Journal of Biomedical Informatics最新文献_第10页

BiFDR: Brain-Inspired Federated Diffusion Transformer with Reinforcement for privacy-preserving molecular generation 基于隐私保护分子生成的脑启发联合扩散变压器

IF 4.5 2区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Biomedical Informatics

Pub Date : 2025-10-01 Epub Date: 2025-09-13 DOI: 10.1016/j.jbi.2025.104910

Hongming Hou , Jing Zhang , Meirun Zhang , Xiucai Ye

Objective:

Generative drug discovery is hampered by challenges in data privacy and the immense computational cost of SOTA models. To surmount these barriers, we developed Brain-Inspired Federated Diffusion with Reinforcement (BiFDR), a privacy-preserving and resource-efficient framework.

Methods:

BiFDR integrates three synergistic modules. A Neuro-inspired Federated Coordinator (NeuroFed) orchestrates secure collaboration via synaptic plasticity-inspired principles, combining server-side pruning with client-side Low-Rank Adaptation (LoRA) and sparse asynchronous updates. A Transformer-based diffusion generator (TransFuse) efficiently creates chemically valid molecules in a compressed latent space using attention mechanisms. Finally, a reinforcement learning agent (T-JORM) steers the generative process towards novel 2D and 3D molecular structures, guided by a multi-faceted, Tanimoto-based reward function.

Results:

Benchmarked against baseline models, BiFDR improving the Quantitative Estimate of Drug-likeness by 13.7%, the Molecular-level Structural Information Score by 5.7%, and the Molecular Interaction Analysis Index by 52.3%. The framework also enhanced synthetic feasibility, reflected by a 9.5% reduction in the Synthetic Accessibility Score. Critically, BiFDR substantially strengthened data privacy, achieving a 43.6% reduction in the mutual information metric.

Conclusion:

BiFDR establishes an effective and efficient paradigm for generative drug discovery. It consistently produces molecules with superior drug-likeness, structural novelty, and interaction potential. By ensuring synthetic accessibility while rigorously preserving privacy and minimizing computational overhead, BiFDR presents a viable and scalable solution for modern, collaborative drug development pipelines.

目的：生成式药物发现受到数据隐私挑战和SOTA模型巨大计算成本的阻碍。为了克服这些障碍，我们开发了一种保护隐私和资源高效的框架——脑启发联合扩散强化（BiFDR）。方法：BiFDR集成三个协同模块。受神经启发的联邦协调器（NeuroFed）通过受突触可塑性启发的原则编排安全协作，将服务器端修剪与客户端低秩适应（LoRA）和稀疏异步更新相结合。基于变压器的扩散发生器（TransFuse）利用注意力机制在压缩的潜在空间中有效地产生化学有效分子。最后，一个强化学习代理（T-JORM）将生成过程转向新的2D和3D分子结构，由一个多方面的、基于谷本的奖励函数指导。结果：以基线模型为基准，BiFDR将药物相似性定量估计提高了13.7%，分子水平结构信息评分提高了5.7%，分子相互作用分析指数提高了52.3%。该框架还增强了综合可行性，综合可达性得分降低了9.5%。关键的是，BiFDR大大加强了数据隐私，实现了互信息度量减少43.6%。结论：BiFDR为生成性药物发现建立了一个有效和高效的范式。它始终如一地产生具有优异的药物相似性、结构新颖性和相互作用潜力的分子。通过确保合成可及性，同时严格保护隐私并最大限度地减少计算开销，BiFDR为现代协作药物开发管道提供了可行且可扩展的解决方案。

{"title":"BiFDR: Brain-Inspired Federated Diffusion Transformer with Reinforcement for privacy-preserving molecular generation","authors":"Hongming Hou , Jing Zhang , Meirun Zhang , Xiucai Ye","doi":"10.1016/j.jbi.2025.104910","DOIUrl":"10.1016/j.jbi.2025.104910","url":null,"abstract":"<div><h3>Objective:</h3><div>Generative drug discovery is hampered by challenges in data privacy and the immense computational cost of SOTA models. To surmount these barriers, we developed Brain-Inspired Federated Diffusion with Reinforcement (BiFDR), a privacy-preserving and resource-efficient framework.</div></div><div><h3>Methods:</h3><div>BiFDR integrates three synergistic modules. A Neuro-inspired Federated Coordinator (NeuroFed) orchestrates secure collaboration via synaptic plasticity-inspired principles, combining server-side pruning with client-side Low-Rank Adaptation (LoRA) and sparse asynchronous updates. A Transformer-based diffusion generator (TransFuse) efficiently creates chemically valid molecules in a compressed latent space using attention mechanisms. Finally, a reinforcement learning agent (T-JORM) steers the generative process towards novel 2D and 3D molecular structures, guided by a multi-faceted, Tanimoto-based reward function.</div></div><div><h3>Results:</h3><div>Benchmarked against baseline models, BiFDR improving the Quantitative Estimate of Drug-likeness by 13.7%, the Molecular-level Structural Information Score by 5.7%, and the Molecular Interaction Analysis Index by 52.3%. The framework also enhanced synthetic feasibility, reflected by a 9.5% reduction in the Synthetic Accessibility Score. Critically, BiFDR substantially strengthened data privacy, achieving a 43.6% reduction in the mutual information metric.</div></div><div><h3>Conclusion:</h3><div>BiFDR establishes an effective and efficient paradigm for generative drug discovery. It consistently produces molecules with superior drug-likeness, structural novelty, and interaction potential. By ensuring synthetic accessibility while rigorously preserving privacy and minimizing computational overhead, BiFDR presents a viable and scalable solution for modern, collaborative drug development pipelines.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"170 ","pages":"Article 104910"},"PeriodicalIF":4.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MedVidDeID: Protecting privacy in clinical encounter video recordings MedVidDeID：在临床遭遇视频记录中保护隐私。

IF 4.5 2区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Biomedical Informatics

Pub Date : 2025-10-01 Epub Date: 2025-08-29 DOI: 10.1016/j.jbi.2025.104901

Sriharsha Mopidevi , Kuk Jin Jang , Basam Alasaly , Sydney Pugh , Jean Park , Ashley Batugo , Sy Hwang , Eric Eaton , Danielle Lee Mowery , Kevin B. Johnson

Objective:

The increasing use of audio-video (AV) data in healthcare has improved patient care, clinical training, and medical and ethnographic research. However, it has also introduced major challenges in preserving patient-provider privacy due to Protected Health Information (PHI) in such data. Traditional de-identification methods are inadequate for AV data, which can reveal identifiable information such as faces, voices, and environmental details. Our goal was to create a pipeline for de-identifying AV healthcare data that minimized the human effort required to guarantee successful de-identification.

Methods:

We combined open-source tools with novel methods and infrastructure into a six-stage pipeline: (1) transcript extraction using WhisperX, (2) transcript de-identification with an adapted PHIlter, (3) audio de-identification through scrubbing, (4) video de-identification using YOLOv11 for pose detection and blurring, (5) recombining de-identified audio and video, and (6) validation and correction via manual quality control (QC). We developed two de-identification strategies to support different tolerances for lossy video images. We evaluated this pipeline using 10 h of simulated clinical AV recordings, comprising nearly 1.1 million video frames and approximately 72,000 words.

Results:

In Precision Privacy Preservation (PPP) mode, MedVidDeId achieved a success rate of 50%, while in Greedy Privacy Preservation (GPP) mode, it achieved a 97.5% success rate. Compared to manual methods for a 15 min video segment, the pipeline reduced de-identification time by 26.7% in PPP and 64.2% in GPP modes.

Conclusion:

The MedVidDeID pipeline offers a viable, efficient hybrid solution for handling AV healthcare data and privacy preservation. Future work will focus on reducing upstream errors at each stage and minimizing the role of the human in the loop.

目的：在医疗保健中越来越多地使用视听（AV）数据，改善了患者护理、临床培训以及医学和人种学研究。然而，由于此类数据中的受保护健康信息（PHI），它也在保护患者-提供者隐私方面带来了重大挑战。传统的去识别方法不适用于自动驾驶数据，因为自动驾驶数据可以显示人脸、声音和环境细节等可识别信息。我们的目标是创建一个去识别AV医疗保健数据的管道，以最大限度地减少保证成功去识别所需的人力。方法：我们将开源工具与新方法和基础设施结合起来，形成了一个六阶段的流水线：(1)使用WhisperX提取文本，(2)使用改编PHIlter进行文本去识别，(3)通过擦除进行音频去识别，(4)使用YOLOv11进行姿态检测和模糊处理进行视频去识别，(5)重新组合去识别的音频和视频，以及(6)通过人工质量控制（QC）进行验证和纠正。我们开发了两种去识别策略来支持有损视频图像的不同容忍度。我们使用10小时的模拟临床AV记录来评估这个管道，包括近110万视频帧和大约72,000个单词。结果：MedVidDeId在精准隐私保护（PPP）模式下的成功率为50%，在贪婪隐私保护（GPP）模式下的成功率为97.5%。与手动方法相比，对于15分钟的视频片段，管道在PPP模式下减少了26.7%的去识别时间，在GPP模式下减少了64.2%。结论：MedVidDeID管道为处理AV医疗数据和隐私保护提供了一种可行、高效的混合解决方案。未来的工作将侧重于减少每个阶段的上游错误，并最大限度地减少人在循环中的作用。

{"title":"MedVidDeID: Protecting privacy in clinical encounter video recordings","authors":"Sriharsha Mopidevi , Kuk Jin Jang , Basam Alasaly , Sydney Pugh , Jean Park , Ashley Batugo , Sy Hwang , Eric Eaton , Danielle Lee Mowery , Kevin B. Johnson","doi":"10.1016/j.jbi.2025.104901","DOIUrl":"10.1016/j.jbi.2025.104901","url":null,"abstract":"<div><h3>Objective:</h3><div>The increasing use of audio-video (AV) data in healthcare has improved patient care, clinical training, and medical and ethnographic research. However, it has also introduced major challenges in preserving patient-provider privacy due to Protected Health Information (PHI) in such data. Traditional de-identification methods are inadequate for AV data, which can reveal identifiable information such as faces, voices, and environmental details. Our goal was to create a pipeline for de-identifying AV healthcare data that minimized the human effort required to guarantee successful de-identification.</div></div><div><h3>Methods:</h3><div>We combined open-source tools with novel methods and infrastructure into a six-stage pipeline: (1) transcript extraction using WhisperX, (2) transcript de-identification with an adapted PHIlter, (3) audio de-identification through scrubbing, (4) video de-identification using YOLOv11 for pose detection and blurring, (5) recombining de-identified audio and video, and (6) validation and correction via manual quality control (QC). We developed two de-identification strategies to support different tolerances for lossy video images. We evaluated this pipeline using 10 h of simulated clinical AV recordings, comprising nearly 1.1 million video frames and approximately 72,000 words.</div></div><div><h3>Results:</h3><div>In Precision Privacy Preservation (PPP) mode, MedVidDeId achieved a success rate of 50%, while in Greedy Privacy Preservation (GPP) mode, it achieved a 97.5% success rate. Compared to manual methods for a 15 min video segment, the pipeline reduced de-identification time by 26.7% in PPP and 64.2% in GPP modes.</div></div><div><h3>Conclusion:</h3><div>The MedVidDeID pipeline offers a viable, efficient hybrid solution for handling AV healthcare data and privacy preservation. Future work will focus on reducing upstream errors at each stage and minimizing the role of the human in the loop.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"170 ","pages":"Article 104901"},"PeriodicalIF":4.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144955583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Strategies for detecting and mitigating dataset shift in machine learning for health predictions: A systematic review 用于健康预测的机器学习中检测和减轻数据集转移的策略：系统综述

IF 4.5 2区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Biomedical Informatics

Pub Date : 2025-10-01 Epub Date: 2025-08-26 DOI: 10.1016/j.jbi.2025.104902

Gabriel Ferreira dos Santos Silva , Fabiano Novaes Barcellos Filho , Roberta Moreira Wichmann , Francisco Costa da Silva Junior , Alexandre Dias Porto Chiavegatto Filho

Objective

This review aims to provide a comprehensive overview of the literature on methods and techniques for identifying and correcting dataset shift in machine learning (ML) applications for health predictions.

Methods

A systematic search was conducted across PubMed, IEEE Xplore, Scopus, and Web of Science, targeting articles published between January 1, 2019, and March 15, 2025. earch strings combined terms related to machine learning, healthcare, and dataset shift. A total of 32 studies were included, and were evaluated based on dataset shift types addressed, detection and correction strategies used, algorithmic choices, and reported impacts on model performance.

Results

The review identified a wide range of dataset shift types, with temporal shift and concept drift being the most commonly addressed. Model-based monitoring and statistical tests were the most frequent detection strategies, while retraining and feature engineering were the predominant correction approaches. Most methods demonstrate moderate interpretability, computational feasibility, and generalizability. However, a lack of standardized performance metrics and external validations limited the comparability of results across studies.

Conclusion

While several promising approaches for managing dataset shift in health-related ML models have been proposed, no single method emerged as broadly generalizable across use cases. The implementation of these techniques in real-world clinical workflows remains limited. Future research should prioritize prospective evaluations, subgroup-specific analyses (e.g., by race, age, or geographic region), and integration into clinical decision-support systems to ensure robust and equitable ML deployment in healthcare settings. A structured summary table and conceptual pipeline diagram are provided to support practical adoption.

目的：本综述旨在对用于健康预测的机器学习（ML）应用中识别和纠正数据集移位的方法和技术的文献进行全面概述。方法系统检索PubMed、IEEE explore、Scopus和Web of Science，检索2019年1月1日至2025年3月15日之间发表的论文。搜索与机器学习、医疗保健和数据集转移相关的组合术语的字符串。共纳入了32项研究，并根据所处理的数据集移位类型、使用的检测和校正策略、算法选择以及对模型性能的影响进行了评估。结果该综述确定了广泛的数据集移位类型，其中时间移位和概念漂移是最常见的。基于模型的监测和统计测试是最常见的检测策略，而再训练和特征工程是主要的校正方法。大多数方法表现出适度的可解释性、计算可行性和通用性。然而，缺乏标准化的性能指标和外部验证限制了研究结果的可比性。虽然已经提出了几种有前途的方法来管理与健康相关的机器学习模型中的数据集转移，但没有一种方法可以在用例中广泛推广。这些技术在实际临床工作流程中的应用仍然有限。未来的研究应优先考虑前瞻性评估，亚组特定分析（例如，按种族，年龄或地理区域），并整合到临床决策支持系统中，以确保在医疗保健环境中稳健和公平的机器学习部署。提供了一个结构化的汇总表和概念管道图，以支持实际采用。

{"title":"Strategies for detecting and mitigating dataset shift in machine learning for health predictions: A systematic review","authors":"Gabriel Ferreira dos Santos Silva , Fabiano Novaes Barcellos Filho , Roberta Moreira Wichmann , Francisco Costa da Silva Junior , Alexandre Dias Porto Chiavegatto Filho","doi":"10.1016/j.jbi.2025.104902","DOIUrl":"10.1016/j.jbi.2025.104902","url":null,"abstract":"<div><h3>Objective</h3><div>This review aims to provide a comprehensive overview of the literature on methods and techniques for identifying and correcting dataset shift in machine learning (ML) applications for health predictions.</div></div><div><h3>Methods</h3><div>A systematic search was conducted across PubMed, IEEE Xplore, Scopus, and Web of Science, targeting articles published between January 1, 2019, and March 15, 2025. earch strings combined terms related to machine learning, healthcare, and dataset shift. A total of 32 studies were included, and were evaluated based on dataset shift types addressed, detection and correction strategies used, algorithmic choices, and reported impacts on model performance.</div></div><div><h3>Results</h3><div>The review identified a wide range of dataset shift types, with temporal shift and concept drift being the most commonly addressed. Model-based monitoring and statistical tests were the most frequent detection strategies, while retraining and feature engineering were the predominant correction approaches. Most methods demonstrate moderate interpretability, computational feasibility, and generalizability. However, a lack of standardized performance metrics and external validations limited the comparability of results across studies.</div></div><div><h3>Conclusion</h3><div>While several promising approaches for managing dataset shift in health-related ML models have been proposed, no single method emerged as broadly generalizable across use cases. The implementation of these techniques in real-world clinical workflows remains limited. Future research should prioritize prospective evaluations, subgroup-specific analyses (e.g., by race, age, or geographic region), and integration into clinical decision-support systems to ensure robust and equitable ML deployment in healthcare settings. A structured summary table and conceptual pipeline diagram are provided to support practical adoption.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"170 ","pages":"Article 104902"},"PeriodicalIF":4.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving large language models for adverse drug reactions named entity recognition via error correction prompt engineering 通过纠错提示工程改进药物不良反应命名实体识别的大型语言模型

IF 4.5 2区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Biomedical Informatics

Pub Date : 2025-10-01 Epub Date: 2025-08-28 DOI: 10.1016/j.jbi.2025.104893

Yunfei Zhang, Wei Liao

The monitoring and analysis of adverse drug reactions (ADRs ) are important for ensuring patient safety and improving treatment outcomes. Accurate identification of drug names, drug components, and ADR entities during named entity recognition (NER) processes is essential for ensuring drug safety and advancing the integration of drug information. Given that existing medical name entity recognition technologies rely on large amounts of manually annotated data for training, they are often less effective when applied to adverse drug reactions due to significant data variability and the high similarity between drug names. This paper proposes a prompt template for ADR that integrates error correction examples. The prompt template includes: 1. Basic prompts with task descriptions, 2. Annotated entity explanations, 3. Annotation guidelines, 4. Annotated samples for few-shot learning, 5. Error correction examples. Additionally, it integrates complex ADR data from the web and constructs a corpus containing three types of entities (drug name, drug components, and adverse drug reactions) using the Begin, Inside, Outside (BIO) annotation method. Finally, we evaluate the effectiveness of each prompt and compare it with the fine-tuned Large Language Model Meta AI (LLaMA) model and the DeepSeek model. Experimental results show that under this prompt template, the F1 score of GPT-3.5 increased from 0.648 to 0.887, and that of GPT-4 increased from 0.757 to 0.921. It is significantly better than the fine-tuned LLaMA model and DeepSeek model. It demonstrates the superiority of the proposed method, and provides a solid foundation for extracting drug-related entity relationships and building knowledge graphs.

药物不良反应（adr）的监测和分析对于确保患者安全和改善治疗效果非常重要。在命名实体识别（NER）过程中，准确识别药品名称、药物成分和ADR实体对于确保药品安全和推进药品信息整合至关重要。鉴于现有的医学名称实体识别技术依赖于大量人工标注的数据进行训练，由于数据的显著可变性和药品名称之间的高度相似性，它们在应用于药物不良反应时往往效果较差。本文提出了一个集成纠错实例的ADR提示模板。提示符模板包括：1。2、基本提示和任务描述。注释实体解释；注释指南；5.少射学习的标注样本；纠错示例。此外，它集成了来自网络的复杂ADR数据，并使用Begin， Inside, Outside （BIO）注释方法构建了一个包含三种类型实体（药物名称，药物成分和药物不良反应）的语料库。最后，我们评估了每个提示的有效性，并将其与经过微调的大型语言模型元AI （LLaMA）模型和DeepSeek模型进行了比较。实验结果表明，在该提示模板下，GPT-3.5的F1评分从0.648提高到0.887，GPT-4的F1评分从0.757提高到0.921。它明显优于经过微调的LLaMA模型和DeepSeek模型。验证了该方法的优越性，为药物相关实体关系的提取和知识图谱的构建提供了坚实的基础。

{"title":"Improving large language models for adverse drug reactions named entity recognition via error correction prompt engineering","authors":"Yunfei Zhang, Wei Liao","doi":"10.1016/j.jbi.2025.104893","DOIUrl":"10.1016/j.jbi.2025.104893","url":null,"abstract":"<div><div>The monitoring and analysis of adverse drug reactions (ADRs ) are important for ensuring patient safety and improving treatment outcomes. Accurate identification of drug names, drug components, and ADR entities during named entity recognition (NER) processes is essential for ensuring drug safety and advancing the integration of drug information. Given that existing medical name entity recognition technologies rely on large amounts of manually annotated data for training, they are often less effective when applied to adverse drug reactions due to significant data variability and the high similarity between drug names. This paper proposes a prompt template for ADR that integrates error correction examples. The prompt template includes: 1. Basic prompts with task descriptions, 2. Annotated entity explanations, 3. Annotation guidelines, 4. Annotated samples for few-shot learning, 5. Error correction examples. Additionally, it integrates complex ADR data from the web and constructs a corpus containing three types of entities (drug name, drug components, and adverse drug reactions) using the Begin, Inside, Outside (BIO) annotation method. Finally, we evaluate the effectiveness of each prompt and compare it with the fine-tuned Large Language Model Meta AI (LLaMA) model and the DeepSeek model. Experimental results show that under this prompt template, the F1 score of GPT-3.5 increased from 0.648 to 0.887, and that of GPT-4 increased from 0.757 to 0.921. It is significantly better than the fine-tuned LLaMA model and DeepSeek model. It demonstrates the superiority of the proposed method, and provides a solid foundation for extracting drug-related entity relationships and building knowledge graphs.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"170 ","pages":"Article 104893"},"PeriodicalIF":4.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SynthMedic: Utilizing large language models for synthetic discharge summary generation, correction and validation SynthMedic：利用大型语言模型生成、校正和验证综合放电摘要。

IF 4.5 2区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Biomedical Informatics

Pub Date : 2025-10-01 Epub Date: 2025-09-15 DOI: 10.1016/j.jbi.2025.104906

Georgi Grazhdanski , Vasil Vasilev , Sylvia Vassileva , Dimitar Taskov , Izabel Antova , Ivan Koychev , Svetla Boytcheva

Background and Objective:

Synthetic clinical texts can improve transparency and reduce bias and costs when training and evaluating specialized language models in the medical domain. Synthetic texts are freely shareable, as they contain no real patient information, and can be customized for a specific task. The objective of this study is to develop a methodology for generating, validating, and correcting synthetic discharge summaries using LLMs without requiring any real patient data.

Methods:

The proposed approach uses an LLM to generate synthetic discharge summaries for specific diseases and standard medical references from Merck Manuals to ground the generation in internationally accepted medical practices. We validate the generated summaries using LLMs as well as by human expert validation. In addition, we propose a method for automatic correction of the generated discharge summaries using Knowledge Graphs to ensure medical factual correctness.

Results:

The conducted human expert evaluation shows that the generated synthetic discharge summaries are credible and factually accurate when provided with the medical reference context. The generated summaries achieve a System Usability Score of 94.35% based on a comprehensive rubric evaluated by medical professionals and a score of 93.65% on the Faithfulness metric evaluated by an LLM.

Conclusions:

The proposed methodology can be utilized to generate high-quality synthetic discharge summaries for various diseases. The generated synthetic corpus consists of 900 discharge summaries in English representing nine socially significant diseases and is publicly available under an open license. The community can take advantage of the corpus and proposed methodology to train complex machine learning models, helping medical professionals in their daily work without using real patient data.

背景和目的：在训练和评估医学领域的专业语言模型时，合成临床文本可以提高透明度，减少偏见和成本。合成文本可以自由共享，因为它们不包含真实的患者信息，并且可以针对特定任务进行定制。本研究的目的是开发一种方法，在不需要任何真实患者数据的情况下，使用llm生成、验证和纠正合成出院摘要。方法：提出的方法使用法学硕士生成特定疾病的综合出院摘要和默克手册中的标准医疗参考资料，以使生成符合国际公认的医疗实践。我们使用llm和人类专家验证来验证生成的摘要。此外，我们提出了一种使用知识图自动更正生成的出院摘要的方法，以确保医学事实的正确性。结果：人工专家评估表明，在提供医疗参考环境时，生成的综合出院摘要是可信的和事实准确的。根据医学专业人员评估的综合指标，生成的摘要达到了94.35%的系统可用性得分，而根据法学硕士评估的忠诚度指标，生成的摘要达到了93.65%的系统可用性得分。结论：该方法可用于生成各种疾病的高质量综合出院摘要。生成的合成语料库由900个英文出院摘要组成，代表9种具有社会意义的疾病，并在开放许可下公开提供。社区可以利用语料库和提出的方法来训练复杂的机器学习模型，在不使用真实患者数据的情况下帮助医疗专业人员进行日常工作。

{"title":"SynthMedic: Utilizing large language models for synthetic discharge summary generation, correction and validation","authors":"Georgi Grazhdanski , Vasil Vasilev , Sylvia Vassileva , Dimitar Taskov , Izabel Antova , Ivan Koychev , Svetla Boytcheva","doi":"10.1016/j.jbi.2025.104906","DOIUrl":"10.1016/j.jbi.2025.104906","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Synthetic clinical texts can improve transparency and reduce bias and costs when training and evaluating specialized language models in the medical domain. Synthetic texts are freely shareable, as they contain no real patient information, and can be customized for a specific task. The objective of this study is to develop a methodology for generating, validating, and correcting synthetic discharge summaries using LLMs without requiring any real patient data.</div></div><div><h3>Methods:</h3><div>The proposed approach uses an LLM to generate synthetic discharge summaries for specific diseases and standard medical references from Merck Manuals to ground the generation in internationally accepted medical practices. We validate the generated summaries using LLMs as well as by human expert validation. In addition, we propose a method for automatic correction of the generated discharge summaries using Knowledge Graphs to ensure medical factual correctness.</div></div><div><h3>Results:</h3><div>The conducted human expert evaluation shows that the generated synthetic discharge summaries are credible and factually accurate when provided with the medical reference context. The generated summaries achieve a System Usability Score of 94.35% based on a comprehensive rubric evaluated by medical professionals and a score of 93.65% on the Faithfulness metric evaluated by an LLM.</div></div><div><h3>Conclusions:</h3><div>The proposed methodology can be utilized to generate high-quality synthetic discharge summaries for various diseases. The generated synthetic corpus consists of 900 discharge summaries in English representing nine socially significant diseases and is publicly available under an open license. The community can take advantage of the corpus and proposed methodology to train complex machine learning models, helping medical professionals in their daily work without using real patient data.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"170 ","pages":"Article 104906"},"PeriodicalIF":4.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145080834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Overcoming data challenges through enriched validation and targeted sampling to measure whole-person health in electronic health records 通过丰富的验证和有针对性的抽样来克服数据挑战，以测量电子健康记录中的整个人的健康。

IF 4.5 2区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Biomedical Informatics

Pub Date : 2025-10-01 Epub Date: 2025-09-02 DOI: 10.1016/j.jbi.2025.104904

Sarah C. Lotspeich , Sheetal Kedar , Rabeya Tahir , Aidan D. Keleghan , Amelia Miranda , Stephany N. Duda , Michael P. Bancks , Brian J. Wells , Ashish K. Khanna , Joseph Rigdon

Objective:

The allostatic load index (ALI) is a 10-component composite measure of whole-person health, which reflects the multiple interrelated physiological regulatory systems that underlie healthy functioning. Data from electronic health records (EHR) present a huge opportunity to operationalize the ALI in learning health systems; however, these data are prone to missingness and errors. Validation (e.g., through chart reviews) can provide better-quality data, but realistically, only a subset of patients’ data can be validated, and most protocols do not recover missing data.

Methods:

Using a representative sample of 1000 patients from the EHR at an extensive learning health system (100 of whom could be validated), we propose methods to design, conduct, and analyze statistically efficient and robust studies of ALI and healthcare utilization. Employing semiparametric maximum likelihood estimation, we robustly incorporate all available patient information into statistical models. Using targeted design strategies, we examine ways to select the most informative patients for validation. Incorporating clinical expertise, we devise a novel validation protocol to promote EHR data quality and completeness.

Results:

Chart reviews uncovered few errors (99% matched source documents) and recovered some missing data through auxiliary information in patients’ charts. On average, validation increased the number of non-missing ALI components per patient from 6 to 7. Through simulations based on preliminary data, residual sampling was identified as the most informative strategy for completing our validation study. Incorporating validation data, statistical models indicated that worse whole-person health (higher ALI) was associated with higher odds of engaging in the healthcare system, adjusting for age.

Conclusion:

Targeted validation with an enriched protocol can ensure the quality and promote the completeness of EHR data. Findings from our validation study were incorporated into analyses as we operationalize the ALI as a scalable whole-person health measure that predicts healthcare utilization in the learning health system.

目的：适应负荷指数（ALI）是一个由10个成分组成的整体人体健康指标，它反映了健康功能背后的多个相互关联的生理调节系统。来自电子健康记录（EHR）的数据为在学习卫生系统中实施ALI提供了巨大的机会；然而，这些数据容易丢失和错误。验证（例如，通过图表审查）可以提供更高质量的数据，但实际上，只有一小部分患者的数据可以被验证，而且大多数方案不能恢复丢失的数据。方法：从一个广泛的学习型卫生系统的电子病历中选取1000名患者作为代表性样本（其中100人可以被验证），我们提出了设计、实施和分析ALI和医疗保健利用的统计有效和稳健研究的方法。采用半参数最大似然估计，我们稳健地将所有可用的患者信息纳入统计模型。使用有针对性的设计策略，我们检查了选择最具信息性的患者进行验证的方法。结合临床专业知识，我们设计了一种新的验证方案，以提高电子病历数据的质量和完整性。结果：图表审核发现的错误很少（99%与源文档匹配），并通过患者图表中的辅助信息恢复了一些缺失的数据。平均而言，验证将每位患者非缺失ALI成分的数量从6个增加到7个。通过基于初步数据的模拟，残差抽样被确定为完成我们验证研究的最具信息性的策略。结合验证数据，统计模型表明，整体健康状况较差（ALI较高）与参与医疗保健系统的几率较高相关，并根据年龄进行调整。结论：采用丰富的方案进行有针对性的验证，可以保证电子病历数据的质量，提高数据的完整性。我们验证研究的结果被纳入分析，因为我们将ALI作为可扩展的全人健康测量来预测学习健康系统中的医疗保健利用。

{"title":"Overcoming data challenges through enriched validation and targeted sampling to measure whole-person health in electronic health records","authors":"Sarah C. Lotspeich , Sheetal Kedar , Rabeya Tahir , Aidan D. Keleghan , Amelia Miranda , Stephany N. Duda , Michael P. Bancks , Brian J. Wells , Ashish K. Khanna , Joseph Rigdon","doi":"10.1016/j.jbi.2025.104904","DOIUrl":"10.1016/j.jbi.2025.104904","url":null,"abstract":"<div><h3>Objective:</h3><div>The allostatic load index (ALI) is a 10-component composite measure of whole-person health, which reflects the multiple interrelated physiological regulatory systems that underlie healthy functioning. Data from electronic health records (EHR) present a huge opportunity to operationalize the ALI in learning health systems; however, these data are prone to missingness and errors. Validation (e.g., through chart reviews) can provide better-quality data, but realistically, only a subset of patients’ data can be validated, and most protocols do not recover missing data.</div></div><div><h3>Methods:</h3><div>Using a representative sample of 1000 patients from the EHR at an extensive learning health system (100 of whom could be validated), we propose methods to design, conduct, and analyze statistically efficient and robust studies of ALI and healthcare utilization. Employing semiparametric maximum likelihood estimation, we robustly incorporate all available patient information into statistical models. Using targeted design strategies, we examine ways to select the most informative patients for validation. Incorporating clinical expertise, we devise a novel validation protocol to promote EHR data quality and completeness.</div></div><div><h3>Results:</h3><div>Chart reviews uncovered few errors (99% matched source documents) and recovered some missing data through auxiliary information in patients’ charts. On average, validation increased the number of non-missing ALI components per patient from 6 to 7. Through simulations based on preliminary data, residual sampling was identified as the most informative strategy for completing our validation study. Incorporating validation data, statistical models indicated that worse whole-person health (higher ALI) was associated with higher odds of engaging in the healthcare system, adjusting for age.</div></div><div><h3>Conclusion:</h3><div>Targeted validation with an enriched protocol can ensure the quality and promote the completeness of EHR data. Findings from our validation study were incorporated into analyses as we operationalize the ALI as a scalable whole-person health measure that predicts healthcare utilization in the learning health system.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"170 ","pages":"Article 104904"},"PeriodicalIF":4.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145000662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of Single-Cell perturbation response based on Direction-Constrained diffusion Schrödinger Bridge 基于方向约束扩散的单细胞微扰响应预测Schrödinger桥

IF 4.5 2区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Biomedical Informatics

Pub Date : 2025-10-01 Epub Date: 2025-09-21 DOI: 10.1016/j.jbi.2025.104915

Yiqing Luo , Lin Liu , Yaxin Fu , Yi Deng , Lin Tang

Objective

Predicting transcriptional responses to external perturbations at the single-cell level is essential for understanding gene regulatory networks, drug discovery, and personalized interventions. The exponential increase in perturbation conditions creates data sparsity, making it difficult to capture dynamic responses and necessitating computational modeling.

Methods

We present Direction-Constrained Diffusion Schrödinger Bridge (DC-DSB), a generative framework that learns probabilistic trajectories between unperturbed and post-perturbation distributions by minimizing path-space KL divergence. To enhance conditional control, DC-DSB integrates hierarchical representations derived from experimental variables and biological prior knowledge. We further introduce a direction-constrained conditioning strategy that injects condition signals along the biologically relevant perturbation trajectory, thereby improving modeling quality and training stability.

Results

DC-DSB improves expression prediction accuracy and generalization to unseen combinations over baselines. By modeling dynamic expression trajectories and co-expression structures under perturbation, DC-DSB enables the discovery of synergistic and antagonistic gene interactions and supports the progressive reconstruction of regulatory pathways.

Conclusion

DC-DSB provides a biologically consistent and generalizable framework for single-cell perturbation modeling. Its trajectory-based and condition-aware architecture overcomes the limitations of static mappings and facilitates downstream analyses in gene regulation and drug discovery.

目的预测单细胞水平对外部扰动的转录反应对于理解基因调控网络、药物发现和个性化干预至关重要。扰动条件的指数增长造成数据稀疏性，使得难以捕获动态响应并需要计算建模。方法我们提出了方向约束扩散Schrödinger桥（DC-DSB），这是一个生成框架，通过最小化路径空间KL散度来学习无扰动和后扰动分布之间的概率轨迹。为了增强条件控制，DC-DSB集成了由实验变量和生物先验知识派生的层次表示。我们进一步引入了一种方向约束的条件反射策略，该策略沿着生物相关的扰动轨迹注入条件信号，从而提高建模质量和训练稳定性。结果dc - dsb在基线上提高了表达预测的准确性和对未见组合的泛化。通过模拟扰动下的动态表达轨迹和共表达结构，DC-DSB能够发现协同和拮抗基因相互作用，并支持调控途径的逐步重建。结论dc - dsb为单细胞微扰建模提供了生物学一致性和可推广的框架。其基于轨迹和条件感知的结构克服了静态映射的局限性，促进了基因调控和药物发现的下游分析。

{"title":"Prediction of Single-Cell perturbation response based on Direction-Constrained diffusion Schrödinger Bridge","authors":"Yiqing Luo , Lin Liu , Yaxin Fu , Yi Deng , Lin Tang","doi":"10.1016/j.jbi.2025.104915","DOIUrl":"10.1016/j.jbi.2025.104915","url":null,"abstract":"<div><h3>Objective</h3><div>Predicting transcriptional responses to external perturbations at the single-cell level is essential for understanding gene regulatory networks, drug discovery, and personalized interventions. The exponential increase in perturbation conditions creates data sparsity, making it difficult to capture dynamic responses and necessitating computational modeling.</div></div><div><h3>Methods</h3><div>We present Direction-Constrained Diffusion Schrödinger Bridge (DC-DSB), a generative framework that learns probabilistic trajectories between unperturbed and post-perturbation distributions by minimizing path-space KL divergence. To enhance conditional control, DC-DSB integrates hierarchical representations derived from experimental variables and biological prior knowledge. We further introduce a direction-constrained conditioning strategy that injects condition signals along the biologically relevant perturbation trajectory, thereby improving modeling quality and training stability.</div></div><div><h3>Results</h3><div>DC-DSB improves expression prediction accuracy and generalization to unseen combinations over baselines. By modeling dynamic expression trajectories and co-expression structures under perturbation, DC-DSB enables the discovery of synergistic and antagonistic gene interactions and supports the progressive reconstruction of regulatory pathways.</div></div><div><h3>Conclusion</h3><div>DC-DSB provides a biologically consistent and generalizable framework for single-cell perturbation modeling. Its trajectory-based and condition-aware architecture overcomes the limitations of static mappings and facilitates downstream analyses in gene regulation and drug discovery.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"170 ","pages":"Article 104915"},"PeriodicalIF":4.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resource-efficient instruction tuning of large language models for biomedical named entity recognition 生物医学命名实体识别大型语言模型的资源高效指令调优

IF 4.5 2区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Biomedical Informatics

Pub Date : 2025-10-01 Epub Date: 2025-08-21 DOI: 10.1016/j.jbi.2025.104896

Hui Liu , Ziyi Chen , Peilin Li , Yuan-Zhi Liu , Xiangtao Liu , Ronald X. Xu , Mingzhai Sun

Objective:

Large language models (LLMs) have exhibited remarkable efficacy in natural language processing (NLP) tasks, with fine-tuning for Biomedical Named Entity Recognition (BioNER) receiving significant research attention. However, the substantial computational demands associated with fine-tuning large-scale models constrain their development and deployment. Consequently, this study investigates parameter-efficient fine-tuning (PEFT) techniques to optimize LLMs for BioNER under limited computational resources. By leveraging these methods, competitive model performance is maintained while preserving in-domain generalization capability.

Methods:

In this study, we employed the PEFT method QLoRA to fine-tune the open-source Llama3.1 model, developing the NERLlama3.1 model specifically designed for the BioNER task. First, an LLM instruction tuning dataset was created using BioNER datasets such as NCBI-disease, BC5CDR-chem, and BC2GM-gene. Next, the Llama3.1-8B model was fine-tuned using the QLoRA method on a single 16GB memory GPU. Furthermore, during the inference phase, we introduced a prompt engineering technique called self-consistency NER prompting (SCNP). This approach leverages the diversity of outputs generated by LLMs to significantly enhance NER performance. Finally, we also developed a multi-task BioNER-capable model, NERLlama3.1-MT, to investigate the capability of fine-tuned LLMs in addressing multi-task BioNER scenarios.

Results:

The NERLlama3.1 model achieved F1-scores of 0.8977, 0.9402, and 0.8530 on the NCBI-disease, BC5CDR-chemical, and BG2GM-gene datasets, respectively. Furthermore, when evaluated on previously unseen datasets, it attained F1-scores of 0.6867 on BC5CDR-disease, 0.6800 on NLM-chemical, and 0.8378 on NLM-gene. These results demonstrate that NERLlama3.1 not only outperforms fully fine-tuned LLMs but also exhibits superior in-domain generalization capabilities when compared to the BERT-base model. Additionally, this work represents the first exploration of fine-tuning LLMs for multi-task BioNER.

Conclusion:

NERLlama3.1 outperformed LLMs fine-tuned with full parameter updates, despite requiring significantly fewer computational resources. Moreover, it exhibited substantially superior in-domain generalization capabilities compared to traditional pre-trained language models. Its low resource demands, high performance, and strong generalization enhance its applicability and utility across diverse clinical BioNER tasks.

目的：大型语言模型（LLMs）在自然语言处理（NLP）任务中表现出显著的有效性，其中生物医学命名实体识别（BioNER）的微调受到了重要的研究关注。然而，与微调大规模模型相关的大量计算需求限制了它们的开发和部署。因此，本研究探讨了参数高效微调（PEFT）技术，以在有限的计算资源下优化BioNER的llm。通过利用这些方法，在保持域内泛化能力的同时保持了具有竞争力的模型性能。方法：本研究采用PEFT方法QLoRA对开源Llama3.1模型进行微调，开发专门针对BioNER任务设计的NERLlama3.1模型。首先，利用NCBI-disease、BC5CDR-chem、BC2GM-gene等BioNER数据集创建LLM指令调优数据集。接下来，在单个16GB内存GPU上，使用QLoRA方法对Llama3.1-8B模型进行微调。此外，在推理阶段，我们引入了一种提示工程技术，称为自一致性NER提示（SCNP）。这种方法利用llm产生的输出的多样性来显著提高NER性能。最后，我们还开发了一个多任务BioNER-capable模型NERLlama3.1-MT，以研究微调llm在解决多任务BioNER场景中的能力。结果：NERLlama3.1模型在ncbi -疾病、bc5cdr -化学和bg2gm -基因数据集上的f1得分分别为0.8977、0.9402和0.8530。此外，当对以前未见过的数据集进行评估时，BC5CDR-disease的f1得分为0.6867，NLM-chemical的得分为0.6800，NLM-gene的得分为0.8378。这些结果表明，与基于bert的模型相比，NERLlama3.1不仅优于完全微调的llm，而且具有优越的域内泛化能力。此外，这项工作代表了对多任务bioner微调llm的首次探索。结论：尽管需要的计算资源显着减少，但NERLlama3.1优于使用全参数更新进行微调的llm。此外，与传统的预训练语言模型相比，它表现出了显著优越的领域内泛化能力。它的低资源需求、高性能和强泛化增强了它在各种临床BioNER任务中的适用性和实用性。

{"title":"Resource-efficient instruction tuning of large language models for biomedical named entity recognition","authors":"Hui Liu , Ziyi Chen , Peilin Li , Yuan-Zhi Liu , Xiangtao Liu , Ronald X. Xu , Mingzhai Sun","doi":"10.1016/j.jbi.2025.104896","DOIUrl":"10.1016/j.jbi.2025.104896","url":null,"abstract":"<div><h3>Objective:</h3><div>Large language models (LLMs) have exhibited remarkable efficacy in natural language processing (NLP) tasks, with fine-tuning for Biomedical Named Entity Recognition (BioNER) receiving significant research attention. However, the substantial computational demands associated with fine-tuning large-scale models constrain their development and deployment. Consequently, this study investigates parameter-efficient fine-tuning (PEFT) techniques to optimize LLMs for BioNER under limited computational resources. By leveraging these methods, competitive model performance is maintained while preserving in-domain generalization capability.</div></div><div><h3>Methods:</h3><div>In this study, we employed the PEFT method QLoRA to fine-tune the open-source Llama3.1 model, developing the NERLlama3.1 model specifically designed for the BioNER task. First, an LLM instruction tuning dataset was created using BioNER datasets such as NCBI-disease, BC5CDR-chem, and BC2GM-gene. Next, the Llama3.1-8B model was fine-tuned using the QLoRA method on a single 16GB memory GPU. Furthermore, during the inference phase, we introduced a prompt engineering technique called self-consistency NER prompting (SCNP). This approach leverages the diversity of outputs generated by LLMs to significantly enhance NER performance. Finally, we also developed a multi-task BioNER-capable model, NERLlama3.1-MT, to investigate the capability of fine-tuned LLMs in addressing multi-task BioNER scenarios.</div></div><div><h3>Results:</h3><div>The NERLlama3.1 model achieved F1-scores of 0.8977, 0.9402, and 0.8530 on the NCBI-disease, BC5CDR-chemical, and BG2GM-gene datasets, respectively. Furthermore, when evaluated on previously unseen datasets, it attained F1-scores of 0.6867 on BC5CDR-disease, 0.6800 on NLM-chemical, and 0.8378 on NLM-gene. These results demonstrate that NERLlama3.1 not only outperforms fully fine-tuned LLMs but also exhibits superior in-domain generalization capabilities when compared to the BERT-base model. Additionally, this work represents the first exploration of fine-tuning LLMs for multi-task BioNER.</div></div><div><h3>Conclusion:</h3><div>NERLlama3.1 outperformed LLMs fine-tuned with full parameter updates, despite requiring significantly fewer computational resources. Moreover, it exhibited substantially superior in-domain generalization capabilities compared to traditional pre-trained language models. Its low resource demands, high performance, and strong generalization enhance its applicability and utility across diverse clinical BioNER tasks.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"170 ","pages":"Article 104896"},"PeriodicalIF":4.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Measuring and visualizing healthcare process variability 测量和可视化医疗保健过程可变性。

IF 4.5 2区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Biomedical Informatics

Pub Date : 2025-10-01 Epub Date: 2025-09-23 DOI: 10.1016/j.jbi.2025.104918

Pengfei Yin , Abel Armas Cervantes , Daniel Capurro

Importance

Understanding factors that contribute to clinical variability in patient care is critical, as unwarranted variability can lead to increased adverse events and prolonged hospital stays. Determining when this variability becomes excessive can be a step in optimizing patient outcomes and healthcare efficiency.

Objective

Explore the association between clinical variation and clinical outcomes. This study aims to identify the point in time when the relationship between clinical variation and length of stay (LOS) becomes significant.

Methods

This cohort study uses MIMIC-IV, a dataset collecting electronic health records of the Beth Israel Deaconess Medical Center in the United States. We focused on adult patients who underwent elective coronary bypass surgery, generating 847 patient observations. Demographic factors such as age, race, insurance type, and the Charlson Comorbidity Index (CCI) were recorded. We performed a variability analysis where patients’ clinical processes are represented as sequences of events. The data was segmented based on the initial day of recorded activity to establish observation windows. Using a regression analysis, we identified the temporal window where variability’s impact on LOS becomes independently significant.

Result

Regression analysis revealed that patients in the top 20 % of the variability distance group experienced an 81 % increase in LOS (95 % CI: 1.72 to 1.91, p < 0.001). Insurance types, such as Medicare and Other, were associated with 18 % (95 % CI: 0.73 to 0.92, p < 0.001) and 21 % (95 % CI: 0.71 to 0.88, p < 0.001) decreases in LOS, respectively. Neither age nor race significantly affected LOS, but a higher CCI was associated with a 3.3 % increase in LOS (95 % CI: 1.02 to 1.05, p < 0.001). These findings indicate that higher variability and CCI significantly influence LOS, with insurance type also playing a crucial role.

Conclusion

In the studied cohort, patient journeys with greater variability were associated with longer LOS with a dose–response relationship: the higher the variability, the longer LOS. This study presents a standardized way to measure and visualize variability in clinical processes and measure its impact on patient-relevant outcomes.

重要性：了解导致患者护理临床变异性的因素至关重要，因为无根据的变异性可能导致不良事件增加和住院时间延长。确定这种可变性何时变得过度，是优化患者结果和医疗效率的一个步骤。目的：探讨临床变异与临床转归的关系。本研究旨在找出临床变异与住院时间（LOS）之间关系显著的时间点。方法：本队列研究使用MIMIC-IV数据集，收集美国贝斯以色列女执事医疗中心的电子健康记录。我们关注的是接受择期冠状动脉搭桥手术的成年患者，共观察了847例患者。记录年龄、种族、保险类型、Charlson共病指数（CCI）等人口统计学因素。我们进行了变异性分析，其中患者的临床过程表示为事件序列。根据记录的活动起始日对数据进行分割，建立观察窗口。通过回归分析，我们确定了可变性对LOS的影响变得独立显著的时间窗口。结果：回归分析显示，在变异性距离组中排名前20位 %的患者的LOS增加了81 %（95 % CI: 1.72至1.91,p ）。结论：在所研究的队列中，变异性较大的患者路程与较长的LOS相关，并具有剂量-反应关系：变异性越高，LOS越长。本研究提出了一种标准化的方法来测量和可视化临床过程中的变异性，并测量其对患者相关结果的影响。

{"title":"Measuring and visualizing healthcare process variability","authors":"Pengfei Yin , Abel Armas Cervantes , Daniel Capurro","doi":"10.1016/j.jbi.2025.104918","DOIUrl":"10.1016/j.jbi.2025.104918","url":null,"abstract":"<div><h3>Importance</h3><div>Understanding factors that contribute to clinical variability in patient care is critical, as unwarranted variability can lead to increased adverse events and prolonged hospital stays. Determining when this variability becomes excessive can be a step in optimizing patient outcomes and healthcare efficiency.</div></div><div><h3>Objective</h3><div>Explore the association between clinical variation and clinical outcomes. This study aims to identify the point in time when the relationship between clinical variation and length of stay (LOS) becomes significant.</div></div><div><h3>Methods</h3><div>This cohort study uses MIMIC-IV, a dataset collecting electronic health records of the Beth Israel Deaconess Medical Center in the United States. We focused on adult patients who underwent elective coronary bypass surgery, generating 847 patient observations. Demographic factors such as age, race, insurance type, and the Charlson Comorbidity Index (CCI) were recorded. We performed a variability analysis where patients’ clinical processes are represented as sequences of events. The data was segmented based on the initial day of recorded activity to establish observation windows. Using a regression analysis, we identified the temporal window where variability’s impact on LOS becomes independently significant.</div></div><div><h3>Result</h3><div>Regression analysis revealed that patients in the top 20 % of the variability distance group experienced an 81 % increase in LOS (95 % CI: 1.72 to 1.91, p < 0.001). Insurance types, such as Medicare and Other, were associated with 18 % (95 % CI: 0.73 to 0.92, p < 0.001) and 21 % (95 % CI: 0.71 to 0.88, p < 0.001) decreases in LOS, respectively. Neither age nor race significantly affected LOS, but a higher CCI was associated with a 3.3 % increase in LOS (95 % CI: 1.02 to 1.05, p < 0.001). These findings indicate that higher variability and CCI significantly influence LOS, with insurance type also playing a crucial role.</div></div><div><h3>Conclusion</h3><div>In the studied cohort, patient journeys with greater variability were associated with longer LOS with a dose–response relationship: the higher the variability, the longer LOS. This study presents a standardized way to measure and visualize variability in clinical processes and measure its impact on patient-relevant outcomes.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"170 ","pages":"Article 104918"},"PeriodicalIF":4.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145149215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparing clinical decision support systems for improving follow-up of abnormal cervical cancer screening test results 比较临床决策支持系统对改善异常宫颈癌筛查结果随访的作用。

IF 4.5 2区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Biomedical Informatics

Pub Date : 2025-10-01 Epub Date: 2025-09-09 DOI: 10.1016/j.jbi.2025.104908

Steven J. Atlas , Timothy E. Burdick , Adam Wright , Wenyan Zhao , Shoshana Hort , David G. Aman , Mathan Thillaiyapillai , E. John Orav , Amy J. Wint , Rebecca E. Smith , Katherine L. Gallagher , Molly L. Housman , Frank Y. Chang , Courtney J. Diamond , Li Zhou , Jennifer S. Haas , Anna N.A. Tosteson

Background

Many individuals with abnormal cervical cancer screening test results do not receive timely follow-up care. Clinical decision support systems (CDSS) to improve follow-up are challenged by difficulty identifying clinical elements and applying complex guideline recommendations. As part of a multisite trial, two CDSS models were implemented: one used natural language processes to evaluate extracted data outside of the electronic health record (EHR) (System A); the other used commercial EHR functionality using LOINC-defined result fields (System B). This secondary analysis compared the accuracy and trial outcomes among sites using these two CDSS models.

Methods

Primary care clinics (32 in System A and 12 in System B) were randomly assigned to usual care, CDSS alone, or CDSS with patient outreach with or without navigation. CDSS identified individuals with overdue abnormal screening results and specified the recommended follow-up and time interval. CDSS accuracy was assessed by manual chart review. Patient outreach consisted of portal/mailed letters plus a single phone call. Navigation included one or more phone calls to address barriers to care. Completion of recommended follow-up at 120 days after enrollment was the primary outcome. Clinic was the unit of randomization, and the patient was the unit of analysis.

Results

Between October 2020 and December 2021, 2596 patients with abnormal results were identified by the CDSS. CDSS true positives were 61.3 % in System A and 70.4 % in System B. CDSS alone versus usual care did not improve outcomes in either system. CDSS with patient outreach with or without navigation versus usual care significantly increased follow-up rates in System A (38.2 % or 37.2 % vs 23.5 %, p < 0.001) and System B (25.4 % or 23 % vs. 19.7 %, p = 0.044).

Conclusions

Two CDSS models developed to identify overdue abnormal cervical cancer screening test results had moderate accuracy. Both models with patient outreach with or without navigation – but not CDSS alone – increased recommended follow-up. Future CDSS for cervical cancer screening may be improved with open-source tools developed in public–private partnerships.

背景：许多宫颈癌筛查结果异常的个体没有得到及时的随访。临床决策支持系统（CDSS），以提高随访困难识别临床因素和应用复杂的指南建议的挑战。作为多站点试验的一部分，实施了两个CDSS模型：一个使用自然语言过程来评估电子健康记录（EHR）（系统a）之外提取的数据；另一个使用商业EHR功能，使用loc定义的结果字段（系统B）。该二次分析比较了使用这两种CDSS模型的站点的准确性和试验结果。方法：初级保健诊所（A系统32家，B系统12家）被随机分配到常规护理、单独CDSS或CDSS患者外展有或没有导航。CDSS对筛查结果逾期异常的个体进行识别，并规定了建议的随访和时间间隔。CDSS的准确性通过人工图表审查来评估。患者外展包括门户/邮寄信件加上一个电话。导航包括一个或多个电话，以解决护理障碍。在入组后120 天完成推荐的随访是主要结局。临床是随机化的单位，病人是分析的单位。结果：在2020年10月至2021年12月期间，CDSS发现了2596例异常结果患者。在A系统中CDSS真阳性为61.3 %，在b系统中为70.4 %。单独使用CDSS与常规护理相比，对两种系统的结果都没有改善。与常规护理相比，有导航或没有导航的CDSS患者外展显著增加了A系统的随访率（38.2 %或37.2 % vs 23.5 %,p ）。结论：两种用于识别逾期异常宫颈癌筛查结果的CDSS模型具有中等准确性。这两种模型都增加了推荐的随访，包括有或没有导航的患者外展，而不是单独的CDSS。未来用于子宫颈癌筛查的CDSS可以通过公私合作开发的开源工具得到改进。

{"title":"Comparing clinical decision support systems for improving follow-up of abnormal cervical cancer screening test results","authors":"Steven J. Atlas , Timothy E. Burdick , Adam Wright , Wenyan Zhao , Shoshana Hort , David G. Aman , Mathan Thillaiyapillai , E. John Orav , Amy J. Wint , Rebecca E. Smith , Katherine L. Gallagher , Molly L. Housman , Frank Y. Chang , Courtney J. Diamond , Li Zhou , Jennifer S. Haas , Anna N.A. Tosteson","doi":"10.1016/j.jbi.2025.104908","DOIUrl":"10.1016/j.jbi.2025.104908","url":null,"abstract":"<div><h3>Background</h3><div>Many individuals with abnormal cervical cancer screening test results do not receive timely follow-up care. Clinical decision support systems (CDSS) to improve follow-up are challenged by difficulty identifying clinical elements and applying complex guideline recommendations. As part of a multisite trial, two CDSS models were implemented: one used natural language processes to evaluate extracted data outside of the electronic health record (EHR) (System A); the other used commercial EHR functionality using LOINC-defined result fields (System B). This secondary analysis compared the accuracy and trial outcomes among sites using these two CDSS models.</div></div><div><h3>Methods</h3><div>Primary care clinics (32 in System A and 12 in System B) were randomly assigned to usual care, CDSS alone, or CDSS with patient outreach with or without navigation. CDSS identified individuals with overdue abnormal screening results and specified the recommended follow-up and time interval. CDSS accuracy was assessed by manual chart review. Patient outreach consisted of portal/mailed letters plus a single phone call. Navigation included one or more phone calls to address barriers to care. Completion of recommended follow-up at 120 days after enrollment was the primary outcome. Clinic was the unit of randomization, and the patient was the unit of analysis.</div></div><div><h3>Results</h3><div>Between October 2020 and December 2021, 2596 patients with abnormal results were identified by the CDSS. CDSS true positives were 61.3 % in System A and 70.4 % in System B. CDSS alone versus usual care did not improve outcomes in either system. CDSS with patient outreach with or without navigation versus usual care significantly increased follow-up rates in System A (38.2 % or 37.2 % vs 23.5 %, p < 0.001) and System B (25.4 % or 23 % vs. 19.7 %, p = 0.044).</div></div><div><h3>Conclusions</h3><div>Two CDSS models developed to identify overdue abnormal cervical cancer screening test results had moderate accuracy. Both models with patient outreach with or without navigation – but not CDSS alone – increased recommended follow-up. Future CDSS for cervical cancer screening may be improved with open-source tools developed in public–private partnerships.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"170 ","pages":"Article 104908"},"PeriodicalIF":4.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145040048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0