Intelligence-based medicine最新文献_第8页

A drug recommendation system based on response prediction: Integrating gene expression and K-mer fragmentation of drug SMILES using LightGBM 基于反应预测的药物推荐系统：利用LightGBM整合药物SMILES的基因表达和K-mer碎片化

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-01-27 DOI: 10.1016/j.ibmed.2025.100206

Sajid Naveed , Mujtaba Husnain

Medical experts and physicians examine the gene expression abnormality in glioblastoma (GBM) cancer patients to identify the drug response. The main objective of this research is to build a machine learning (ML) based model for improve the outcome of cancer medication to save the time and effort of medical practitioners. Developing a drug response recommendation system is our goal that uses the gene expression data of cancer cell lines to predict the response of anticancer drugs in terms of half-maximal inhibitory concentration (IC50). Genetic data from a GBM cancer patient is used as input into a system to predict and recommend the response of multiple anticancer drugs in a particular cancer sample. In this research, we used K-mer molecular fragmentation to process drug SMILES in a novel way, which enabled us to build a competent model that provides drug response. We used the Light Gradient Boosting Machine (LightGBM) regression algorithm and Genomics of Drug Sensitivity of Cancer (GDSC) data for this proposed recommendation system. The results showed that all predicted IC50 values are fall within the range of the real values when examining GBM data. Two drugs, temozolomide and carmustine, were predicted with a Mean Squared Error (MSE) of 0.10 and 0.11 respectively, and 0.41 in unseen test samples. These recommended responses were then verified by expert doctors, who confirmed that the responses to these drugs were very close to the actual response. These recommendation are also effective in slowing the growth of these tumors and improving patients quality of life by monitoring medication effects.

医学专家和医生检查胶质母细胞瘤（GBM）癌症患者的基因表达异常，以确定药物反应。本研究的主要目的是建立一个基于机器学习（ML）的模型，以改善癌症药物治疗的结果，从而节省医生的时间和精力。我们的目标是开发一种药物反应推荐系统，利用癌细胞系的基因表达数据，以半最大抑制浓度（IC50）来预测抗癌药物的反应。来自GBM癌症患者的遗传数据被用作系统的输入，以预测和推荐多种抗癌药物对特定癌症样本的反应。在这项研究中，我们利用K-mer分子碎片以一种新颖的方式处理药物SMILES，这使我们能够建立一个提供药物反应的胜任模型。我们使用光梯度增强机（Light Gradient Boosting Machine, LightGBM）回归算法和癌症药物敏感性基因组学（Genomics of Drug Sensitivity of Cancer， GDSC）数据来构建这个推荐系统。结果表明，对GBM数据的预测IC50值均落在实际值的范围内。替莫唑胺和卡莫司汀两种药物的预测均方误差（MSE）分别为0.10和0.11，未见样品的预测均方误差为0.41。这些建议的反应然后由专家医生验证，他们确认对这些药物的反应非常接近实际反应。这些建议也有效地减缓这些肿瘤的生长，并通过监测药物效果来改善患者的生活质量。

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引用次数: 0

Meta-learning driven multi disease fuzzy neural framework for clinical risk prediction 基于元学习驱动的多疾病模糊神经框架临床风险预测

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-11-21 DOI: 10.1016/j.ibmed.2025.100315

Kubra Noor, Ubaida Fatima, Fahim Raees

Rising rates of chronic illnesses, including heart disease, diabetes, and cancer, demand precise and scalable early diagnostic approaches. Utilizing three datasets UCI Heart Disease, PIMA Indians Diabetes, and Breast Cancer Wisconsin we suggest a metalearning-inspired hybrid approach combining Fuzzy C-Means clustering and artificial neural networks for multidisease risk prediction. Fuzzy logic is used to cluster each dataset to model intraclass variation; cluster-specific neural networks are then trained to catch patterns. Fuzzy membership ratings are used to combine final forecasts. Achieving 85.25 % (heart disease), 81.2 % (diabetes), and 95.1 % (cancer) accuracy, respectively, the suggested system shows great accuracy, disease-wide generalization, and interpretability. The results show improved predictions for complex and varied patient profiles, confirming that the system is strong and useful for real-world health analysis.

包括心脏病、糖尿病和癌症在内的慢性病发病率不断上升，需要精确和可扩展的早期诊断方法。利用UCI心脏病、PIMA印第安人糖尿病和威斯康星州乳腺癌三个数据集，我们提出了一种元学习启发的混合方法，将模糊c均值聚类和人工神经网络相结合，用于多疾病风险预测。利用模糊逻辑对各数据集进行聚类，模拟类内变化；然后训练特定于集群的神经网络来捕捉模式。模糊隶属度评级用于组合最终预测。该系统分别达到85.25%（心脏病）、81.2%（糖尿病）和95.1%（癌症）的准确率，显示出很高的准确性、疾病通用性和可解释性。结果表明，对复杂和不同的患者概况的预测有所改善，证实了该系统在现实世界的健康分析中是强大和有用的。

引用次数: 0

Design and implementation of a low-cost malaria diagnostic system based on convolutional neural network 基于卷积神经网络的低成本疟疾诊断系统的设计与实现

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-07-09 DOI: 10.1016/j.ibmed.2025.100272

Ekobo Akoa Brice , Ndoumbe Jean , Mohamadou Madina

This work focuses on the design and implementation of an intelligent system that can diagnose malaria from blood smear images. This system takes data in the image format and provides an instant and automated diagnosis to output the result of the patient’s condition on a screen. The methodology for achieving the system is based on the CNN (convolutional neural network). The latter has the specificity to function as a feature extractor and image classifier. The software part thus obtained is implemented in an electronic device that serves as a kit mounted with our care. The establishment of such a system has innumerable assets, such as rapidity during diagnosis by a laboratory technician or not; its portability that will facilitate its use wherever needed. From an ergonomic and functional point of view, the system has a real impact in the diagnosis of a large-scale malaria endemic. The CNN was trained on a large dataset of blood smears and was able to accurately classify infected and uninfected samples with high sensitivity and specificity. Insofar as the system carried out after testing on several samples reaches an average sensitivity of 89.50% and an average precision of 89%, this improves decision-making on the diagnosis of malaria. The system thus created allows malaria to be diagnosed at low cost from blood smear images. The use of CNNs in this project has the advantage of automatically extracting features from blood smear images and classifying them efficiently. The major advantage of the proposed system is its portability and lower cost. The performance of the proposed algorithm was evaluated on a publicly available malaria data set.

这项工作的重点是设计和实现一个智能系统，可以从血液涂片图像诊断疟疾。该系统以图像格式获取数据，并提供即时和自动的诊断，将患者的病情结果输出到屏幕上。实现该系统的方法是基于CNN（卷积神经网络）。后者具有作为特征提取器和图像分类器的特异性。由此获得的软件部分在一个电子设备中实现，该设备作为一个工具包安装在我们的护理中。建立这样一个系统具有无数的优势，例如实验室技术人员在诊断过程中是否快速；它的可移植性将使它在任何需要的地方都能使用。从人体工程学和功能的角度来看，该系统对大规模疟疾地方病的诊断具有实际影响。CNN在一个大型的血液涂片数据集上进行训练，能够以高灵敏度和特异性准确地对感染和未感染的样本进行分类。在对几个样本进行测试后，该系统的平均灵敏度达到89.50%，平均精度达到89%，这改善了疟疾诊断的决策。这样创建的系统可以通过血液涂片图像以低成本诊断疟疾。在本课题中使用cnn具有从血液涂片图像中自动提取特征并进行高效分类的优点。该系统的主要优点是可移植性和低成本。在一个公开可用的疟疾数据集上评估了所提出算法的性能。

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引用次数: 0

Arkangel AI: A conversational agent for real-time, evidence-based medical question-answering Arkangel AI：实时、循证医学问答的对话代理

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-07-11 DOI: 10.1016/j.ibmed.2025.100274

Maria Camila Villa, Natalia Castano-Villegas, Isabella Llano, Julian Martinez, Maria Fernanda Guevara, Jose Zea, Laura Velásquez

Introduction

Large Language Models (LLMs) have been trained and tested on several medical question-answering (QA) datasets built from medical licensing exams and natural interactions between doctors and patients to fine-tune them for specific health-related tasks.

Objective

We aimed to develop LLM-powered Conversational Agents (CAs) equipped to produce fast, accurate, and real-time responses to medical queries in different clinical and scientific scenarios. This paper presents Arkangel AI, our first conversational agent and research assistant.

Methods

The model is based on a system containing five LLMs; each is classified within a specific workflow with pre-defined instructions to produce the best search strategy and provide evidence-based answers. We assessed accuracy, intra/inter-class variability, and Cohen's Kappa using the question-answer (QA) dataset MedQA. Additionally, we used the PubMedQA dataset and assessed both databases using the RAGAS framework, including Context, Response Relevance, and Faithfulness. Traditional statistical analysis was performed with hypothesis tests and 95 % IC.

Results

Accuracy for MedQA (n: 1273) was 90.26 % and Cohen's kappa was 87 %, surpassing current SoTAs for other LLMs (GPT-4o, MedPaLM2). The model retrieved 80 % of the expected articles and provided relevant answers in 82 % of PubMedQA.

Conclusion

Arkangel AI showed proficient retrieval and reasoning abilities and unbiased responses. Evenly distributed medical QA datasets to train improved LLMs and external validation for the model with real-world physicians in clinical scenarios are needed. Clinical decision-making remains in the hands of trained healthcare professionals.

大型语言模型（llm）已经在几个医学问答（QA）数据集上进行了培训和测试，这些数据集来自医疗许可考试和医生和患者之间的自然互动，以微调它们以适应特定的健康相关任务。我们的目标是开发基于llm的会话代理（CAs），以便在不同的临床和科学场景中对医疗查询产生快速、准确和实时的响应。本文介绍了Arkangel AI，我们的第一个会话代理和研究助理。方法基于一个包含5个llm的系统建立模型；每个都在特定的工作流中进行分类，并带有预定义的指令，以产生最佳搜索策略并提供基于证据的答案。我们使用问答（QA）数据集MedQA评估准确性、类内/类间变异性和Cohen Kappa。此外，我们使用PubMedQA数据集，并使用RAGAS框架评估两个数据库，包括上下文、响应相关性和可信度。结果MedQA （n: 1273）的准确率为90.26%,Cohen’s kappa为87%，超过了目前其他LLMs （gpt - 40、MedPaLM2）的SoTAs。该模型检索了80%的预期文章，并在82%的PubMedQA中提供了相关答案。结论arkangel人工智能具有良好的检索推理能力和无偏性反应。需要均匀分布的医疗QA数据集来训练改进的llm，并在临床场景中与现实世界的医生一起对模型进行外部验证。临床决策仍然掌握在训练有素的医疗保健专业人员手中。

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引用次数: 0

Machine learning approach in predicting early antenatal care initiation at first trimester among reproductive women in Somalia: an analysis with SHAP explanations 预测索马里育龄妇女妊娠早期产前护理的机器学习方法：基于SHAP解释的分析

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-04-21 DOI: 10.1016/j.ibmed.2025.100252

Jamilu Sani , Mohamed Mustaf Ahmed

Introduction

Timely antenatal care (ANC) initiation is essential for maternal and neonatal health, enabling the early detection of risks and ensuring optimal care. In Somalia, delayed initiation of ANC poses a significant health risk. This study applied machine learning (ML) models to predict early ANC initiation among Somali women and identify key predictors using SHapley Additive exPlanations (SHAP).

Methods

Data from the 2020 Somali Health and Demographic Survey were analyzed, focusing on ANC timing in 3138 women aged 15–49. Six ML models (Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbors, and XGBoost) were assessed for accuracy, precision, recall, F1-score, and AUROC. Feature importance was evaluated using SHAP to interpret the influence of each predictor.

Results

Random Forest achieved the highest performance, with an accuracy of 70 %, precision of 0.69, recall of 0.71, and AUROC of 0.74, closely followed by XGBoost, which achieved an accuracy of 69 % and AUROC of 0.72. SHAP analysis identified the place of delivery, residence, and age group as the most influential predictors of early ANC initiation, with the number of births in the past five years showing a significant negative impact.

Conclusion

Machine learning models, particularly Random Forest and XGBoost, effectively predicted early ANC initiation, highlighting significant demographic and healthcare access-related predictors. These findings suggest targeted interventions focusing on delivery location preferences, residential factors, and age-specific approaches to improve early ANC attendance in Somalia.

及时开展产前保健（ANC）对孕产妇和新生儿健康至关重要，能够及早发现风险并确保最佳护理。在索马里，推迟启动非裔国民大会对健康构成重大风险。本研究应用机器学习（ML）模型预测索马里妇女早期ANC的发生，并使用SHapley加性解释（SHAP）确定关键预测因素。方法分析2020年索马里健康和人口调查的数据，重点分析3138名15-49岁妇女的ANC时间。对6个ML模型（逻辑回归、支持向量机、决策树、随机森林、k近邻和XGBoost）的准确性、精密度、召回率、f1评分和AUROC进行了评估。使用SHAP评估特征重要性，以解释每个预测因子的影响。结果random Forest的准确率为70%，精密度为0.69，召回率为0.71，AUROC为0.74，XGBoost紧随其后，准确率为69%，AUROC为0.72。SHAP分析确定，分娩地点、居住地和年龄组是早期ANC发生的最具影响力的预测因素，过去5年的出生数量显示出显著的负面影响。机器学习模型，特别是Random Forest和XGBoost，可以有效预测早期ANC的发生，突出了重要的人口统计学和医疗保健相关预测因子。这些发现表明，有针对性的干预措施侧重于递送地点偏好、居住因素和针对特定年龄的方法，以提高索马里ANC的早期出勤率。

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引用次数: 0

Fully automatic content-aware tiling pipeline for pathology whole slide images 全自动内容感知平铺管道病理整个幻灯片图像

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-11-24 DOI: 10.1016/j.ibmed.2025.100318

Falah Jabar , Lill-Tove Rasmussen Busund , Biagio Ricciuti , Masoud Tafavvoghi , Thomas K. Kilvaer , David J. Pinato , Mette Pøhl , Sigve Andersen , Tom Donnem , David J. Kwiatkowski , Mehrdad Rakaee

Tiling (or patching) histology Whole Slide Images (WSIs) is a required initial step in the development of deep learning (DL) models. Gigapixel-scale WSIs must be divided into smaller, manageable image tiles. Standard WSI tiling techniques often exclude diagnostically important tissue regions or include regions with artifacts such as folds, blurs, and pen-markings, which can significantly degrade DL model performance and analysis. This paper introduces WSI-SmartTiling, a fully automated, deep learning-based, content-aware WSI tiling pipeline designed to include maximal information content from WSI. A supervised DL model for artifact detection was developed using pixel-based semantic segmentation at high magnification (20× and 40x) to classify WSI regions as either artifacts or qualified tissue. The model was trained on a diverse dataset and validated using both internal and external datasets. Quantitative and qualitative evaluations demonstrated its superiority, outperforming state-of-the-art methods with accuracy, precision, recall, and F1 scores exceeding 95 % across all artifact types, along with Dice scores above 94 %. In addition, WSI-SmartTiling integrates a generative adversarial network model to reconstruct tissue regions obscured by pen-markings in various colors, ensuring relevant valuable areas are preserved. Lastly, while excluding artifacts, the pipeline efficiently tiles qualified tissue regions with minimum tissue loss.

In conclusion, this high-resolution preprocessing pipeline can significantly improve pathology WSI-based feature extraction and DL-based classification by minimizing tissue loss and providing high-quality – artifact-free – tissue tiles. The WSI-SmartTiling pipeline is publicly available on GitHub.

铺贴（或修补）组织学全幻灯片图像（wsi）是开发深度学习（DL）模型所需的第一步。千兆像素级wsi必须划分为更小的、可管理的图像块。标准的WSI平铺技术通常会排除诊断上重要的组织区域，或者包括褶皱、模糊和笔标记等伪影区域，这些区域会显著降低DL模型的性能和分析。本文介绍了WSI- smarttiling，这是一个全自动的、基于深度学习的、内容感知的WSI平铺管道，旨在包含来自WSI的最大信息内容。在高倍率（20倍和40倍）下，使用基于像素的语义分割开发了一个用于伪像检测的监督深度学习模型，将WSI区域分类为伪像或合格组织。该模型在不同的数据集上进行训练，并使用内部和外部数据集进行验证。定量和定性评估证明了它的优越性，在准确性、精密度、召回率方面优于最先进的方法，在所有神器类型中F1得分超过95%，Dice得分超过94%。此外，WSI-SmartTiling集成了生成对抗网络模型，以重建被各种颜色的笔标记遮挡的组织区域，确保保留相关的有价值的区域。最后，在排除伪影的同时，管道有效地以最小的组织损失覆盖合格的组织区域。总之，这种高分辨率的预处理管道可以通过最大限度地减少组织损失和提供高质量的无伪影组织块，显著改善基于病理wsi的特征提取和基于dl的分类。WSI-SmartTiling管道在GitHub上是公开的。

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引用次数: 0

Optimizing melanoma diagnosis: A hybrid deep learning and quantum computing approach for enhanced lesion classification 优化黑色素瘤诊断：用于增强病变分类的混合深度学习和量子计算方法

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-06-21 DOI: 10.1016/j.ibmed.2025.100264

Maria Frasca , Ilaria Cutica , Gabriella Pravettoni , Davide La Torre

Melanoma is one of the most aggressive forms of skin cancer, necessitating advanced diagnostic tools to improve early detection. This study presents a novel AI-driven approach that combines deep neural networks with quantum computing techniques for enhanced lesion classification. Specifically, we employ a U-Net model for segmentation and a hybrid Convolutional Neural Network - Quantum Neural Network (CNN-QNN) for classification. Our approach achieves a precision of 99.67 %, recall of 99.67 %, and an overall accuracy of 99.35 % on the HAM10000 dataset. Additionally, we report a sensitivity of 99.4 %, a specificity of 99.2 %, and a macro F1-score of 99.5 %, significantly surpassing traditional CNN-based classifiers. This hybrid model outperforms conventional deep learning approaches, demonstrating its potential for aiding dermatologists in clinical decision-making. A comparative analysis with state-of-the-art models further validates the effectiveness of our method.

黑色素瘤是最具侵袭性的皮肤癌之一，需要先进的诊断工具来提高早期发现。本研究提出了一种新的人工智能驱动方法，将深度神经网络与量子计算技术相结合，以增强病变分类。具体来说，我们使用U-Net模型进行分割，使用混合卷积神经网络-量子神经网络（CNN-QNN）进行分类。我们的方法在HAM10000数据集上实现了99.67%的准确率、99.67%的召回率和99.35%的总体准确率。此外，我们报告的灵敏度为99.4%，特异性为99.2%，宏观f1评分为99.5%，显著超过传统的基于cnn的分类器。这种混合模型优于传统的深度学习方法，证明了它在帮助皮肤科医生进行临床决策方面的潜力。与最先进模型的对比分析进一步验证了我们方法的有效性。

引用次数: 0

Artificial neural network based automatic detection of motor evoked potentials 基于人工神经网络的运动诱发电位自动检测

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-09-13 DOI: 10.1016/j.ibmed.2025.100295

Bethel Osuagwu , Hongli Huang , Emily L. McNicol , Vellaisamy A.L. Roy , Aleksandra Vučkovič

Introduction

Motor evoked potentials (MEP) are detected using various methods that determine signal changepoints. The current detection methods perform well given a high signal to noise ratio. However, performance can diminish with artefact such as those arising due to poor signal quality and unwanted electrical potentials. Part of the problem is likely because the methods ignore the morphology of a signal making it impossible to differentiate noise from MEPs.

Methods

For the first time, we investigated a new detection method able to learn MEP morphology using artificial neural networks. To build an MEP detection model, we trained deep neural networks with architectures based on combined CNN and LSTM or self-attention mechanism, using sample MEP data recorded from able-bodied individuals. The MEP detection capability of the models was compared with that of a changepoint based detection method.

Results

Our models reached test accuracy of up to 89.7 ± 1.5 % on average. In a real-world setting evaluation, our models achieved average detection accuracy of up to 94.7 ± 1.2 %, compared with 76.4 ± 5.3 % for the standard changepoint detection method (p = 0.004).

Conclusion

Artificial neural network models can be used for improved automated detection of MEPs.

运动诱发电位（MEP）的检测使用各种方法来确定信号的变化点。当前的检测方法在高信噪比条件下表现良好。然而，由于信号质量差和不需要的电势而产生的伪影会降低性能。部分问题可能是因为这些方法忽略了信号的形态，从而无法区分噪声和mep。方法首次研究了一种基于人工神经网络的MEP形态学检测方法。为了构建MEP检测模型，我们使用健全个体的MEP样本数据，训练了基于CNN和LSTM（自注意机制）相结合的深层神经网络架构。将模型的MEP检测能力与基于变化点的检测方法进行了比较。结果模型的检测准确率平均可达89.7±1.5%。在现实环境评估中，我们的模型实现了高达94.7±1.2%的平均检测精度，而标准变化点检测方法的平均检测精度为76.4±5.3% （p = 0.004）。结论人工神经网络模型可用于改进mep的自动检测。

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引用次数: 0

Deep learning based detection of endometriosis lesions in laparoscopic images with 5-fold cross-validation 基于深度学习的腹腔镜子宫内膜异位症病变检测及5倍交叉验证

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-03-08 DOI: 10.1016/j.ibmed.2025.100230

Shujaat Ali Zaidi , Varin Chouvatut , Chailert Phongnarisorn , Dussadee Praserttitipong

Endometriosis, a complex gynecological condition, presents significant diagnostic challenges due to the subtle and varied appearance of its lesions. This study leverages deep learning to classify endometriosis lesions in laparoscopic images using the Gynecologic Laparoscopy Endometriosis Dataset (GLENDA). Three deep learning models VGG19, ResNet50, and Inception V3 were trained and evaluated with 5-fold cross-validation to enhance generalizability and mitigate overfitting. Robust data augmentation techniques were applied to address dataset limitations. The models were tasked with classifying lesions into pathological and nonpathological categories. Experimental results demonstrated strong performance, with VGG19, ResNet50, and Inception V3 achieving accuracies of 0.89, 0.91, and 0.93, respectively. Inception V3 outperformed the others, highlighting its efficacy for this task. The findings underscore the potential of deep learning in improving endometriosis diagnosis, offering a reliable tool for clinicians. This study contributes to the growing field of AI-driven medical image analysis, emphasizing the value of cross-validation and data augmentation in enhancing model performance for specialized medical applications.

子宫内膜异位症是一种复杂的妇科疾病，由于其病变的微妙和多样的外观，提出了重大的诊断挑战。本研究利用妇科腹腔镜子宫内膜异位症数据集（GLENDA），利用深度学习对腹腔镜图像中的子宫内膜异位症病变进行分类。三个深度学习模型VGG19， ResNet50和Inception V3进行了训练和评估，并进行了5倍交叉验证，以增强泛化性并减少过拟合。应用稳健的数据增强技术来解决数据集的局限性。这些模型的任务是将病变分为病理和非病理两类。实验结果显示了较强的性能，VGG19、ResNet50和Inception V3的准确率分别为0.89、0.91和0.93。Inception V3的表现优于其他版本，突出了它在此任务中的有效性。研究结果强调了深度学习在改善子宫内膜异位症诊断方面的潜力，为临床医生提供了可靠的工具。这项研究促进了人工智能驱动的医学图像分析领域的发展，强调了交叉验证和数据增强在提高专业医疗应用的模型性能方面的价值。

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引用次数: 0

A Bayesian framework for LLM-enhanced history-taking in recurrent medical conditions to improve treatment outcomes: An empirical evaluation 一个贝叶斯框架的法学硕士增强历史采取复发性医疗条件，以提高治疗效果：经验评估

Intelligence-based medicine

Pub Date : 2025-01-01 Epub Date: 2025-07-31 DOI: 10.1016/j.ibmed.2025.100282

Timothy Suraj

This paper introduces a novel Bayesian framework integrating Large Language Models (LLMs) into medical history-taking specifically for recurrent medical conditions, aiming to overcome limitations of traditional methods and improve treatment outcomes. Unlike existing AI applications in healthcare that primarily focus on diagnostic classification or prediction in acute settings, our approach emphasizes iterative diagnostic refinement and explainable AI within a Bayesian probabilistic framework, offering a unique strategy for personalized management of recurrent conditions. We empirically evaluate this framework by analyzing the current limitations in clinical history-taking practices and leveraging the capabilities of modern LLMs to generate more comprehensive patient narratives, improve pattern recognition across longitudinal data, and enhance the identification of subtle disease precursors. Our review of preliminary implementations suggests that LLM integration into clinical workflows may reduce diagnostic errors, improve treatment adherence, and enable more personalized therapeutic interventions. However, significant challenges remain regarding clinical validation, privacy concerns, and integration with existing healthcare systems. We conclude that LLMs represent a promising tool for treating recurrent medical conditions when deployed as physician augmentation rather than replacement technologies.

本文介绍了一种新的贝叶斯框架，将大语言模型（llm）集成到病史采集中，专门针对复发性疾病，旨在克服传统方法的局限性，提高治疗效果。与医疗保健领域现有的人工智能应用主要侧重于急性环境中的诊断分类或预测不同，我们的方法强调在贝叶斯概率框架内迭代诊断改进和可解释的人工智能，为复发性疾病的个性化管理提供了独特的策略。我们通过分析目前临床病史采集实践的局限性，并利用现代法学硕士的能力来生成更全面的患者叙述，改善纵向数据的模式识别，并增强对细微疾病前兆的识别，对该框架进行了实证评估。我们对初步实施的回顾表明，将LLM整合到临床工作流程中可以减少诊断错误，提高治疗依从性，并实现更个性化的治疗干预。然而，在临床验证、隐私问题和与现有医疗保健系统的集成方面，仍然存在重大挑战。我们得出的结论是，llm是治疗复发性疾病的一个很有前途的工具，当作为医生增强而不是替代技术部署时。

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引用次数: 0