Pub Date : 2026-02-01Epub Date: 2026-01-10DOI: 10.1016/j.jbi.2026.104984
Jialou Wang , Pingfan Wang , Wai Lok Woo , Kandianos Emmanouil Sakalidis , Florentina Johanna Hettinga , Angela Rodrigues , Helen Dawes , Gavin Daniel Tempest
Understanding the drivers of well-being from longitudinal behavioural data is a fundamental challenge in biomedical informatics, where traditional analyses often conflate correlation with causation. This paper presents a rigorous application of causal inference to disentangle the drivers of well-being from complex longitudinal self-report data ( enrolled; analysed after a priori completeness threshold of of 28 daily entries). We introduce a novel computational metric, the Behaviour Self-Regulation Score (BSRS), to quantify both trait-like (long-term) and state-like (short-term) behavioural consistency from daily reports of physical activity and sleep. Employing causal graphical models and propensity score methods, we estimate the causal effects of these behavioural patterns, controlling for motivational and perceptual confounders. Our analysis uncovers distinct causal pathways: while long-term self-regulation (BSRS-L) has a stable positive causal effect, short-term behavioural consistency (BSRS-S) demonstrates a significantly stronger causal impact on daily well-being, despite a near-zero correlation. Furthermore, we demonstrate that features selected via our causal framework significantly improve the predictive accuracy of well-being in machine learning models compared to conventional feature selection methods. This work contributes a robust methodological framework for causal analysis of longitudinal self-report data and provides evidence that causally-informed modelling can identify more potent targets for digital health interventions.
{"title":"Quantifying the effect of Behaviour Self-Regulation on well-being through causal analysis: A methodological framework for longitudinal health data","authors":"Jialou Wang , Pingfan Wang , Wai Lok Woo , Kandianos Emmanouil Sakalidis , Florentina Johanna Hettinga , Angela Rodrigues , Helen Dawes , Gavin Daniel Tempest","doi":"10.1016/j.jbi.2026.104984","DOIUrl":"10.1016/j.jbi.2026.104984","url":null,"abstract":"<div><div>Understanding the drivers of well-being from longitudinal behavioural data is a fundamental challenge in biomedical informatics, where traditional analyses often conflate correlation with causation. This paper presents a rigorous application of causal inference to disentangle the drivers of well-being from complex longitudinal self-report data (<span><math><mrow><mi>N</mi><mo>=</mo><mn>141</mn></mrow></math></span> enrolled; <span><math><mrow><mi>N</mi><mo>=</mo><mn>94</mn></mrow></math></span> analysed after a priori completeness threshold of <span><math><mrow><mo>≥</mo><mn>20</mn></mrow></math></span> of 28 daily entries). We introduce a novel computational metric, the Behaviour Self-Regulation Score (BSRS), to quantify both trait-like (long-term) and state-like (short-term) behavioural consistency from daily reports of physical activity and sleep. Employing causal graphical models and propensity score methods, we estimate the causal effects of these behavioural patterns, controlling for motivational and perceptual confounders. Our analysis uncovers distinct causal pathways: while long-term self-regulation (BSRS-L) has a stable positive causal effect, short-term behavioural consistency (BSRS-S) demonstrates a significantly stronger causal impact on daily well-being, despite a near-zero correlation. Furthermore, we demonstrate that features selected via our causal framework significantly improve the predictive accuracy of well-being in machine learning models compared to conventional feature selection methods. This work contributes a robust methodological framework for causal analysis of longitudinal self-report data and provides evidence that causally-informed modelling can identify more potent targets for digital health interventions.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"174 ","pages":"Article 104984"},"PeriodicalIF":4.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145951928","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}
Pub Date : 2026-01-01Epub Date: 2025-11-29DOI: 10.1016/j.jbi.2025.104965
Jianghai Zhou , Jike Ge , Zuqin Chen , Jie Tan , You Li
Objective
Depression is a serious mental disorder that significantly affects patients’ work ability and social functioning. With the rapid development of artificial intelligence, researchers have begun to explore automatic depression detection methods based on multimodal data. However, multimodal data are often accompanied by a large amount of noise. Existing methods usually lack sufficient feature screening after extraction and are directly applied to downstream tasks, which may limit the model’s generalization ability. In addition, current multimodal fusion strategies still face several challenges.
Methods
To address these challenges, we propose a novel multimodal depression detection model that integrates three modalities: audio, vision, and text. The model extracts depression-related key features through a multi-level attention mechanism and achieves efficient multimodal feature fusion using skip connections with a residual structure.
Results
Experiments conducted on the DAIC-WOZ dataset showed that the proposed method achieved a mean absolute error (MAE) of 3.13 and a root mean square error (RMSE) of 3.59, outperforming existing state-of-the-art models. The generalization ability of the model was further validated on the E-DAIC dataset, demonstrating its effectiveness and robustness.
Conclusion
The proposed method provides an efficient and reliable solution for depression detection using multimodal data and multi-level attention mechanisms. The findings highlight the significant value of multimodal learning in the medical field and offer strong support for the development of AI-assisted clinical decision-making systems.
{"title":"MDD-MARF: a multimodal depression detection model based on multi-level attention mechanism and residual fusion","authors":"Jianghai Zhou , Jike Ge , Zuqin Chen , Jie Tan , You Li","doi":"10.1016/j.jbi.2025.104965","DOIUrl":"10.1016/j.jbi.2025.104965","url":null,"abstract":"<div><h3>Objective</h3><div>Depression is a serious mental disorder that significantly affects patients’ work ability and social functioning. With the rapid development of artificial intelligence, researchers have begun to explore automatic depression detection methods based on multimodal data. However, multimodal data are often accompanied by a large amount of noise. Existing methods usually lack sufficient feature screening after extraction and are directly applied to downstream tasks, which may limit the model’s generalization ability. In addition, current multimodal fusion strategies still face several challenges.</div></div><div><h3>Methods</h3><div>To address these challenges, we propose a novel multimodal depression detection model that integrates three modalities: audio, vision, and text. The model extracts depression-related key features through a multi-level attention mechanism and achieves efficient multimodal feature fusion using skip connections with a residual structure.</div></div><div><h3>Results</h3><div>Experiments conducted on the DAIC-WOZ dataset showed that the proposed method achieved a mean absolute error (MAE) of 3.13 and a root mean square error (RMSE) of 3.59, outperforming existing state-of-the-art models. The generalization ability of the model was further validated on the E-DAIC dataset, demonstrating its effectiveness and robustness.</div></div><div><h3>Conclusion</h3><div>The proposed method provides an efficient and reliable solution for depression detection using multimodal data and multi-level attention mechanisms. The findings highlight the significant value of multimodal learning in the medical field and offer strong support for the development of AI-assisted clinical decision-making systems.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"173 ","pages":"Article 104965"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645673","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}
Pub Date : 2026-01-01Epub Date: 2025-12-24DOI: 10.1016/j.jbi.2025.104972
Sen Niu , Xiaohong Han , Liu Cao , Ye Tian , Ding Yuan , Longlong Cheng
We present CA-NEE, a Conditional-Aware one-stage model for overlapping and nested biomedical event extraction. CA-NEE integrates an event-type-aware conditioning mechanism with token-pair relation modeling to jointly identify triggers, argument spans, and roles. A Conditional Layer Normalization (CLN) dynamically adapts token representations to candidate event types, and a parallel word-pair scorer predicts span boundaries and roles in a single pass. Evaluations on GENIA11 and GENIA13 show consistent gains in Trigger Classification (TC) and Argument Classification (AC) over strong baselines, particularly on complex overlapping and nested structures. These results demonstrate that CA-NEE offers an effective and efficient solution for biomedical event extraction.
{"title":"Fusion framework: Conditional-aware one-stage nested event extraction model","authors":"Sen Niu , Xiaohong Han , Liu Cao , Ye Tian , Ding Yuan , Longlong Cheng","doi":"10.1016/j.jbi.2025.104972","DOIUrl":"10.1016/j.jbi.2025.104972","url":null,"abstract":"<div><div>We present CA-NEE, a Conditional-Aware one-stage model for overlapping and nested biomedical event extraction. CA-NEE integrates an event-type-aware conditioning mechanism with token-pair relation modeling to jointly identify triggers, argument spans, and roles. A Conditional Layer Normalization (CLN) dynamically adapts token representations to candidate event types, and a parallel word-pair scorer predicts span boundaries and roles in a single pass. Evaluations on <strong>GENIA11</strong> and <strong>GENIA13</strong> show consistent gains in Trigger Classification (TC) and Argument Classification (AC) over strong baselines, particularly on complex overlapping and nested structures. These results demonstrate that CA-NEE offers an effective and efficient solution for biomedical event extraction.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"173 ","pages":"Article 104972"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145843751","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}
Pub Date : 2026-01-01Epub Date: 2025-12-11DOI: 10.1016/j.jbi.2025.104970
Hongyun Zeng , Huiwei Zhou , Weihong Yao , Hao Zhou , Yan Zhao , Zhecheng Wang
Objectives
Hypothesis generation (HG) aims to reveal meaningful hidden relationships between scientific terms from literature for accelerating innovation in drug discovery, disease prognosis and treatment. Recent studies have successfully employed the dynamic nature of term-pair relations for HG. However, the existing methods focus on capturing the evolution of term pairs by modeling the temporal meaning of terms themselves, which is hard to accurately model intricate spatio-temporal relations between term pairs.
Methods
In this paper, a Temporal Self and Interactive Evolution (TSIE) modeling method is proposed to accurately characterize complex dynamics of term-pair relations in HG. Specifically, for each term pair, we first employ Gated Recurrent Unit (GRU) to model its Temporal Self-evolution (TSE) and Temporal Interactive Evolution (TIE) for learning its TSE Embedding (TSE_emb) and TIE Embedding (TIE_emb), respectively. Then, we adopt a dual-tower Transformer to further model the temporal dependencies of both TSE_emb and TIE_emb, which are finally integrated by a gated fusion layer for inferring the future connectivity of the term pair.
Results
Experiments on three real-world datasets Immunotherapy, Virology, and Neurology demonstrate that TSIE can effectively capture complex evolutional patterns for biomedical hypothesis generation and achieve the state-of-the-art performance.
Conclusion
This paper proposes a novel TSIE method to learn temporal interactive difference features and enhance the model’s understanding of temporal relation inference. Our TSIE learns both TSE and TIE to effectively model the dynamic relationship between terms. By adapting a dual-tower Transformer encoder, TSIE can further model the temporal dependencies of TSE and TIE.
{"title":"Modeling temporal self and interactive evolution for biomedical hypothesis generation","authors":"Hongyun Zeng , Huiwei Zhou , Weihong Yao , Hao Zhou , Yan Zhao , Zhecheng Wang","doi":"10.1016/j.jbi.2025.104970","DOIUrl":"10.1016/j.jbi.2025.104970","url":null,"abstract":"<div><h3>Objectives</h3><div>Hypothesis generation (HG) aims to reveal meaningful hidden relationships between scientific terms from literature for accelerating innovation in drug discovery, disease prognosis and treatment. Recent studies have successfully employed the dynamic nature of term-pair relations for HG. However, the existing methods focus on capturing the evolution of term pairs by modeling the temporal meaning of terms themselves, which is hard to accurately model intricate spatio-temporal relations between term pairs.</div></div><div><h3>Methods</h3><div>In this paper, a Temporal Self and Interactive Evolution (TSIE) modeling method is proposed to accurately characterize complex dynamics of term-pair relations in HG. Specifically, for each term pair, we first employ Gated Recurrent Unit (GRU) to model its Temporal Self-evolution (TSE) and Temporal Interactive Evolution (TIE) for learning its TSE Embedding (TSE_emb) and TIE Embedding (TIE_emb), respectively. Then, we adopt a dual-tower Transformer to further model the temporal dependencies of both TSE_emb and TIE_emb, which are finally integrated by a gated fusion layer for inferring the future connectivity of the term pair.</div></div><div><h3>Results</h3><div>Experiments on three real-world datasets Immunotherapy, Virology, and Neurology demonstrate that TSIE can effectively capture complex evolutional patterns for biomedical hypothesis generation and achieve the state-of-the-art performance.</div></div><div><h3>Conclusion</h3><div>This paper proposes a novel TSIE method to learn temporal interactive difference features and enhance the model’s understanding of temporal relation inference. Our TSIE learns both TSE and TIE to effectively model the dynamic relationship between terms. By adapting a dual-tower Transformer encoder, TSIE can further model the temporal dependencies of TSE and TIE.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"173 ","pages":"Article 104970"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145742797","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}
Pub Date : 2026-01-01Epub Date: 2025-12-18DOI: 10.1016/j.jbi.2025.104968
Yin-Long Liu , Yuanchao Li , Rui Feng , Jiaxin Chen , Yiming Wang , Yu-Ang Chen , Nan Ding , Jiahong Yuan , Zhen-Hua Ling
Objective:
This study aims to extend the counterintuitive observation that Automatic Speech Recognition (ASR) errors can be beneficial for Alzheimer’s Disease (AD) detection. Our objective is to conduct a large-scale investigation to validate this phenomenon and, more importantly, to elucidate the specific mechanisms by which ASR errors can serve as valuable diagnostic clues for distinguishing individuals with AD from Healthy Controls (HC).
Methods:
We employed 18 ASR models, in both their original and fine-tuned versions, to generate 36 sets of transcripts from the ADReSS dataset. We also synthesized speech from both manual and ASR transcripts using a text-to-speech (TTS) model. Knowledge-based features and pre-trained embeddings were extracted and fed into two proposed AD detection models : a self-attention model and a cross-attention-based interpretability model. To uncover the underlying mechanisms, we conducted a multi-faceted set of analyses, including examinations of ASR error types, words affected by ASR errors, linguistic comparisons, attention weight analysis, and case studies.
Results:
We demonstrate that transcripts generated by certain ASR models achieve higher AD detection accuracy than gold-standard manual transcripts. This performance gain stems not from errors in general or a high Word Error Rate (WER), but from specific and asymmetric error patterns. Our analyses reveal that these patterns amplify some pre-existing linguistic deficits in AD speech (e.g., disfluencies), thereby increasing the feature-level divergence between the AD and HC groups. Furthermore, we show that these diagnostic clues are effectively preserved when speech is synthesized from ASR transcripts, holding significant implications for data augmentation strategies in AD research.
Conclusion:
The specific, asymmetric error patterns introduced by certain ASR models enhance the distinction between AD and HC groups by amplifying pathological linguistic deficits associated with AD. This work suggests a paradigm shift for clinical ASR development: optimizing models not merely for transcription accuracy, but for their downstream diagnostic utility.
{"title":"Beyond manual transcripts: Exploring the potential of automatic speech recognition errors in improving Alzheimer’s disease detection","authors":"Yin-Long Liu , Yuanchao Li , Rui Feng , Jiaxin Chen , Yiming Wang , Yu-Ang Chen , Nan Ding , Jiahong Yuan , Zhen-Hua Ling","doi":"10.1016/j.jbi.2025.104968","DOIUrl":"10.1016/j.jbi.2025.104968","url":null,"abstract":"<div><h3>Objective:</h3><div>This study aims to extend the counterintuitive observation that Automatic Speech Recognition (ASR) errors can be beneficial for Alzheimer’s Disease (AD) detection. Our objective is to conduct a large-scale investigation to validate this phenomenon and, more importantly, to elucidate the specific mechanisms by which ASR errors can serve as valuable diagnostic clues for distinguishing individuals with AD from Healthy Controls (HC).</div></div><div><h3>Methods:</h3><div>We employed 18 ASR models, in both their original and fine-tuned versions, to generate 36 sets of transcripts from the ADReSS dataset. We also synthesized speech from both manual and ASR transcripts using a text-to-speech (TTS) model. Knowledge-based features and pre-trained embeddings were extracted and fed into two proposed AD detection models : a self-attention model and a cross-attention-based interpretability model. To uncover the underlying mechanisms, we conducted a multi-faceted set of analyses, including examinations of ASR error types, words affected by ASR errors, linguistic comparisons, attention weight analysis, and case studies.</div></div><div><h3>Results:</h3><div>We demonstrate that transcripts generated by certain ASR models achieve higher AD detection accuracy than gold-standard manual transcripts. This performance gain stems not from errors in general or a high Word Error Rate (WER), but from specific and asymmetric error patterns. Our analyses reveal that these patterns amplify some pre-existing linguistic deficits in AD speech (e.g., disfluencies), thereby increasing the feature-level divergence between the AD and HC groups. Furthermore, we show that these diagnostic clues are effectively preserved when speech is synthesized from ASR transcripts, holding significant implications for data augmentation strategies in AD research.</div></div><div><h3>Conclusion:</h3><div>The specific, asymmetric error patterns introduced by certain ASR models enhance the distinction between AD and HC groups by amplifying pathological linguistic deficits associated with AD. This work suggests a paradigm shift for clinical ASR development: optimizing models not merely for transcription accuracy, but for their downstream diagnostic utility.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"173 ","pages":"Article 104968"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798054","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}
Pub Date : 2026-01-01Epub Date: 2025-11-30DOI: 10.1016/j.jbi.2025.104963
Antonin Prochazka, Jan Zeman
Objective
To enhance the cross-domain generalization of thyroid-nodule segmentation models by augmenting limited ultrasound training data with synthetic images generated by a fine-tuned Stable Diffusion model.
Methods
Three public thyroid ultrasound datasets with heterogeneous acquisition characteristics were used: TN3K (training + testing), TDID, and TUCC. The denoising UNet inside Stable Diffusion v1.4 was fine-tuned on 2303 TN3K nodules and then used to synthesize realistic thyroid nodules. Using the model’s inpainting capability, same number of synthetic nodules were inserted into original ultrasound images. The combined data were then used to train ResUNet, DeepLabV3+ and MITUnet segmentation networks with identical hyper-parameters. Performance between the models trained on native data only and native + synthetic data was quantified with the Dice similarity coefficient (Dice score) and Intersection-over-Union (IoU).
Results
Across the in-domain TN3K test set (n = 614), performance gains were modest, with the best improvements reaching + 2.2 % in Dice score for DeepLabV3+. In contrast, substantial gains were observed on the external datasets. On the TDID dataset (n = 462), DeepLabV3+ improved from 38.2 % to 59.1 % Dice (+20.9 %), while MITUNet and ResUNet also gained up by 7.1 % and 6.9 % respectively. On the TUCC dataset (n = 192), DeepLabV3+ improved by 11.4 % in Dice, MITUNet by 6.9 %, and ResUNet by 3.1 %. All improvements—except for in-domain TN3K—were statistically significant (p < 0.01, paired t-test or Wilcoxon signed-rank test), confirming that synthetic images generated by Stable Diffusion enhance cross-domain segmentation robustness.
Conclusion
Augmenting ultrasound dataset with synthetic images generated by a task-specific Stable Diffusion model substantially improves the robustness of thyroid nodule segmentation across datasets acquired with different devices, at different institutions, and by different operators.
{"title":"Domain adaptation of stable diffusion for ultrasound inpainting: a synthetic data approach for enhanced thyroid nodule segmentation","authors":"Antonin Prochazka, Jan Zeman","doi":"10.1016/j.jbi.2025.104963","DOIUrl":"10.1016/j.jbi.2025.104963","url":null,"abstract":"<div><h3>Objective</h3><div>To enhance the cross-domain generalization of thyroid-nodule segmentation models by augmenting limited ultrasound training data with synthetic images generated by a fine-tuned Stable Diffusion model.</div></div><div><h3>Methods</h3><div>Three public thyroid ultrasound datasets with heterogeneous acquisition characteristics were used: TN3K (training + testing), TDID, and TUCC. The denoising UNet inside Stable Diffusion v1.4 was fine-tuned on 2303 TN3K nodules and then used to synthesize realistic thyroid nodules. Using the model’s inpainting capability, same number of synthetic nodules were inserted into original ultrasound images. The combined data were then used to train ResUNet, DeepLabV3+ and MITUnet segmentation networks with identical hyper-parameters. Performance between the models trained on native data only and native + synthetic data was quantified with the Dice similarity coefficient (Dice score) and Intersection-over-Union (IoU).</div></div><div><h3>Results</h3><div>Across the in-domain TN3K test set (n = 614), performance gains were modest, with the best improvements reaching + 2.2 % in Dice score for DeepLabV3+. In contrast, substantial gains were observed on the external datasets. On the TDID dataset (n = 462), DeepLabV3+ improved from 38.2 % to 59.1 % Dice (+20.9 %), while MITUNet and ResUNet also gained up by 7.1 % and 6.9 % respectively. On the TUCC dataset (n = 192), DeepLabV3+ improved by 11.4 % in Dice, MITUNet by 6.9 %, and ResUNet by 3.1 %. All improvements—except for in-domain TN3K—were statistically significant (p < 0.01, paired <em>t</em>-test or Wilcoxon signed-rank test), confirming that synthetic images generated by Stable Diffusion enhance cross-domain segmentation robustness.</div></div><div><h3>Conclusion</h3><div>Augmenting ultrasound dataset with synthetic images generated by a task-specific Stable Diffusion model substantially improves the robustness of thyroid nodule segmentation across datasets acquired with different devices, at different institutions, and by different operators.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"173 ","pages":"Article 104963"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145661395","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}
Pub Date : 2026-01-01Epub Date: 2025-12-29DOI: 10.1016/j.jbi.2025.104975
Bruria Ben Shahar , Yuval Shahar , Shai Jaffe , Odeya Cohen , Erez Shalom , Maya Selivanova , Ephraim Rimon , Irit Hochberg , Ayelet Goldstein
Background
Evidence-based clinical guidelines (GLs) are essential for standardizing care, yet often difficult to apply. Most clinical decision support systems (CDSSs) assume continuous application, which misaligns with the episodic nature of real-world workflows.
Objectives
To design, implement, and evaluate e-Picard, a CDSS that provides GL-based recommendations through episodic, intermittent, on-demand consultations. The system supports retrospective assessment of past care and prospective identification of required actions. The evaluation focused on system validity and on its potential, in a retrospective simulation on real-world data, to enhance staff adherence to the GLs and to assess the potential effect of varying the frequency of the consultations.
Methods
The system development involved three preprocessing steps: (1) acquisition of free-text GLs with domain experts; (2) modeling procedural logic as workflows; and (3) flattening these into declarative temporal patterns for retrospective quality assessment and prospective recommendations. At runtime, e-Picard analyzes offline patient data to identify missed actions, computes compliance using fuzzy logic, and generates context-specific recommendations. e-Picard was applied to pressure-ulcer (PU) and diabetes management (DM) GLs, adapted for episodic use. Technical validation was performed on records from 43 PU and 82 DM patients. A retrospective simulation using 1,000 patients per domain estimated potential increases in adherence under varying consultation frequencies.
Results
Technical manual validation showed high correctness (≥99 %) and completeness (up to 98 %), based on 3,110 PU and 12,538 DM data instances (i.e., clinical measurements or actions), across various clinical scenarios over two-week observation periods.
Retrospective simulation covered 57,860 PU and 100,940 DM data instances with estimated adherence potentially increasing from 68 %–69 % to 89 %–97 % for PU and from 14 %–15 % to 60 %–87 % for DM, in the real-world data retrospective simulation, assuming full adherence of the staff to the system’s recommendations, depending on the scenario. Higher consultation frequency yielded greater gains, and adherence variability across hospital units and patient subgroups was reduced.
Conclusions
Episodic CDSSs can deliver accurate, context-aware recommendations in environments with intermittent use and incomplete data, with the potential, assuming that the real-world data retrospective simulation results hold, to enhance adherence and consistency in care.
{"title":"Integrating retrospective quality assessment with real-time guideline application to support the episodic application of clinical guidelines over significant time periods","authors":"Bruria Ben Shahar , Yuval Shahar , Shai Jaffe , Odeya Cohen , Erez Shalom , Maya Selivanova , Ephraim Rimon , Irit Hochberg , Ayelet Goldstein","doi":"10.1016/j.jbi.2025.104975","DOIUrl":"10.1016/j.jbi.2025.104975","url":null,"abstract":"<div><h3>Background</h3><div>Evidence-based clinical guidelines (GLs) are essential for standardizing care, yet often difficult to apply. Most clinical decision support systems (CDSSs) assume continuous application, which misaligns with the episodic nature of real-world workflows.</div></div><div><h3>Objectives</h3><div>To design, implement, and evaluate e-Picard, a CDSS that provides GL-based recommendations through episodic, intermittent, on-demand consultations. The system supports retrospective assessment of past care and prospective identification of required actions. The evaluation focused on system validity and on its potential, in a retrospective simulation on real-world data, to enhance staff adherence to the GLs and to assess the potential effect of varying the frequency of the consultations.</div></div><div><h3>Methods</h3><div>The system development involved three preprocessing steps: (1) acquisition of free-text GLs with domain experts; (2) modeling procedural logic as workflows; and (3) flattening these into declarative temporal patterns for retrospective quality assessment and prospective recommendations. At runtime, e-Picard analyzes offline patient data to identify missed actions, computes compliance using fuzzy logic, and generates context-specific recommendations. e-Picard was applied to pressure-ulcer (PU) and diabetes management (DM) GLs, adapted for episodic use. Technical validation was performed on records from 43 PU and 82 DM patients. A retrospective simulation using 1,000 patients per domain estimated potential increases in adherence under varying consultation frequencies.</div></div><div><h3>Results</h3><div>Technical manual validation showed high correctness (≥99 %) and completeness (up to 98 %), based on 3,110 PU and 12,538 DM data instances (i.e., clinical measurements or actions), across various clinical scenarios over two-week observation periods.</div><div>Retrospective simulation covered 57,860 PU and 100,940 DM data instances with estimated adherence potentially increasing from 68 %–69 % to 89 %–97 % for PU and from 14 %–15 % to 60 %–87 % for DM, in the real-world data retrospective simulation, assuming full adherence of the staff to the system’s recommendations, depending on the scenario. Higher consultation frequency yielded greater gains, and adherence variability across hospital units and patient subgroups was reduced.</div></div><div><h3>Conclusions</h3><div>Episodic CDSSs can deliver accurate, context-aware recommendations in environments with intermittent use and incomplete data, with the potential, assuming that the real-world data retrospective simulation results hold, to enhance adherence and consistency in care.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"173 ","pages":"Article 104975"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878473","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}
Pub Date : 2026-01-01Epub Date: 2025-12-22DOI: 10.1016/j.jbi.2025.104973
Pengli Lu , Chao Dong , Jingjin Xue , Fentang Gao
Automatic ICD coding refers to the process of using artificial intelligence methods to automatically extract information related to diseases, symptoms, diagnoses, treatments, and other relevant details from electronic health records, and convert it into codes that comply with the International Classification of Diseases (ICD) standard. Automatic ICD coding technology has been gradually improved with the advancement of deep learning, but in practical deployment, it still faces challenges such as inconsistent semantics, ambiguous labels, and limited interpretability. To address these issues, we propose a novel automatic ICD coding framework MKHCNet (Mamba-Knowledge-HPLA-ContraNorm Network) which integrates unstructured clinical knowledge representation, long-range dependency modeling, and contrastive normalization techniques to enhance coding performance. Specifically, we construct a disease semantic knowledge graph to enrich ICD label representations, employ the Mamba network to capture cross-domain dependencies, apply the ContraNorm module to enhance label separability, and propose the Hierarchical Position Label Attention (HPLA) mechanism to achieve fine-grained, attention-based interpretability. Finally, with the purpose of capturing complex nonlinear relationships more effectively and better adapting to complex patterns in EHR data, FastKAN acts as a classifier and utilizes radial basis function (RBF) for feature transformation. We conducted systematic experiments on the benchmark datasets MIMIC-FULL and MIMIC-50. The experimental results show that MKHCNet improves MaAUC and P8 by 2.1% and 0.3% on MIMIC-FULL respectively compared with the best existing mainstream model. Furthermore, case studies demonstrate that the model is able to effectively identify complex semantic cues and provide strong clinical interpretability.
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