Journal of Biomedical Informatics最新文献

{"title":"Direct Effects Multiplicative Inference (DEMI): causal prediction from pairwise clinical relationships, applied to antidepressant response.","authors":"Farrokh Alemi, Yili Lin, Hadeel R A Elyazori, Vladimir Franzuela Cardenas, Niloofar Ramezani, Kevin Lybarger","doi":"10.1016/j.jbi.2026.105111","DOIUrl":"https://doi.org/10.1016/j.jbi.2026.105111","url":null,"abstract":"<p><strong>Objective: </strong>This paper introduces Direct Effects Multiplicative Inference (DEMI), a causal algorithm that reasons through a medical knowledgebase and predicts clinical outcomes from direct, unmediated effects. This study compares performance of DEMI to logistic regression, a common inference mechanism available for analysis of patient-level tabular data.</p><p><strong>Methods: </strong>We extracted 430 million relationships between pairs of medical concepts from the All of Us database. DEMI uses this knowledgebase to predict likelihood of outcomes, in three steps: (1) patient features are temporally ordered using pairwise precedence counts; (2) each feature's total effect on the outcome is recursively decomposed into direct and indirect (mediated) components by working backward through the temporal order; (3) direct effects are aggregated multiplicatively to estimate the odds of outcomes. DEMI uses the distribution of a pair of variables, as reported in the medical knowledgebase. Logistic regression uses patient-level tabular data, which includes the joint distribution of all variables. Performance was assessed using Area under the Receiver Operating Characteristic curve (AUC).</p><p><strong>Results: </strong>DEMI predicted response to 15 antidepressants with a mean AUC of 0.934 (range, 0.512-0.996). Logistic regression achieved similar discrimination (mean AUC, 0.935; range, 0.512-1.000). At α = 0.05, the two methods did not differ significantly. DEMI achieved comparable discrimination using precomputed pairwise statistics without access to the joint distribution of patient features. Under the same variable-selection rule, DEMI could use on average 113 more predictors per task than logistic regression.</p><p><strong>Conclusion: </strong>DEMI predicted antidepressant response from a medical knowledgebase as well as logistic regression did from patient-level data. Under its structural assumptions (no unmeasured confounding and correct temporal ordering), it does so by isolating direct, unmediated predictors and excluding effects that operate through intermediate variables.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"105111"},"PeriodicalIF":5.9,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149216131","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}

{"title":"Augmenting electronic health records for adverse event detection.","authors":"Gün Kaynar, Zhaoyi You, Richard D Boyce, Takahiro Yakoh, Carl Kingsford","doi":"10.1016/j.jbi.2026.105112","DOIUrl":"https://doi.org/10.1016/j.jbi.2026.105112","url":null,"abstract":"<p><strong>Objective: </strong>Adverse events (AEs) resulting from medical interventions are significant contributors to patient morbidity, mortality, and healthcare costs. Detection of these events using electronic health records (EHRs) can facilitate timely clinical interventions. However, effective detection remains challenging due to severe class imbalance, missing labels, and the complexity of EHR records. Classical machine learning approaches frequently underperform due to insufficient representation of minority adverse event classes and limited capacity to capture interactions among patient demographics, administered medications, and associated complications. This study aims to develop and evaluate a lightweight, NLP-inspired data augmentation framework for structured EHR data that improves minority-class adverse event detection without requiring complex generative models or pre-trained clinical language models.</p><p><strong>Methods: </strong>We introduce TASER-AE (Tokenized Augmentation for Structured EHR-Adverse Events), a novel data augmentation pipeline tailored for structured EHR data, coupled with transformer-based classification. TASER-AE addresses these issues through an NLP-inspired data augmentation framework adapted for EHR, enabling effective minority-class representation in sparse and imbalanced clinical datasets. The augmented records produced by TASER-AE alleviate class imbalance by enriching the representation of minority adverse event classes, which enhances the robustness and predictive performance of the classifier.</p><p><strong>Results: </strong>TASER-AE yields minority-class F1 scores up to 0.67, substantially surpassing classical machine-learning baselines and generative augmentation, and consistently improving on prior edit-based augmentation methods, across multiple adverse event tasks. Experiments conducted on two distinct EHR datasets confirm TASER-AE's ability to substantially improve adverse event detection.</p><p><strong>Conclusion: </strong>These results demonstrate the potential of structured, NLP-inspired augmentation methods to overcome data limitations in clinical predictive modeling, ultimately contributing to improved patient safety outcomes. TASER-AE is available at https://github.com/Kingsford-Group/taserae.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"105112"},"PeriodicalIF":5.9,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149207120","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}

{"title":"Leveraging routine scheduling patterns to unshift Date-Shifted clinical data.","authors":"Andrzej Kudlicki, Bradley A Malin, Elmer V Bernstam","doi":"10.1016/j.jbi.2026.105107","DOIUrl":"https://doi.org/10.1016/j.jbi.2026.105107","url":null,"abstract":"<p><strong>Objective: </strong>Date-shifting is a common strategy for preserving temporal relationships in de-identified clinical data, premised on the assumption that all candidate shifts in a chosen range are equally likely to a recipient. We assess whether routine scheduling patterns - weekday concentration of outpatient activity and predictable institutional holiday closures - invalidate this assumption, and quantify the resulting risk of date recovery as a function of dataset characteristics.</p><p><strong>Methods: </strong>We formalize the unshifting problem as Bayesian inference over candidate shifts, scoring each candidate by the likelihood that reconstructed dates are consistent with weekday and holiday scheduling patterns. Model parameters were estimated empirically from 95.9 million encounters across 4.1 million patient records in one large academic health system over 5 years. We then simulated unshifting on 1,617,988 outpatients (with 2 + distinct event dates) across 22 shift ranges from 7 days to 5 years, comparing the recipient's argmax against the known true shift.</p><p><strong>Results: </strong>Ambulatory visits occurred on weekdays 98.0% of the time, with some encounter subtypes at 100%; a similar pattern affects non-weekend holidays (at least six or seven institutional closure days per year, depending on which fixed holidays fall on weekdays). A recipient targeting only patients where posterior confidence reaches τ ≥ 0.95 correctly recovered the true shift for 5.9% of the population under a 30-day shift, with 88.4% precision. Even under a one-year shift, patients with over 100 distinct encounter dates faced a 34% chance of correct date unshifting.</p><p><strong>Conclusion: </strong>The unshifting vulnerability is substantial and depends predictably on encounter density, shift range, and the institutional holiday calendar. It affects datasets of all sizes, including case reports with relative timelines. The same Bayesian framework that enables unshifting also serves as a pre-release risk assessment tool: data custodians can compute per-patient vulnerability scores and apply targeted mitigations before releasing date-shifted data.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"105107"},"PeriodicalIF":5.9,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149168361","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}

{"title":"Temporally consistent survival prediction for non-uniform longitudinal data","authors":"Harrison Fah ,&nbsp;Russell Greiner ,&nbsp;Roger A. Dixon","doi":"10.1016/j.jbi.2026.104996","DOIUrl":"10.1016/j.jbi.2026.104996","url":null,"abstract":"<div><h3>Objective:</h3><div>Traditional survival prediction models use a patient’s covariates at a single time point to estimate the time until a specific event occurs, such as death or hospital readmission. However, in many longitudinal datasets, patient covariates are recorded at multiple time points, typically with varying intervals. Our objective is to learn a survival prediction model by training on longitudinal datasets with non-uniform time intervals between covariate measurements, both within and across patient trajectories.</div></div><div><h3>Methods:</h3><div>We propose a new algorithm, Temporally Consistent Multi-Task Logistic Regression (TC-MTLR), which incorporates concepts from distributional reinforcement learning to model survival outcomes. Unlike existing dynamic survival prediction algorithms, TC-MTLR is designed to leverage the non-uniformity of longitudinal measurements. We evaluate this method against two standard and two dynamic survival prediction algorithms across three short and three long longitudinal datasets, including two related to healthcare.</div></div><div><h3>Results:</h3><div>On short datasets, TC-MTLR achieves top performance in Concordance Index (C-Index) and Uncensored Mean Average Error (MAE-Uncensored) while displaying mixed results according to Integrated Brier Score (IBS) and Pseudo-Observable MAE (MAE-PO). However, on long datasets, TC-MTLR achieves similar C-Index performance as the other survival predictions methods while outperforming them according to MAE-PO and achieving top performance according to MAE-Uncensored and IBS.</div></div><div><h3>Conclusion:</h3><div>TC-MTLR effectively utilizes the non-uniform temporal structure of longitudinal datasets, offering a competitive and often superior alternative to existing survival prediction models.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"176 ","pages":"Article 104996"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191965","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}

{"title":"Learning dynamic binary treatment policies under treatment selection bias: A Conservative Q-Learning approach with representation balancing","authors":"Animesh Kumar Paul,&nbsp;Russell Greiner","doi":"10.1016/j.jbi.2026.104994","DOIUrl":"10.1016/j.jbi.2026.104994","url":null,"abstract":"<div><h3>Objective:</h3><div>Learn safe, robust dynamic treatment regimes (DTRs) from observational trajectories that exhibit treatment selection bias, using an offline reinforcement learning (RL) approach.</div></div><div><h3>Methods:</h3><div>We propose <em>CQL-RB</em>, which augments Conservative Q-Learning (CQL) with a representation-balancing penalty based on an integral probability metric (IPM) (instantiated as either a maximum mean discrepancy (MMD) or an energy-distance penalty). The penalty aligns latent patient representations across treatment groups to reduce action-conditioned distribution shift while preserving CQL’s conservative policy estimation. We evaluate CQL-RB on two clinically realistic simulators: <em>EpiCare</em> (eight environments) and <em>AhnChemo</em> from <em>DTR-Bench</em>, both modeling longitudinal healthcare decisions with binary actions at each stage. To emulate selection bias, we implement clinician-like behavior policies that assign treatment as a function of patient covariates. Baselines include BOWL, ACWL, T-RL, RL-NN, and standard CQL. Outcomes are expected return and adverse-event counts from simulator rollouts; model selection uses weighted importance sampling off-policy evaluation on held-out data. Ablations vary both the IPM weight <span><math><mi>β</mi></math></span> and the choice of IPM metric.</div></div><div><h3>Results:</h3><div>Across all eight EpiCare environments and the challenging AhnChemo task, CQL-RB with either MMD or energy-distance penalties consistently achieves higher returns than competing methods while yielding lower (or comparable) adverse-event rates. Removing the balancing term degrades both return and safety, confirming its contribution. Performance is robust for moderate penalty weights (e.g., <span><math><mrow><mi>β</mi><mspace></mspace><mo>∈</mo><mspace></mspace><mrow><mo>{</mo><mn>1</mn><mo>,</mo><mn>10</mn><mo>,</mo><mn>100</mn><mo>}</mo></mrow></mrow></math></span>), with degradation only at overly large values (e.g., <span><math><mrow><mi>β</mi><mo>≥</mo><mn>1000</mn></mrow></math></span> for MMD or <span><math><mrow><mi>β</mi><mo>=</mo><mn>10000</mn></mrow></math></span> for energy distance).</div></div><div><h3>Conclusion:</h3><div>Representation balancing materially strengthens conservative offline RL for DTR learning under treatment selection bias. By aligning patient representations without altering CQL’s safety mechanics, CQL-RB delivers policies that are both <em>effective</em> (higher returns) and <em>safer</em> (fewer adverse events) in realistic healthcare simulations. These findings underscore the importance of addressing treatment selection bias when learning robust and safe dynamic treatment policies.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"176 ","pages":"Article 104994"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146179225","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}

{"title":"Multi-channel causal variational autoencoder for multimodal biomedical causal disentanglement","authors":"Safaa Al-Ali,&nbsp;Irene Balelli,&nbsp;Alzheimer’s Disease Neuroimaging Initiative","doi":"10.1016/j.jbi.2026.104995","DOIUrl":"10.1016/j.jbi.2026.104995","url":null,"abstract":"<div><div>The multimodal nature of clinical assessment and decision-making, and the high rate of healthcare data generation, motivate the need to develop approaches specifically tailored to the analysis of these complex and potentially high-dimensional multimodal datasets. This poses both technical and conceptual challenges: how can such heterogeneous data be analyzed jointly? How can modality-specific information be identified from shared information? Variational autoencoders (VAEs) offer a robust framework for learning latent representations of complex data distributions, while being flexible enough to adapt to different data types and structures, and having already been successfully applied for latent disentanglement of multimodal (multi-channel) data. We aim at tackling multi-channel disentanglement from a causal perspective, and seek at identifying causal relationships between channels, beyond simple statistical associations. To do that, we propose Multi-Channel Causal VAE (MC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>VAE), a novel causal disentanglement approach for multi-channel data, whose objective is to jointly learn modality-specific latent representations from a multi-channel dataset, and identify a causal structure between the latent channels. Each channel is projected into its own latent space, where a causal discovery step is integrated to learn the hidden causal graph. Finally, the decoder takes into account the discovered graph to predict the data. Covariate of interest can be integrated as well when available, and accounted in the causal graph structure. Extensive experiments on synthetically generated multi-channel datasets demonstrate the ability of MC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>VAE in effectively uncovering the underlying latent causal structures across multiple channels, hence making it a strong candidate for real-world multi-channel causal disentanglement. Application to multi-channel data on neurodegeneration extracted from the Alzheimer’s Disease Neuroimaging Initiative highlights the existence of a biologically meaningful latent causal structure, whose pertinence is supported by multiple previous experimental and modeling work, and provides actionable insight for disease progression.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"176 ","pages":"Article 104995"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137458","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}

{"title":"A study of large language models for patient information extraction: Model architecture, fine-tuning strategy, and multi-task instruction tuning","authors":"Cheng Peng ,&nbsp;Xinyu Dong ,&nbsp;Mengxian Lyu ,&nbsp;Daniel Paredes ,&nbsp;Yaoyun Zhang ,&nbsp;Yonghui Wu","doi":"10.1016/j.jbi.2026.105034","DOIUrl":"10.1016/j.jbi.2026.105034","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Natural language processing (NLP) is a key technology to extract important patient information from clinical narratives to support healthcare applications. The rapid development of large language models (LLMs) has revolutionized patient information extraction in the clinical domain, yet critical strategies for effectively adopting LLMs for optimal performance need further exploration. This study examines LLMs’ effectiveness in patient information extraction, focusing on LLM architectures, fine-tuning strategies, and multi-task instruction tuning techniques for developing robust and generalizable patient information extraction systems.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;This study aims to explore key strategies of adopting LLMs for clinical concept and relation extraction tasks, including: (1) encoder-only or decoder-only LLMs, (2) prompt-based parameter-efficient fine-tuning (PEFT) algorithms, and (3) multi-task instruction tuning on few-shot learning performance. We benchmarked a suite of LLMs, including encoder-only LLMs (BERT, GatorTron) and decoder-only LLMs (GatorTronGPT, Llama 3.1, GatorTronLlama), across five widely used benchmarking datasets. We compared traditional full-size fine-tuning and prompt-based PEFT. We explored a multi-task instruction tuning framework that combines both tasks across four datasets to evaluate the zero-shot and few-shot learning performance using the leave-one-dataset-out strategy.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;For single-task clinical CE, the two decoder-only LLMs (Llama 3.1 and GatorTronLlama) achieved the best performance, with average F1 scores of 0.8964 and 0.8981, respectively, across the five datasets, outperforming other LLMs with average F1 improvement of 0.7 ∼ 3.3%. Encoder-only LLMs with prompt-based learning outperformed those implemented using classification. For RE, the prompt-based PEFT strategy demonstrated remarkable performance, with an F1 improvement of up to 15.9% over traditional fine-tuning on all datasets. All three decoder-only LLMs outperformed encoder-only LLMs, increasing average F1 score by 1.8 to 6.6%, with GatorTronLlama achieving the best performance with an average F1 score of 0.8978. Multi-task instruction tuning showed remarkable improvements, boosting zero-shot and few-shot F1 scores by 1.1 ∼ 37.8% compared to those without multi-task fine-tuning. Notably, generative LLMs with multitask instruction tuning using only 20% of the full dataset achieved similar performance comparable to the full-size fine-tuning (&lt;0.005 in terms of F1 score).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;Our findings support generative LLMs with PEFT as a cost-effective solution for patient information extraction. In addition, multi-task instruction tuning significantly improves the zero-shot and few-shot performance, contributing to better generalizability. This study provides practical guidelines to develop LLM-based scalable, adaptable, and high-performing pati","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"178 ","pages":"Article 105034"},"PeriodicalIF":4.5,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147574145","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}

{"title":"On the transfer learning behavior of domain-specific vision–language models in screening mammography","authors":"Aisha Urooj Khan ,&nbsp;Gokul Ramasamy ,&nbsp;Muhammad Danish Khan ,&nbsp;John Garrett ,&nbsp;Tyler Bradshaw ,&nbsp;Lonie Salkowski ,&nbsp;Imon Banerjee","doi":"10.1016/j.jbi.2026.104986","DOIUrl":"10.1016/j.jbi.2026.104986","url":null,"abstract":"<div><div>Vision-Language models have shown remarkable performance for natural images and text. Given the homology of the anatomy, high gray-scale image dimension, and the unbalanced datasets, the traditional VLMs do not adapt well to radiological applications. In this work, we empirically adapted image encoder trained within domain-specific VLMs to be applied in two downstream tasks for 2D mammogram image analysis: tissue density estimation and BI-RADS prediction. We study the transfer learning behavior using linear probing, fine-tuning, and online self distillation. We analyze that knowledge driven domain-specific VLM backbones with frozen weights perform better than MammoClip VLM model as well as supervised baselines such as ViT and CNNs even with only 5% of training data. Generalization capabilities are further studied of these models on two external datasets.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"175 ","pages":"Article 104986"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043748","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}

{"title":"KATMed: a knowledge-augmented transformer model for contraindication-aware medication recommendation in comorbidities","authors":"Ziqian Qiao ,&nbsp;Shaofu Lin ,&nbsp;Jiatong Fan ,&nbsp;Jianhui Chen ,&nbsp;Zhiyi Tang ,&nbsp;Zitong Zhang","doi":"10.1016/j.jbi.2026.104991","DOIUrl":"10.1016/j.jbi.2026.104991","url":null,"abstract":"<div><div>Drug-disease contraindications in comorbidities (DDCC) pose a significant challenge and priority in clinical treatment. These contraindications exhibit a prototypical long-tail distribution, characterized by low-frequency, highly diverse, and substantial individual variability. Such distinct properties impose significant limitations on electronic health record-based medication recommendation modeling, ultimately elevating safety risks in clinical practice. To address this challenge, this study proposes KATMed, a knowledge-augmented transformer model for contraindication-aware medication recommendation in comorbidities. The model employs Transformer-based encoding of patient records and leverages two self-supervised tasks to capture rich temporal and semantic dependencies. Based on this foundation, a hybrid knowledge-augmented framework is developed to integrate bidirectional medication-related clinical associations. Positive disease-procedure associations are modeled by using a dynamic semantic relevance matrix to expand the input information, thereby enhancing the model’s feature learning capability on sparse yet diverse comorbidity records. Negative DDCC rules are incorporated as differentiable logical constraints in the loss function to suppress unsafe medications. Experiments on the MIMIC-III and MIMIC-IV datasets show that KATMed significantly improves performance, achieving a 5.2% increase in accuracy and a 2.04% reduction in safety violations.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"175 ","pages":"Article 104991"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146100243","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}

{"title":"Ontology-grounded knowledge graphs for mitigating hallucinations in large language models for clinical question answering","authors":"Mohamed Ali,&nbsp;Zaki Taha,&nbsp;Mohamed Mabrouk Morsey","doi":"10.1016/j.jbi.2026.104993","DOIUrl":"10.1016/j.jbi.2026.104993","url":null,"abstract":"<div><h3>Objective:</h3><div>Large Language Models (LLMs) show strong potential in biomedical informatics but frequently generate hallucinated or factually incorrect responses, limiting their clinical utility. This study aims to develop and evaluate a GraphRAG framework using an ontology-grounded knowledge graph that mitigates hallucinations in clinical question answering.</div></div><div><h3>Methods:</h3><div>We designed a domain-specific Resource Description Framework (RDF)/Web Ontology Language (OWL) ontology and knowledge graph using clinical and hospital data from multiple Egyptian institutions. The ontology was integrated with LLMs to enforce structured semantic grounding during question answering. Clinical questions were evaluated under three conditions: (i) baseline ChatGPT-4, (ii) DeepSeek-R1, and (iii) our ontology-grounded framework. Accuracy was evaluated against clinically reported reference answers derived from five peer-reviewed Egyptian hospital studies.</div></div><div><h3>Results:</h3><div>Our GraphRAG framework significantly outperformed baseline models. While ChatGPT-4 achieved 37% accuracy and DeepSeek-R1 achieved 52%, the ontology-grounded approach achieved 98% accuracy (59/60 questions). The hallucination rate was reduced from approximately 63% in ChatGPT-4 and 48% in DeepSeek-R1 to just 1.7% in our framework, representing a relative hallucination reduction of more than 61% relative to state-of-the-art LLMs. The framework further ensured consistent, reproducible answers aligned with clinical knowledge, demonstrating its robustness for healthcare applications.</div></div><div><h3>Conclusion:</h3><div>Ontology-grounded knowledge graphs provide a reliable and verifiable method for mitigating hallucinations in LLM-based clinical question answering. By embedding structured clinical semantics into the reasoning process, the framework enhances factual accuracy, reproducibility, and safety in biomedical informatics. This work highlights the critical role of semantic knowledge representation in building trustworthy Artificial Intelligence (AI) systems for healthcare decision support.</div></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"175 ","pages":"Article 104993"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076154","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}