Journal of the American Medical Informatics Association最新文献

{"title":"Semi-supervised learning from small annotated data and large unlabeled data for fine-grained Participants, Intervention, Comparison, and Outcomes entity recognition.","authors":"Fangyi Chen, Gongbo Zhang, Yilu Fang, Yifan Peng, Chunhua Weng","doi":"10.1093/jamia/ocae326","DOIUrl":"10.1093/jamia/ocae326","url":null,"abstract":"<p><strong>Objective: </strong>Extracting PICO elements-Participants, Intervention, Comparison, and Outcomes-from clinical trial literature is essential for clinical evidence retrieval, appraisal, and synthesis. Existing approaches do not distinguish the attributes of PICO entities. This study aims to develop a named entity recognition (NER) model to extract PICO entities with fine granularities.</p><p><strong>Materials and methods: </strong>Using a corpus of 2511 abstracts with PICO mentions from 4 public datasets, we developed a semi-supervised method to facilitate the training of a NER model, FinePICO, by combining limited annotated data of PICO entities and abundant unlabeled data. For evaluation, we divided the entire dataset into 2 subsets: a smaller group with annotations and a larger group without annotations. We then established the theoretical lower and upper performance bounds based on the performance of supervised learning models trained solely on the small, annotated subset and on the entire set with complete annotations, respectively. Finally, we evaluated FinePICO on both the smaller annotated subset and the larger, initially unannotated subset. We measured the performance of FinePICO using precision, recall, and F1.</p><p><strong>Results: </strong>Our method achieved precision/recall/F1 of 0.567/0.636/0.60, respectively, using a small set of annotated samples, outperforming the baseline model (F1: 0.437) by more than 16%. The model demonstrates generalizability to a different PICO framework and to another corpus, which consistently outperforms the benchmark in diverse experimental settings (P-value < .001).</p><p><strong>Discussion: </strong>We developed FinePICO to recognize fine-grained PICO entities from text and validated its performance across diverse experimental settings, highlighting the feasibility of using semi-supervised learning (SSL) techniques to enhance PICO entities extraction. Future work can focus on optimizing SSL algorithms to improve efficiency and reduce computational costs.</p><p><strong>Conclusion: </strong>This study contributes a generalizable and effective semi-supervised approach leveraging large unlabeled data together with small, annotated data for fine-grained PICO extraction.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"555-565"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and evaluation of a 4M taxonomy from nursing home staff text messages using a fine-tuned generative language model.","authors":"Matthew Steven Farmer, Mihail Popescu, Kimberly Powell","doi":"10.1093/jamia/ocaf006","DOIUrl":"10.1093/jamia/ocaf006","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to explore the utilization of a fine-tuned language model to extract expressions related to the Age-Friendly Health Systems 4M Framework (What Matters, Medication, Mentation, and Mobility) from nursing home worker text messages, deploy automated mapping of these expressions to a taxonomy, and explore the created expressions and relationships.</p><p><strong>Materials and methods: </strong>The dataset included 21 357 text messages from healthcare workers in 12 Missouri nursing homes. A sample of 860 messages was annotated by clinical experts to form a \"Gold Standard\" dataset. Model performance was evaluated using classification metrics including Cohen's Kappa (κ), with κ ≥ 0.60 as the performance threshold. The selected model was fine-tuned. Extractions were clustered, labeled, and arranged into a structured taxonomy for exploration.</p><p><strong>Results: </strong>The fine-tuned model demonstrated improved extraction of 4M content (κ = 0.73). Extractions were clustered and labeled, revealing large groups of expressions related to care preferences, medication adjustments, cognitive changes, and mobility issues.</p><p><strong>Discussion: </strong>The preliminary development of the 4M model and 4M taxonomy enables knowledge extraction from clinical text messages and aids future development of a 4M ontology. Results compliment themes and findings in other 4M research.</p><p><strong>Conclusion: </strong>This research underscores the need for consensus building in ontology creation and the role of language models in developing ontologies, while acknowledging their limitations in logical reasoning and ontological commitments. Further development and context expansion with expert involvement of a 4M ontology are necessary.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"535-544"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833468/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The substance-exposed birthing person-infant/child dyad and health information exchange in the United States.","authors":"Fabienne C Bourgeois, Amrita Sinha, Gaurav Tuli, Marvin B Harper, Virginia K Robbins, Sydney Jeffrey, John S Brownstein, Shahla M Jilani","doi":"10.1093/jamia/ocae315","DOIUrl":"10.1093/jamia/ocae315","url":null,"abstract":"<p><strong>Objective: </strong>Timely access to data is needed to improve care for substance-exposed birthing persons and their infants, a significant public health problem in the United States. We examined the current state of birthing person and infant/child (dyad) data-sharing capabilities supported by health information exchange (HIE) standards and HIE network capabilities for data exchange to inform point-of-care needs assessment for the substance-exposed dyad.</p><p><strong>Material and methods: </strong>A cross-map analysis was performed using a set of dyadic data elements focused on pediatric development and longitudinal supportive care for substance-exposed dyads (70 birthing person and 110 infant/child elements). Cross-mapping was conducted to identify definitional alignment to standardized data fields within national healthcare data exchange standards, the United States Core Data for Interoperability (USCDI) version 4 (v4) and Fast Healthcare Interoperability Resources (FHIR) release 4 (R4), and applicable structured vocabulary standards or terminology associated with USCDI. Subsequent survey analysis examined representative HIE network sharing capabilities, focusing on USCDI and FHIR usage.</p><p><strong>Results: </strong>91.11% of dyadic data elements cross-mapped to at least 1 USCDI v4 standardized data field (87.80% of those structured) and 88.89% to FHIR R4. 75% of the surveyed HIE networks reported supporting USCDI versions 1 or 2 and the capability to use FHIR, though demand is limited.</p><p><strong>Discussion: </strong>HIE of clinical and supportive care data for substance-exposed dyads is supported by current national standards, though limitations exist.</p><p><strong>Conclusion: </strong>These findings offer a dyadic-focused framework for electronic health record-centered data exchange to inform bedside care longitudinally across clinical touchpoints and population-level health.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"417-425"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833485/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142923823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utility of word embeddings from large language models in medical diagnosis.","authors":"Shahram Yazdani, Ronald Claude Henry, Avery Byrne, Isaac Claude Henry","doi":"10.1093/jamia/ocae314","DOIUrl":"10.1093/jamia/ocae314","url":null,"abstract":"<p><strong>Objective: </strong>This study evaluates the utility of word embeddings, generated by large language models (LLMs), for medical diagnosis by comparing the semantic proximity of symptoms to their eponymic disease embedding (\"eponymic condition\") and the mean of all symptom embeddings associated with a disease (\"ensemble mean\").</p><p><strong>Materials and methods: </strong>Symptom data for 5 diagnostically challenging pediatric diseases-CHARGE syndrome, Cowden disease, POEMS syndrome, Rheumatic fever, and Tuberous sclerosis-were collected from PubMed. Using the Ada-002 embedding model, disease names and symptoms were translated into vector representations in a high-dimensional space. Euclidean and Chebyshev distance metrics were used to classify symptoms based on their proximity to both the eponymic condition and the ensemble mean of the condition's symptoms.</p><p><strong>Results: </strong>The ensemble mean approach showed significantly higher classification accuracy, correctly classifying between 80% (Cowden disease) to 100% (Tuberous sclerosis) of the sample disease symptoms using the Euclidean distance metric. In contrast, the eponymic condition approach using Euclidian distance metric and Chebyshev distances, in general, showed poor symptom classification performance, with erratic results (0%-100% accuracy), largely ranging between 0% and 3% accuracy.</p><p><strong>Discussion: </strong>The ensemble mean captures a disease's collective symptom profile, providing a more nuanced representation than the disease name alone. However, some misclassifications were due to superficial semantic similarities, highlighting the need for LLM models trained on medical corpora.</p><p><strong>Conclusion: </strong>The ensemble mean of symptom embeddings improves classification accuracy over the eponymic condition approach. Future efforts should focus on medical-specific training of LLMs to enhance their diagnostic accuracy and clinical utility.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"526-534"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation.","authors":"Shalmali Joshi, Iñigo Urteaga, Wouter A C van Amsterdam, George Hripcsak, Pierre Elias, Benjamin Recht, Noémie Elhadad, James Fackler, Mark P Sendak, Jenna Wiens, Kaivalya Deshpande, Yoav Wald, Madalina Fiterau, Zachary Lipton, Daniel Malinsky, Madhur Nayan, Hongseok Namkoong, Soojin Park, Julia E Vogt, Rajesh Ranganath","doi":"10.1093/jamia/ocae301","DOIUrl":"10.1093/jamia/ocae301","url":null,"abstract":"<p><p>The primary practice of healthcare artificial intelligence (AI) starts with model development, often using state-of-the-art AI, retrospectively evaluated using metrics lifted from the AI literature like AUROC and DICE score. However, good performance on these metrics may not translate to improved clinical outcomes. Instead, we argue for a better development pipeline constructed by working backward from the end goal of positively impacting clinically relevant outcomes using AI, leading to considerations of causality in model development and validation, and subsequently a better development pipeline. Healthcare AI should be \"actionable,\" and the change in actions induced by AI should improve outcomes. Quantifying the effect of changes in actions on outcomes is causal inference. The development, evaluation, and validation of healthcare AI should therefore account for the causal effect of intervening with the AI on clinically relevant outcomes. Using a causal lens, we make recommendations for key stakeholders at various stages of the healthcare AI pipeline. Our recommendations aim to increase the positive impact of AI on clinical outcomes.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"589-594"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833492/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dataset and benchmark for hospital course summarization with adapted large language models.","authors":"Asad Aali, Dave Van Veen, Yamin Ishraq Arefeen, Jason Hom, Christian Bluethgen, Eduardo Pontes Reis, Sergios Gatidis, Namuun Clifford, Joseph Daws, Arash S Tehrani, Jangwon Kim, Akshay S Chaudhari","doi":"10.1093/jamia/ocae312","DOIUrl":"10.1093/jamia/ocae312","url":null,"abstract":"<p><strong>Objective: </strong>Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) depict remarkable capabilities in automating real-world tasks, their capabilities for healthcare applications such as synthesizing BHCs from clinical notes have not been shown. We introduce a novel preprocessed dataset, the MIMIC-IV-BHC, encapsulating clinical note and BHC pairs to adapt LLMs for BHC synthesis. Furthermore, we introduce a benchmark of the summarization performance of 2 general-purpose LLMs and 3 healthcare-adapted LLMs.</p><p><strong>Materials and methods: </strong>Using clinical notes as input, we apply prompting-based (using in-context learning) and fine-tuning-based adaptation strategies to 3 open-source LLMs (Clinical-T5-Large, Llama2-13B, and FLAN-UL2) and 2 proprietary LLMs (Generative Pre-trained Transformer [GPT]-3.5 and GPT-4). We evaluate these LLMs across multiple context-length inputs using natural language similarity metrics. We further conduct a clinical study with 5 clinicians, comparing clinician-written and LLM-generated BHCs across 30 samples, focusing on their potential to enhance clinical decision-making through improved summary quality. We compare reader preferences for the original and LLM-generated summary using Wilcoxon signed-rank tests. We further request optional qualitative feedback from clinicians to gain deeper insights into their preferences, and we present the frequency of common themes arising from these comments.</p><p><strong>Results: </strong>The Llama2-13B fine-tuned LLM outperforms other domain-adapted models given quantitative evaluation metrics of Bilingual Evaluation Understudy (BLEU) and Bidirectional Encoder Representations from Transformers (BERT)-Score. GPT-4 with in-context learning shows more robustness to increasing context lengths of clinical note inputs than fine-tuned Llama2-13B. Despite comparable quantitative metrics, the reader study depicts a significant preference for summaries generated by GPT-4 with in-context learning compared to both Llama2-13B fine-tuned summaries and the original summaries (P<.001), highlighting the need for qualitative clinical evaluation.</p><p><strong>Discussion and conclusion: </strong>We release a foundational clinically relevant dataset, the MIMIC-IV-BHC, and present an open-source benchmark of LLM performance in BHC synthesis from clinical notes. We observe high-quality summarization performance for both in-context proprietary and fine-tuned open-source LLMs using both quantitative metrics and a qualitative clinical reader study. Our research effectively integrates elements from the data assimilation pipeline: our methods use (1) clinical data sources to integrate, (2) data translation, and (3) knowledge creation, while our evaluation strategy paves the way for (4) deployment.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"470-479"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833472/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the individual.","authors":"Suzanne Bakken","doi":"10.1093/jamia/ocaf020","DOIUrl":"10.1093/jamia/ocaf020","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":"32 3","pages":"415-416"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833475/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients.","authors":"Junbo Shen, Bing Xue, Thomas Kannampallil, Chenyang Lu, Joanna Abraham","doi":"10.1093/jamia/ocae316","DOIUrl":"10.1093/jamia/ocae316","url":null,"abstract":"<p><strong>Objective: </strong>Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.</p><p><strong>Materials and methods: </strong>This retrospective cohort study used data from the electronic health records of adult surgical patients over 4 years (2018-2021). Six key postoperative complications for cardiac surgery were assessed: acute kidney injury, atrial fibrillation, cardiac arrest, deep vein thrombosis or pulmonary embolism, blood transfusion, and other intraoperative cardiac events. We compared surgVAE's prediction performance against widely-used ML models and advanced representation learning and generative models under 5-fold cross-validation.</p><p><strong>Results: </strong>89 246 surgeries (49% male, median [IQR] age: 57 [45-69]) were included, with 6502 in the targeted cardiac surgery cohort (61% male, median [IQR] age: 60 [53-70]). surgVAE demonstrated generally superior performance over existing ML solutions across postoperative complications of cardiac surgery patients, achieving macro-averaged AUPRC of 0.409 and macro-averaged AUROC of 0.831, which were 3.4% and 3.7% higher, respectively, than the best alternative method (by AUPRC scores). Model interpretation using Integrated Gradients highlighted key risk factors based on preoperative variable importance.</p><p><strong>Discussion and conclusion: </strong>Our advanced representation learning framework surgVAE showed excellent discriminatory performance for predicting postoperative complications and addressing the challenges of data complexity, small cohort sizes, and low-frequency positive events. surgVAE enables data-driven predictions of patient risks and prognosis while enhancing the interpretability of patient risk profiles.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"459-469"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833467/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A perspective on individualized treatment effects estimation from time-series health data.","authors":"Ghadeer O Ghosheh, Moritz Gögl, Tingting Zhu","doi":"10.1093/jamia/ocae323","DOIUrl":"https://doi.org/10.1093/jamia/ocae323","url":null,"abstract":"<p><strong>Objectives: </strong>The objective of this study is to provide an overview of the current landscape of individualized treatment effects (ITE) estimation, specifically focusing on methodologies proposed for time-series electronic health records (EHRs). We aim to identify gaps in the literature, discuss challenges, and propose future research directions to advance the field of personalized medicine.</p><p><strong>Materials and methods: </strong>We conducted a comprehensive literature review to identify and analyze relevant works on ITE estimation for time-series data. The review focused on theoretical assumptions, types of treatment settings, and computational frameworks employed in the existing literature.</p><p><strong>Results: </strong>The literature reveals a growing body of work on ITE estimation for tabular data, while methodologies specific to time-series EHRs are limited. We summarize and discuss the latest advancements, including the types of models proposed, the theoretical foundations, and the computational approaches used.</p><p><strong>Discussion: </strong>The limitations and challenges of current ITE estimation methods for time-series data are discussed, including the lack of standardized evaluation metrics and the need for more diverse and representative datasets. We also highlight considerations and potential biases that may arise in personalized treatment effect estimation.</p><p><strong>Conclusion: </strong>This work provides a comprehensive overview of ITE estimation for time-series EHR data, offering insights into the current state of the field and identifying future research directions. By addressing the limitations and challenges, we hope to encourage further exploration and innovation in this exciting and under-studied area of personalized medicine.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558469","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":"Integrating State-Space Modeling, Parameter Estimation, Deep Learning, and Docking Techniques in Drug Repurposing: A Case Study on COVID-19 Cytokine Storm.","authors":"Abhisek Bakshi, Kaustav Gangopadhyay, Sujit Basak, Rajat K De, Souvik Sengupta, Abhijit Dasgupta","doi":"10.1093/jamia/ocaf035","DOIUrl":"https://doi.org/10.1093/jamia/ocaf035","url":null,"abstract":"<p><strong>Objective: </strong>This study addresses the significant challenges posed by emerging SARS-CoV-2 variants, particularly in developing diagnostics and therapeutics. Drug repurposing is investigated by identifying critical regulatory proteins impacted by the virus, providing rapid and effective therapeutic solutions for better disease management.</p><p><strong>Materials and methods: </strong>We employed a comprehensive approach combining mathematical modeling and efficient parameter estimation to study the transient responses of regulatory proteins in both normal and virus-infected cells. Proportional-integral-derivative (PID) controllers were used to pinpoint specific protein targets for therapeutic intervention. Additionally, advanced deep learning models and molecular docking techniques were applied to analyze drug-target and drug-drug interactions, ensuring both efficacy and safety of the proposed treatments. This approach was applied to a case study focused on the cytokine storm in COVID-19, centering on Angiotensin-converting enzyme 2 (ACE2), which plays a key role in SARS-CoV-2 infection.</p><p><strong>Results: </strong>Our findings suggest that activating ACE2 presents a promising therapeutic strategy, whereas inhibiting AT1R seems less effective. Deep learning models, combined with molecular docking, identified Lomefloxacin and Fostamatinib as stable drugs with no significant thermodynamic interactions, suggesting their safe concurrent use in managing COVID-19-induced cytokine storms.</p><p><strong>Discussion: </strong>The results highlight the potential of ACE2 activation in mitigating lung injury and severe inflammation caused by SARS-CoV-2. This integrated approach accelerates the identification of safe and effective treatment options for emerging viral variants.</p><p><strong>Conclusion: </strong>This framework provides an efficient method for identifying critical regulatory proteins and advancing drug repurposing, contributing to the rapid development of therapeutic strategies for COVID-19 and future global pandemics.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450819","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}