AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献

{"title":"Safeguarding Privacy in Genome Research: A Comprehensive Framework for Authors.","authors":"Maryam Ghasemian, Lynette Hammond Gerido, Erman Ayday","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>As genomic research continues to advance, sharing of genomic data and research outcomes has become increasingly important for fostering collaboration and accelerating scientific discovery. However, such data sharing must be balanced with the need to protect the privacy of individuals whose genetic information is being utilized. This paper presents a bidirectional framework for evaluating privacy risks associated with data shared (both in terms of summary statistics and research datasets) in genomic research papers, particularly focusing on re-identification risks such as membership inference attacks (MIA). The framework consists of a structured workflow that begins with a questionnaire designed to capture researchers' (authors') self-reported data sharing practices and privacy protection measures. Responses are used to calculate the risk of re-identification for their study (paper) when compared with the National Institutes of Health (NIH) genomic data sharing policy. Any gaps in compliance help us to identify potential vulnerabilities and encourage the researchers to enhance their privacy measures before submitting their research for publication. The paper also demonstrates the application of this framework, using published genomic research as case study scenarios to emphasize the importance of implementing bidirectional frameworks to support trustworthy open science and genomic data sharing practices.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"177-186"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Standardized Guideline for Assessing Extracted Electronic Health Records Cohorts: A Scoping Review.","authors":"Nattanit Songthangtham, Ratchada Jantraporn, Elizabeth Weinfurter, Gyorgy Simon, Wei Pan, Sripriya Rajamani, Steven G Johnson","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Assessing how accurately a cohort extracted from Electronic Health Records (EHR) represents the intended target population, or cohort fitness, is critical but often overlooked in secondary EHR data use. This scoping review aimed to (1) identify guidelines for assessing cohort fitness and (2) determine their thoroughness by examining whether they offer sufficient detail and computable methods for researchers. This scoping review follows the JBI guidance for scoping reviews and is refined based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) checklists. Searches were performed in Medline, Embase, and Scopus. From 1,904 results, 30 articles and 2 additional references were reviewed. Nine articles (28.13%) include a framework for evaluating cohort fitness but only 5 (15.63%) contain sufficient details and quantitative methodologies. Overall, a more comprehensive guideline that provides best practices for measuring the cohort fitness is still needed.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"527-536"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Analysis of Patient Similarity Measures for Outcome Prediction.","authors":"Deyi Li, Alan S L Yu, Mei Liu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Personalized medicine aims to improve clinical outcomes by tailoring treatments to individual patients based on genetic, phenotypic, or psychosocial characteristics, leveraging insights from similar patients. This is particularly necessary for managing diseases with significant variability in their causes, progressions and prognoses. Accurate measurement of patient similarity is crucial in this context, as it enables the identification of a high-quality cohort of similar patients, thereby enhancing clinical decision making with better evidence. However, previous studies have not comprehensively compared different patient similarity measures in large-scale retrospective analyses of electronic health records (EHRs). In this study, we conducted a comparative analysis of four patient similarity measures focusing on feature weighting mechanisms, using EHR data from 46,968 hospitalized patients. For evaluation, we assessed the patient similarity measures for predicting acute kidney injury, readmission, and mortality. Our results showed that using grid-searched weights to combine features based by their types outperformed all other methods.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"270-279"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150746/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Implemented Real-World-Data Pipeline for Standardization of Electronic Health Records in Precision Oncology.","authors":"Kory Kreimeyer, Durrant Barasa, Mohamed Sherief, Xiaorui Shi, Marvin Borja, Srinivasan Yegnasubramanian, Valsamo Anagnostou, Joseph C Murray, Taxiarchis Botsis","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Several use cases in precision oncology require accurately extracting and standardizing Real-World Data from Electronic Health Records (EHRs). We developed the infrastructure and a toolset incorporating data mining and natural language processing scripts to automatically retrieve selected descriptive and common endpoint variables from EHRs. This toolset was evaluated against a reference dataset of 106 lung cancer and 45 sarcoma patient cases pulled from two databases complying with the Precision Oncology Core Data Model (Precision-DM) and maintained by the Johns Hopkins Molecular Tumor Board and a research team. We accurately retrieved most descriptive EHR fields but less efficiently extracted the Date of Diagnosis and Treatment Start Date that supported calculating the Age at Diagnosis, Overall Survival, and Time to First Treatment (accuracy range 50%-86%). Our infrastructure and Precision-DM-based standardization could inspire similar efforts in other cancer centers, however, the toolset should be enhanced to improve accuracy in certain variables.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"242-249"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150718/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging SNOMED CT for patient cohort identification over heterogeneous EHR data.","authors":"Xubing Hao, Yan Huang, Licong Cui, Xiaojin Li","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>SNOMED CT is extensively employed to standardize data across diverse patient datasets and support cohort identification, with studies revealing its benefits and challenges. In this work, we developed a SNOMED CT-driven cohort query system over a heterogeneous Optum^® de-identified COVID-19 Electronic Health Record dataset leveraging concept mappings between ICD-9-CM/ICD-10-CM and SNOMED CT. We evaluated the benefits and challenges of using SNOMED CT to perform cohort queries based on both query code sets and actual patients retrieved from the database, leveraging the original ICD-9-CM and ICD-10-CM as baselines. Manual review of 80 random cases revealed 65 cases containing 148 true positive codes and 25 cases containing 63 false positive codes. The manual evaluation also revealed issues in code naming, mappings, and hierarchical relations. Overall, our study indicates that while the SNOMED CT-driven query system holds considerable promise for comprehensive cohort queries, careful attention must be given to the challenges offalsely included codes and patients.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"205-214"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Healthcare Data Integration: A Machine Learning Approach to Harmonizing Laboratory Labels.","authors":"Mehmet F Bagci, Samantha R Spierling, Anna L Ritko, Truong Nguyen, Brian D Modena, Yusuf Ozturk","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Variations in laboratory test names across healthcare systems-stemming from inconsistent terminologies, abbreviations, misspellings, and assay vendors-pose significant challenges to the integration and analysis of clinical data. These discrepancies hinder interoperability and complicate efforts to extract meaningful insights for both clinical research and patient care. In this study, we propose a machine learning-driven solution, enhanced by natural language processing techniques, to standardize lab test names. By employing feature extraction methods that analyze both string similarity and the distributional properties of test results, we improve the harmonization of test names, resulting in a more robust dataset. Our model achieves a 99% accuracy rate in matching lab names, showcasing the potential of AI-driven approaches in resolving long-standing standardization challenges. Importantly, this method enhances the reliability and consistency of clinical data, which is crucial for ensuring accurate results in large-scale clinical studies and improving the overall efficiency of informatics-based research and diagnostics.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"65-73"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable Artificial Intelligence (XAI) in the Era of Large Language Models: Applying an XAI Framework in Pediatric Ophthalmology Diagnosis using the Gemini Model.","authors":"Dipak P Upadhyaya, Katrina Prantzalos, Pedram Golnari, Aasef G Shaikh, Subhashini Sivagnanam, Amitava Majumdar, Fatema F Ghasia, Satya S Sahoo","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Amblyopia is a neurodevelopmental disorder affecting children's visual acuity, requiring early diagnosis for effective treatment. Traditional diagnostic methods rely on subjective evaluations of eye tracking recordings from high fidelity eye tracking instruments performed by specialized pediatric ophthalmologists, often unavailable in rural, low resource clinics. As such, there is an urgent need to develop a scalable, low cost, high accuracy approach to automatically analyze eye tracking recordings. Large Language Models (LLM) show promise in accurate detection of amblyopia; our prior work has shown that the Google Gemini model, guided by expert ophthalmologists, can detect control and amblyopic subjects from eye tracking recordings. However, there is a clear need to address the issues of transparency and trust in medical applications of LLMs. To bolster the reliability and interpretability of LLM analysis of eye tracking records, we developed a Feature Guided Interprative Prompting (FGIP) framework focused on critical clinical features. Using the Google Gemini model, we classify high-fidelity eye-tracking data to detect amblyopia in children and apply the Quantus framework to evaluate the classification results across key metrics (faithfulness, robustness, localization, and complexity). These metrics provide a quantitative basis for understanding the model's decision-making process. This work presents the first implementation of an Explainable Artificial Intelligence (XAI) framework to systematically characterize the results generated by the Gemini model using high-fidelity eye-tracking data to detect amblyopia in children. Results demonstrated that the model accurately classified control and amblyopic subjects, including those with nystagmus while maintaining transparency and clinical alignment. The results of this study support the development of a scalable and interpretable clinical decision support (CDS) tool using LLMs that has the potential to enhance the trustworthiness of AI applications.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"566-575"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRISeqClassifier: A Deep Learning Toolkit for Precise MRI Sequence Classification.","authors":"Jinqian Pan, Qi Chen, Chengkun Sun, Renjie Liang, Jiang Bian, Jie Xu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Magnetic Resonance Imaging (MRI) is a crucial diagnostic tool in medicine, widely used to detect and assess various health conditions. Different MRI sequences, such as T1-weighted, T2-weighted, and FLAIR, serve distinct roles by highlighting different tissue characteristics and contrasts. However, distinguishing them based solely on the description file is currently impossible due to confusing or incorrect annotations. Additionally, there is a notable lack of effective tools to differentiate these sequences. In response, we developed a deep learning-based toolkit tailored for small, unrefined MRI datasets. This toolkit enables precise sequence classification and delivers performance comparable to systems trained on large, meticulously curated datasets. Utilizing lightweight model architectures and incorporating a voting ensemble method, the toolkit enhances accuracy and stability. It achieves a 99% accuracy rate using only 10% of the data typically required in other research. The code is available at https://github.com/JinqianPan/MRISeqClassifier.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"405-413"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility Assessment of a Wearable App to Manage Symptoms of Postural Orthostatic Tachycardia Syndrome Using Real-Time Heart Rate Monitoring.","authors":"Aileen S Gabriel, Te-Yi Tsai, C Mahony Reategui-Rivera, Patricia Rocco, Aref Smiley, Clayton Powers, Jeanette P Brown, Joseph Finkelstein","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Postural Tachycardia Syndrome (POTS) is a chronic condition characterized by orthostatic intolerance and a significant rise in heart rate upon standing. Patients often experience debilitating symptoms, such as brain fog and chronic fatigue, which hinder daily functioning. Non-pharmacological management strategies, particularly pacing, are crucial for reducing symptom fluctuations and improving quality of life. Heart rate monitoring plays a key role in effective pacing, enabling patients to plan activities and prevent severe symptom onset. Recent technological advancements have increased interest in wearable devices for managing chronic conditions. This study examines the feasibility of using wearable technology to support symptom management in POTS patients. Through an Exploratory- Descriptive Qualitative approach, five key themes emerged, including personalized management strategies and the beneficial impact of real-time feedback. The findings suggest that wearable devices can enhance self-management, improve communication with healthcare providers, and empower patients to take a more proactive approach to their care.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"159-166"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Scanner to Science: Reusing Clinically Acquired Medical Images for Research.","authors":"Jenna M Schabdach, Remo M S Williams, Joseph Logan, Viveknarayanan Padmanabhan, Russell D'Aiello Iii, Johnny Mclaughlin, Alexander Gonzalez, Edward M Krause, Gregory E Tasian, Susan Sotardi, Aaron F Alexander-Bloch","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Growth in the field of medical imaging research has revealed a need for larger volume and variety in available data. This need could be met using curated clinically acquired data, but the process for getting this data from the scanners to the scientists is complex and lengthy. We present a manifest-driven modular Extract, Transform, and Load (ETL) process named Locutus designed to appropriately handle difficulties present in the process of reusing clinically acquired medical imaging data. The design of Locutus was based on four foundational assumptions about medical data, research data, and communication. All parts of a workflow must communicate with each other and be adaptable to unique data delivery requests. In addition, the workflow must be robust to possible errors and uncertainties in clinically-acquired data, which may require human intervention to resolve. With these assumptions in mind,Locutus presents a five-phase workflow for downloading, deidentifying, and delivering unique requests for imaging data. The phases include initialization, data preparation, extraction of data from the research server to a pre-deidentification data warehouse, transformation into deidentified space, and loading into post-deidentification data warehouse. To date, this workflow has been used to process 32,962 imaging accessions for research use. This number is expected to grow as technical challenges are addressed and the role of humans is expected to shift from frequent intervention to regular monitoring.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2025 ","pages":"471-480"},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12150695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}