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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.02730">arXiv:2411.02730</a> <span> [<a href="https://arxiv.org/pdf/2411.02730">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A Natural Language Processing Approach to Support Biomedical Data Harmonization: Leveraging Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+Z">Zexu Li</a>, <a href="/search/cs?searchtype=author&query=Prabhu%2C+S+P">Suraj P. Prabhu</a>, <a href="/search/cs?searchtype=author&query=Popp%2C+Z+T">Zachary T. Popp</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+S+S">Shubhi S. Jain</a>, <a href="/search/cs?searchtype=author&query=Balakundi%2C+V">Vijetha Balakundi</a>, <a href="/search/cs?searchtype=author&query=Ang%2C+T+F+A">Ting Fang Alvin Ang</a>, <a href="/search/cs?searchtype=author&query=Au%2C+R">Rhoda Au</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+J">Jinying Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.02730v1-abstract-short" style="display: inline;"> Biomedical research requires large, diverse samples to produce unbiased results. Automated methods for matching variables across datasets can accelerate this process. Research in this area has been limited, primarily focusing on lexical matching and ontology based semantic matching. We aimed to develop new methods, leveraging large language models (LLM) and ensemble learning, to automate variable… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.02730v1-abstract-full').style.display = 'inline'; document.getElementById('2411.02730v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.02730v1-abstract-full" style="display: none;"> Biomedical research requires large, diverse samples to produce unbiased results. Automated methods for matching variables across datasets can accelerate this process. Research in this area has been limited, primarily focusing on lexical matching and ontology based semantic matching. We aimed to develop new methods, leveraging large language models (LLM) and ensemble learning, to automate variable matching. Methods: We utilized data from two GERAS cohort (European and Japan) studies to develop variable matching methods. We first manually created a dataset by matching 352 EU variables with 1322 candidate JP variables, where matched variable pairs were positive and unmatched pairs were negative instances. Using this dataset, we developed and evaluated two types of natural language processing (NLP) methods, which matched variables based on variable labels and definitions from data dictionaries: (1) LLM-based and (2) fuzzy matching. We then developed an ensemble-learning method, using the Random Forest model, to integrate individual NLP methods. RF was trained and evaluated on 50 trials. Each trial had a random split (4:1) of training and test sets, with the model's hyperparameters optimized through cross-validation on the training set. For each EU variable, 1322 candidate JP variables were ranked based on NLP-derived similarity scores or RF's probability scores, denoting their likelihood to match the EU variable. Ranking performance was measured by top-n hit ratio (HRn) and mean reciprocal rank (MRR). Results:E5 performed best among individual methods, achieving 0.90 HR-30 and 0.70 MRR. RF performed better than E5 on all metrics over 50 trials (P less than 0.001) and achieved an average HR 30 of 0.98 and MRR of 0.73. LLM-derived features contributed most to RF's performance. One major cause of errors in automatic variable matching was ambiguous variable definitions within data dictionaries. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.02730v1-abstract-full').style.display = 'none'; document.getElementById('2411.02730v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">32 pages, 2 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.03741">arXiv:2410.03741</a> <span> [<a href="https://arxiv.org/pdf/2410.03741">pdf</a>, <a href="https://arxiv.org/format/2410.03741">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Towards Democratization of Subspeciality Medical Expertise </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=O%27Sullivan%2C+J+W">Jack W. O'Sullivan</a>, <a href="/search/cs?searchtype=author&query=Palepu%2C+A">Anil Palepu</a>, <a href="/search/cs?searchtype=author&query=Saab%2C+K">Khaled Saab</a>, <a href="/search/cs?searchtype=author&query=Weng%2C+W">Wei-Hung Weng</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+Y">Yong Cheng</a>, <a href="/search/cs?searchtype=author&query=Chu%2C+E">Emily Chu</a>, <a href="/search/cs?searchtype=author&query=Desai%2C+Y">Yaanik Desai</a>, <a href="/search/cs?searchtype=author&query=Elezaby%2C+A">Aly Elezaby</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D+S">Daniel Seung Kim</a>, <a href="/search/cs?searchtype=author&query=Lan%2C+R">Roy Lan</a>, <a href="/search/cs?searchtype=author&query=Tang%2C+W">Wilson Tang</a>, <a href="/search/cs?searchtype=author&query=Tapaskar%2C+N">Natalie Tapaskar</a>, <a href="/search/cs?searchtype=author&query=Parikh%2C+V">Victoria Parikh</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+S+S">Sneha S. Jain</a>, <a href="/search/cs?searchtype=author&query=Kulkarni%2C+K">Kavita Kulkarni</a>, <a href="/search/cs?searchtype=author&query=Mansfield%2C+P">Philip Mansfield</a>, <a href="/search/cs?searchtype=author&query=Webster%2C+D">Dale Webster</a>, <a href="/search/cs?searchtype=author&query=Gottweis%2C+J">Juraj Gottweis</a>, <a href="/search/cs?searchtype=author&query=Barral%2C+J">Joelle Barral</a>, <a href="/search/cs?searchtype=author&query=Schaekermann%2C+M">Mike Schaekermann</a>, <a href="/search/cs?searchtype=author&query=Tanno%2C+R">Ryutaro Tanno</a>, <a href="/search/cs?searchtype=author&query=Mahdavi%2C+S+S">S. Sara Mahdavi</a>, <a href="/search/cs?searchtype=author&query=Natarajan%2C+V">Vivek Natarajan</a>, <a href="/search/cs?searchtype=author&query=Karthikesalingam%2C+A">Alan Karthikesalingam</a>, <a href="/search/cs?searchtype=author&query=Ashley%2C+E">Euan Ashley</a> , et al. (1 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.03741v1-abstract-short" style="display: inline;"> The scarcity of subspecialist medical expertise, particularly in rare, complex and life-threatening diseases, poses a significant challenge for healthcare delivery. This issue is particularly acute in cardiology where timely, accurate management determines outcomes. We explored the potential of AMIE (Articulate Medical Intelligence Explorer), a large language model (LLM)-based experimental AI syst… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03741v1-abstract-full').style.display = 'inline'; document.getElementById('2410.03741v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.03741v1-abstract-full" style="display: none;"> The scarcity of subspecialist medical expertise, particularly in rare, complex and life-threatening diseases, poses a significant challenge for healthcare delivery. This issue is particularly acute in cardiology where timely, accurate management determines outcomes. We explored the potential of AMIE (Articulate Medical Intelligence Explorer), a large language model (LLM)-based experimental AI system optimized for diagnostic dialogue, to potentially augment and support clinical decision-making in this challenging context. We curated a real-world dataset of 204 complex cases from a subspecialist cardiology practice, including results for electrocardiograms, echocardiograms, cardiac MRI, genetic tests, and cardiopulmonary stress tests. We developed a ten-domain evaluation rubric used by subspecialists to evaluate the quality of diagnosis and clinical management plans produced by general cardiologists or AMIE, the latter enhanced with web-search and self-critique capabilities. AMIE was rated superior to general cardiologists for 5 of the 10 domains (with preference ranging from 9% to 20%), and equivalent for the rest. Access to AMIE's response improved cardiologists' overall response quality in 63.7% of cases while lowering quality in just 3.4%. Cardiologists' responses with access to AMIE were superior to cardiologist responses without access to AMIE for all 10 domains. Qualitative examinations suggest AMIE and general cardiologist could complement each other, with AMIE thorough and sensitive, while general cardiologist concise and specific. Overall, our results suggest that specialized medical LLMs have the potential to augment general cardiologists' capabilities by bridging gaps in subspecialty expertise, though further research and validation are essential for wide clinical utility. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03741v1-abstract-full').style.display = 'none'; document.getElementById('2410.03741v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.07911">arXiv:2403.07911</a> <span> [<a href="https://arxiv.org/pdf/2403.07911">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Callahan%2C+A">Alison Callahan</a>, <a href="/search/cs?searchtype=author&query=McElfresh%2C+D">Duncan McElfresh</a>, <a href="/search/cs?searchtype=author&query=Banda%2C+J+M">Juan M. Banda</a>, <a href="/search/cs?searchtype=author&query=Bunney%2C+G">Gabrielle Bunney</a>, <a href="/search/cs?searchtype=author&query=Char%2C+D">Danton Char</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+J">Jonathan Chen</a>, <a href="/search/cs?searchtype=author&query=Corbin%2C+C+K">Conor K. Corbin</a>, <a href="/search/cs?searchtype=author&query=Dash%2C+D">Debadutta Dash</a>, <a href="/search/cs?searchtype=author&query=Downing%2C+N+L">Norman L. Downing</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+S+S">Sneha S. Jain</a>, <a href="/search/cs?searchtype=author&query=Kotecha%2C+N">Nikesh Kotecha</a>, <a href="/search/cs?searchtype=author&query=Masterson%2C+J">Jonathan Masterson</a>, <a href="/search/cs?searchtype=author&query=Mello%2C+M+M">Michelle M. Mello</a>, <a href="/search/cs?searchtype=author&query=Morse%2C+K">Keith Morse</a>, <a href="/search/cs?searchtype=author&query=Nallan%2C+S">Srikar Nallan</a>, <a href="/search/cs?searchtype=author&query=Pandya%2C+A">Abby Pandya</a>, <a href="/search/cs?searchtype=author&query=Revri%2C+A">Anurang Revri</a>, <a href="/search/cs?searchtype=author&query=Sharma%2C+A">Aditya Sharma</a>, <a href="/search/cs?searchtype=author&query=Sharp%2C+C">Christopher Sharp</a>, <a href="/search/cs?searchtype=author&query=Thapa%2C+R">Rahul Thapa</a>, <a href="/search/cs?searchtype=author&query=Wornow%2C+M">Michael Wornow</a>, <a href="/search/cs?searchtype=author&query=Youssef%2C+A">Alaa Youssef</a>, <a href="/search/cs?searchtype=author&query=Pfeffer%2C+M+A">Michael A. Pfeffer</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+N+H">Nigam H. Shah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.07911v2-abstract-short" style="display: inline;"> The impact of using artificial intelligence (AI) to guide patient care or operational processes is an interplay of the AI model's output, the decision-making protocol based on that output, and the capacity of the stakeholders involved to take the necessary subsequent action. Estimating the effects of this interplay before deployment, and studying it in real time afterwards, are essential to bridge… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07911v2-abstract-full').style.display = 'inline'; document.getElementById('2403.07911v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.07911v2-abstract-full" style="display: none;"> The impact of using artificial intelligence (AI) to guide patient care or operational processes is an interplay of the AI model's output, the decision-making protocol based on that output, and the capacity of the stakeholders involved to take the necessary subsequent action. Estimating the effects of this interplay before deployment, and studying it in real time afterwards, are essential to bridge the chasm between AI model development and achievable benefit. To accomplish this, the Data Science team at Stanford Health Care has developed a Testing and Evaluation (T&E) mechanism to identify fair, useful and reliable AI models (FURM) by conducting an ethical review to identify potential value mismatches, simulations to estimate usefulness, financial projections to assess sustainability, as well as analyses to determine IT feasibility, design a deployment strategy, and recommend a prospective monitoring and evaluation plan. We report on FURM assessments done to evaluate six AI guided solutions for potential adoption, spanning clinical and operational settings, each with the potential to impact from several dozen to tens of thousands of patients each year. We describe the assessment process, summarize the six assessments, and share our framework to enable others to conduct similar assessments. Of the six solutions we assessed, two have moved into a planning and implementation phase. Our novel contributions - usefulness estimates by simulation, financial projections to quantify sustainability, and a process to do ethical assessments - as well as their underlying methods and open source tools, are available for other healthcare systems to conduct actionable evaluations of candidate AI solutions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07911v2-abstract-full').style.display = 'none'; document.getElementById('2403.07911v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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