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arXiv 2022年

作者： Yan, Rui Qu, Liangqiong Wei, Qingyue Huang, Shih-Cheng Shen, Liyue Rubin, Daniel Xing, Lei Zhou, Yuyin The Institute for Computational and Mathematical Engineering Stanford University StanfordCA94305 United States The Department of Statistics and Actuarial Science The Institute of Data Science The University of Hong Kong 999077 Hong Kong The Department of Biomedical Data Science Stanford University StanfordCA94305 United States The Department of Electrical Engineering Stanford University StanfordCA94305 United States The Department of Biomedical Informatics Stanford University StanfordCA94305 United States The Department of Radiation Oncology Stanford University StanfordCA94305 United States The Department of Computer Science and Engineering University of California Santa CruzCA95064 United States

The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enables privacy-preserving collaborative learning among different institutions, but it generally suffers from performance deterioration due to heterogeneous data distributions and a lack of quality labeled data. In this paper, we present a robust and label-efficient self-supervised FL framework for medical image analysis. Our method introduces a novel Transformer-based self-supervised pre-training paradigm that pre-trains models directly on decentralized target task datasets using masked image modeling, to facilitate more robust representation learning on heterogeneous data and effective knowledge transfer to downstream models. Extensive empirical results on simulated and real-world medical imaging non-IID federated datasets show that masked image modeling with Transformers significantly improves the robustness of models against various degrees of data heterogeneity. Notably, under severe data heterogeneity, our method, without relying on any additional pre-training data, achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training. In addition, we show that our federated self-supervised pre-training methods yield models that generalize better to out-of-distribution data and perform more effectively when fine-tuning with limited labeled data, compared to existing FL algorithms. The code is available at https://***/rui-yan/SSL-FL. © 2022, CC BY.

关键词： Deep learning

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Risk-based decision making: estimands for sequential prediction under interventions

arXiv

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arXiv 2023年

作者： Luijken, Kim Morzywolek, Pawel van Amsterdam, Wouter Cinà, Giovanni Hoogland, Jeroen Keogh, Ruth Krijthe, Jesse Magliacane, Sara van Ommen, Thijs Peek, Niels Putter, Hein van Smeden, Maarten Sperrin, Matthew Wang, Junfeng Weir, Daniala Didelez, Vanessa van Geloven, Nan Department of Epidemiology Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Statistics University of Washington Seattle United States Department of Medical Informatics Amsterdam University Medical Centers Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam Netherlands Department of Medical Statistics London School of Hygiene & Tropical Medicine Keppel Street London United Kingdom Pattern Recognition and Bio-Informatics Group EEMCS Delft University of Technology Delft Netherlands Amsterdam Machine Learning Lab University of Amsterdam Amsterdam Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology Department of Pharmaceutical Sciences Utrecht University Utrecht Netherlands Department of Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics/Computer Science University of Bremen Bremen Germany

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. © 2023, CC BY.

关键词： Forecasting

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Iterative Prompt Refinement for Mining Gene Relationships from ChatGPT

International Journal of Artificial Intelligence and Robotic...

引用

International Journal of Artificial Intelligence and Robotics Research 2024年第2期01卷

作者： Yibo Chen Jeffrey Gao Marius Petruc Richard D. Hammer Mihail Popescu Dong Xu Institute for Data Science and Informatics Bond Life Sciences Center University of Missouri Columbia MO 65211 USA Marriotts Ridge High School Marriottsville MD 21104 USA Institute for Data Science and Informatics University of Missouri Columbia MO 65211 USA Department of Pathology and Anatomical Sciences University of Missouri Columbia MO 65211 USA Department of Biomedical Informatics Biostatistics and Medical Epidemiology University of Missouri Columbia MO 65211 USA Department of Electrical Engineering and Computer Science Bond Life Sciences Center Institute for Data Science and Informatics University of Missouri Columbia MO 65211 USA

ChatGPT has demonstrated its potential as a surrogate knowledge graph. Trained on extensive data sources, including open-access publications, peer-reviewed research articles, and biomedical websites, ChatGPT extracted information on gene relationships and biological pathways so that it can be used to predict them. However, a major challenge is model hallucination, that is, high false positive rates. To assess and address this challenge, we systematically evaluated ChatGPT’s capacity for predicting gene relationships using GPT-3.5-turbo, GPT-4, and GPT-4o. Benchmarking against the KEGG Pathway database as the ground truth, we experimented with diverse prompting strategies, targeting gene relationships of activation, inhibition, and phosphorylation. We introduced an innovative iterative prompt refinement technique. By assessing prompt efficacy using metrics such as F-1 score, precision, and recall, GPT-4 suggested improved prompts. A refined prompt, which combines a specialized role with explanatory text, significantly enhanced the performance. Going beyond pairwise gene relationships, we also deciphered complex gene interplays, such as gene interaction chains and pathways pertinent to diseases such as non-small cell lung cancer. Direct prompts showed limited success, but “least-to-most” prompting exhibited significant potentials for such network constructions. The methods in this study may be used for other bioinformatics prediction problems.

关键词： ChatGPT prompt refinement bioinformatics knowledge graph gene relation

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TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools

arXiv

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arXiv 2025年

作者： Gao, Shanghua Zhu, Richard Kong, Zhenglun Noori, Ayush Su, Xiaorui Ginder, Curtis Tsiligkaridis, Theodoros Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA United States Cardiovascular Division Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States MIT Lincoln Laboratory LexingtonMA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative CambridgeMA United States

Precision therapeutics require multimodal adaptive models that generate personalized treatment recommendations. We introduce TXAGENT, an AI agent that leverages multi-step reasoning and real-time biomedical knowledge retrieval across a toolbox of 211 tools to analyze drug interactions, contraindications, and patient-specific treatment strategies. TXAGENT evaluates how drugs interact at molecular, pharmacokinetic, and clinical levels, identifies contraindications based on patient comorbidities and concurrent medications, and tailors treatment strategies to individual patient characteristics, including age, genetic factors, and disease progression. TXAGENT retrieves and synthesizes evidence from multiple biomedical sources, assesses interactions between drugs and patient conditions, and refines treatment recommendations through iterative reasoning. It selects tools based on task objectives and executes structured function calls to solve therapeutic tasks that require clinical reasoning and cross-source validation. The TOOLUNIVERSE consolidates 211 tools from trusted sources, including all US FDA-approved drugs since 1939 and validated clinical insights from Open Targets. TXAGENT outperforms leading LLMs, tool-use models, and reasoning agents across five new benchmarks: DrugPC, BrandPC, GenericPC, TreatmentPC, and DescriptionPC, covering 3,168 drug reasoning tasks and 456 personalized treatment scenarios. It achieves 92.1% accuracy in open-ended drug reasoning tasks, surpassing GPT-4o by up to 25.8% and outperforming DeepSeek-R1 (671B) in structured multi-step reasoning. TXAGENT generalizes across drug name variants and descriptions, maintaining a variance of © 2025, CC BY.

关键词： Drug discovery

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A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds

arXiv

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arXiv 2024年

作者： Orlichenko, Anton Qu, Gang Zhou, Ziyu Liu, Anqi Deng, Hong-Wen Ding, Zhengming Stephen, Julia M. Wilson, Tony W. Calhoun, Vince D. Wang, Yu-Ping Department of Biomedical Engineering Tulane University New OrleansLA70118 United States Department of Computer Science Tulane University New OrleansLA70118 United States Center for Biomedical Informatics and Genomics Tulane Integrated Institute of Data & Health Sciences Tulane University New OrleansLA70112 United States Institute for Human Neuroscience Boys Town National Research Hospital Boys TownNE United States Mind Research Network AlbuquerqueNM United States Georgia State University Georgia Institute of Technology Emory University AtlantaGA United States

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results:We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data con- ditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds. © 2024, CC BY.

关键词： Neurodegenerative diseases

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Evaluating explainability for graph neural networks

arXiv

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arXiv 2022年

作者： Agarwal, Chirag Queen, Owen Lakkaraju, Himabindu Zitnik, Marinka Media and Data Science Research Lab Adobe Noida201304 India Department of Computer Science University of Tennessee KnoxvilleTN37996 United States Harvard Business School BostonMA02163 United States Department of Biomedical Informatics Harvard University BostonMA02115 United States Broad Institute of MIT Harvard CambridgeMA02142 United States Harvard Data Science Initiative CambridgeMA02138 United States Department of Computer Science Harvard University BostonMA02134 United States

As explanations are increasingly used to understand the behavior of graph neural networks (GNNs), evaluating the quality and reliability of GNN explanations is crucial. However, assessing the quality of GNN explanations is challenging as existing graph datasets have no or unreliable ground-truth explanations. Here, we introduce a synthetic graph data generator, SHAPEGGEN, which can generate a variety of benchmark datasets (e.g., varying graph sizes, degree distributions, homophilic vs. heterophilic graphs) accompanied by ground-truth explanations. The flexibility to generate diverse synthetic datasets and corresponding ground-truth explanations allows SHAPEGGEN to mimic the data in various real-world areas. We include SHAPEGGEN and several real-world graph datasets in a graph explainability library, GRAPHXAI. In addition to synthetic and real-world graph datasets with ground-truth explanations, GRAPHXAI provides data loaders, data processing functions, visualizers, GNN model implementations, and evaluation metrics to benchmark GNN explainability methods. © 2022, CC BY.

关键词： data handling

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Large Language Models in Mental Health Care: a Scoping Review

arXiv

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arXiv 2024年

作者： Hua, Yining Liu, Fenglin Yang, Kailai Li, Zehan Na, Hongbin Sheu, Yi-Han Zhou, Peilin Moran, Lauren V. Ananiadou, Sophia CliDon, David A. Beam, Andrew Torous, John Department of Epidemiology Harvard T.H. Chan School of Public Health MA United States Department of Psychiatry Beth Israel Deaconess Medical Center MA United States Institute of Biomedical Engineering Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science The University of Manchester Manchester United Kingdom School of Biomedical Informatics University of Texas Health Science at Houston Houston United States Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Center for Precision Psychiatry Massachusetts General Hospital MA United States Department of Psychiatry Harvard Medical School MA United States Data Science and Analytics Thrust Hong Kong University of Science and Technology Guangzhou China Division of Psychotic Disorders McLean Hospital MA United States The Alan Turing Institute London United Kingdom

The integration of large language models (LLMs) in mental health care is an emerging field. There is a need to systematically review the application outcomes and delineate the advantages and limitations in clinical settings. This review aims to provide a comprehensive overview of the use of LLMs in mental health care, assessing their efficacy, challenges, and potential for future applications. A systematic search was conducted across multiple databases including PubMed, Web of science, Google Scholar, arXiv, medRxiv, and PsyArXiv in November 2023. All forms of original research, peer-reviewed or not, published or disseminated between October 1, 2019, and December 2, 2023, are included without language restrictions if they used LLMs developed after T5 and directly addressed research questions in mental health care settings. From an initial pool of 313 articles, 34 met the inclusion criteria based on their relevance to LLM application in mental health care and the robustness of reported outcomes. Diverse applications of LLMs in mental health care are identified, including diagnosis, therapy, patient engagement enhancement, etc. Key challenges include data availability and reliability, nuanced handling of mental states, and effective evaluation methods. Despite successes in accuracy and accessibility improvement, gaps in clinical applicability and ethical considerations were evident, pointing to the need for robust data, standardized evaluations, and interdisciplinary collaboration. LLMs hold substantial promise for enhancing mental health care. For their full potential to be realized, emphasis must be placed on developing robust datasets, development and evaluation frameworks, ethical guidelines, and interdisciplinary collaborations to address current limitations. © 2024, CC BY.

关键词： Electronic health record

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Monitoring strategies for continuous evaluation of deployed clinical prediction models

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Journal of biomedical informatics 2025年 168卷 104854页

作者： Grace Y E Kim Conor K Corbin François Grolleau Michael Baiocchi Jonathan H Chen Center for Biomedical Informatics Research Stanford California USA. Electronic address: yek1354@stanford.edu. Center for Biomedical Informatics Research Stanford California USA Department of Biomedical Data Science Stanford California USA. Center for Biomedical Informatics Research Stanford California USA. Department of Epidemiology and Population Health Stanford California USA. Division of Hospital Medicine Stanford Stanford California USA Clinical Excellence Research Center Stanford California USA.

OBJECTIVE:As machine learning adoption in clinical practice continues to grow, deployed classifiers must be continuously monitored and updated (retrained) to protect against data drift that stems from inevitable changes, including evolving medical practices and shifting patient populations. However, successful clinical machine learning classifiers will lead to a change in care which may change the distribution of features, labels, and their relationship. For example, "high risk" cases that were correctly identified by the model may ultimately get labeled as "low risk" thanks to an intervention prompted by the model's alert. Classifier surveillance systems naive to such deployment-induced feedback loops will estimate lower model performance and lead to degraded future classifier retrains. The objective of this study is to simulate the impact of these feedback loops, propose feedback aware monitoring strategies as a solution, and assess the performance of these alternative monitoring strategies through simulations. METHODS:We propose Adherence Weighted and Sampling Weighted Monitoring as two feedback loop-aware surveillance strategies. Through simulation we evaluate their ability to accurately appraise post deployment model performance and to initiate safe and accurate classifier retraining. RESULTS:Measured across accuracy, area under the receiver operating characteristic curve, average precision, brier score, expected calibration error, F1, precision, sensitivity, and specificity, in the presence of feedback loops, Adherence Weighted and Sampling Weighted strategies have the highest fidelity to the ground truth classifier performance while standard approaches yield the most inaccurate estimations. Furthermore, in simulations with true data drift, retraining using standard unweighted approaches results in a AUROC score of 0.52 (drop from 0.72). In contrast, retraining based on Adherence Weighted and Sampling Weighted strategies recover performance to 0.67 which is co

关键词： Clinical prediction Feedback loops Machine learning Model retraining Post deployment performance monitoring clinical informatics

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Planning with Diffusion Models for Target-Oriented Dialogue Systems

arXiv

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arXiv 2025年

作者： Du, Hanwen Peng, Bo Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States

Target-Oriented Dialogue (TOD) remains a significant challenge in the LLM era, where strategic dialogue planning is crucial for directing conversations toward specific targets. However, existing dialogue planning methods generate dialogue plans in a step-by-step sequential manner, and may suffer from compounding errors and myopic actions. To address these limitations, we introduce a novel dialogue planning framework, DiffTOD, which leverages diffusion models to enable non-sequential dialogue planning. DiffTOD formulates dialogue planning as a trajectory generation problem with conditional guidance, and leverages a diffusion language model to estimate the likelihood of the dialogue trajectory. To optimize the dialogue action strategies, DiffTOD introduces three tailored guidance mechanisms for different target types, offering flexible guidance towards diverse TOD targets at test time. Extensive experiments across three diverse TOD settings show that DiffTOD can effectively perform non-myopic lookahead exploration and optimize action strategies over a long horizon through non-sequential dialogue planning, and demonstrates strong flexibility across complex and diverse dialogue scenarios. Our code and data are accessible through https://***/r/DiffTOD. © 2025, CC BY.

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Deep learning models can predict violence and threats against healthcare providers using clinical notes

npj Mental Health Research

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npj Mental Health Research 2024年第1期3卷 61页

作者： Dobbins, Nicholas J. Chipkin, Jacqueline Byrne, Tim Ghabra, Omar Siar, Julia Sauder, Mitchell Huijon, R. Michael Black, Taylor M. Biomedical Informatics & Data Science Johns Hopkins University Baltimore MD United States Biomedical Informatics & Medical Education University of Washington Seattle WA United States Psychiatry & Behavioral Sciences University of Washington Seattle WA United States Analytics UW Medicine University of Washington Seattle WA United States

Violence, verbal abuse, threats, and sexual harassment of healthcare providers by patients is a major challenge for healthcare organizations around the world, contributing to staff turnover, distress, absenteeism, reduced job satisfaction, and worsening mental and physical health. To enable interventions prior to possible violent episodes, we trained two deep learning models to predict violence against healthcare workers 3 days prior to violent events for case and control patients. The first model is a document classification model using clinical notes, and the second is a baseline regression model using largely structured data. Our document classification model achieved an F1 score of 0.75 while our model using structured data achieved an F1 of 0.72, both exceeding the predictive performance of a psychiatry team who reviewed the same documents (0.5 F1). To aid in the explainability and understanding of risk factors for violent events, we additionally trained a named entity recognition classifier on annotations of the same corpus, which achieved an overall F1 of 0.7. This study demonstrates the first deep learning model capable of predicting violent events within healthcare settings using clinical notes, surpassing the first published baseline of human experts. We anticipate our methods can be generalized and extended to enable intervention at other hospital systems. © The Author(s) 2024.

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