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arXiv 2022年

作者： Wan, Ke Tanioka, Kensuke Shimokawa, Toshio Department of Medical Data Science Wakayama Medical University Japan Department of Biomedical Sciences and Informatics Doshisha University Japan

The increasing scientific attention given to precision medicine based on real-world data has led many recent studies to clarify the relationships between treatment effects and patient characteristics. However, this is challenging because of ubiquitous heterogeneity in the treatment effect for individuals and the real-world data on their backgrounds being complex and noisy. Because of their flexibility, various heterogeneous treatment effect (HTE) machine learning (ML)estimation methods have been proposed. However, most ML methods incorporate black-box models that hamper direct interpretation of the interrelationships between individuals’ characteristics and the treatments’ effects. This study proposes an ML method for estimating HTE based on the rule ensemble method termed RuleFit. The main advantages of RuleFit are interpretability and accuracy. However, HTEs are always defined in the potential outcome framework, and RuleFit cannot be applied directly. Thus, we modified RuleFit and proposed a method to estimate HTEs that directly interpret the interrelationships among the individuals’ features from the model. The actual data from the HIV study, ACTG 175 dataset, was used to illustrate the interpretation based on the ensemble of rules created by the proposed method. In addition, the numerical results confirm that the proposed method has high prediction accuracy compared to the previous method, indicating that the proposed method establishes an interpretable model with sufficient prediction accuracy. © 2022, CC BY.

关键词： Numerical methods

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FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation

arXiv

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arXiv 2023年

作者： Li, Can Lai, Dejian Jiang, Xiaoqian Zhang, Kai Department of Biostatistics and Data Science School of Public Health The University of Texas Health Science Center at Houston HoustonTX United States Department of Health Data Science and Artificial Intelligence McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX United States

Liver transplantation often faces fairness challenges across subgroups defined by sensitive attributes such as age group, gender, and race/ethnicity. Machine learning models for outcome prediction can introduce additional biases. Therefore, we introduce Fairness through the Equitable Rate of Improvement in Multitask Learning (FERI) algorithm for fair predictions of graft failure risk in liver transplant patients. FERI constrains subgroup loss by balancing learning rates and preventing subgroup dominance in the training process. Our results show that FERI maintained high predictive accuracy with AUROC and AUPRC comparable to baseline models. More importantly, FERI demonstrated an ability to improve fairness without sacrificing accuracy. Specifically, for the gender, FERI reduced the demographic parity disparity by 71.74%, and for the age group, it decreased the equalized odds disparity by 40.46%. Therefore, the FERI algorithm advanced fairness-aware predictive modeling in healthcare and provides an invaluable tool for equitable healthcare systems. © 2023, CC BY.

关键词： Contrastive Learning

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Distributed management of patient data-sharing informed consents for clinical research

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Computers in Biology and Medicine 2024年 180卷 108956页

作者： Pham, Anh Edelson, Maxim Nouri, Armin Kuo, Tsung-Ting UCSD Health Department of Biomedical Informatics University of California San Diego La Jolla CA United States UCSD Department of Computer Science and Engineering University of California San Diego La Jolla CA United States Department of Biomedical Engineering Fu Foundation School of Engineering Columbia University New YorkNY United States Department of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States Department of Surgery School of Medicine Yale University New HavenCT United States

Background: The consent protocol is now a critical part in the overall orchestration of clinical research. We aimed to demonstrate the feasibility of an Ethereum-based informed consent system, which includes an immutable and automated channel of consent matching, to simultaneously assure patient privacy and increase the efficiency of researchers’ data access. Method: We simulated a multi-site scenario, each assigned 10000 consent records. A consent record contained one patient's data-sharing preference with regards to seven data categories. We developed a blockchain-based infrastructure with a smart contract to record consents on-chain, and to query consenting patients corresponding to specific criteria. We measured our system's recording efficiency against a baseline design and verified accuracy by testing an exhaustive list of possible queries. Results: Our method achieved ∼3–4% lead with an average insertion speed of ∼2 s per record per node on either a 3-, 4- or 5-node network, and 100 % accuracy. It also outperformed other solutions in external validation. Discussion: The speed we achieved is reasonable in a real-world system under the realistic assumption that patients may not change their minds too frequently, with the added benefit of immutability. Furthermore, the per-insertion time did improve slightly as the number of network nodes increased, attesting to the benefit of node parallelism as it suggests no attrition of insertion efficiency due to scale of nodes. Conclusions: Our work confirms the technical feasibility of a blockchain-based consent mechanism, assuring patients with an immutable audit trail, and providing researchers with an efficient way to reach their cohorts. © 2024

关键词： Clinical research

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Assessing the Impact of Post-COVID Clinics on 6-Month Health Care Utilization for Patients With Long COVID: A Single-Center Experience

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Mayo Clinic Proceedings: Innovations, Quality and Outcomes 2025年第3期9卷 100603-100603页

作者： Gong, Kirby D. Afshar, Ali S. Brown, Fred Alavi, Reza Ganesh, Ravindra Kharrazi, Hadi ArtiMed Inc. Baltimore MD Division of General Internal Medicine Mayo Clinic Rochester MN Division of Biomedical Informatics and Data Science Johns Hopkins School of Medicine Baltimore MD

Objective: To assess the impact of post-COVID clinics by examining the association between their early usage and downstream health care utilization. Patients and Methods: In a case-control study spanning data from March 11, 2020 to June 1, 2023, patients with Long COVID were identified from a major health system using diagnosis codes. The Fast, Large-Scale Almost Matching Exactly algorithm was used to match patients who presented early to post-COVID clinics with patients with Long COVID who did not attend such clinics. Matching was performed on demographic characteristics, acute COVID severity, comorbidities, diagnosis date, and vaccination, to reduce confounders for the comparison of the health care utilization and mortality between cohorts. Results: When exactly matching on all 46 features, the algorithm yielded 2814 matched patients, of whom 692 (24.6%;66.6% females;mean [SD] age, 48.8 [14.5] years) were seen in post-COVID clinics within the first 6 months and 2122 (75.4%;64.1% females;mean [SD] age, 49.7 [15.2] years) who were not. The average treatment effect (95% CI) of early post-COVID clinic usage was −0.60 (−0.83 to −0.39) on inpatient visits, −0.19 (−0.26 to −0.11) on emergency department visits, 7.62 (6.96-8.56) on outpatient visits, −$3467 (−$6267 to −$754) on estimated costs, and −0.006 (−0.010 to −0.003) on mortality. Conclusion: Early usage of post-COVID clinics by patients with Long COVID is associated with not only fewer downstream inpatient stays, emergency department visits, estimated costs, and reduced mortality within the first 6 months but also greater outpatient utilization. Results suggest early post-COVID clinic involvement shifts care to outpatient settings, potentially reducing costs and mortality. © 2025 The Authors

关键词： COVID coronavirus disease COVID-19 COronaVIrus Disease 2019 ED emergency department EHR electronic health record ICD International Classification of Diseases SARS-CoV-2 severe acute respiratory syndrome coronavirus 2

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引用

18th World Congress on Medical and Health informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： Sun, Yingcheng Tang, Jiaqi Butler, Alex Liu, Cong Fang, Yilu Weng, Chunhua Department of Biomedical Informatics Columbia University New YorkNYNY10032 United States Data Science Institute Columbia University New YorkNY United States Department of Medicine Columbia University New YorkNY United States

ISBN: (纸本)9781643682648

The rapid growth of clinical trials launched in recent years poses significant challenges for accurate and efficient trial search. Keyword-based clinical trial search engines require users to construct effective queries, which can be a difficult task given complex information needs. In this study, we present an interactive clinical trial search interface that retrieves trials similar to a target clinical trial. It enables user configuration of 13 clinical trial features and 4 metrics (Jaccard similarity, semantic-based similarity, temporal overlap and geographical distance) to measure pairwise trial similarities. Among 1,007 coronavirus disease 2019 (COVID-19) trials conducted in the United States, 91.9% were found to have similar trials with the similarity threshold being 0.85 and 43.8% were highly similar with the threshold 0.95. A simulation study using 3 groups of similar trials curated by COVID-19 clinical trial reviews demonstrates the precision and recall of the search interface. © 2022 International Medical informatics Association (IMIA) and IOS Press.

关键词： COVID-19

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Comfort Shoulder (Rhomboid Major) Muscles Chair Identifier System

Comfort Shoulder (Rhomboid Major) Muscles Chair Identifier S...

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International Conference on Communications, Information System and Computer Engineering (CISCE)

作者： Ata Jahangir Moshayedi Yuexin Yu Kimia Savoji Mehran Emadi Andani School of Information Engineering Jiangxi University of Science and Technology Ganzhou Jiangxi China Biomedical Data Science and Informatics School of computing Clemson University Clemson South Carolina USA Department of Neurosciences Biomedicine and Movement Sciences University of Verona Verona Italy

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Nowadays, Sitting is one of the frequent activities of humans. particularly with the rise of office work, which is leading to an increase in the sitting periods for many individuals. Research highlights the importance of studying sitting posture to enhance seat quality, correct sitting methods, and prevent various health issues like neck and shoulder strains, etc. This article focuses on designing an IoT-based system that enables web-based data visualization without specialized software that can enable distance sampling. Through temperature sensors (DS18b20 and SHT11) and an FSR sensor, the system can identify distinct patterns in the main rhomboid muscles (the most important muscles of humans). The results show a significant relationship between individuals' body mass (BMI) and the observed muscle patterns, highlighting the importance of this correlation for researchers and industry designers alike.

关键词： Temperature sensors Pediatrics Shape Ergonomics Muscles Software Neck

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Visual Explanation of the Assessment of Certainty of Evidence for Systematic Review and Meta-analysis

Visual Explanation of the Assessment of Certainty of Evidenc...

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IEEE Workshop on Visual Analytics in Healthcare (VAHC)

作者： Syed Arsalan Ahmed Naqvi Kunwer Sufyan Faisal Manal Imran Muhammad Ali Khan Kaneez Zahra Rubab Khakwani M. Hassan Murad Huan He Irbaz Bin Riaz Department of Oncology Mayo Clinic Ziauddin University Dow University of Health Sciences The University of Arizona Mayo Clinic Evidence Based Practice Center Mayo Clinic Department of Biomedical Informatics and Data Science Yale University

ISBN: (数字)9798331522162

ISBN: (纸本)9798331522179

Practitioners of evidence-based medicine need to know the level of certainty in the evidence they are applying to patient care. We use a living interactive evidence synthesis framework to create and maintain living, interactive systematic reviews (LISRs). With each new update, it is critical to report any changes to the confidence level or certainty of synthesized evidence (CoE) for patient important endpoints. Ascertaining CoE is a complex task and thus challenging in the setting of LISRs. Therefore, we propose a hybrid approach, which leverages an interactive web-based visualization techniques to accelerate the CoE evaluation.

关键词： Visual analytics Conferences data visualization Medical services Systematic literature review

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Gene interactions analysis of brain spatial transcriptome for Alzheimer's disease

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Genes & Diseases 2024年第6期11卷 525-540页

作者： Shengran Wang Jonathan Greenbaum Chuan Qiu Russell HSwerdlow Mohammad Haeri Yun Gong Hui Shen Hongmei Xiao Hongwen Deng Reproductive Medicine Center The Sun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhouGuangdong 510120China Center for System Biology Data Sciences and Reproductive HealthSchool of Basic Medical ScienceCentral South UniversityChangshaHunan 410008China Tulane Center of Biomedical Informatics and Genomics Deming Department of MedicineTulane University School of MedicineTulane UniversityNew OrleansLA 70112USA Department of Pathology and KU Alzheimer's Disease Research Center University of Kansas Medical CenterKansas CityKS 66160USA Institute of Reproductive&Stem Cell Engineering School of Basic Medical ScienceCentral South UniversityChangshaHunan 410008China Center of Reproductive Health School of Basic Medical ScienceCentral South UniversityChangshaHunan 410008China

Recent studies have explored the spatial transcriptomics patterns of Alzheimer's disease(AD)brain by spatial sequencing in mouse models,enabling the identification of unique genome-wide transcriptomic features associated with different spatial regions and pathological ***,the dynamics of gene interactions that occur during amyloid-βaccumulation remain largely *** this study,we performed analyses on ligand-receptor communication,transcription factor regulatory network,and spot-specific network to reveal the dependence and the dynamics of gene associations/interactions on spatial regions and pathological status with mouse and human *** first used a spatial transcriptomics dataset of the AppNL-G-F knock-in AD and wild-type mouse *** revealed 17 ligand-receptor pairs with opposite tendencies throughout the amyloid-βaccumulation process and showed the specific ligand-receptor interactions across the hippocampus layers at different extents of pathological *** then identified nerve function related transcription factors in the hippocampus and entorhinal cortex,as well as genes with different transcriptomic association degrees in AD versus wild-type ***,another independent spatial transcriptomics dataset from different AD mouse models and human single-nuclei RNA-seq data/Alzdata database were used for *** is the first study to identify various gene associations throughout amyloid-βaccumulation based on spatial transcriptomics,establishing the foundations to reveal advanced and in-depth AD etiology from a novel perspective based on the comprehensive analyses of gene interactions that are spatio-temporal dependent.

关键词： Alzheimer's disease Amyloid-β Ligand-receptor Spatial transcriptomics Transcription factor

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Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources

arXiv

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arXiv 2024年

作者： Li, Yiming Viswaroopan, Deepthi He, William Li, Jianfu Zuo, Xu Xu, Hua Tao, Cui McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Department of Electrical & Computer Engineering Pratt School of Engineering Duke University 305 Tower Engineering Building DurhamNC27708 United States Department of Artificial Intelligence and Informatics Mayo Clinic JacksonvilleFL32224 United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT06510 United States

Objective Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of these products. Traditional deep learning models are adept at learning intricate feature representations and dependencies in sequential data, but often require extensive labeled data. In contrast, large language models (LLMs) excel in understanding contextual information, but exhibit unstable performance on named entity recognition (NER) tasks, possibly due to their broad but unspecific training. This study aims to evaluate the effectiveness of LLMs and traditional deep learning models in AE extraction, and to assess the impact of ensembling these models on performance. Methods In this study, we utilized reports and posts from the Vaccine Adverse Event Reporting System (VAERS) (n=621), Twitter (n=9,133), and Reddit (n=131) as our corpora. Our goal was to extract three types of entities: vaccine, shot, and adverse event (ae). We explored and fine-tuned (except GPT-4) multiple LLMs, including GPT-2, GPT-3.5, GPT-4, Llama-2 7b, and Llama-2 13b, as well as traditional deep learning models like Recurrent neural network (RNN) and Bidirectional Encoder Representations from Transformers for biomedical Text Mining (BioBERT). To enhance performance, we created ensembles of the three models with the best performance. For evaluation, we used strict and relaxed F1 scores to evaluate the performance for each entity type, and micro-average F1 was used to assess the overall performance. Results The ensemble model achieved the highest performance in "vaccine," "shot," and "ae," with strict F1-scores of 0.878, 0.930, and 0.925, respectively, along with a micro-average score of 0.903. These results underscore the significance of fine-tuning models for specific tasks and demonstrate the effectiveness of ensemble methods in enhancing performance. Conclusion In conclusion,

关键词： Vaccines

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GENIE: Generative Note Information Extraction model for structuring EHR data

arXiv

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arXiv 2025年

作者： Ying, Huaiyuan Yuan, Hongyi Lu, Jinsen Qu, Zitian Zhao, Yang Zhao, Zhengyun Kohane, Isaac Cai, Tianxi Yu, Sheng Department of Statistics and Data Science Tsinghua University Beijing China Zhili College Tsinghua University Beijing China Weiyang College Tsinghua University Beijing China Department of Biomedical Informatics Harvard Medical School Boston United States

Electronic Health Records (EHRs) hold immense potential for advancing healthcare, offering rich, longitudinal data that combines structured information with valuable insights from unstructured clinical notes. However, the unstructured nature of clinical text poses significant challenges for secondary applications. Traditional methods for structuring EHR free-text data, such as rule-based systems and multi-stage pipelines, are often limited by their time-consuming configurations and inability to adapt across clinical notes from diverse healthcare settings. Few systems provide a comprehensive attribute extraction for terminologies. While giant large language models (LLMs) like GPT-4 and LLaMA 405B excel at structuring tasks, they are slow, costly, and impractical for large-scale use. To overcome these limitations, we introduce GENIE, a Generative Note Information Extraction system that leverages LLMs to streamline the structuring of unstructured clinical text into usable data with standardized format. GENIE processes entire paragraphs in a single pass, extracting entities, assertion statuses, locations, modifiers, values, and purposes with high accuracy. Its unified, end-to-end approach simplifies workflows, reduces errors, and eliminates the need for extensive manual intervention. Using a robust data preparation pipeline and fine-tuned small scale LLMs, GENIE achieves competitive performance across multiple information extraction tasks, outperforming traditional tools like cTAKES and MetaMap and can handle extra attributes to be extracted. GENIE strongly enhances real-world applicability and scalability in healthcare systems. By open-sourcing the model and test data, we aim to encourage collaboration and drive further advancements in EHR structurization. © 2025, CC BY-NC-SA.

关键词： Electronic health record

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