Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes. This process is often constrained by having a relatively small number of patient records for training the model. We demonstrate that using patient representation schemes inspired from techniques in natural language processing can increase the accuracy of clinical prediction models by transferring information learned from the entire patient population to the task of training a specific model, where only a subset of the population is relevant. Such patient representation schemes enable a 3.5% mean improvement in AUROC on five prediction tasks compared to standard baselines, with the average improvement rising to 19% when only a small number of patient records are available for training the clinical prediction model.
SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction
School of Computer Science, Peking University（北京大学计算机学院）；National Research and Engineering Center of Software Engineering, Peking University（北京大学国家软件工程研究与工程中心）；Center on Frontiers of Computing Studies, Peking University（北京大学计算前沿研究中心）
Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records
University of Pittsburgh（匹兹堡大学）；Intelligent Systems Program（智能系统计划）；School of Computing and Information（计算与信息学院）；Department of Medicine（医学院）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Health Information Management（健康信息管理系）；Department of Biomedical Informatics（生物医学信息学系）；Clinical and Translational Science Institute（临床与转化科学研究所）
本文提出 Fair Patient Model（FPM），通过加权损失函数从 EHR 中学习公平患者表示，在 MIMIC-III 的四项预测任务中改善公平性并保持可比准确率。
Building models for health prediction based on Electronic Health Records (EHR) has become an active research area. EHR patient journey data consists of patient time-ordered clinical events/visits from patients. Most existing studies focus on modeling long-term dependencies between visits, without explicitly taking short-term correlations between consecutive visits into account, where irregular time intervals, incorporated as auxiliary information, are fed into health prediction models to capture latent progressive patterns of patient journeys. We present a novel deep neural network with four m…
Northwestern University Feinberg School of Medicine（西北大学范伯格医学院）
Rare diseases are very difficult to identify among large number of other possible diagnoses. Better availability of patient data and improvement in machine learning algorithms empower us to tackle this problem computationally. In this paper, we target one such rare disease - cardiac amyloidosis. We aim to automate the process of identifying potential cardiac amyloidosis patients with the help of machine learning algorithms and also learn most predictive factors. With the help of experienced cardiologists, we prepared a gold standard with 73 positive (cardiac amyloidosis) and 197 negative insta…
PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals
School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院）；Department of Psychology, Princeton University（普林斯顿大学心理学系）；School of Social Work, University of Pittsburgh（匹兹堡大学社会工作学院）；Department of Psychiatry and Behavioral Sciences, Stanford University（斯坦福大学精神病学与行为科学系）
Cross-Lingual Mental Health Ontologies for Indian Languages: Bridging Patient Expression and Clinical Understanding through Explainable AI and Human-in-the-Loop Validation
University of Florida（佛罗里达大学）；University of Kansas（堪萨斯大学）；University of Maryland（马里兰大学）；IIT Kharagpur（印度理工学院卡拉格普尔分校）
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Auditing machine learning-based (ML) healthcare tools for bias is critical to preventing patient harm, especially in communities that disproportionately face health inequities. General frameworks are becoming increasingly available to measure ML fairness gaps between groups. However, ML for health (ML4H) auditing principles call for a contextual, patient-centered approach to model assessment. Therefore, ML auditing tools must be (1) better aligned with ML4H auditing principles and (2) able to illuminate and characterize communities vulnerable to the most harm. To address this gap, we propose s…
In this work, we modify and apply self-supervision techniques to the domain of medical health insurance claims. We model patients' healthcare claims history analogous to free-text narratives, and introduce pre-trained `prior knowledge', later utilized for patient outcome predictions on a challenging task: predicting Covid-19 hospitalization, given a patient's pre-Covid-19 insurance claims history. Results suggest that pre-training on insurance claims not only produces better prediction performance, but, more importantly, improves the model's `clinical trustworthiness' and model stability/relia…
University of Pittsburgh（匹兹堡大学）；North Allegheny Intermediate High School（北阿勒格尼中级高中）；The Hong Kong Polytechnic University（香港理工大学）；Weill Cornell Medicine（威尔康奈尔医学院）
Health literacy is the central focus of Healthy People 2030, the fifth iteration of the U.S. national goals and objectives. People with low health literacy usually have trouble understanding health information, following post-visit instructions, and using prescriptions, which results in worse health outcomes and serious health disparities. In this study, we propose to leverage natural language processing techniques to improve health literacy in patient education materials by automatically translating illiterate languages in a given sentence. We scraped patient education materials from four onl…
Mining Social Determinants of Health for Heart Failure Patient 30-Day Readmission via Large Language Model
Department of Computer Science, Emory University（计算机科学系，埃默里大学）；Nell Hodgson Woodruff School of Nursing, Emory University（Nell Hodgson Woodruff护理学院，埃默里大学）
McLean Hospital（麦克莱恩医院）；Harvard Medical School（哈佛医学院）；Brandeis University（布兰迪斯大学）
Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health records (EHRs) span a wide range of topics and vocabulary; therefore, a psychiatric readmission prediction model must begin with a robust and interpretable topic extraction component. We created a data pipeline for using document vector similarity metrics to perform topic extraction o…
A Dataset and Resources for Identifying Patient Health Literacy Information from Clinical Notes
National Library of Medicine（国家医学图书馆）；Massachusetts Institute of Technology（麻省理工学院）；Yonsei University College of Medicine（延世大学医学院）；Beth Israel Deaconess Medical Center（贝斯以色列医疗中心）；NYC Health + Hospitals - Lincoln（纽约市卫生局-林肯医院）
AmarDoctor: An AI-Driven, Multilingual, Voice-Interactive Digital Health Application for Primary Care Triage and Patient Management to Bridge the Digital Health Divide for Bengali Speakers
MedAi Bangladesh Limited（MedAi孟加拉有限公司）；Central Police Hospital, Dhaka, Bangladesh（达卡警察医院）；University of Birmingham, UK（英国伯明翰大学）
The Hong Kong Polytechnic University（香港理工大学）；Nanjing University（南京大学）
Huge volumes of patient queries are daily generated on online health forums, rendering manual doctor allocation a labor-intensive task. To better help patients, this paper studies a novel task of doctor recommendation to enable automatic pairing of a patient to a doctor with relevant expertise. While most prior work in recommendation focuses on modeling target users from their past behavior, we can only rely on the limited words in a query to infer a patient's needs for privacy reasons. For doctor modeling, we study the joint effects of their profiles and previous dialogues with other patients…
University of British Columbia（不列颠哥伦比亚大学）；York University（约克大学）
The proliferation of text messaging for mobile health is generating a large amount of patient-doctor conversations that can be extremely valuable to health care professionals. We present ConVIScope, a visual text analytic system that tightly integrates interactive visualization with natural language processing in analyzing patient-doctor conversations. ConVIScope was developed in collaboration with healthcare professionals following a user-centered iterative design. Case studies with six domain experts suggest the potential utility of ConVIScope and reveal lessons for further developments.
