Disease prediction holds considerable significance in modern healthcare, because of its crucial role in facilitating early intervention and implementing effective prevention measures. However, most recent disease prediction approaches heavily rely on laboratory test outcomes (e.g., blood tests and medical imaging from X-rays). Gaining access to such data for precise disease prediction is often a complex task from the standpoint of a patient and is always only available post-patient consultation. To make disease prediction available from patient-side, we propose Personalized Medical Disease Prediction (PoMP), which predicts diseases using patient health narratives including textual descriptions and demographic information. By applying PoMP, patients can gain a clearer comprehension of their conditions, empowering them to directly seek appropriate medical specialists and thereby reducing the time spent navigating healthcare communication to locate suitable doctors. We conducted extensive experiments using real-world data from Haodf to showcase the effectiveness of PoMP.
Chronic pain patient narratives allow for the estimation of current pain intensity
INESC-ID；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；Faculty of Medicine, University of Porto（波尔图大学医学院）；i3S - Instituto de Investigação e Inovação em Saúde, University of Porto（波尔图大学健康研究与创新研究所）；Centro Hospitalar Universitário São João（圣若昂大学医院中心）；Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto（波尔图大学医学院健康技术与服务研究中心）
InsightFlow: LLM-Driven Synthesis of Patient Narratives for Mental Health into Causal Models
Department of Mathematics, IIT Delhi（印度德里理工学院数学系）；Department of Electrical Engineering, IIT Delhi（印度德里理工学院电气工程系）；Department of Computer Science and Engineering, IIIT Manipur（曼尼普尔理工学院计算机科学与工程系）
Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida（健康结局与生物医学信息学系，佛罗里达大学医学院）；Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida（心血管医学部，医学院，佛罗里达大学）；Division of Hematology & Oncology, Department of Medicine, College of Medicine, University of Florida（血液学与肿瘤学部，医学院，佛罗里达大学）
A Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseases
Imperial College London（帝国理工学院）；Pangaea Data Limited（Pangaea Data有限公司）；Hong Kong Baptist University（香港浸会大学）
临床医生可以依靠诸如国际疾病分类（ICD）之类的医疗编码系统来从电子健康记录（EHR）中识别患有疾病的患者。然而，由于缺乏细节和特异性以及错误编码的可能性，最近的研究表明ICD代码在实际临床实践中通常不能准确地识别患者的特定疾病，因此，使用它们来寻找患者进行研究或试验可能导致高失败率和错过未编码的患者。对所有患者进行大规模的人工检查是不可行的，因为它成本高且速度慢。本文提出了一种可扩展的工作流程，该工作流程利用EHR中的结构化数据和非结构化文本注释，采用包括NLP，AutoML和临床医生在环机制在内的技术来构建机器学习分类器，以识别具有给定疾病的大规模患者，特别是那些目前可能被ICD代码错误编码或遗漏的患者。在MIMIC-III数据集中进行了案例研究，其中与简单使用ICD代码对黄金测试子集识别卵巢癌患者相比，所提出的工作流程在F1评分方面表现出更高的分类性能（0.901 vs 0.814）、肺癌（0.859 vs 0.828）、癌症恶病质（0.862 vs 0.650）和狼疮性肾炎（0.959 vs 0.855）。此外，利用非结构化笔记的拟议工作流程始终优于使用结构化数据的基线，仅增加F1（卵巢癌0.901 vs 0.719，肺癌0.859 vs 0.787，癌症恶病质0.862 vs 0.838和狼疮性肾炎0.959 vs 0.785）。在大型测试集上的实验也证明了所提出的工作流…
Patterns vs. Patients: Evaluating LLMs against Mental Health Professionals on Personality Disorder Diagnosis through First-Person Narratives
IDEAS Research Institute（IDEAS研究 institute）；Adam Mickiewicz University（亚当·密茨凯维奇大学）；AMU Center for Artificial Intelligence（AMU人工智能中心）；Poznań University of Medical Sciences（波兹南医学科学大学）；Maria Curie-Skłodowska University（玛丽·居里-斯克洛多夫斯卡大学）
An Underexplored Frontier: Large Language Models for Rare Disease Patient Education and Communication -- A scoping review
Division of Computational Health Sciences, Department of Surgery, University of Minnesota（计算健康科学系，外科部，明尼苏达大学）；Department of Electrical and Computer Engineering, University of Minnesota（电气与计算机工程系，明尼苏达大学）；Clinical Bioinformatics Lab, Université Paris Cité, Institut Imagine, INSERM UMR 1163（临床生物信息学实验室，巴黎城市大学，Imagine机构，INSERM UMR 1163）；Medical Informatics Department, Hôpital Necker-Enfants Malades, AP-HP（医学信息学部，Necker-Enfants Malades医院，AP-HP）
Optimizing Large Language Models for Detecting Symptoms of Comorbid Depression or Anxiety in Chronic Diseases: Insights from Patient Messages
Stanford University（斯坦福大学）；Stanford School of Medicine（斯坦福医学院）；Stanford Health Care（斯坦福医疗保健系统）；Google LLC（谷歌公司）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Veterans Affairs Palo Alto Health Care System（帕洛阿尔托退伍军人事务医疗保健系统）
What Do Patients Say About Their Disease Symptoms? Deep Multilabel Text Classification With Human-in-the-Loop Curation for Automatic Labeling of Patient Self Reports of Problems
A Method for Characterizing Disease Progression from Acute Kidney Injury to Chronic Kidney Disease
Department of Biomedical Informatics, Columbia University（哥伦比亚大学生物医学信息学系）；Department of Medicine, Division of Nephrology, Columbia University（哥伦比亚大学医学系肾脏病科）；Department of Medicine, Division of General Medicine, Columbia University（哥伦比亚大学医学系全科医学）
Natural Language Processing with Deep Learning for Medical Adverse Event Detection from Free-Text Medical Narratives: A Case Study of Detecting Total Hip Replacement Dislocation
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Karolinska Institutet（卡罗林斯卡学院）；Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）
