Automated clinical coding using off-the-shelf large language models
Canon Medical Research Europe（佳能医疗研究欧洲）；Queen Mary University of London（伦敦玛丽女王大学）；University of Edinburgh（爱丁堡大学）；Anglia Ruskin University（安格利亚鲁斯金大学）；The Alan Turing Institute（艾伦·图灵研究所）；University of Warwick（华威大学）
Joseph S. Boyle, Antanas Kascenas, Pat Lok, Maria Liakata, Alison Q. O'Neil
备注：
Accepted to the NeurIPS 2023 workshop Deep Generative Models For Health (DGM4H). 9 pages, 3 figures
The task of assigning diagnostic ICD codes to patient hospital admissions is typically performed by expert human coders. Efforts towards automated ICD coding are dominated by supervised deep learning models. However, difficulties in learning to predict the large number of rare codes remain a barrier to adoption in clinical practice. In this work, we leverage off-the-shelf pre-trained generative large language models (LLMs) to develop a practical solution that is suitable for zero-shot and few-shot code assignment, with no need for further task-specific training. Unsupervised pre-training alone does not guarantee precise knowledge of the ICD ontology and specialist clinical coding task, therefore we frame the task as information extraction, providing a description of each coded concept and asking the model to retrieve related mentions. For efficiency, rather than iterating over all codes, we leverage the hierarchical nature of the ICD ontology to sparsely search for relevant codes.
KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs
The University of New South Wales（新南威尔士大学）；University of Göttingen（哥廷根大学）；Macquarie University（麦考瑞大学）；Beijing Intelligent Decision Medical Technology Co. Ltd（北京智决医疗科技有限公司）
Coding-Free and Privacy-Preserving Agentic Framework for Data-Driven Clinical Research
Infmedix, Co., Ltd.（Infmedix公司）；Department of Transdisciplinary Studies, Seoul National University（首尔国立大学跨学科研究系）；Healthcare AI Research Institute, Seoul National University Hospital（首尔国立大学医院医疗人工智能研究所）；Department of Medicine, Seoul National University College of Medicine（首尔国立大学医学院医学系）；Department of Transdisciplinary Medicine, Seoul National University Hospital（首尔国立大学医院跨学科医学系）；Department of Radiology, Massachusetts General Hospital and Harvard Medical School（麻省总医院放射科及哈佛医学院）
From Documents to Spans: Span-Centric Learning for LLM-based Explainable Medical Coding
School of Biomedical Engineering, Division of Life Sciences and Medicine, USTC（生物医学工程学院，生命科学与医学系，中国科学技术大学）；MIRACLE Center, Suzhou Institute for Advance Research, USTC（MIRACLE中心，苏州先进研究院，中国科学技术大学）；Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology（江苏省多模态数字孪生技术重点实验室）；State Key Laboratory of Precision and Intelligent Chemistry, USTC（精密与智能化学国家重点实验室）
Fine-tuning foundational models to code diagnoses from veterinary health records
Colorado State University (CSU) Data Science Research Institute（科罗拉多州立大学（CSU）数据科学研究院）；CSU College of Veterinary Medicine and Biomedical Sciences（科罗拉多州立大学兽医医学与生物科学学院）；CSU Veterinary Teaching Hospital（科罗拉多州立大学兽医教学医院）；Southern California Clinical and Translational Science Institute（南加州临床与转化科学研究院）
Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale
Australian e-Health Research Centre, CSIRO, Australia（澳大利亚电子健康研究中心，CSIRO，澳大利亚）；National Library of Medicine, National Institutes of Health, USA（国家医学图书馆，国立卫生研究院，美国）
University of Edinburgh（爱丁堡大学）；University College London（伦敦大学学院）；Edinburgh Futures Institute（爱丁堡未来学院）；Epic Systems Corporation（Epic系统公司）；University College London Hospitals NHS Foundation Trust（伦敦大学学院医院NHS基金会信托）；Aalto University（阿尔托大学）；University of Oxford（牛津大学）
Clinical coding is the task of transforming medical information in a patient's health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on th…
Model selection meets clinical semantics: Optimizing ICD-10-CM prediction via LLM-as-Judge evaluation, redundancy-aware sampling, and section-aware fine-tuning
National Kaohsiung University of Science and Technology（高雄科技大学）；National Health Research Institutes（国家卫生研究院）；Kaohsiung Medical University（高雄医学大学）；Taitung MacKay Memorial Hospital（台东马偕纪念医院）；MacKay Medical University（马偕医科大学）；Kaohsiung Medical University Hospital（高雄医学大学附设医院）
Healthcare providers usually record detailed notes of the clinical care delivered to each patient for clinical, research, and billing purposes. Due to the unstructured nature of these narratives, providers employ dedicated staff to assign diagnostic codes to patients' diagnoses using the International Classification of Diseases (ICD) coding system. This manual process is not only time-consuming but also costly and error-prone. Prior work demonstrated potential utility of Machine Learning (ML) methodology in automating this process, but it has relied on large quantities of manually labeled data…
Detecting HIV-Related Stigma in Clinical Narratives Using Large Language Models
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida（佛罗里达大学医学院健康结果与生物医学信息学系）；Department of Epidemiology, College of Public Health and Health Professions, University of Florida（佛罗里达大学公共卫生与健康专业学院流行病学系）；Preston A. Wells, Jr. Center for Brain Tumor Therapy, Lillian S. Wells Department of Neurosurgery, University of Florida（佛罗里达大学莉莉安·S·威尔斯神经外科系普雷斯顿·A·威尔斯脑肿瘤治疗中心）
A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment
Microsoft Healthcare & Life Sciences（微软医疗与生命科学）；Microsoft Research Montréal（微软研究院蒙特利尔）；Mila, Université de Montréal（蒙特利尔大学米拉研究所）；IKIM, University Hospital Essen（埃森大学医院人工智能医学研究所）；Cancer Research Center Cologne Essen（科隆埃森癌症研究中心）；German Cancer Consortium（德国癌症联盟）；TU Dortmund（多特蒙德工业大学）
School of Biomedical Informatics（生物医学信息学院）；The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
Neural network-based representations ("embeddings") have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more advanced embedding methods and representations (e.g., ELMo, BERT) have further pushed the state-of-the-art in NLP, yet there are no common best practices for how to integrate these representations into clinical tasks. The purpose of this study, then, is to explore the space of possible options in utilizing these new models for clinical concept extraction, including comparing these to traditional …
Imperial College London（伦敦帝国理工学院）；Transformative AI（变革性人工智能公司）
Past studies on the ICD coding problem focus on predicting clinical codes primarily based on the discharge summary. This covers only a small fraction of the notes generated during each hospital stay and leaves potential for improving performance by analysing all the available clinical notes. We propose a hierarchical transformer architecture that uses text across the entire sequence of clinical notes in each hospital stay for ICD coding, and incorporates embeddings for text metadata such as their position, time, and type of note. While using all clinical notes increases the quantity of data su…
DeepImagine: Clinical Trial Outcome Prediction via Stepwise Local Counterfactual Imaginations
Laboratory for Emerging Intelligence, University of California, San Diego（新兴智能实验室，加州大学圣地亚哥分校）；Department of Dermatology, University of California, San Diego（皮肤科系，加州大学圣地亚哥分校）；University of Chicago（芝加哥大学）；Elsevier（艾尔斯特出版社）
An Agentic AI System for Multi-Framework Communication Coding
Duke School of Medicine（杜克大学医学院）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；MedStar Health National Center for Human Factors in Healthcare（MedStar Health国家医疗人因中心）；University of Pennsylvania（宾夕法尼亚大学）；Duke Cancer Institute（杜克癌症研究所）；Duke University（杜克大学）
Language Models as Semantic Teachers: Post-Training Alignment for Medical Audio Understanding
Eindhoven University of Technology, The Netherlands（埃因霍温理工大学，荷兰）；Kyutai, France（Kyutai公司，法国）；Eindhoven Artificial Intelligence Systems Institute, The Netherlands（埃因霍温人工智能系统研究所，荷兰）
Artificial intelligence to improve clinical coding practice in Scandinavia: a crossover randomized controlled trial
Norwegian Centre for E-health Research（挪威电子健康研究中心）；UiT The Arctic University of Norway（挪威北极大学）；University Hospital of North Norway（北挪威大学医院）；The Norwegian Centre for Clinical Artificial Intelligence（挪威临床人工智能中心）
Exploring the Consistency, Quality and Challenges in Manual and Automated Coding of Free-text Diagnoses from Hospital Outpatient Letters
University of Manchester（曼彻斯特大学）；The Northern Care Alliance NHS Foundation Trust（北部护理联盟国民保健信托基金会）；Manchester University NHS Foundation Trust（曼彻斯特大学国民保健信托基金会）；University Hospitals of Morecambe Bay NHS Foundation Trust（莫克姆湾大学医院国民保健信托基金会）；Intelligent Medical Objects, Inc.（智能医疗对象公司）；Columbia University（哥伦比亚大学）
University of Pittsburgh（匹兹堡大学）；National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；The Ohio State University（俄亥俄州立大学）
Linking clinical narratives to standardized vocabularies and coding systems is a key component of unlocking the information in medical text for analysis. However, many domains of medical concepts lack well-developed terminologies that can support effective coding of medical text. We present a framework for developing natural language processing (NLP) technologies for automated coding of under-studied types of medical information, and demonstrate its applicability via a case study on physical mobility function. Mobility is a component of many health measures, from post-acute care and surgical o…
Infusing clinical knowledge into tokenisers for language models
The UCL Institute of Health Informatics（伦敦大学学院健康信息学研究所）；Advanced Care Research Centre（高级护理研究中心）；The Usher Institute（乌舍研究所）；College of Medicine and Veterinary Medicine（医学与兽医学院）；University of Edinburgh（爱丁堡大学）；School of Literatures, Languages and Cultures（文学、语言与文化学院）；Edinburgh Futures Institute（爱丁堡未来研究所）
