A Clinical Decision Support System (CDSS) is designed to enhance clinician decision-making by combining system-generated recommendations with medical expertise. Given the high costs, intensive labor, and time-sensitive nature of medical treatments, there is a pressing need for efficient decision support, especially in complex emergency scenarios. In these scenarios, where information can be limited, an advanced CDSS framework that leverages AI (artificial intelligence) models to effectively reduce diagnostic uncertainty has utility. Such an AI-enabled CDSS framework with quantified uncertainty promises to be practical and beneficial in the demanding context of real-world medical care. In this study, we introduce the concept of Medical Entropy, quantifying uncertainties in patient outcomes predicted by neural machine translation based on the ICD-9 code of procedures. Our experimental results not only show strong correlations between procedure and diagnosis sequences based on the simple ICD-9 code but also demonstrate the promising capacity to model trends of uncertainties during hospitalizations through a data-driven approach.
DarwinAI Corp.（DarwinAI公司）；University of Waterloo（滑铁卢大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）
The COVID-19 pandemic continues to have a devastating global impact, and has placed a tremendous burden on struggling healthcare systems around the world. Given the limited resources, accurate patient triaging and care planning is critical in the fight against COVID-19, and one crucial task within care planning is determining if a patient should be admitted to a hospital's intensive care unit (ICU). Motivated by the need for transparent and trustworthy ICU admission clinical decision support, we introduce COVID-Net Clinical ICU, a neural network for ICU admission prediction based on patient cl…
Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inserm（法国国家健康与医学研究院）；Pixyl Research and Development Laboratory（Pixyl研发实验室）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. Particularly, end users are reluctant to rely on the rough predictions of DL models. Uncertainty quantification methods have been proposed in the literature as a potential response to reduce the rough decision provided by the DL black box and thus increase the interpretability and the acceptability of the result by the final user. In this review, we propose an overview of the existing methods to quantify uncertainty associate…
Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts
Platform of Bioinformatics（生物信息平台）；Lausanne University Hospital（洛桑大学医院）；Faculty of Biology and Medicine（生物学与医学学院）；University of Lausanne（洛桑大学）；Leenaards Memory Centre（Leenaards记忆中心）；Department of Clinical Neurosciences（临床神经科学系）
University of Science and Technology of China（中国科学技术大学）；HUAWEI Technologies（华为技术有限公司）
Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. Considering that manual diagnosis could be error-prone and time-consuming, many intelligent approaches based on clinical text mining have been proposed to perform automatic diagnosis. However, these methods may not achieve satisfactory results due to the following challenges. First, most of the diagnosis codes are rare, and the distribution is extremely unbalanced. Second, existing methods are challenging to capture the correlation between diag…
Indiana University-Purdue University Indianapolis（印第安纳大学-普渡大学印第安纳波利斯分校）；Emory University（埃默里大学）
Coding diagnosis and procedures in medical records is a crucial process in the healthcare industry, which includes the creation of accurate billings, receiving reimbursements from payers, and creating standardized patient care records. In the United States, Billing and Insurance related activities cost around $471 billion in 2012 which constitutes about 25% of all the U.S hospital spending. In this paper, we report the performance of a natural language processing model that can map clinical notes to medical codes, and predict final diagnosis from unstructured entries of history of present illn…
Tsinghua University（清华大学）；Tsinghua National Laboratory of Information Science and Technology（清华信息科学与技术国家实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；Huangshi Central Hospital of Edong Healthcare Group, Hubei Polytechnic University（湖北理工学院鄂东医疗集团黄石市中心医院）
Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis witho…
nference LLC（恩弗伦斯有限责任公司）；Massachusetts Institute of Technology（麻省理工学院）；University of Pennsylvania Health System（宾夕法尼亚大学医疗系统）
Deep learning models in healthcare may fail to generalize on data from unseen corpora. Additionally, no quantitative metric exists to tell how existing models will perform on new data. Previous studies demonstrated that NLP models of medical notes generalize variably between institutions, but ignored other levels of healthcare organization. We measured SciBERT diagnosis sentiment classifier generalizability between medical specialties using EHR sentences from MIMIC-III. Models trained on one specialty performed better on internal test sets than mixed or external test sets (mean AUCs 0.92, 0.87…
Berliner Hochschule für Technik (BHT)（柏林工程应用技术大学）；Charité Berlin（柏林夏里特医学院）；Leibniz University Hannover（汉诺威莱布尼茨大学）
The use of deep neural models for diagnosis prediction from clinical text has shown promising results. However, in clinical practice such models must not only be accurate, but provide doctors with interpretable and helpful results. We introduce ProtoPatient, a novel method based on prototypical networks and label-wise attention with both of these abilities. ProtoPatient makes predictions based on parts of the text that are similar to prototypical patients - providing justifications that doctors understand. We evaluate the model on two publicly available clinical datasets and show that it outpe…
Sberbank Artificial Intelligence Laboratory（俄罗斯联邦储蓄银行人工智能实验室）；New York University（纽约大学）
In this paper we study the problem of predicting clinical diagnoses from textual Electronic Health Records (EHR) data. We show the importance of this problem in medical community and present comprehensive historical review of the problem and proposed methods. As the main scientific contributions we present a modification of Bidirectional Encoder Representations from Transformers (BERT) model for sequence classification that implements a novel way of Fully-Connected (FC) layer composition and a BERT model pretrained only on domain data. To empirically validate our model, we use a large-scale Ru…
Enhancing Trustworthy Clinical Diagnosis Decision-Making in Large Language Models via Etiology-Aware Attention Supervision
Zhejiang University（浙江大学）；University of Chinese Academy of Sciences（中国科学院大学）；First Affiliated Hospital, College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）
Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Jacobi Medical Center, Albert Einstein College of Medicine（雅各布医疗中心，阿尔伯特·爱因斯坦学院）；Department of Emergency Medicine, Beth Israel Deaconess Medical Center（贝斯以色列医疗中心急诊科）；Leaning machines（Leanings machines）
Knowledge-Augmented Multimodal Clinical Rationale Generation for Disease Diagnosis with Small Language Models
Hong Kong Baptist University（香港 Baptist 大学）；Shanxi University（山西大学）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海先进研究院）；Hong Kong University of Science and Technology（香港科技大学）；The University of Manchester（曼彻斯特大学）
Insight: A Multi-Modal Diagnostic Pipeline using LLMs for Ocular Surface Disease Diagnosis
Clinical Research Center, University of California, Berkeley（加州大学伯克利分校临床研究中心）；University of California, Berkeley（加州大学伯克利分校）；California Institute of Technology（加州理工学院）；University of California, Davis（加州大学戴维斯分校）；University of Hong Kong（香港大学）
Reverse Physician-AI Relationship: Full-process Clinical Diagnosis Driven by a Large Language Model
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Peking University Third Hospital（北京大学第三医院）
Same Patient, Different Words, Different Diagnosis? Evaluating Semantic Stability in Clinical LLMs
Department of Computer Science and Engineering, College of Engineering, Qatar University（卡塔尔大学计算机科学与工程系）；College of Science and Engineering, Hamad Bin Khalifa University (HBKU)（哈马德·本·卡伊夫大学（HBKU）理学院）；Primary Health Care Corporation (PHCC)（初级卫生保健公司）；Birmingham City University（伯明翰城市大学）
Petuum Inc.（Petuum 公司）；Department of Psychiatry, University of Pittsburgh Medical Center（匹兹堡大学医学中心精神科）
$\textbf{Objective}$ Develop an automatic diagnostic system which only uses textual admission information from Electronic Health Records (EHRs) and assist clinicians with a timely and statistically proved decision tool. The hope is that the tool can be used to reduce mis-diagnosis. $\textbf{Materials and Methods}$ We use the real-world clinical notes from MIMIC-III, a freely available dataset consisting of clinical data of more than forty thousand patients who stayed in intensive care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. We proposed a Convolutional Neural Ne…
Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes
Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia（费城儿童医院雷蒙德·G·佩雷尔曼细胞与分子治疗中心）；University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
United Imaging Intelligence（联影智能）；Manning College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校曼宁信息与计算机科学学院）；University of Massachusetts Medical School（马萨诸塞大学医学院）；Miner School of Computer and Information Sciences, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校迈纳计算机与信息科学学院）
Shanghai Jiao Tong University（上海交通大学）；Shanghai Mental Health Center（上海市精神卫生中心）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）
In a depression-diagnosis-directed clinical session, doctors initiate a conversation with ample emotional support that guides the patients to expose their symptoms based on clinical diagnosis criteria. Such a dialogue system is distinguished from existing single-purpose human-machine dialog systems, as it combines task-oriented and chit-chats with uniqueness in dialogue topics and procedures. However, due to the social stigma associated with mental illness, the dialogue data related to depression consultation and diagnosis are rarely disclosed. Based on clinical depression diagnostic criteria …
LLM Sensitivity Evaluation Framework for Clinical Diagnosis
School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机科学学院）；Key Laboratory of Trustworthy Distributed Computing and Service(BUPT), Ministry of Education（教育部可信分布式计算与服务重点实验室）；Nanyang Technological University（南洋理工大学）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；College of AI, Tsinghua University（清华大学人工智能学院）
Universidad Carlos III de Madrid（卡洛斯三世大学）；Hospital Fundación Jiménez Díaz e Instituto de Investigación, FJD（希门尼斯·迪亚斯基金会医院及研究所（FJD））；CIEMAT（环境、能源与技术研究中心）；Centre for Biomedical Network Research on Rare Diseases (CIBERER)（罕见疾病生物医学网络研究中心（CIBERER））
Although rare diseases are characterized by low prevalence, approximately 300 million people are affected by a rare disease. The early and accurate diagnosis of these conditions is a major challenge for general practitioners, who do not have enough knowledge to identify them. In addition to this, rare diseases usually show a wide variety of manifestations, which might make the diagnosis even more difficult. A delayed diagnosis can negatively affect the patient's life. Therefore, there is an urgent need to increase the scientific and medical knowledge about rare diseases. Natural Language Proce…
A Specialized Large Language Model for Clinical Reasoning and Diagnosis in Rare Diseases
Guangzhou Women and Children's Medical Center, Guangzhou Medical University（广州医科大学附属广州市妇女儿童医疗中心）；Tianjin Medical University（天津医科大学）；Guangzhou National Laboratory（广州国家实验室）；Children's Hospital of Fudan University（复旦大学附属儿童医院）
Benchmarking Multi-turn Medical Diagnosis: Hold, Lure, and Self-Correction
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；New York University（纽约大学）；The University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Chain-of-Thought Reasoning with Large Language Models for Clinical Alzheimer's Disease Assessment and Diagnosis
Stevens Institute of Technology（斯蒂文斯理工学院）；Surgical Trauma Intensive Care Unit（外科创伤重症护理科）；Institute of Brain Science（脑科学研究所）；National Yang Ming Chiao Tung University（国立阳明交通大学）
