Multi-label natural language processing to identify diagnosis and procedure codes from MIMIC-III inpatient notes
Indiana University - Purdue University Indianapolis（印第安纳大学与普渡大学印第安纳波利斯联合大学）；Emory University（埃默里大学）
A. K. Bhavani Singh, Mounika Guntu, Ananth Reddy Bhimireddy, Judy W. Gichoya, Saptarshi Purkayastha
备注：
This is a shortened version of the Capstone Project that was accepted by the Faculty of Indiana University, in partial fulfillment of the requirements for the degree of Master of Science in Health Informatics
In the United States, 25% or greater than 200 billion dollars of hospital spending accounts for administrative costs that involve services for medical coding and billing. With the increasing number of patient records, manual assignment of the codes performed is overwhelming, time-consuming and error-prone, causing billing errors. Natural language processing can automate the extraction of codes/labels from unstructured clinical notes, which can aid human coders to save time, increase productivity, and verify medical coding errors. Our objective is to identify appropriate diagnosis and procedure codes from clinical notes by performing multi-label classification. We used de-identified data of critical care patients from the MIMIC-III database and subset the data to select the ten (top-10) and fifty (top-50) most common diagnoses and procedures, which covers 47.45% and 74.12% of all admissions respectively. We implemented state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) to fine-tune the language model on 80% of the data and validated on the remaining 20%. The model achieved an overall accuracy of 87.08%, an F1 score of 85.82%, and an AUC of 91.76% for top-10 codes. For the top-50 codes, our model achieved an overall accuracy of 93.76%, an F1 score of 92.24%, and AUC of 91%. When compared to previously published research, our model outperforms in predicting codes from the clinical text. We discuss approaches to generalize the knowledge discovery process of our MIMIC-BERT to other clinical notes. This can help human coders to save time, prevent backlogs, and additional costs due to coding errors.
MAP: Evaluation and Multi-Agent Enhancement of Large Language Models for Inpatient Pathways
The Chinese University of Hong Kong（香港中文大学）；Guangzhou Hospital of Integrated Traditional Chinese and Western Medicine（广州市中西医结合医院）；Shenzhen Traditional Chinese Medicine Hospital（深圳市中医院）；Zhongshan Hospital Fudan University（复旦大学附属中山医院）
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…
MIMIC-IV-Ext-PE: Using a large language model to predict pulmonary embolism phenotype in the MIMIC-IV dataset
Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Baylor College of Medicine（贝勒医学院）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）；Massachusetts Institute of Technology（麻省理工学院）
SUP-MIMIC: A Multi-Task Clinical Diagnosis Benchmark for Evaluating LLMs' Robustness to Contradictory Evidence
Beijing University of Technology（北京工业大学）；Beijing Institute of Technology（北京理工大学）；China-Japan Friendship Hospital（中日友好医院）；Dongbei University of Finance and Economics（东北财经大学）
Multitask deep learning has been applied to patient outcome prediction from text, taking clinical notes as input and training deep neural networks with a joint loss function of multiple tasks. However, the joint training scheme of multitask learning suffers from inter-task interference, and diagnosis prediction among the multiple tasks has the generalizability issue due to rare diseases or unseen diagnoses. To solve these challenges, we propose a hypernetwork-based approach that generates task-conditioned parameters and coefficients of multitask prediction heads to learn task-specific predicti…
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…
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…
DRG-LLaMA : Tuning LLaMA Model to Predict Diagnosis-related Group for Hospitalized Patients
Mayo Clinic Health System（妙佑医疗国际医疗系统）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Mayo Clinic（妙佑医疗国际）；Carle Illinois College of Medicine（卡勒伊利诺伊医学院）
Sun Yat-sen University（中山大学）；Hong Kong Polytechnic University（香港理工大学）；Rutgers University（罗格斯大学）；Hong Kong University of Science and Technology（香港科技大学）；Tencent Jarvis Lab（腾讯Jarvis实验室）
With the rise of telemedicine, the task of developing Dialogue Systems for Medical Diagnosis (DSMD) has received much attention in recent years. Different from early researches that needed to rely on extra human resources and expertise to help construct the system, recent researches focused on how to build DSMD in a purely data-driven manner. However, the previous data-driven DSMD methods largely overlooked the system interpretability, which is critical for a medical application, and they also suffered from the data sparsity issue at the same time. In this paper, we explore how to bring interp…
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…
Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing
University of Florida（佛罗里达大学）；UF Health Shands Hospital（UF Health Shands医院）；University of California Los Angeles（加州大学洛杉矶分校）；UF Clinical and Translational Science Institute（UF临床与转化科学研究所）；North Florida/South Georgia Veterans Health Service（北佛罗里达/南乔治亚退伍军人卫生服务处）；College of Nursing, University of Florida（佛罗里达大学护理学院）；College of Public Health and Health Professions & College of Medicine, University of Florida（佛罗里达大学公共卫生与健康专业学院及医学院）
DKG-LLM : A Framework for Medical Diagnosis and Personalized Treatment Recommendations via Dynamic Knowledge Graph and Large Language Model Integration
University of Qom（库姆大学）；Imperial College London（帝国理工学院）；Institute for Health Research Aragón（阿拉贡健康研究所）；Islamic Azad University Science And Research Branch（伊斯兰阿扎德大学科学与研究分校）
Radiology reports contain a diverse and rich set of clinical abnormalities documented by radiologists during their interpretation of the images. Comprehensive semantic representations of radiological findings would enable a wide range of secondary use applications to support diagnosis, triage, outcomes prediction, and clinical research. In this paper, we present a new corpus of radiology reports annotated with clinical findings. Our annotation schema captures detailed representations of pathologic findings that are observable on imaging ("lesions") and other types of clinical problems ("medica…
OverdoseMoE: A Multi-Expert Framework for Opioid Overdose Risk Prediction
Manning College of Information and Computer Sciences, UMass 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）；Center for Healthcare Organization and Implementation Research, VA Bedford Health Care（VA贝德福德医疗保健中心医疗组织与实施研究中心）；Miner School of Computer and Information Sciences, UMass Lowell（马萨诸塞大学洛厄尔分校迈纳计算机与信息科学学院）
Federal University of Santa Catarina（圣卡塔琳娜联邦大学）；Healthcare（3778医疗）；Syrian-Lebanese Hospital（叙利亚-黎巴嫩医院）
ICD coding from electronic clinical records is a manual, time-consuming and expensive process. Code assignment is, however, an important task for billing purposes and database organization. While many works have studied the problem of automated ICD coding from free text using machine learning techniques, most use records in the English language, especially from the MIMIC-III public dataset. This work presents results for a dataset with Brazilian Portuguese clinical notes. We develop and optimize a Logistic Regression model, a Convolutional Neural Network (CNN), a Gated Recurrent Unit Neural Ne…
Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Loyola University Chicago（芝加哥洛约拉大学）；Boston Children’s Hospital Harvard Medical School（波士顿儿童医院哈佛医学院）；University of Colorado-Anschutz（科罗拉多大学安舒茨分校）
Prediction of medical codes from clinical notes is both a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation will save significant time and excessive effort spent by human coders today. However, the biggest challenge is directly identifying appropriate medical codes out of several thousands of high-dimensional codes from unstructured free-text clinical notes. In the past three years, with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LTSM) networks, there have been vast improvements in tackling the mos…
Unsupervised pretraining is an integral part of many natural language processing systems, and transfer learning with language models has achieved remarkable results in many downstream tasks. In the clinical application of medical code assignment, diagnosis and procedure codes are inferred from lengthy clinical notes such as hospital discharge summaries. However, it is not clear if pretrained models are useful for medical code prediction without further architecture engineering. This paper conducts a comprehensive quantitative analysis of various contextualized language models' performance, pre…
In the context of the Electronic Health Record, automated diagnosis coding of patient notes is a useful task, but a challenging one due to the large number of codes and the length of patient notes. We investigate four models for assigning multiple ICD codes to discharge summaries taken from both MIMIC II and III. We present Hierarchical Attention-GRU (HA-GRU), a hierarchical approach to tag a document by identifying the sentences relevant for each label. HA-GRU achieves state-of-the art results. Furthermore, the learned sentence-level attention layer highlights the model decision process, allo…
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）
ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance
Language Technologies Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所）；University of California, Berkeley（加州大学伯克利分校）；University of California, San Francisco（加州大学旧金山分校）；Department of EECS, University of California, Berkeley（加州大学伯克利分校电子工程与计算机科学系）
Ranking-Aware Prompt Optimization for Multimodal Clinical Diagnosis
Harvard University（哈佛大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；Brown University（布朗大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Georgia Institute of Technology（佐治亚理工学院）
Emulating Clinician Cognition via Self-Evolving Deep Clinical Research
Gaoling School of Artificial Intelligence（首都经济贸易大学人工智能学院）；Renmin University of China（中国人民大学）；Peking University Third Hospital（北京大学第三医院）；Baidu Inc.（百度公司）；Chinese PLA General Hospital（中国人民解放军总医院）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly（联合NTU-UBC老年积极生活方式卓越研究中心）；Nanyang Technological University（南洋理工大学）
University of Memphis（孟菲斯大学）；Adobe Research（奥多比研究院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Clinical notes in Electronic Health Records (EHR) present rich documented information of patients to inference phenotype for disease diagnosis and study patient characteristics for cohort selection. Unsupervised user embedding aims to encode patients into fixed-length vectors without human supervisions. Medical concepts extracted from the clinical notes contain rich connections between patients and their clinical categories. However, existing unsupervised approaches of user embeddings from clinical notes do not explicitly incorporate medical concepts. In this study, we propose a concept-aware …
Clinical notes in electronic health records contain highly heterogeneous writing styles, including non-standard terminology or abbreviations. Using these notes in predictive modeling has traditionally required preprocessing (e.g. taking frequent terms or topic modeling) that removes much of the richness of the source data. We propose a pretrained hierarchical recurrent neural network model that parses minimally processed clinical notes in an intuitive fashion, and show that it improves performance for discharge diagnosis classification tasks on the Medical Information Mart for Intensive Care I…
Auckland University of Technology（奥克兰理工大学）；Universidad Tecnológica de Bolívar（玻利瓦尔理工大学）
Accurate mortality prediction allows Intensive Care Units (ICUs) to adequately benchmark clinical practice and identify patients with unexpected outcomes. Traditionally, simple statistical models have been used to assess patient death risk, many times with sub-optimal performance. On the other hand deep learning holds promise to positively impact clinical practice by leveraging medical data to assist diagnosis and prediction, including mortality prediction. However, as the question of whether powerful Deep Learning models attend correlations backed by sound medical knowledge when generating pr…
