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, allows easier error analysis, and suggests future directions for improvement.
College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校信息与计算机科学学院）；Department of Computer Science, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校计算机科学系）；Center for Healthcare Organization and Implementation Research, Veterans Affairs Bedford Healthcare System（退伍军人事务部贝德福德医疗系统医疗保健组织与实施研究中心）
Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with an average of 3,000+ tokens. This task is challenging due to the high-dimensional space of multi-label assignment (155,000+ ICD code candidates) and the long-tail challenge - Many ICD codes are infrequently assigned yet infrequent ICD codes are important clinically. This study addresses the long-tail challenge by transforming this multi-label classification task into an autoregressive generation task. Specifically, we first introduce a novel pretraining objective to generate…
Background and Objective: Code assignment is of paramount importance in many levels in modern hospitals, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious and subjective, and it requires medical coders with extensive training. This study aims to evaluate the performance of deep-learning-based systems to automatically map clinical notes to ICD-9 medical codes. Methods: The evaluations of this research are focused on end-to-end learning methods without manually defined rules. Traditional machine learning algorithms, …
Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）
Manual annotation of ICD-9 codes is a time consuming and error-prone process. Deep learning based systems tackling the problem of automated ICD-9 coding have achieved competitive performance. Given the increased proliferation of electronic medical records, such automated systems are expected to eventually replace human coders. In this work, we investigate how a simple typo-based adversarial attack strategy can impact the performance of state-of-the-art models for the task of predicting the top 50 most frequent ICD-9 codes from discharge summaries. Preliminary results indicate that a malicious …
Automatic ICD coding is the task of assigning codes from the International Classification of Diseases (ICD) to medical notes. These codes describe the state of the patient and have multiple applications, e.g., computer-assisted diagnosis or epidemiological studies. ICD coding is a challenging task due to the complexity and length of medical notes. Unlike the general trend in language processing, no transformer model has been reported to reach high performance on this task. Here, we investigate in detail ICD coding using PubMedBERT, a state-of-the-art transformer model for biomedical language u…
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）
Clinicians may rely on medical coding systems such as International Classification of Diseases (ICD) to identify patients with diseases from Electronic Health Records (EHRs). However, due to the lack of detail and specificity as well as a probability of miscoding, recent studies suggest the ICD codes often cannot characterise patients accurately for specific diseases in real clinical practice, and as a result, using them to find patients for studies or trials can result in high failure rates and missing out on uncoded patients. Manual inspection of all patients at scale is not feasible as it i…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）
Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with average length of 3,000+ tokens. This task is challenging due to a high-dimensional space of multi-label assignment (tens of thousands of ICD codes) and the long-tail challenge: only a few codes (common diseases) are frequently assigned while most codes (rare diseases) are infrequently assigned. This study addresses the long-tail challenge by adapting a prompt-based fine-tuning technique with label semantics, which has been shown to be effective under few-shot setting. To fu…
Medical document understanding has gained much attention recently. One representative task is the International Classification of Disease (ICD) diagnosis code assignment. Existing work adopts either RNN or CNN as the backbone network because the vanilla BERT cannot handle well long documents (>2000 to kens). One issue shared across all these approaches is that they are over specific to the ICD code assignment task, losing generality to give the whole document-level and sentence-level embedding. As a result, it is not straight-forward to direct them to other downstream NLU tasks. Motivated by t…
International Classification of Diseases(ICD) is an authoritative health care classification system of different diseases and conditions for clinical and management purposes. Considering the complicated and dedicated process to assign correct codes to each patient admission based on overall diagnosis, we propose a hierarchical deep learning model with attention mechanism which can automatically assign ICD diagnostic codes given written diagnosis. We utilize character-aware neural language models to generate hidden representations of written diagnosis descriptions and ICD codes, and design an a…
Social Determinants of Health Prediction for ICD-9 Code with Reasoning Models
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；University of Illinois Chicago Peoria（伊利诺伊大学芝加哥分校皮奥里亚分校）
Medical coding is a complex task, requiring assignment of a subset of over 72,000 ICD codes to a patient's notes. Modern natural language processing approaches to these tasks have been challenged by the length of the input and size of the output space. We limit our model inputs to a small window around medical entities found in our documents. From those local contexts, we build contextualized representations of both ICD codes and entities, and aggregate over these representations to form document-level predictions. In contrast to existing methods which use a representation fixed either in size…
University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）；Semantic Health（语义健康公司）
The International Classification of Diseases (ICD) system is the international standard for classifying diseases and procedures during a healthcare encounter and is widely used for healthcare reporting and management purposes. Assigning correct codes for clinical procedures is important for clinical, operational, and financial decision-making in healthcare. Contextual word embedding models have achieved state-of-the-art results in multiple NLP tasks. However, these models have yet to achieve state-of-the-art results in the ICD classification task since one of their main disadvantages is that t…
Clinical interactions are initially recorded and documented in free text medical notes. ICD coding is the task of classifying and coding all diagnoses, symptoms and procedures associated with a patient's visit. The process is often manual and extremely time-consuming and expensive for hospitals. In this paper, we propose a machine learning model, BERT-XML, for large scale automated ICD coding from EHR notes, utilizing recently developed unsupervised pretraining that have achieved state of the art performance on a variety of NLP tasks. We train a BERT model from scratch on EHR notes, learning w…
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）
Stanford University（斯坦福大学）；Rush University Medical Center（拉什大学医学中心）
Documentation errors increase healthcare costs and cause unnecessary patient deaths. As the standard language for diagnoses and billing, ICD codes serve as the foundation for medical documentation worldwide. Despite the prevalence of electronic medical records, hospitals still witness high levels of ICD miscoding. In this paper, we propose to automatically document ICD codes with far-field speech recognition. Far-field speech occurs when the microphone is located several meters from the source, as is common with smart homes and security systems. Our method combines acoustic signal processing w…
Reykjavik University（雷克雅未克大学）；Primary Health Care Service of the Capital Area（首都地区初级卫生保健服务中心）；University of Iceland（冰岛大学）；Development Centre for Primary Health Care in Iceland（冰岛初级卫生保健发展中心）
Clinical Text Notes (CTNs) contain physicians' reasoning process, written in an unstructured free text format, as they examine and interview patients. In recent years, several studies have been published that provide evidence for the utility of machine learning for predicting doctors' diagnoses from CTNs, a task known as ICD coding. Data annotation is time consuming, particularly when a degree of specialization is needed, as is the case for medical data. This paper presents a method of augmenting a sparsely annotated dataset of Icelandic CTNs with a machine-learned imputation in a semi-self-su…
Learning to represent free text is a core task in many clinical machine learning (ML) applications, as clinical text contains observations and plans not otherwise available for inference. State-of-the-art methods use large language models developed with immense computational resources and training data; however, applying these models is challenging because of the highly varying syntax and vocabulary in clinical free text. Structured information such as International Classification of Disease (ICD) codes often succinctly abstracts the most important facts of a clinical encounter and yields good…
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…
University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Bedford Veterans Affairs Medical Center（贝德福德退伍军人事务医疗中心）；University of Massachusetts Medical School（马萨诸塞大学医学院）
Automated ICD coding, which assigns the International Classification of Disease codes to patient visits, has attracted much research attention since it can save time and labor for billing. The previous state-of-the-art model utilized one convolutional layer to build document representations for predicting ICD codes. However, the lengths and grammar of text fragments, which are closely related to ICD coding, vary a lot in different documents. Therefore, a flat and fixed-length convolutional architecture may not be capable of learning good document representations. In this paper, we proposed a M…
Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. The current scenario of assigning codes is a manual process which is very expensive, time-consuming and error prone. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) and Deep Learning (DL) methods and techniques to resolve the problem of manual coding of clinical narratives and to assist human coders to assign clinical codes more accurately and efficiently. …
Institute for Language, Cognition and Computation, University of Edinburgh（爱丁堡大学语言、认知与计算研究所）；Usher Institute, University of Edinburgh（爱丁堡大学乌谢尔研究所）；School of Literatures, Languages and Cultures, University of Edinburgh（爱丁堡大学文学、语言与文化学院）
Large-Scale Multi-Label Text Classification (LMTC) includes tasks with hierarchical label spaces, such as automatic assignment of ICD-9 codes to discharge summaries. Performance of models in prior art is evaluated with standard precision, recall, and F1 measures without regard for the rich hierarchical structure. In this work we argue for hierarchical evaluation of the predictions of neural LMTC models. With the example of the ICD-9 ontology we describe a structural issue in the representation of the structured label space in prior art, and propose an alternative representation based on the de…
National Institute of Technology Karnataka Surathkal（卡纳塔克苏拉特卡尔国家理工学院）
Existing Clinical Decision Support Systems (CDSSs) largely depend on the availability of structured patient data and Electronic Health Records (EHRs) to aid caregivers. However, in case of hospitals in developing countries, structured patient data formats are not widely adopted, where medical professionals still rely on clinical notes in the form of unstructured text. Such unstructured clinical notes recorded by medical personnel can also be a potential source of rich patient-specific information which can be leveraged to build CDSSs, even for hospitals in developing countries. If such unstruc…
Fine-Tuning Open-Source Large Language Models to Improve Their Performance on Radiation Oncology Tasks: A Feasibility Study to Investigate Their Potential Clinical Applications in Radiation Oncology
Mayo Clinic Arizona（梅奥诊所亚利桑那院区）；University of Georgia（佐治亚大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Early Prediction of Alzheimers Disease Leveraging Symptom Occurrences from Longitudinal Electronic Health Records of US Military Veterans
Manning College of Information & 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）；Center for Healthcare Organization & Implementation Research, VA Bedford Health Care System（弗吉尼亚州贝德福德医疗系统医疗保健组织与实施研究中心）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Center of Biomedical and Health Research in Data Sciences, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校数据科学生物医学与健康研究中心）；UMass Chan Medical School（马萨诸塞大学陈医学院）；Miner School of Computer & 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）
Gender Bias in Large Language Models for Healthcare: Assignment Consistency and Clinical Implications
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Singapore General Hospital（新加坡中央医院）；University of Antwerp（安特卫普大学）；Duke University School of Medicine（杜克大学医学院）；SingHealth（新加坡保健服务集团）；Singapore National Eye Centre（新加坡国家眼科中心）；Weill Cornell Medicine（威尔康奈尔医学院）；Mass General Brigham（麻省总医院布里格姆）；Harvard Medical School（哈佛医学院）；National University of Singapore（新加坡国立大学）
Dalhousie University（达尔豪斯大学）；Semantic Health（语义健康公司）；University of Toronto（多伦多大学）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Machine Learning (ML) is widely used to automatically extract meaningful information from Electronic Health Records (EHR) to support operational, clinical, and financial decision-making. However, ML models require a large number of annotated examples to provide satisfactory results, which is not possible in most healthcare scenarios due to the high cost of clinician-labeled data. Active Learning (AL) is a process of selecting the most informative instances to be labeled by an expert to further train a supervised algorithm. We demonstrate the effectiveness of AL in multi-label text classificati…
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…
RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Records
Department of Computer Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；Department of Pediatrics, University of Illinois College of Medicine Peoria（伊利诺伊大学皮奥里亚医学院儿科系）
Beuth University of Applied Sciences Berlin（柏林比乌特应用科学大学）；Charité Berlin（柏林夏里特医学院）
Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities. We simulate patients at admission time, when decision support can be especially valuable, and contribute a novel admission to discharge task with four common outcome prediction targets: Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction. The ideal system should infer outcomes based on symptoms, pre-conditions and risk factors of a patient. We evaluate the effectiveness of language models to handle this scenario and propo…
Practical Design and Benchmarking of Generative AI Applications for Surgical Billing and Coding
Duke University Medical Center（杜克大学医学中心）；Patient Revenue Management Organization, Duke University Health System（杜克大学卫生系统患者收入管理组织）；Duke Pratt School of Engineering（杜克普拉特工程学院）；Duke University School of Medicine（杜克大学医学院）；MSTAT
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…
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）
Accurate and timely detection of medical adverse events (AEs) from free-text medical narratives is challenging. Natural language processing (NLP) with deep learning has already shown great potential for analyzing free-text data, but its application for medical AE detection has been limited. In this study we proposed deep learning based NLP (DL-NLP) models for efficient and accurate hip dislocation AE detection following total hip replacement from standard (radiology notes) and non-standard (follow-up telephone notes) free-text medical narratives. We benchmarked these proposed models with a wid…
University of Florence（佛罗伦萨大学）；Institute for cancer research, prevention and clinical network (ISPRO)（癌症研究、预防与临床网络研究所（ISPRO））
We report about the application of state-of-the-art deep learning techniques to the automatic and interpretable assignment of ICD-O3 topography and morphology codes to free-text cancer reports. We present results on a large dataset (more than 80 000 labeled and 1 500 000 unlabeled anonymized reports written in Italian and collected from hospitals in Tuscany over more than a decade) and with a large number of classes (134 morphological classes and 61 topographical classes). We compare alternative architectures in terms of prediction accuracy and interpretability and show that our best model ach…
Kimia Lab, University of Waterloo（滑铁卢大学Kimia实验室）；University of Waterloo（滑铁卢大学）；Kimia Lab（Kimia实验室）；Canada（加拿大）；Kimia Lab, University of Waterloo, Canada（滑铁卢大学Kimia实验室，加拿大）
A Pathology report is arguably one of the most important documents in medicine containing interpretive information about the visual findings from the patient's biopsy sample. Each pathology report has a retention period of up to 20 years after the treatment of a patient. Cancer registries process and encode high volumes of free-text pathology reports for surveillance of cancer and tumor diseases all across the world. In spite of their extremely valuable information they hold, pathology reports are not used in any systematic way to facilitate computational pathology. Therefore, in this study, w…
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）
