Question Answering (QA) in clinical notes has gained a lot of attention in the past few years. Existing machine reading comprehension approaches in clinical domain can only handle questions about a single block of clinical texts and fail to retrieve information about multiple patients and their clinical notes. To handle more complex questions, we aim at creating knowledge base from clinical notes to link different patients and clinical notes, and performing knowledge base question answering (KBQA). Based on the expert annotations available in the n2c2 dataset, we first created the ClinicalKBQA dataset that includes around 9K QA pairs and covers questions about seven medical topics using more than 300 question templates. Then, we investigated an attention-based aspect reasoning (AAR) method for KBQA and analyzed the impact of different aspects of answers (e.g., entity, type, path, and context) for prediction. The AAR method achieves better performance due to the well-designed encoder and attention mechanism. From our experiments, we find that both aspects, type and path, enable the model to identify answers satisfying the general conditions and produce lower precision and higher recall. On the other hand, the aspects, entity and context, limit the answers by node-specific information and lead to higher precision and lower recall.
Objectives: To adapt and evaluate a deep learning language model for answering why-questions based on patient-specific clinical text. Materials and Methods: Bidirectional encoder representations from transformers (BERT) models were trained with varying data sources to perform SQuAD 2.0 style why-question answering (why-QA) on clinical notes. The evaluation focused on: 1) comparing the merits from different training data, 2) error analysis. Results: The best model achieved an accuracy of 0.707 (or 0.760 by partial match). Training toward customization for the clinical language helped increase 6…
The Ohio State University（俄亥俄州立大学）；University of Michigan（密歇根大学）；George Mason University（乔治梅森大学）
Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts. Studies show that neural QA models trained on one corpus may not generalize well to new clinical texts from a different institute or a different patient group, where large-scale QA pairs are not readily available for model retraining. To address this challenge, we propose a simple yet effective framework, CliniQG4QA, which leverages question generation (QG) to synthesize QA pairs on new clinical contexts and boosts QA models without requiring manual annotations. In order …
Institute for Infocomm Research (I2R), A*STAR（新加坡科技研究局资讯通信研究院）
There is growing interest in the automated extraction of relevant information from clinical dialogues. However, it is difficult to collect and construct large annotated resources for clinical dialogue tasks. Recent developments in natural language processing suggest that large-scale pre-trained language backbones could be leveraged for such machine comprehension and information extraction tasks. Yet, due to the gap between pre-training and downstream clinical domains, it remains challenging to exploit the generic backbones for domain-specific applications. Therefore, in this work, we propose a…
ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes
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）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）
Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）
The recent developments in the field of biomedicine have made large volumes of biomedical literature available to the medical practitioners. Due to the large size and lack of efficient searching strategies, medical practitioners struggle to obtain necessary information available in the biomedical literature. Moreover, the most sophisticated search engines of age are not intelligent enough to interpret the clinicians' questions. These facts reflect the urgent need of an information retrieval system that accepts the queries from medical practitioners' in natural language and returns the answers …
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）
Clinical semantic parsing (SP) is an important step toward identifying the exact information need (as a machine-understandable logical form) from a natural language query aimed at retrieving information from electronic health records (EHRs). Current approaches to clinical SP are largely based on traditional machine learning and require hand-building a lexicon. The recent advancements in neural SP show a promise for building a robust and flexible semantic parser without much human effort. Thus, in this paper, we aim to systematically assess the performance of two such neural SP models for EHR q…
Reading and understanding text is one important component in computer aided diagnosis in clinical medicine, also being a major research problem in the field of NLP. In this work, we introduce a question-answering task called MedQA to study answering questions in clinical medicine using knowledge in a large-scale document collection. The aim of MedQA is to answer real-world questions with large-scale reading comprehension. We propose our solution SeaReader--a modular end-to-end reading comprehension model based on LSTM networks and dual-path attention architecture. The novel dual-path attention…
Do Clinical Question Answering Systems Really Need Specialised Medical Fine Tuning?
University of Delhi（德里大学）；Jamia Hamdard（贾迈亚哈马尔德大学）；Cornell University（康奈尔大学）；Expedia Group（Expedia集团）；DSEU-Okhla；Center for SDGC（SDGC中心）；Stanford University（斯坦福大学）
LongHealth: A Question Answering Benchmark with Long Clinical Documents
Technical University of Munich（慕尼黑工业大学）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；University Hospital Aachen（亚琛大学医院）；Kaduceo；Berliner Hochschule für Technik (BHT)（柏林工业大学）；Mass General Brigham, Harvard Medical School（麻省总医院布莱根医疗集团，哈佛医学院）；Maastricht University（马斯特里赫特大学）；University Hospital RWTH Aachen（亚琛工业大学医院）；Technical University Dresden（德累斯顿工业大学）；German Heart Center Munich（慕尼黑德国心脏中心）
Georgia Institute of Technology（佐治亚理工学院）；Google Inc（谷歌公司）；Sutter Health（萨特医疗中心）
The text of clinical notes can be a valuable source of patient information and clinical assessments. Historically, the primary approach for exploiting clinical notes has been information extraction: linking spans of text to concepts in a detailed domain ontology. However, recent work has demonstrated the potential of supervised machine learning to extract document-level codes directly from the raw text of clinical notes. We propose to bridge the gap between the two approaches with two novel syntheses: (1) treating extracted concepts as features, which are used to supplement or replace the text…
EHRNote-ChatQA: A Benchmark for Evidence-Grounded Multi-Turn Clinical Question Answering over Longitudinal Discharge Summaries
KAIST（韩国科学技术院）；Seoul National University（首尔大学）；Seoul National University Bundang Hospital（首尔大学盆唐医院）；SAIHST, Sungkyunkwan University（成均馆大学）；Yonsei University College of Medicine（延世大学医学院）；Gangnam Severance Hospital（江南塞弗伦斯医院）；Severance Hospital（塞弗伦斯医院）；Seoul Medical Center（首尔医疗中心）；Seoul National University Hospital（首尔大学医院）；National Cancer Center（国立癌症中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Samsung Medical Center（三星医疗中心）
University of Washington（华盛顿大学）；George Mason University（乔治梅森大学）
Symptom information is primarily documented in free-text clinical notes and is not directly accessible for downstream applications. To address this challenge, information extraction approaches that can handle clinical language variation across different institutions and specialties are needed. In this paper, we present domain generalization for symptom extraction using pretraining and fine-tuning data that differs from the target domain in terms of institution and/or specialty and patient population. We extract symptom events using a transformer-based joint entity and relation extraction metho…
UMass-Amherst（马萨诸塞大学阿默斯特分校）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；IBM Research, Cambridge（IBM剑桥研究院）
We explore state-of-the-art neural models for question answering on electronic medical records and improve their ability to generalize better on previously unseen (paraphrased) questions at test time. We enable this by learning to predict logical forms as an auxiliary task along with the main task of answer span detection. The predicted logical forms also serve as a rationale for the answer. Further, we also incorporate medical entity information in these models via the ERNIE architecture. We train our models on the large-scale emrQA dataset and observe that our multi-task entity-enriched mode…
University of Bristol（布里斯托大学）；University of Sussex（萨塞克斯大学）
This research on data extraction methods applies recent advances in natural language processing to evidence synthesis based on medical texts. Texts of interest include abstracts of clinical trials in English and in multilingual contexts. The main focus is on information characterized via the Population, Intervention, Comparator, and Outcome (PICO) framework, but data extraction is not limited to these fields. Recent neural network architectures based on transformers show capacities for transfer learning and increased performance on downstream natural language processing tasks such as universal…
HealthBranches: Synthesizing Clinically-Grounded Question Answering Datasets via Decision Pathways
University of Calabria（卡拉布里亚大学）；University of Catanzaro（卡坦扎罗大学）；University of Eastern Piedmont（东皮埃蒙特大学）；University of Pisa（比萨大学）；University of Cambridge（剑桥大学）
Goergen Institute for Data Science（格根数据科学研究所）；University of Rochester（罗切斯特大学）
Clinical concept extraction often begins with clinical Named Entity Recognition (NER). Often trained on annotated clinical notes, clinical NER models tend to struggle with tagging clinical entities in user queries because of the structural differences between clinical notes and user queries. User queries, unlike clinical notes, are often ungrammatical and incoherent. In many cases, user queries are compounded of multiple clinical entities, without comma or conjunction words separating them. By using as dataset a mixture of annotated clinical notes and synthesized user queries, we adapt a clini…
Consiglio Nazionale delle Ricerche (CNR)（意大利国家研究委员会）；Politecnico di Milano（米兰理工大学）；Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Milano（米兰大医院基金会）
Information extraction from narrative clinical notes is useful for patient care, as well as for secondary use of medical data, for research or clinical purposes. Many studies focused on information extraction from English clinical texts, but less dealt with clinical notes in languages other than English. This study tested the feasibility of using 'off the shelf' information extraction algorithms to identify medical concepts from Italian clinical notes. We used MetaMap to map medical concepts to the Unified Medical Language System (UMLS). The study addressed two questions: (Q1) to understand if…
Question-Answering System Extracts Information on Injection Drug Use from Clinical Notes
Oak Ridge National Laboratory（橡树岭国家实验室）；Vanderbilt University（范德堡大学）；Department of Veterans Affairs（美国退伍军人事务部）；Stanford University School of Medicine（斯坦福大学医学院）；Veterans Affairs Great Lakes Health Care System（退伍军人事务部大湖区医疗系统）
East China University of Science and Technology（华东理工大学）；Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属瑞金医院）
Clinical text structuring is a critical and fundamental task for clinical research. Traditional methods such as taskspecific end-to-end models and pipeline models usually suffer from the lack of dataset and error propagation. In this paper, we present a question answering based clinical text structuring (QA-CTS) task to unify different specific tasks and make dataset shareable. A novel model that aims to introduce domain-specific features (e.g., clinical named entity information) into pre-trained language model is also proposed for QA-CTS task. Experimental results on Chinese pathology reports…
!MSA at AraHealthQA 2025 Shared Task: Enhancing LLM Performance for Arabic Clinical Question Answering through Prompt Engineering and Ensemble Learning
Faculty of Computer Science, MSA University（计算机科学学院，MSA大学）
IBM T. J. Watson Research Center（IBM T. J. 沃森研究中心）；IBM Almaden Research Center（IBM阿尔马登研究中心）
Large-scale clinical data is invaluable to driving many computational scientific advances today. However, understandable concerns regarding patient privacy hinder the open dissemination of such data and give rise to suboptimal siloed research. De-identification methods attempt to address these concerns but were shown to be susceptible to adversarial attacks. In this work, we focus on the vast amounts of unstructured natural language data stored in clinical notes and propose to automatically generate synthetic clinical notes that are more amenable to sharing using generative models trained on r…
NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes
University of Massachusetts（马萨诸塞大学）；University of Minnesota（明尼苏达大学）；Microsoft（微软公司）；University of Edinburgh（爱丁堡大学）；University of Massachusetts, Lowell（马萨诸塞大学洛厄尔分校）
De-identification is not enough: a comparison between de-identified and synthetic clinical notes
Department of Computer Science, University of Manitoba（曼尼托巴大学计算机科学系）；McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心麦克威廉姆斯生物医学信息学院）
IBM Research（IBM研究院）；Arizona State University（亚利桑那州立大学）
Objective: Using natural language processing (NLP) to find sentences that state treatment plans in a clinical note, would automate plan extraction and would further enable their use in tools that help providers and care managers. However, as in the most NLP tasks on clinical text, creating gold standard to train and test NLP models is tedious and expensive. Fortuitously, sometimes but not always clinical notes contain sections with a heading that identifies the section as a plan. Leveraging contents of such labeled sections as a noisy training data, we assessed accuracy of NLP models trained w…
Accenture Labs（埃森哲实验室）；Indian Institute of Science（印度科学学院）；Arizona State University（亚利桑那州立大学）；Duke University（杜克大学）
Understanding causal narratives communicated in clinical notes can help make strides towards personalized healthcare. Extracted causal information from clinical notes can be combined with structured EHR data such as patients' demographics, diagnoses, and medications. This will enhance healthcare providers' ability to identify aspects of a patient's story communicated in the clinical notes and help make more informed decisions. In this work, we propose annotation guidelines, develop an annotated corpus and provide baseline scores to identify types and direction of causal relations between a pai…
Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Princeton University（普林斯顿大学）；New York University（纽约大学）
Clinical notes contain information about patients that goes beyond structured data like lab values and medications. However, clinical notes have been underused relative to structured data, because notes are high-dimensional and sparse. This work develops and evaluates representations of clinical notes using bidirectional transformers (ClinicalBERT). ClinicalBERT uncovers high-quality relationships between medical concepts as judged by humans. ClinicalBert outperforms baselines on 30-day hospital readmission prediction using both discharge summaries and the first few days of notes in the intens…
