An Empirical Study of UMLS Concept Extraction from Clinical Notes using Boolean Combination Ensembles
University of Minnesota（明尼苏达大学）；Dartmouth College（达特茅斯学院）；Mayo Clinic（梅奥诊所）；The University of Texas Health Science Center（德克萨斯大学健康科学中心）
Greg M. Silverman, Raymond L. Finzel, Michael V. Heinz, Jake Vasilakes, Jacob C. Solinsky, Reed McEwan, Benjamin C. Knoll, Christopher J. Tignanelli, Hongfang L… 展开作者
Greg M. Silverman, Raymond L. Finzel, Michael V. Heinz, Jake Vasilakes, Jacob C. Solinsky, Reed McEwan, Benjamin C. Knoll, Christopher J. Tignanelli, Hongfang Liu, Hua Xu, Xiaoqian Jiang, Genevieve B. Melton, Serguei VS Pakhomov
Our objective in this study is to investigate the behavior of Boolean operators on combining annotation output from multiple Natural Language Processing (NLP) systems across multiple corpora and to assess how filtering by aggregation of Unified Medical Language System (UMLS) Metathesaurus concepts affects system performance for Named Entity Recognition (NER) of UMLS concepts. We used three corpora annotated for UMLS concepts: 2010 i2b2 VA challenge set (31,161 annotations), Multi-source Integrated Platform for Answering Clinical Questions (MiPACQ) corpus (17,457 annotations including UMLS concept unique identifiers), and Fairview Health Services corpus (44,530 annotations). Our results showed that for UMLS concept matching, Boolean ensembling of the MiPACQ corpus trended towards higher performance over individual systems. Use of an approximate grid-search can help optimize the precision-recall tradeoff and can provide a set of heuristics for choosing an optimal set of ensembles.
Contextual word embedding models, such as BioBERT and Bio_ClinicalBERT, have achieved state-of-the-art results in biomedical natural language processing tasks by focusing their pre-training process on domain-specific corpora. However, such models do not take into consideration expert domain knowledge. In this work, we introduced UmlsBERT, a contextual embedding model that integrates domain knowledge during the pre-training process via a novel knowledge augmentation strategy. More specifically, the augmentation on UmlsBERT with the Unified Medical Language System (UMLS) Metathesaurus was perfor…
University of Edinburgh（爱丁堡大学）；University College London（伦敦大学学院）；Health Data Research UK（英国健康数据研究所）
The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of data annotation from clinical experts. We propose a method using ontologies and weak supervision. The approach includes two steps: (i) Text-to-UMLS, linking text mentions to concepts in Unified Medical Language System (UMLS), with a named entity linking tool (e.g. SemEHR) and weak supervision based on customised rules and Bidirectional Encoder Representations from Transformers (BERT) based contextual representations, a…
Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes
University of Edinburgh（爱丁堡大学）；Health Data Research UK（英国健康数据研究所）；University of Oxford（牛津大学）；University College London（伦敦大学学院）；The University of Manchester（曼彻斯特大学）
CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications
Centre for Big Data Research in Health, University of New South Wales（健康大数据研究中心，新南威尔士大学）；Eastern Heart Clinic, Prince of Wales Hospital（东部心脏诊所，王室医院）；NSW Ambulance Aeromedical Operations, Bankstown Helicopter Base（新南威尔士州急救航空医疗运作，班克stown直升机基地）；Department of Anaesthesia, Saint George Hospital（麻醉科，圣乔治医院）；Department of Cardiology, Prince of Wales Hospital（心内科，王室医院）；School of Clinical Medicine, University of New South Wales（临床医学学院，新南威尔士大学）
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…
En-ViMedNER: An English-Vietnamese Parallel Biomedical Corpus with UMLS Semantic Type Annotations
College of Engineering and Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；Faculty of Engineering and IT, University of Technology Sydney（悉尼科技大学工程与信息技术学院）；Center for AI Research, VinUniversity（VinUniversity人工智能研究中心）；Monash University, Australia（莫纳什大学）
National Research Council Canada（加拿大国家研究委员会）；Privacy Analytics Inc.（隐私分析公司）
Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. We present the first attempt to apply state-of-the-art entity recognition approaches on a newly released dataset, MedMentions. This dataset contains over 4000 biomedical abstracts, annotated for UMLS semantic types. In comparison to existing datasets, MedMentions contains a far greater number of entity types, and thus represents a more challenging but realistic scenario in a real-world setting. We explore a number of relevant dimensions, including the use of con…
King’s College London（伦敦国王学院）；University College London（伦敦大学学院）；Health Data Research UK（英国健康数据研究所）；King’s College Hospital NHS Foundation Trust（国王学院医院国民保健服务基金会信托）
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of Information Extraction (IE) technologies to enable clinical analysis. We present the open-source Medical Concept Annotation Toolkit (MedCAT) that provides: a) a novel self-supervised machine learning algorithm for extracting concepts using any concept vocabulary including UMLS/SNOMED-CT; b) a feature-rich annotation interface for customising and training IE models; and c) integrations to the broader CogStack ecosystem for vendor-agnostic health system deployment. We show improved performanc…
This paper presents a Lisp architecture for a portable NLP system, termed LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard, customized and in-house developed NLP tools. Our system facilitates portability across different institutions and data systems by incorporating an enriched Common Data Model (CDM) to standardize necessary data elements. It utilizes UMLS to perform domain adaptation when integrating generic domain NLP tools. It also features stand-off annotations that are specified by positional reference to the original document. We built an interval tree based s…
Paris University（巴黎大学）；AP-HP（巴黎公共援助医院）；LIMSI（法国计算机科学与自动化研究所）；CNRS（法国国家科学研究中心）；Univ. Paris-Sud（巴黎南大学）；Université Paris-Saclay（巴黎萨克雷大学）；Sorbonne Université（索邦大学）；Inserm（法国国家健康与医学研究院）；Univ Paris 13（巴黎第十三大学）；LIMICS（医学信息学与电子健康知识工程实验室）
We aimed to enhance the performance of a supervised model for clinical named-entity recognition (NER) using medical terminologies. In order to evaluate our system in French, we built a corpus for 5 types of clinical entities. We used a terminology-based system as baseline, built upon UMLS and SNOMED. Then, we evaluated a biGRU-CRF, and an hybrid system using the prediction of the terminology-based system as feature for the biGRU-CRF. In English, we evaluated the NER systems on the i2b2-2009 Medication Challenge for Drug name recognition, which contained 8,573 entities for 268 documents. In Fre…
This paper presents a novel prototype for biomedical term normalization of electronic health record excerpts with the Unified Medical Language System (UMLS) Metathesaurus. Despite being multilingual and cross-lingual by design, we first focus on processing clinical text in Spanish because there is no existing tool for this language and for this specific purpose. The tool is based on Apache Lucene to index the Metathesaurus and generate mapping candidates from input text. It uses the IXA pipeline for basic language processing and resolves ambiguities with the UKB toolkit. It has been evaluated …
Entity linking is the task of linking mentions of named entities in natural language text, to entities in a curated knowledge-base. This is of significant importance in the biomedical domain, where it could be used to semantically annotate a large volume of clinical records and biomedical literature, to standardized concepts described in an ontology such as Unified Medical Language System (UMLS). We observe that with precise type information, entity disambiguation becomes a straightforward task. However, fine-grained type information is usually not available in biomedical domain. Thus, we prop…
Healthcare domain generates a lot of unstructured and semi-structured text. Natural Language processing (NLP) has been used extensively to process this data. Deep Learning based NLP especially Large Language Models (LLMs) such as BERT have found broad acceptance and are used extensively for many applications. A Language Model is a probability distribution over a word sequence. Self-supervised Learning on a large corpus of data automatically generates deep learning-based language models. BioBERT and Med-BERT are language models pre-trained for the healthcare domain. Healthcare uses typical NLP …
We used MetaMap and YTEX as a basis for the construc- tion of two separate systems to participate in the 2013 ShARe/CLEF eHealth Task 1[9], the recognition of clinical concepts. No modifications were directly made to these systems, but output concepts were filtered using stop concepts, stop concept text and UMLS semantic type. Con- cept boundaries were also adjusted using a small collection of rules to increase precision on the strict task. Overall MetaMap had better per- formance than YTEX on the strict task, primarily due to a 20% perfor- mance improvement in precision. In the relaxed task Y…
Clinical text classification is an important problem in medical natural language processing. Existing studies have conventionally focused on rules or knowledge sources-based feature engineering, but only a few have exploited effective feature learning capability of deep learning methods. In this study, we propose a novel approach which combines rule-based features and knowledge-guided deep learning techniques for effective disease classification. Critical Steps of our method include identifying trigger phrases, predicting classes with very few examples using trigger phrases and training a conv…
University of Antwerp（安特卫普大学）；University of Groningen（格罗宁根大学）
In this paper, we report a knowledge-based method for Word Sense Disambiguation in the domains of biomedical and clinical text. We combine word representations created on large corpora with a small number of definitions from the UMLS to create concept representations, which we then compare to representations of the context of ambiguous terms. Using no relational information, we obtain comparable performance to previous approaches on the MSH-WSD dataset, which is a well-known dataset in the biomedical domain. Additionally, our method is fast and easy to set up and extend to other domains. Suppl…
Integrating UMLS Knowledge into Large Language Models for Medical Question Answering
National University of Singapore（新加坡国立大学）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；University of Tokyo（东京大学）；National Institute of Advanced Industrial Science and Technology（日本产业技术综合研究所）；Singapore General Hospital（新加坡中央医院）；Zhejiang Lab（之江实验室）；National Institutes of Health（美国国立卫生研究院）；Yale University（耶鲁大学）
Isfahan University of Technology（伊斯法罕理工大学）；University of Isfahan（伊斯法罕大学）
Recent years have seen particular interest in using electronic medical records (EMRs) for secondary purposes to enhance the quality and safety of healthcare delivery. EMRs tend to contain large amounts of valuable clinical notes. Learning of embedding is a method for converting notes into a format that makes them comparable. Transformer-based representation models have recently made a great leap forward. These models are pre-trained on large online datasets to understand natural language texts effectively. The quality of a learning embedding is influenced by how clinical notes are used as inpu…
AD-CDO: A Lightweight Ontology for Representing Eligibility Criteria in Alzheimer's Disease Clinical Trials
University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；Indiana University School of Medicine（印第安纳大学医学院）；Mayo Clinic in Florida（佛罗里达梅奥诊所）
University of Minnesota（明尼苏达大学）；Arizona State University（亚利桑那州立大学）；Mayo Clinic（梅奥诊所）
Since no effective therapies exist for Alzheimer's disease (AD), prevention has become more critical through lifestyle factor changes and interventions. Analyzing electronic health records (EHR) of patients with AD can help us better understand lifestyle's effect on AD. However, lifestyle information is typically stored in clinical narratives. Thus, the objective of the study was to demonstrate the feasibility of natural language processing (NLP) models to classify lifestyle factors (e.g., physical activity and excessive diet) from clinical texts. We automatically generated labels for the trai…
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Amazon（亚马逊）；Northwestern University（西北大学）
Predicting patient mortality is an important and challenging problem in the healthcare domain, especially for intensive care unit (ICU) patients. Electronic health notes serve as a rich source for learning patient representations, that can facilitate effective risk assessment. However, a large portion of clinical notes are unstructured and also contain domain specific terminologies, from which we need to extract structured information. In this paper, we introduce an embedding framework to learn semantically-plausible distributed representations of clinical notes that exploits the semantic corr…
IHC-LLMiner: Automated extraction of tumour immunohistochemical profiles from PubMed abstracts using large language models
Institute of Health Informatics, University College London（伦敦大学学院健康信息学研究所）；Research Department of Pathology, University College London（伦敦大学学院病理学研究部）；Department of Pathology, Hadassah-Hebrew University Medical Center（哈达萨-希伯来大学医学中心病理学系）；Department of Cellular Pathology, University College London Hospitals NHS Foundation Trust（伦敦大学学院医院NHS信托基金会细胞病理学系）；School of Health & Wellbeing, University of Glasgow（格拉斯哥大学健康与福祉学院）
