Objective: Currently, a major limitation for natural language processing (NLP) analyses in clinical applications is that a concept can be referenced in various forms across different texts. This paper introduces Multi-Ontology Refined Embeddings (MORE), a novel hybrid framework for incorporating domain knowledge from multiple ontologies into a distributional semantic model, learned from a corpus of clinical text.
Materials and Methods: We use the RadCore and MIMIC-III free-text datasets for the corpus-based component of MORE. For the ontology-based part, we use the Medical Subject Headings (MeSH) ontology and three state-of-the-art ontology-based similarity measures. In our approach, we propose a new learning objective, modified from the Sigmoid cross-entropy objective function.
Results and Discussion: We evaluate the quality of the generated word embeddings using two established datasets of semantic similarities among biomedical concept pairs. On the first dataset with 29 concept pairs, with the similarity scores established by physicians and medical coders, MORE's similarity scores have the highest combined correlation (0.633), which is 5.0% higher than that of the baseline model and 12.4% higher than that of the best ontology-based similarity measure.On the second dataset with 449 concept pairs, MORE's similarity scores have a correlation of 0.481, with the average of four medical residents' similarity ratings, and that outperforms the skip-gram model by 8.1% and the best ontology measure by 6.9%.
Objective: This work aimed to demonstrate the effectiveness of a hybrid approach based on Sentence BERT model and retrofitting algorithm to compute relatedness between any two biomedical concepts. Materials and Methods: We generated concept vectors by encoding concept preferred terms using ELMo, BERT, and Sentence BERT models. We used BioELMo and Clinical ELMo. We used Ontology Knowledge Free (OKF) models like PubMedBERT, BioBERT, BioClinicalBERT, and Ontology Knowledge Injected (OKI) models like SapBERT, CoderBERT, KbBERT, and UmlsBERT. We trained all the BERT models using Siamese network on …
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)（国家生物技术信息中心（NCBI），国家医学图书馆（NLM），国立卫生研究院（NIH））；The Jackson Laboratory for Genomic Medicine（杰克逊基因组医学实验室）
Automatic phenotype concept recognition from unstructured text remains a challenging task in biomedical text mining research. Previous works that address the task typically use dictionary-based matching methods, which can achieve high precision but suffer from lower recall. Recently, machine learning-based methods have been proposed to identify biomedical concepts, which can recognize more unseen concept synonyms by automatic feature learning. However, most methods require large corpora of manually annotated data for model training, which is difficult to obtain due to the high cost of human an…
BACKGROUND: In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical literature. We trained different neural embedding models on 1.7 million articles from the PubMed Open Access dataset, and evaluated them based on a biomedical benchmark set containing 100 sentence pairs annotated by human experts and a smaller contradiction subset derived from the original benchmark set. RESULTS: With a Pearson correlation of 0.819, our best unsupervised model based on the Paragraph Vector Distribut…
ISPO: An Integrated Ontology of Symptom Phenotypes for Semantic Integration of Traditional Chinese Medical Data
Beijing Jiaotong University（北京交通大学）；Hubei Provincial Hospital of Traditional Chinese Medicine（湖北省中医院）；China Academy of Chinese Medical Sciences（中国中医科学院）；Hubei University of Chinese Medicine（湖北中医药大学）；Guang'anmen Hospital, China Academy of Chinese Medical Sciences（中国中医科学院广安门医院）；Zhejiang University（浙江大学）
Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；University of Washington（华盛顿大学）
Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, resulting in a need to de-duplicate entities when merging ontologies. We propose a method for enriching entities in an ontology with external definition and context information, and use this additional information for ontology alignment. We develop a neural architecture capable of encoding the additional information when available, and show that the addition of external data results in an F1-score of 0.69 on the Ontolo…
Predicting Failures of LLMs to Link Biomedical Ontology Terms to Identifiers Evidence Across Models and Ontologies
Department of Neurology（神经学系）；Rehabilitation University of Illinois at Chicago Chicago, IL, USA（伊利诺伊大学芝加哥分校康复学院）；Lab for Applied Artificial Intelligence（应用人工智能实验室）；Engineering Program（工程学院）
Universidad Nacional de Educación a Distancia (UNED)（西班牙国立远程教育大学）
This registered report introduces the largest, and for the first time, reproducible experimental survey on biomedical sentence similarity with the following aims: (1) to elucidate the state of the art of the problem; (2) to solve some reproducibility problems preventing the evaluation of most of current methods; (3) to evaluate several unexplored sentence similarity methods; (4) to evaluate an unexplored benchmark, called Corpus-Transcriptional-Regulation; (5) to carry out a study on the impact of the pre-processing stages and Named Entity Recognition (NER) tools on the performance of the sent…
Chan Zuckerberg Initiative（陈·扎克伯格倡议）；University of Massachusetts（马萨诸塞大学）
Tools to explore scientific literature are essential for scientists, especially in biomedicine, where about a million new papers are published every year. Many such tools provide users the ability to search for specific entities (e.g. proteins, diseases) by tracking their mentions in papers. PubMed, the most well known database of biomedical papers, relies on human curators to add these annotations. This can take several weeks for new papers, and not all papers get tagged. Machine learning models have been developed to facilitate the semantic indexing of scientific papers. However their perfor…
Mapping Biomedical Ontology Terms to IDs: Effect of Domain Prevalence on Prediction Accuracy
Computer Science Department（计算机科学系）；Missouri State University（密苏里州立大学）；Kummer Institute Center for AI and AS（Kummer研究所人工智能与自动系统中心）；Missouri University of Science & Technology（密苏里科技大学）；Cooperative Engineering Program（合作工程计划）
GraphPrompt: Graph-Based Prompt Templates for Biomedical Synonym Prediction
University of Washington（华盛顿大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Carnegie Mellon University（卡内基梅隆大学）；Nanyang Technological University（南洋理工大学）；Peking University（北京大学）
Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field
Knowledge Media Institute, The Open University, Milton Keynes, UK（开放大学知识媒体研究所）；Department of Business and Law, University of Milano-Bicocca, Milan, IT（米兰-比科卡大学商业与法律系）
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
Motivation: Ontologies are widely used in biology for data annotation, integration, and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotations commonly used in ontologies include class labels, descriptions, or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally unexploited by ontology-based analysis methods such as semantic similarity measures. Results: We propose a novel method, OPA2Vec, to gene…
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）
The metadata about scientific experiments published in online repositories have been shown to suffer from a high degree of representational heterogeneity---there are often many ways to represent the same type of information, such as a geographical location via its latitude and longitude. To harness the potential that metadata have for discovering scientific data, it is crucial that they be represented in a uniform way that can be queried effectively. One step toward uniformly-represented metadata is to normalize the multiple, distinct field names used in metadata (e.g., lat lon, lat and long) …
DeepRAG: Integrating Hierarchical Reasoning and Process Supervision for Biomedical Multi-Hop QA
Department of Information Science, University of Pittsburgh（信息科学系，匹兹堡大学）；Department of Biomedical Informatics, University of Pittsburgh（生物医学信息学系，匹兹堡大学）；Department of Health Information Management and ISP, University of Pittsburgh（健康信息管理与信息科学系，匹兹堡大学）
This paper presents the formal release of MedMentions, a new manually annotated resource for the recognition of biomedical concepts. What distinguishes MedMentions from other annotated biomedical corpora is its size (over 4,000 abstracts and over 350,000 linked mentions), as well as the size of the concept ontology (over 3 million concepts from UMLS 2017) and its broad coverage of biomedical disciplines. In addition to the full corpus, a sub-corpus of MedMentions is also presented, comprising annotations for a subset of UMLS 2017 targeted towards document retrieval. To encourage research in Bi…
Purdue School of Engineering and Technology, IUPUI（印第安纳大学与普渡大学印第安纳波利斯联合分校普渡工程与技术学院）
Document clustering is a text mining technique used to provide better document search and browsing in digital libraries or online corpora. A lot of research has been done on biomedical document clustering that is based on using existing ontology. But, associations and co-occurrences of the medical concepts are not well represented by using ontology. In this research, a vector representation of concepts of diseases and similarity measurement between concepts are proposed. They identify the closest concepts of diseases in the context of a corpus. Each document is represented by using the vector …
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation
School of Electrical Engineering and Computer Science, Ohio University（俄亥俄大学电气与计算机工程学院）；University of Southern California（南加利福尼亚大学）；School of Computer Science, McGill University（麦吉尔大学计算机学院）；Northwestern University（西北大学）；University of Washington（华盛顿大学）
In this paper, we discuss the utility and deficiencies of existing ontology resources for a number of language processing applications. We describe a technique for increasing the semantic type coverage of a specific ontology, the National Library of Medicine's UMLS, with the use of robust finite state methods used in conjunction with large-scale corpus analytics of the domain corpus. We call this technique "semantic rerendering" of the ontology. This research has been done in the context of Medstract, a joint Brandeis-Tufts effort aimed at developing tools for analyzing biomedical language (i.…
University of Oxford（牛津大学）；Samsung Research（三星研究院）
Ontology alignment (a.k.a ontology matching (OM)) plays a critical role in knowledge integration. Owing to the success of machine learning in many domains, it has been applied in OM. However, the existing methods, which often adopt ad-hoc feature engineering or non-contextual word embeddings, have not yet outperformed rule-based systems especially in an unsupervised setting. In this paper, we propose a novel OM system named BERTMap which can support both unsupervised and semi-supervised settings. It first predicts mappings using a classifier based on fine-tuning the contextual embedding model …
Feasibility of Structuring Stress Documentation Using an Ontology-Guided Large Language Model
College of Nursing, Seoul National University（首尔国立大学护理学院）；The Research Institute of Nursing Science, Seoul National University（首尔国立大学护理科学研究所）；Center of Human-Caring Nurse Leaders for the Future by the Brain Korea (BK 21) four project, College of Nursing, Seoul National University（首尔国立大学护理学院，韩国脑计划（BK21）第四阶段未来人文关怀护理领军人才中心）；College of Medicine, Seoul National University（首尔国立大学医学院）
In the era of clinical information explosion, a good strategy for clinical text summarization is helpful to improve the clinical workflow. The ideal summarization strategy can preserve important information in the informative but less organized, ill-structured clinical narrative texts. Instead of using pure statistical learning approaches, which are difficult to interpret and explain, we utilized knowledge of computational linguistics with human experts-curated biomedical knowledge base to achieve the interpretable and meaningful clinical text summarization. Our research objective is to use th…
IBM Research（IBM研究院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Northeastern University（东北大学）
Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable. This can be a barrier to model uptake in important domains such as biomedicine. There has been recent work on general interpretable representation learning (Onoe and Durrett, 2020), but these domain-agnostic representations do not readily transfer to the important domain of biomedicine. In this paper, we create a new entity type system and training set from a large corpus of biomedical texts by mapping entities to…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Adobe Research（Adobe研究院）
Compared to the general news domain, information extraction (IE) from biomedical text requires much broader domain knowledge. However, many previous IE methods do not utilize any external knowledge during inference. Due to the exponential growth of biomedical publications, models that do not go beyond their fixed set of parameters will likely fall behind. Inspired by how humans look up relevant information to comprehend a scientific text, we present a novel framework that utilizes external knowledge for joint entity and relation extraction named KECI (Knowledge-Enhanced Collective Inference). …
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…
Academia Sinica（中央研究院）；National Yang Ming University（国立阳明大学）；National Tsing-Hua University（国立清华大学）；National Central University（国立中央大学）
The advancement of biomedical named entity recognition (BNER) and biomedical relation extraction (BRE) researches promotes the development of text mining in biological domains. As a cornerstone of BRE, robust BNER system is required to identify the mentioned NEs in plain texts for further relation extraction stage. However, the current BNER corpora, which play important roles in these tasks, paid less attention to achieve the criteria for BRE task. In this study, we present Revised JNLPBA corpus, the revision of JNLPBA corpus, to broaden the applicability of a NER corpus from BNER to BRE task.…
MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering
National Library of Medicine, Division of Intramural Research（国家医学图书馆，院内研究部）；University of Illinois at Urbana-Champaign, Department of Computer Science（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；University of Michigan Medical School（密歇根大学医学院）
Virginia Commonwealth University（弗吉尼亚联邦大学）；University of Minnesota（明尼苏达大学）
Vector space methods that measure semantic similarity and relatedness often rely on distributional information such as co--occurrence frequencies or statistical measures of association to weight the importance of particular co--occurrences. In this paper, we extend these methods by incorporating a measure of semantic similarity based on a human curated taxonomy into a second--order vector representation. This results in a measure of semantic relatedness that combines both the contextual information available in a corpus--based vector space representation with the semantic knowledge found in a …
A Natural Language Processing Approach to Support Biomedical Data Harmonization: Leveraging Large Language Models
Boston University Chobanian & Avedisian School of Medicine（波士顿大学乔巴尼安与阿维迪西安医学院）；Boston University Faculty of Computing & Data Sciences（波士顿大学计算与数据科学学院）；Slone Epidemiology Center（斯隆流行病学中心）；Boston University School of Public Health（波士顿大学公共卫生学院）
