Comparative effectiveness of medical concept embedding for feature engineering in phenotyping

Lee, Junghwan; Liu, Cong; Kim, Jae Hyun; Butler, Alex; Shang, Ning; Pang, Chao; Natarajan, Karthik; Ryan, Patrick B.; Ta, Casey; Weng, Chunhua

doi:10.1093/jamiaopen/ooab028

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…8 Previous work in this domain used supervised and unsupervised machine learning to derive phenotypes for several diseases, with different strengths and limitations (see literature review in Note S1). [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] Supervised models rely on classifiers based on manually labeled gold standards for each specific disease, which is time-consuming and not scalable. Unsupervised approaches discover phenotypes purely from the data, trying to aggregate medical concepts commonly appearing together in the patient records.…”

Section: Introductionmentioning

confidence: 99%

Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records

et al. 2021

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De Freitas et al. present Phe2vec, an automated framework for disease phenotyping based on unsupervised learning. Phe2vec derives embeddings of medical concepts from electronic health records and uses them to define phenotypes and measure the association between diseases and patients. The authors demonstrate the method's effectiveness via comparison with both rule-based algorithms and standard automated methods. Phe2vec can be used to identify patient cohorts by simply specifying a seed concept associated to any disease of interest.

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Section: Introductionmentioning

confidence: 99%

Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records

et al. 2021

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“…Although symbolic representation enables quantitative reasoning based on statistical probability, its inclusion in machine learning models that execute numerical operations is challenging. KRL aims to convert objects of interest (entities and relations in KG) into a continuous low-dimensional vector space [75,134] to efficiently measure the semantic correlations between entities and relations and to significantly improve knowledge acquisition, fusion, and inference performance. The KRL model of TransE has demonstrated remarkable outcomes in KG reasoning research.…”

Section: Reasoning Based On Krlmentioning

confidence: 99%

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Duan

Pan

et al. 2023

Big Data Min. Anal.

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Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications. Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field. To this end, we offer an in-depth review of MKG in this work. Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development. Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed. A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG. In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types. Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

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“…Some of the studies evaluated the effectiveness of word embedding models on multiple tasks. Lee et al [135] evaluated Node2Vec, singular value decomposition, Language Identification for Named Entities, Word2Vec, and global vectors for word representation (GloVe) in retrieving relevant medical features for phenotyping tasks. The authors demonstrated that GloVe, when trained on EHR data, outperforms other embedding methods.…”

Section: Language Modelingmentioning

confidence: 99%

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

Bazoge,

Morin,

Daille

et al. 2023

JMIR Med Inform

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Background In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. Objective The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. Methods This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. Results We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). Conclusions CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.

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Comparative effectiveness of medical concept embedding for feature engineering in phenotyping

Cited by 7 publications

References 29 publications

Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records

Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

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