ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2018
Adapting Word Embeddings from Multiple Domains to Symptom Recognition from Psychiatric Notes.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Visualization of medical concepts represented using word embeddings: a scoping review.BMC medical informatics and decision making · 2022Article
- A Comparison of Natural Language Processing Methods for the Classification of Lumbar Spine Imaging Findings Related to Lower Back Pain.Academic radiology · 2022Article
- Psychosis Relapse Prediction Leveraging Electronic Health Records Data and Natural Language Processing Enrichment Methods.Frontiers in psychiatry · 2022Article
- Extracting Family History Information From Electronic Health Records: Natural Language Processing Analysis.JMIR medical informatics · 2021Article
- Article
- Rapamycin - mTOR + BRAF = ? Using relational similarity to find therapeutically relevant drug-gene relationships in unstructured text.Journal of biomedical informatics · 2019Article
- Classification of Pulmonary Nodular Findings based on Characterization of Change using Radiology Reports.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Mental health is increasingly recognized an important topic in healthcare. Information concerning psychiatric symptoms is critical for the timely diagnosis of mental disorders, as well as for the personalization of interventions. However, the diversity and sparsity of psychiatric symptoms make it challenging for conventional natural language processing techniques to automatically extract such information from clinical text. To address this problem, this study takes the initiative to use and adapt word embeddings from four source domains - intensive care, biomedical literature, Wikipedia and Psychiatric Forum - to recognize symptoms in the target domain of psychiatry. We investigated four different approaches including 1) only using word embeddings of the source domain, 2) directly combining data of the source and target to generate word embeddings, 3) assigning different weights to word embeddings, and 4) retraining the word embedding model of the source domain using a corpus of the target domain. To the best of our knowledge, this is the first work of adapting multiple word embeddings of external domains to improve psychiatric symptom recognition in clinical text. Experimental results showed that the last two approaches outperformed the baseline methods, indicating the effectiveness of our new strategies to leverage embeddings from other domains.
Identifiers
29888086PMC5961810W2843023018What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.