ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Empirical Findings on the Role of Structured Data, Unstructured Data, and their Combination for Automatic Clinical Phenotyping.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Detecting Uncoded Self-Harm in Veterans' Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Cohort Study.Journal of medical Internet research · 2026Article
- Article
- Automating clinical phenotyping using natural language processing.Communications medicine · 2026Article
- Optimising the use of electronic medical records for large scale research in psychiatry.Translational psychiatry · 2024Review
- Using Multimodal Data to Improve Precision of Inpatient Event Timelines.Advances in knowledge discovery and data mining : ... Pacific-Asia Conference, PAKDD ..., proceedings. Pacific-Asia Conference on Knowledge Discovery and Data Mining · 2024Article
- Trends and opportunities in computable clinical phenotyping: A scoping review.Journal of biomedical informatics · 2023Article
- Artificial intelligence in clinical decision support systems for oncology.International journal of medical sciences · 2023Review
- Developing an LSTM Model to Identify Surgical Site Infections using Electronic Healthcare Records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The objective of this study is to explore the role of structured and unstructured data for clinical phenotyping by determining which types of clinical phenotypes are best identified using unstructured data (e.g., clinical notes), structured data (e.g., laboratory values, vital signs), or their combination across 172 clinical phenotypes. Specifically, we used laboratory and chart measurements as well as clinical notes from the MIMIC-III critical care database and trained an LSTM using features extracted from each type of data to determine which categories of phenotypes were best identified by structured data, unstructured data, or both. We observed that textual features on their own outperformed structured features for 145 (84%) of phenotypes, and that Doc2Vec was the most effective representation of unstructured data for all phenotypes. When evaluating the impact of adding textual features to systems previously relying only on structured features, we found a statistically significant (p < 0.05) increase in phenotyping performance for 51 phenotypes (primarily involving the circulatory system, injury, and poisoning), one phenotype for which textual features degraded performance (diabetes without complications), and no statistically significant change in performance with the remaining 120 phenotypes. We provide analysis on which phenotypes are best identified by each type of data and guidance on which data sources to consider for future research on phenotype identification.
Indexed as
Identifiers
34457160PMC8378600What 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.