ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
Machine Learning Approaches to Identify Nicknames from A Statewide Health Information Exchange.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The trial behind it
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Who cites it
2 citing papers in PubMed.
- Evaluation of a Parsimonious COVID-19 Outbreak Prediction Model: Heuristic Modeling Approach Using Publicly Available Data Sets.Journal of medical Internet research · 2021Article
- Generalization of Machine Learning Approaches to Identify Notifiable Conditions from a Statewide Health Information Exchange.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patient matching is essential to minimize fragmentation of patient data. Existing patient matching efforts often do not account for nickname use. We sought to develop decision models that could identify true nicknames using features representing the phonetical and structural similarity of nickname pairs. We identified potential male and female name pairs from the Indiana Network for Patient Care (INPC), and developed a series of features that represented their phonetical and structural similarities. Next, we used the XGBoost classifier and hyperparameter tuning to build decision models to identify nicknames using these feature sets and a manually reviewed gold standard. Decision models reported high precision/positive predictive value and accuracy scores for both male and female name pairs despite the low number of true nickname matches in the datasets under study. Ours is one of the first efforts to identify patient nicknames using machine learning approaches.
Identifiers
31259019PMC6568128What OpenQuestion holds
Registered trials
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