Apr 11, 2019 · We developed a classifier to identify potential FH patients using electronic health record (EHR) data at Stanford Health Care.
Apr 11, 2019 · Such machine learning guided strategies can lead to effective identification of the highest risk patients for enhanced management strategies. ...
Our EHR-derived FH classifier is effective in finding candidate patients for further FH screening. Such machine learning guided strategies can lead to effective ...
Jun 15, 2024 · Our machine learning–derived model provides a higher pretest probability of identifying individuals with a molecular diagnosis of FH compared ...
Finding missed cases of familial hypercholesterolemia in health systems using machine learning.
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We trained a random forest classifier using data from known patients (n = 197) and matched non-cases (n = 6590). Our classifier obtained a positive predictive ...
Oct 21, 2019 · The FIND FH model successfully scans large, diverse, and disparate health-care encounter databases to identify individuals with familial ...
The purpose of this study was to derive an algorithm to identify people with suspected monogenic FH for subsequent confirmatory genomic testing and cascade ...
Sep 26, 2022 · Our objective was to create a machine learning model from basic lipid profile data with better screening performance than LDL-C (low-density lipoprotein ...
Sep 25, 2023 · In this review, we discussed how ML can be used for FH screening, diagnosis and risk assessment based on different data sources.