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Case Identification of Depression in Inpatient Electronic Medical Records: Scoping Review
The search terms used to identify this category were not specific to a type of algorithm or method of case identification, as the purpose was to include a broad range of variations in phenotypic methodology (Multimedia Appendix 1).
We adapted an existing data extraction form (Multimedia Appendix 2, Lee et al [18]) to collect the results of our review. Data were extracted by 1 reviewer and then confirmed by a second reviewer.
JMIR Med Inform 2024;12:e49781
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The use of transformer-based methods to understand EMR text data has emerged as a promising new trend in automatic clinical text analysis [32].
In this study, we intend to pretrain an existing model, Bio Clinical BERT [33], with free text data from Alberta hospital EMRs to develop an Alberta hospital notes-specific BERT model (AHN-BERT). The pretrained language model would serve as a feature extraction layer in a neural network to identify inpatient falls.
JMIR Med Inform 2024;12:e48995
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All nursing notes of selected document types were merged into 1 text and converted into a bag-of-words (BOW) vector with the count of words or term frequency-inverse document frequency (TF-IDF) vectorizer by using a Python scikit-learn ML library [29-31].
A binary classification model was developed to identify HAPI cases by considering all patients who developed any stage of PI during a hospital stay as positive cases and patients without PIs as the negative cohort.
JMIR AI 2023;2:e41264
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