e.g. mhealth
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Skip search results from other journals and go to results- 2 JMIR Formative Research
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Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study
The resulting classifier takes plain note text as the input and classifies the note as positive (ie, includes mention of a patient receiving an AF notification, or patient-initiated cardiac testing or electrocardiogram resulting in an AF prediagnosis) or negative. When a classifier is tuned on the labeler model output, it enables generalizing beyond the labeling heuristics encoded in the labeling functions, such that the classifier can recognize more patterns.
Classifier generation process.
JMIR Med Inform 2024;12:e51171
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In both cases, the classifier with the highest accuracy was the classifier for category 1 (sentences with direct mentions of AD). The classifiers with the 2 lowest accuracies were either the classifier for category 5 (sentences with mentions of dry or itchy skin) or the classifier for category 7 (sentences with mentions of asthma) for both the use of Bio Clinical BERT embeddings and the use of BERT Base Uncased embeddings.
JMIR Form Res 2024;8:e52200
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We also evaluated classifiers on top-2 accuracy, which measures the proportion of predictions where the correct event type is among the top 2 highest probability event types predicted by the classifier. The definitions and mathematical formulas of the evaluation metrics are shown in Multimedia Appendix 2. Each of these metrics provides a distinct perspective on the performance of the classifier, and collectively, they offer a comprehensive understanding of how well the classifier is functioning.
JMIR Hum Factors 2024;11:e53378
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Using this data set, we trained a facial expression classifier for children that attained state-of-the-art accuracy on the Child Affective Facial Expression (CAFE) data set [49], the standard benchmark in the field for facial expression recognition (FER) of children.
Despite creating this high-performing model, we have yet to leverage it in adaptive digital therapies such as Guess What.
JMIR Form Res 2023;7:e39917
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The training data set was used to build the classifier, and the test data set was used to assess the performance. The feature values were standardized using the Standard Scaler function in the scikit-learn module before constructing the logistic regression (LR) classifier.
Scikit-learn is a Python module integrating a wide range of state-of-the-art ML algorithms for medium-scale supervised and unsupervised problems [23].
J Med Internet Res 2021;23(2):e23390
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