e.g. mhealth
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Skip search results from other journals and go to results- 37 JMIR Medical Informatics
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The purpose of this study was to develop an algorithm using ML techniques to forecast whether the initial vancomycin regimen to be administered can achieve an AUC24/MIC ratio within the therapeutic range. In other words, the final output of the ML algorithm predicted “yes” or “no” based on whether the AUC24/MIC of vancomycin falls within the therapeutic range of 400 to 600.
J Med Internet Res 2025;27:e63983
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The algorithm takes as input the 3 D ultrasound image and outputs the corresponding predicted segmentation. During development, the algorithm learned to set its internal parameters by minimizing the difference between the predicted segmentation and the segmentations obtained in VR. Two separate models were developed: one for segmenting the embryo and another for the embryonic head.
J Med Internet Res 2025;27:e60887
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Additionally, we trained an e Xtreme gradient boosting (XGBoost) algorithm [42] to predict 5 CCP subgroups with differential risks of outcomes, as described in our previous work [23]. Briefly, the model was trained and calibrated using an isotonic regression algorithm, and internally validated in the discovery cohort. The SHapley Additive ex Planations (SHAP) method was employed to identify each feature’s relative contribution [23,43] and enhance the model’s explainability.
JMIR Public Health Surveill 2025;11:e67840
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In the second part, we fine-tuned the sentence transformer and then used the Doc SCAN algorithm to cluster the synthetic datasets. We chose the sbert-chinese-general-v2 model, which is a model pretrained on the Sim CLUE dataset [30], as the base model due to its outstanding performance on embedding Chinese sentences.
JMIR Form Res 2025;9:e54803
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Reference 1: Preparing physicians for the clinical algorithm eraalgorithm
JMIR Med Educ 2025;11:e55709
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Spelling mistakes: we corrected potential spelling errors by applying the Symmetric Delete spelling correction algorithm (Sym Spell) with the MEDLINE unigram dictionary, which includes over 28 million unique terms.
Punctuation: we removed punctuation from the text.
Vectorization: we vectorized the text into a sequence of numbers in the term frequency–inverse document frequency format [19].
We used Tensorflow and Keras to construct one model each for the prediction of diagnostic codes and billing codes.
JMIR AI 2025;4:e64279
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