e.g. mhealth
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Skip search results from other journals and go to results- 2 JMIR Medical Informatics
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Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study
We used an ensemble of 4 learners (a mix of parametric models and machine learning models): (1) logistic regression, (2) L1 penalized logistic regression (with penalty parameter lambda=0.01), (3) gradient boosting (with n_estimators=200, max_depth=5, and learning_rate=0.1), and (4) random forest (with max_depth=5 and num_trees=20).
JMIR Public Health Surveill 2024;10:e53322
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The ensemble prompt’s output y E is then the mode of these outputs:
y E=mode (y1, y2, ..., ym)
Algorithmically, the ensemble prompting process is as follows:
The rationale behind an ensemble prompt is that by integrating multiple types of prompts, we can use the strengths and counterbalance the weaknesses of each individual prompt, offering a robust and potentially more accurate response.
JMIR Med Inform 2024;12:e55318
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However, Ben-Assuli et al [13] found that using multiple time periods and ensemble ML methods on large-scale data enabled early risk identification in specific patient groups [13]. Stacked ensemble models, including those with boosted tree algorithms, demonstrated strong performance in predicting unplanned patient readmissions by reducing bias from individual models and sensitivities to rare classes [9,10].
JMIR Form Res 2023;7:e41725
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Consequentially, we propose ensemble models to capture static and contextualized input word representations of textual examination data and classify them into 3 labels: healthy, caution required, and critical thyroid conditions. We construct 2 ensemble models and call them static and contextualized ensemble NLP network (SCENT) systems. SCENT version 1, SCENT-v1, is an ensemble or soft voting method for the CNN and ELECTRA ensemble models.
JMIR Med Inform 2021;9(9):e30223
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Classification Accuracy of Hepatitis C Virus Infection Outcome: Data Mining Approach
To select members of the ensemble, we followed a similar procedure to the one used to obtain the best subsets of features. First, all single models were ordered according to classification accuracy over all patients. An ensemble was created with the first model only and then including the following model in the ensemble: if the resulting accuracy was better than before, the model remained in the ensemble, otherwise it was removed.
J Med Internet Res 2021;23(2):e18766
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