Long-term prediction of Iranian blood product supply using LSTM: a 5-year forecast
Authors (first, second and last of 4)
Collection
Counterfactual Prediction Models in Medicine
BMC Medical Informatics and Decision Making is calling for submissions to our Collection on Counterfactual Prediction Models in Medicine. Prediction models are not assured to be usable in counterfactual mode to assess hypothetical interventions. Yet, this important difference in capabilities of prediction models is sometimes overlooked. The aim of this collection is to encourage methodological and applied studies concerning the development, validation, and application of counterfactual prediction models in healthcare.
Articles (11 in this collection)
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From algorithms to action: improving patient care requires causality
Authors (first, second and last of 5)
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Which risk factor best predicts coronary artery disease using artificial neural network method?
Authors (first, second and last of 14)
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InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography
Authors (first, second and last of 4)
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Dirichlet process mixture models to impute missing predictor data in counterfactual prediction models: an application to predict optimal type 2 diabetes therapy
Authors (first, second and last of 6)
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The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients
Authors (first, second and last of 9)
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Predicting delayed methotrexate elimination in pediatric acute lymphoblastic leukemia patients: an innovative web-based machine learning tool developed through a multicenter, retrospective analysis
Authors (first, second and last of 13)
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Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine
Authors (first, second and last of 7)
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An early detection and segmentation of Brain Tumor using Deep Neural Network
Authors (first, second and last of 4)
