Predictive modeling of hospital readmission: challenges and solutions
Hospital readmission prediction is a study to learn models from historical medical data to
predict probability of a patient returning to hospital in a certain period, eg 30 or 90 days, after …
predict probability of a patient returning to hospital in a certain period, eg 30 or 90 days, after …
Density-aware personalized training for risk prediction in imbalanced medical data
Medical events of interest, such as mortality, often happen at a low rate in electronic medical
records, as most admitted patients survive. Training models with this imbalance rate (class …
records, as most admitted patients survive. Training models with this imbalance rate (class …
Sparse gated mixture-of-experts to separate and interpret patient heterogeneity in ehr data
A chalenge in developing machine learning models for patient risk prediction involves
addressing patient heterogeneity and interpreting the model outcome in clinical settings …
addressing patient heterogeneity and interpreting the model outcome in clinical settings …
Dynimp: Dynamic imputation for wearable sensing data through sensory and temporal relatedness
In wearable sensing applications, data is inevitable to be irregularly sampled or partially
missing, which pose challenges for any downstream application. An unique aspect of …
missing, which pose challenges for any downstream application. An unique aspect of …
Towards Robust and Generalizable Machine Learning for Real-World Healthcare Data with Heterogeneity
Z Huo - 2022 - search.proquest.com
The utility of machine learning for enhancing human well-being and health has risen to the
core discussion in both research and real-world application in today's technological front …
core discussion in both research and real-world application in today's technological front …