Medical informed machine learning: A scoping review and future research directions
Combining domain knowledge (DK) and machine learning is a recent research stream to
overcome multiple issues like limited explainability, lack of data, and insufficient robustness …
overcome multiple issues like limited explainability, lack of data, and insufficient robustness …
Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression
Objectives Quantification and early identification of unplanned readmission risk have the
potential to improve the quality of care during hospitalization and after discharge. However …
potential to improve the quality of care during hospitalization and after discharge. However …
Scalable predictive analysis in critically ill patients using a visual open data analysis platform
With the accumulation of large amounts of health related data, predictive analytics could
stimulate the transformation of reactive medicine towards Predictive, Preventive and …
stimulate the transformation of reactive medicine towards Predictive, Preventive and …
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 …
Predicting hospital associated disability from imbalanced data using supervised learning
Hospitalization of elderly patients can lead to serious adverse effects on their functional
capability. Identifying the underlying factors leading to such adverse effects is an active area …
capability. Identifying the underlying factors leading to such adverse effects is an active area …
[HTML][HTML] Predicting readmission charges billed by hospitals: machine learning approach
Background: The Centers for Medicare and Medicaid Services projects that health care
costs will continue to grow over the next few years. Rising readmission costs contribute …
costs will continue to grow over the next few years. Rising readmission costs contribute …
Integrated theory-and data-driven feature selection in gene expression data analysis
The exponential growth of high dimensional biological data has led to a rapid increase in
demand for automated approaches for knowledge production. Existing methods rely on two …
demand for automated approaches for knowledge production. Existing methods rely on two …
Impact of selected pre-processing techniques on prediction of risk of early readmission for diabetic patients in India
R Duggal, S Shukla, S Chandra, B Shukla… - International Journal of …, 2016 - Springer
Diabetes is associated with increased risk of hospital readmission. Predicting risk of
readmission of diabetic patients can facilitate implementing appropriate plans to prevent …
readmission of diabetic patients can facilitate implementing appropriate plans to prevent …
Readmission prediction for patients with heterogeneous medical history: A trajectory-based deep learning approach
Hospital readmission refers to the situation where a patient is re-hospitalized with the same
primary diagnosis within a specific time interval after discharge. Hospital readmission …
primary diagnosis within a specific time interval after discharge. Hospital readmission …
Integrating knowledge from DEX hierarchies into a logistic regression stacking model for predicting ski injuries
Abstract Machine learning models are often unaware of the structure that exists between
attributes. Expert models, on the other hand, provide structured knowledge that is readily …
attributes. Expert models, on the other hand, provide structured knowledge that is readily …