Health Outcomes from Home Hospitalization: Multisource Predictive Modeling

A home away from home: the patient-centered health home.

Combining Non-Parametric based models for multisource predictive forest mapping

Using a single model for forest type predictive mapping will not produce good estimates of confidence in the prediction of individual pixels, even with good overall accuracy. A new strategy which combines several models based on different philosophy could not only reduce the uncertainty of predictive modelling, but also improve the mapping accuracy. In our study, a Artificial Neural Networks, a...

Interpretable Predictive Models for Knowledge Discovery from Home-Care Electronic Health Records

The purpose of this methodological study was to compare methods of developing predictive rules that are parsimonious and clinically interpretable from electronic health record (EHR) home visit data, contrasting logistic regression with three data mining classification models. We address three problems commonly encountered in EHRs: the value of including clinically important variables with littl...

Discharged from a mental health admission ward: is it safe to go home? A review on the negative outcomes of psychiatric hospitalization

Before psychiatry emerged as a medical discipline, hospitalizing individuals with mental disorders was more of a social stigmatizing act than a therapeutic act. After the birth of the mental health disciplines, psychiatric hospitalization was legitimized and has proven to be indispensable, preventing suicides and helping individuals in need. However, despite more than a century passing since th...

Using SAS Predictive Modeling to Investigate the Asthma’s Patient Future Hospitalization Risk

The focus of this study is to develop predictive models to forecast future Asthma hospitalizations for patients diagnosed with Asthma. Using SAS® Software tools such as predictive models provides great benefit to insurance companies, hospitals, and pharmaceutical companies trying to manage their resources and define future plans and policies. The strength areas of predictive modeling are: a) an...