OBJECTIVE To identify patients with heart failure (HF) by using language contained in the electronic medical record (EMR). METHODS We validated 2 methods of identifying HF through the EMR, which offers transcription of clinical notes within 24 hours or less of the encounter. The first method was natural language processing (NLP) of the EMR text. The second method was predictive modeling based...

BACKGROUND Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predi...

OBJECTIVE To compare general and disease-based modeling for fluid resuscitation and vasopressor use in intensive care units. METHODS Retrospective cohort study involving 2944 adult medical and surgical intensive care unit (ICU) patients receiving fluid resuscitation. Within this cohort there were two disease-based groups, 802 patients with a diagnosis of pneumonia, and 143 patients with a dia...

Developing virtual performanceand reliability predictive techniques has become essential for the development of (micro)electronic systems. This paper provides an overview of current predictive methodologies, challenges and requirements for the modeling of microelectronics thermal behavior. Critical modeling issues are discussed, from optimizing Integrated Circuit (IC) packaging thermal performa...

In this paper, we propose ADTreesLogit, a model that integrates the advantage of ADTrees model and the logistic regression model, to improve the predictive accuracy and interpretability of existing churn prediction models. We show that the overall predictive accuracy of ADTreesLogit model compares favorably with that of TreeNet®, a model which won the Gold Prize in the 2003 mobile customer chur...

Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) st...

Colleges have increasingly turned to predictive analytics target at-risk students for additional support. Most of the analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack by systematically comparing two important dimensions: (1) different approaches sample and variable construction how ...

Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an individual model. In this paper, we propose a new method for learning ensembles of ...