Predictive Modeling for Patient Outcomes in Healthcare Decision Systems
DOI:
https://doi.org/10.64137/31079377/IJMSD-V1I2P105Keywords:
Predictive modeling, Healthcare analytics, Electronic health records, Machine learning, Patient outcomes, Clinical decision support, Risk stratificationAbstract
Predictive modeling has become an essential component of modern healthcare decision systems, enabling early identification of patient risks and supporting data-driven clinical decisions. The aim of this study is to create and validate predictive models of outcomes relevant to individual patients by using electronic health record (EHR) data, in an effort to enhance clinical efficiency and resource allocation. The approach consists of pre-processing structured data from clinical records, feature engineering of demographic information, as well as vital signs, laboratory results and medical history, along with the use of machine learning algorithms such as logistic regression, random forests and gradient boosting models. Model performance is assessed with accuracy, area under the receiver operating characteristic curve (AUROC), and calibration. The primary results indicate that machine learning models have the predictive advantage over traditional statistical models for predicting adverse patient outcomes with better discrimination and risk stratification, and remain clinically interpretable. The work suggests that when carefully designed and incorporated into healthcare decision systems, predictive modeling can greatly improve the accuracy of predicting patient outcomes and help to facilitate proactive clinical care that is evidence-based.
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