Statistical Modeling for Predictive Healthcare Analytics

Statistical Modeling for Predictive Healthcare Analytics

Samik Acharya, Sima Das
Copyright: © 2024 |Pages: 29
DOI: 10.4018/979-8-3693-2762-3.ch017
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Abstract

Statistical modeling is crucial in predictive healthcare analytics, utilizing sensor data to forecast outcomes and enhance patient care. Key techniques include machine learning algorithms like decision trees, random forests, and neural networks for risk assessment and treatment prediction. Logistic regression and survival analysis models handle binary outcomes and time-to-event data, respectively. Evaluation metrics like accuracy and AUC-ROC gauge model performance. Techniques for imbalanced data, feature selection, and model interpretability bolster predictive robustness. Bayesian modeling integrates prior knowledge, improving reliability and interpretability. Statistical modeling empowers data-driven clinical decisions, enhancing patient outcomes and personalized medicine.
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1. Introduction To Predictive Healthcare Analytics

Predictive healthcare analytics is rapidly reshaping the medical landscape by harnessing data-driven insights to forecast patient outcomes, optimize resource allocation, and refine decision-making processes. Its significance is paramount, offering healthcare providers the ability to transform vast amounts of data into actionable intelligence, thus improving patient outcomes while reducing costs. This approach encompasses various methodologies, including data collection, preprocessing, predictive modeling, and model validation, culminating in the deployment of predictive models into clinical workflows and decision support systems. The applications of predictive analytics in healthcare are diverse, spanning risk stratification, early disease detection, personalized treatment planning, resource optimization, and population health management. Ultimately, predictive healthcare analytics holds the promise of driving innovation, enhancing patient care, and catalyzing transformative changes across the healthcare industry through data-driven precision and proactive intervention strategies. Predictive analytics is revolutionizing healthcare by leveraging data to forecast patient outcomes, optimize resource allocation, and enhance decision-making. This article provides an overview of predictive healthcare analytics, highlighting its significance and diverse applications across the healthcare landscape.

1.1. Significance of Predictive Healthcare Analytics

Predictive healthcare analytics involves the use of statistical algorithms and machine learning techniques to analyze historical and real-time data, identify patterns, and predict future events or trends in patient health, treatment outcomes, and operational efficiency. Its significance lies in its ability to transform raw data into actionable insights, enabling healthcare providers to make informed decisions, improve patient outcomes, and reduce costs.

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