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Predictive analytics in healthcare can identify patients who are at a higher risk of developing certain diseases. For example, it could estimate whether a person with hypertension is also at risk of developing coronary heart disease or chronic kidney disease.
Patient Readmission Risk Hospitals use predictive modeling to assess the risk of patients being readmitted after discharge. By analyzing patient history, health data, and social determinants, models can identify individuals at a higher risk of readmission.
Machine learning algorithms are used to train and improve these models to help you make better decisions. Predictive modeling is used in many industries and applications and can solve a wide range of issues, such as fraud detection, customer segmentation, disease diagnosis, and stock price prediction.
Modelling enables us to estimate longer term and population wide health benefits, harms, and trade-offs of interventions, integrate evidence from different domains, test the potential for behaviour change through hypothetical what if scenarios, and address issues of cost and cost-effectiveness.
Predictive models allow health plans and employers to identify at-risk patients and forecast their impact on health spending.
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In many cases the overall benefit of using predictive models for improving healthcare will be critically determined by the considerations of implementation costs, actionability, safety, and utility.
By utilizing predictive models and data-driven insights, healthcare organizations can detect potential problems before they arise, anticipate future needs of their patients, and identify trends in population health more quickly and accurately than ever before.
In the context of healthcare, predictive modeling involves developing mathematical models that can predict patient outcomes, such as disease onset, progression, response to treatment, hospital readmissions, and mortality.

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