Predictive analytics analyzes current and historical data to make predictions about future events and trends. The predictions are based on the predictive models that are generated from a machine learning technique that recognizes pattern in the current and historical data.
US20140275807 illustrates the predictive analytics application in the personalized medicine. Personalized medicine refers to the use of a diagnostic to target a therapy at a patient exploiting the patient’s individual health data including genotypic that are most likely to benefit from the therapy. Diagnostics is the first step in defining the precise nature of a patient’s disease state. Alzheimer’s disease diagnosis is complex, particularly in the early stages of disease. Alzheimer’s disease is caused by disorders of the brain and central nervous system. The predictive analytics can predict a response of the patient to a particular therapy. Thus, by analyzing the integrated diagnostic data of the patient, the predictive analytics can predict Alzheimer’s disease at a pre-symptomatic stage.
US2016001935 illustrates a system for monitoring a healthcare provider’s operation and performance, and thus, providing tools to make better business decisions, reduce costs, and improve operating efficiency using the predictive analytics. The system receives data associated with the healthcare provider’s operation and performance. The predictive analytics models determine forecast and predict future trends utilizing the aggregated data. Especially, the predictive analytics combines clinical, supply chain and claims data to allow a healthcare provider to compare and contrast how changes in choice of medical devices, medicines, etc. impact both clinical outcomes and profits by physicians, specialties, and payers.
US20150310179 illustrates the software as a service (SaaS) platform for providing analysis of large database regarding patients’ diagnosis and treatment information. The predictive analytics defines decision points that are relevant to clinical decision-making by generating optimal probabilities and likelihood ratios through analysis of information contained in the database.
US20120165617 illustrates a system for early health and preventive care using data from wearable sensors. Data collected from the sensors is transmitted to a mobile cloud computing platform-as-a-service (PaaS). The predictive analytics analyzes the received data to predict diseases and other conditions to which the patient may be predisposed.
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