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Predictive Analytics, Big Data, And Machine Learning

Pressure ulcers are still considered as one of the most serious problems that occur in elderly people especially in situations of degenerative diseases. It becomes important to come up with ways nurses deliver care to that type of patient though new technologies in healthcare. With Artificial Intelligence machine learning, physicians can provide predictive analytic models for learning of the generation or degeneration of the condition (Intersystems, 2018). Predictive analytics depends on artificial intelligence where computer are taught how to learn the occurrence or behaviour of a particular medical condition. In many cases this can be helpful to determine the kind of treatment a patient can be put in before they condition gets worse. Machine learning can enable the nurse or physician understand the patient’s pressure ulcers, and apply the required treatment during care to reduce the risk of generation (Intersystems, 2018).  It helps the nurse to evaluate the risk degree with individualized production of pressure ulcer prevention protocol. 

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Machine learning involves the use of degree evaluation scales like the Norton and Waterlow scales which can detect areas that are susceptible to pressure ulcers especially for patients that have reached the most severe stages of the illness. By using artificial intelligence, it is also possible to tell the life span for the patient so they can prepare themselves early (Intersystems, 2018). With machine learning, it is possible for the physical to modify the type of care according to recorded factors, change in treatment will then be mobilized  as a comfort message is administered  especially the heels, bony projections among other factors related to pressure ulcers (Saria, 2016).

In summary predictive analytics and machine learning is very important development in medicine and nursing care for pressure ulcer prevention, it will identify the risk factors influence how they occur and how nurses will behave in relation to the causal factor.

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