Machine Learning In Health Care

MACHINE LEARNING AND HEALTH CARE

Rama Bhagwat, Student, Bachelor in Computer Application1, Sonia Vijaykumar, Student, Bachelor in Computer Application2,

KLES’s Institute, Hubballi

Abstract:

Healthcare informatics, a multi-disciplinary field has become synonymous with the technological advancements and big data challenges. With the need to reduce healthcare costs and the movement towards personalized healthcare, the healthcare industry faces changes in three core areas namely, electronic record management, data integration, and computer aided diagnoses. Machine learning a complex field in itself offers a wide range of tools, techniques, and frameworks that can be exploited to address these challenges

Introduction:

Machine learning provides a way to find patterns and reason about data, which enables healthcare professionals to move to personalized care known as precision medicine. There are many possibilities for how machine learning can be used in healthcare, and all of them depend on having sufficient data and permission to use it. Previously, alerts and recommendations for a medical practice have been developed based on external studies, and hard-coded into their software. However, that can limit the accuracy of that data because it might come from different populations and environments. Machine learning, on the other hand, can be refined using data that is available in that particular environment (e.g., anonymized patient information from a hospital and the area it serves). An example of how healthcare providers can take advantage of machine learning is offering the ability to predict hospital readmission for chronically ill patients. Identifying those patients most at risk of being readmitted makes it possible for providers to offer better post-discharge support. By lowering the rate of readmission, it not only improves the lives of those most at risk, it also helps save precious healthcare dollars, which can be used for wellness and prevention

Predicting the risk of readmission following a hospital stay is just one example of how machine learning can be applied to solve some of the most pressing issues in healthcare delivery. Other examples include: • Finding combinations of drugs that should not be taken together • Classifying imagery, such as mole scans, to identify disease • Assisting with decisions about what condition a patient might have, or what treatment might be the best.

This is an exciting field that seeks to assist healthcare providers—whether practicing in the hospital or in the

Community—in creating better health outcomes for their patients. We have only just begun to explore the