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Machine Learning in Healthcare: Barrier or Benefit?

By Pharma Tech Outlook | Friday, December 07, 2018

AI and machine learning applications are continuously being deployed in every industry, especially in the healthcare sector. This scenario provides a great hope for patients to have advanced diagnosis and treatments. Simultaneously, the healthcare industry has become one of the leading industries to offer numerous opportunities, and there exists a huge scope for the implementation of machine learning technologies. As IoT devices create a massive volume of data, their management creates complexities in today’s business functions. Here, ML works as a solution to reduce these complexities with the assurance of quality, reliability, and automated data analytics.

ML in healthcare data management is highly imperative today due to the increased number of patients compared to the average number of patients ten years earlier. In the case of heart patients, the presence of the heart disease in their body can be classified into absent, less likely, likely, highly likely, and present. The machine learning algorithms will automatically analyze and predict the characteristics of heart disease and categorize into one of these five types.

Oracle Analytics Cloud is considered to be the best option to leverage ML training and application workflows to evaluate the quality of historical, study-data-tabulation-model (SDTM) mapping model. Likewise, AI and ML applications perform various other tasks such as managing continuous data collection from various data sources such as mobile devices, verify consistency across data transformations, and streamline safety case processing tasks.

AI and ML in healthcare will have to perform way better by using its highly disruptive and new technological applications. Through the analysis of big data, machine learning technologies can provide new insights into the disease processes that could open up new treatment possibilities in the medical field. As ML monitors and analyzes the regular pattern of the medical data from previous medical records, it will then help the physicians respond to patients with separate personalized medications. This will result in a more efficient treatment which can give additional medical preferences and treatments for millions of patients with reduced risks and increased benefits.

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