Is Machine Learning the Future of Medical Diagnosis & Treatment?
By Data-Core Systems | 11/02/2018

From Internet searches to voice recognition services, machine learning processes have become commonplace in our daily lives, and they continue to expand into various industries.Already, machine learning is being used by healthcare professionals to detect skin cancer, lung cancer and eye disease based on image recognition patterns and algorithms.One study found that these processes are, in many cases, more effective than clinicians at tasks involving pattern recognition. Another area of healthcare where machine learning could prove to be especially useful is in the diagnosis, prognosis and treatment of individuals with complex medical conditions like mental illnesses.

Before delving into the uses of machine learning, it is important to understand exactly what machine learning is. Broadly defined, machine learning is an application of artificial intelligence that enables computational systems to automatically learn and improve from experience.One of the most distinctive features of these processes is that they do not require programming by humans. Instead, machine learning algorithms are constantly figuring out new, updated predictions and solutions to problems based on what it “learns” from data sets.

Currently, the healthcare industry uses a more traditional approach to creating solutions. Typically, medical decisions surrounding diagnosis, prognosis and treatment are determined based on generalizations about a hypothetical population that are derived from a sample of individuals.However, for patients with mental illnesses, this method falls short because it disregards the complex variables of each case. These variables—including interactions within and among environmental, behavioral, cognitive, emotional and biological systems—are unique to the individual and are not accounted for in generalizations. For this reason, data used in traditional decision-making methods, although derived from statistically significant research, is not clinically meaningful.

Today, machine learning is a promising alternative to traditional decision models in healthcare because of its ability to focus on the individual by considering both clinical and biological data. It can facilitate an analysis of complex multivariate relationships and verify generalizations with a clear reporting of probabilities. In other words, machine learning can help to take the guesswork out medical decisions.Therefore, the adoption of such practices would allow healthcare professionals to make more accurate diagnoses and prognoses as well as design better treatment plans for patients with mental illnesses or other complex conditions.

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