Machine Learning Magic in the Segment of Mental Illness

Machine learning has changed the picture of the world enormously. Not just in the realm of businesses, Machine learning has been doing wonders in the entertainment industry, e-commerce world, robotics, etc. but healthcare sector also has come up with more accurate diagnosis and treatments with its implementations.

Machine learning has been reported to bring up immense improvement in manual processes involved in the treatment of patients with accurate records of patients. In near future, it may be used for some life-saving surgeries too as precision is guaranteed by it.

ML algorithms have been proved to be beneficial for the treatment process of unvarying nature. Machine learning has confirmed more precision with streams dealing with large image datasets, such as cardiology, pathology, radiology, cardiology, etc. 

 Latest news in Healthcare 

• This data-based technology, machine learning, has been in the headlines because of day by day increasing innovation. Another feather of success is recently added by Google by developing a machine learning algorithm on mammograms in recognition of cancerous tumours. 
• A JAMA article announced the diagnosis of diabetic retinopathy through the retinal picture. 
• Stanford claimed to recognize skin cancer with a deep learning algorithm.  

Efforts are still on to derive more efficiency in the various disciplines of medical science through machine learning algorithms and the latest development has been made in the sector of mental illness. Have a look underneath for more details on machine learning’s contribution to psychological sickness. 

 Machine Learning in Mental Illness 

To gain more precision in recognition of the mental illness of patients suffering from the symptoms of depression and psychosis, a Birmingham researching team has put forth a new mode of being cent per cent accurate with the aid of machine learning.

The study and their observation of research were made public via the journal 'Schizophrenia Bulletin'. 

Earlier it was a challenging task for the doctors to come up with an accurate diagnosis based on the symptoms of individuals. The symptoms of every individual differ from one another, moreover symptoms resemble many illnesses altogether. The mental health team gives a finding of primary illness that has symptoms of secondary illness. 

A research team of the University of Birmingham’s Institute of Mental Health and Centre for Human Brain Health was looking to incorporate machine learning to structure an accurate model to find out the analytical accuracy of a cohort of patients with symptoms of two diseases. The outcome of their research was published by them in Schizophrenia Bulletin.

It was found that a major proportion of patients displayed co-morbidities (having symptoms of two illnesses at a time), which raised the complexity for clinicians as regard as deciding diagnosis and treatment for patients not showing comorbidity. It doesn’t mean that patients are not diagnosed correctly but the diagnostic tools we have are not sufficient enough to get a clear diagnosis of clinical and neurobiological truth. 

The researcher scrutinized responses to clinical interviews and answers to their list of questions and data of more than 300 patients who participated in the Pronia study. During their research, the researchers recognized a small segment of patients who were suffering from psychosis and had no symptoms of depression or were found dealing with depression and displayed no psychotic symptoms. With the usage of this data, they recognized the ML algorithm for both, depressions and psychotic separately. As a result, they got into position to implement machine learning mode to patients with symptoms of both the disease. This is how their objective to develop an accurate model for the detection of disease was accomplished. 

Despite the breakthrough, machine learning has a lot more to help us with.

It was later found by the researchers that with the model developed by them, the possibility of diagnosing depression is more accurate and patients with symptoms of psychosis and depression together were considered to be having the issue of depression only. It indicated that depression was predominant in the mixed illness. 

The team found that patients with depression as a primary illness were more likely to be diagnosed accurately than the patients having psychosis. 

Conclusion:

From the above research, one can conclude the big role machine learning is playing in the medical sector and finding patterns of mental health. However, more researches and developments are required to identify and respond to the ethical and practical implications of machine learning in healthcare.