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The Role of Big Data in Advancing Mental Health Research

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By Sourav Banerjee, Founder & CTO, of United We Care

Mental health is a complex and multifaceted field that has long eluded definitive answers and solutions. It includes a broad spectrum of illnesses and ailments, each impacted by a multitude of elements, such as stressors in the environment and genetic predisposition.

Understanding the intricacies of mental health requires the collection and analysis of vast amounts of data. Big data, with its capacity to handle and process immense datasets, has emerged as a powerful tool for research on mental health.

Why Mental Health is Complex

Mental health is a complex topic to decipher for so many reasons. Firstly, it encompasses a diverse range of disorders, from anxiety and depression to schizophrenia and bipolar disorder. Since each of these disorders has distinct traits and underlying causes, it is difficult to create a diagnosis and treatment plan that works for everyone.

Additionally, mental health disorders often present with a wide spectrum of symptoms and manifestations. For instance, two individuals with depression may exhibit entirely different sets of symptoms, making it difficult to establish consistent diagnostic criteria.

This complexity is further compounded by the fact that many mental health conditions co-occur, leading to overlapping symptomatology. About 3 percent of the population has more than one mental illness at a time, according to the NCBI. Someone suffering from depression may be suffering from bipolar disorder at the same time, making it difficult to establish a specific set of treatments that should be ideally followed.

Big data can be used to identify new risk factors for mental health disorders, develop new treatment approaches, and improve the delivery of mental health care. Big data has the potential to make mental health care more personalized, effective, and accessible.

Mental Health Diagnosis and Psychological Research is Statistical

Statistical methods are frequently used in the diagnosis of mental illness and in psychological research. Statistical analyses are used by researchers to identify patterns, correlations, and trends in large datasets. These analyses aid in making sense of the vast and varied information gathered in mental health studies.

For instance, when studying the effectiveness of a new treatment for depression, researchers need to analyze the outcomes for a significant number of patients. They employ statistical methods to determine if the treatment results in a statistically significant improvement in patients’ symptoms. This approach is essential to ensure that research findings are reliable and not merely coincidental.

A 2022 study by the Kaiser Permanente Mental Health Research Network found that electronic health record data could be used to identify patients at high risk for suicide attempts with 90% accuracy. The study analyzed data from over 2 million Kaiser Permanente patients and found that a combination of factors, including mental health diagnoses, medication use, and social factors, could be used to predict which patients were most likely to attempt suicide.

The Involvement of Both Genetic Factors and Psychological Stressors

Recognizing the role of both hereditary and environmental stressors is essential to understanding mental health. Many mental health conditions are influenced by genetic predisposition, as some people may be more genetically predisposed to disorders like bipolar disorder or schizophrenia. However, these genetic predispositions do not guarantee that an individual will develop the condition.

Environmental stressors, including traumatic experiences, chronic stress, and substance abuse, also play a crucial role in the onset and course of mental health disorders. Complex interactions exist between genetic and environmental factors, and the relative importance of each varies depending on the person and the situation. It is difficult to prove causation beyond a reasonable doubt because of this complexity.

The Challenge of Establishing Causality

Establishing causality is a critical aspect of mental health research, but it is a daunting task because we don’t fully understand the intricacies of how mental health works. The human mind is an immensely intricate system, and mental health disorders involve a delicate interplay of genetics, brain chemistry, life experiences, and environmental factors.

While we can observe correlations and associations in data, teasing out causal relationships is challenging due to the multifaceted nature of mental health. For instance, we may observe a genetic predisposition for a particular disorder, but we cannot definitively say that this genetic factor directly causes the disorder. It may be influenced by various other factors, including environmental stressors or biological mechanisms that are not yet fully understood.

Big data, with its capacity to collect and analyze extensive datasets, provides a promising avenue for shedding light on these complex causal relationships. By examining a vast array of variables and their interactions, researchers can begin to unravel the intricate web of factors contributing to mental health conditions. Big data plays an important role in the field of mental health in the prediction, automation, and analysis of such disorders.

Risk Profiling, Relapse Prediction, and Prognosis Prediction

One of the significant challenges in mental health research is the prediction of risk, relapse, and prognosis. Big data has the potential to address these challenges by providing a wealth of information for analysis.

Risk profiling involves identifying individuals at high risk of developing a particular mental health condition. By analyzing data from a diverse range of sources, including genetic markers, environmental factors, and behavioral patterns, researchers can develop risk profiles that help identify those who may benefit from early interventions or preventive measures.

Relapse prediction is crucial for individuals with chronic mental health conditions. By continuously monitoring and analyzing various data points, such as medication adherence, lifestyle factors, and psychological well-being, big data can assist in predicting when a person might be at risk of a relapse. This knowledge enables healthcare providers to offer timely support and interventions.

Prognosis prediction involves estimating the course and outcome of a mental health condition for an individual. Big data can help develop predictive models that consider various factors, such as treatment response, adherence, and personal history. These models can guide treatment decisions and improve the long-term management of mental health conditions.

A 2020 study by IBM Watson Health found that social media data could be used to identify individuals at risk for depression with 70% accuracy. The study analyzed data from over 500,000 Twitter users and found that a combination of factors, including the use of certain keywords and phrases, the frequency of posting, and the sentiment of posts, could be used to identify individuals at risk for depression.

Variability in Symptoms and Manifestations

The wide variability in symptoms and manifestations of mental health issues poses a significant challenge to accurate diagnosis and treatment. Big data can help address this challenge by enabling researchers to analyze and identify patterns within large datasets.

For example, by collecting and analyzing information from diverse sources, such as electronic health records, wearable devices, and patient self-reports, researchers can identify common symptom clusters and patterns associated with specific mental health conditions. This information can lead to more precise and personalized diagnostic criteria and treatment approaches.

Conclusion

The role of big data in mental health research is transformative. It enables researchers to tackle the complexity of mental health by employing statistical methods, considering genetic and environmental factors, and addressing the challenges of establishing causality. Moreover, big data helps unravel the variability in symptoms and manifestations and establishes strong directional knowledge graphs that guide the exploration of causality. With the power of big data, we are better equipped than ever to advance our understanding of mental health and improve the lives of those affected by these conditions.

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