Approved Research

Diagnosis of Bipolar Disorder using various static and dynamic parameters

Lay summary

Introduction:

Bipolar disorder poses a significant challenge to mental health, characterized by extreme mood fluctuations and impacting various aspects of life. This research proposal aims to develop a comprehensive system that integrates static and dynamic parameters associated with bipolar disorder to create personalized profiles for individuals. By employing machine learning algorithms on previously captured data, the system seeks to predict episodes of bipolar disorder and provide timely feedback and interventions.

Objective:

The primary objective of this research is to create a robust software system that seamlessly combines static parameters, such as retinal vascular parameters, gamma-aminobutyric acid (GABA) levels, and structural brain parameters, with dynamic parameters like sleep patterns, eating patterns, energy levels, mood swings, and heart rate. By leveraging these diverse indicators, we aim to enhance the accuracy and reliability of predicting bipolar disorder episodes.

Methodology:

1. Data Collection: Utilize the collected datasets, which include static parameters from medical imaging, genetic information, and biomarker analysis. Additionally, leverage dynamic parameters obtained through continuous monitoring of daily activities, sleep patterns, and physiological responses.
2. Machine Learning Algorithms: Implement state-of-the-art machine learning algorithms to analyze the collected data. This involves training models to recognize patterns and associations between parameters and bipolar disorder episodes.
3. Personalized Profiling: Develop a system capable of creating individualized profiles for users, which will serve as a foundation for predicting the onset of bipolar disorder episodes.
4. Prediction and Feedback System: Implement a real-time prediction system that evaluates an individual's current state and risk level for bipolar disorder episodes. The system will provide timely feedback and interventions to mitigate the impact of potential episodes.

Expected Outcomes:

1. A sophisticated software system capable of integrating static and dynamic parameters for accurate prediction of bipolar disorder episodes.
2. Personalized profiles that capture individual variations and patterns, enhancing the system's predictive capabilities.
3. Timely interventions and feedback mechanisms to support individuals in managing their mental health effectively.

Public Health Impact:

This system will be of great help in early recognition of bipolar disorder symptoms so that patients can afford to take preventive action on it before the symptoms take over.

It can also serve to track symptoms and assess treatment outcomes in the long run. Therefore, due to the absence of such a system in the clinical setting, in India, this project will provide positive outcomes to the bipolar disorder patient population.