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Shanmukh Alle – Dual Degree ECE

Shanmukh Alle received his MS  Dual Degree in Electronics and Communication Engineering (ECE). His research work was supervised by Prof. Deva Priyakumar. Here’s a summary of Shanmukh Alles thesis Machine Learning for Disease Diagnosis and Severity Assessment:

Machine learning methods have found applications in various branches of healthcare including disease diagnosis, disease progression and outcome prediction, drug design, etc. The computational nature of such methods democratizes medical expertise by enabling access to diagnosis and outcome prediction in the absence of a medical expert.

In this thesis we present two different applications of AI in health care. We present LPGNet an efficient deep learning based model for diagnosis of Parkinson’s disease from Gait. Next, We present the statistical analysis and models built for severity and mortality prediction in hospitalized Indian COVID-19 patients. In the first part of the thesis we present LPGNet an efficient method for diagnosis of Parkinson’s Disease (PD) from gait. PD is a chronic and progressive neurological disorder that results in rigidity, tremors and postural instability. There is no definite medical test to diagnose PD and diagnosis is mostly a clinical exercise. Although guidelines exist, about 10-30% of the patients are wrongly diagnosed with PD. Hence, there is a need for an accurate, unbiased and fast method for diagnosis. In this study, we propose LPGNet, a fast and accurate method to diagnose PD from gait. LPGNet uses Linear Prediction Residuals (LPR) to extract discriminating patterns from gait recordings and then uses a 1D convolution neural network with depth-wise separable convolutions to perform diagnosis. We also undertake an analysis of various crossvalidation strategies used in literature in PD diagnosis from gait and find that most methods are affected by some form of data leakage between various folds which leads to unnecessarily large models and inflated performance due to overfitting. The analysis clears the path for future works in correctly evaluating their methods. LPGNet achieves an AUC of 0.91 with a 21 times speedup and about 99% lesser parameters in the model compared to the state of the art which would enable the method to be implemented in embedded systems to enable automated Parkinson’s diagnosis to be cheap and widely accessible. In the second part of the thesis we present the statistical analysis to understand and build models for severity and mortality prediction in hospitalized Indian COVID-19 patients. The clinical course of coronavirus disease 2019 (COVID19) infection is highly variable with the vast majority recovering uneventfully but a small fraction progressing to severe disease and death. Appropriate and timely supportive care can reduce mortality and it is critical to evolve better patient risk stratification based on simple clinical data, so as to perform effective triage during strains on the healthcare infrastructure. We analyse a diverse set of 70 bio-markers including physiological, haematological and genomic parameters from 544 COVID-19 patients from New Delhi, India to identify significant markers and build risk stratification and mortality prediction models. An XGboost classifier yielded the best performance on risk stratification (AUC score of 0.83). A logistic regression model yielded the best performance on mortality prediction (AUC score of 0.92) when tested on a holdout test set of 115 patients. Examination of the data in comparison to a similar dataset with a Wuhan cohort of 375 patients was undertaken to understand the much lower mortality rates in India and the possible reasons thereof. The comparison indicated higher survival rate in the Delhi cohort even when patients had similar parameters as the Wuhan patients who died. Our analysis of medications administered points to usage of steroids early on which may have helped in the survival of severe patients. This study helps in identifying high-risk patients and provides insights into the low mortality rate in India.