Uncertainty-Aware Ensembling in Multi-Modal AI and its Applications in Digital Health for Neurodegenerative Disorders
Common neurodegenerative disorders such as Alzheimer's dementia and Parkinson's disease are increasingly recognised as leading causes of death and disability with debilitating symptoms such as progressive cognitive decline, communication breakdown, motor dysfunction and accompanying psychi...
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| Format: | Thèse |
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Massachusetts Institute of Technology
2022
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| Accès en ligne: | https://hdl.handle.net/1721.1/140988 |
| _version_ | 1826188187230273536 |
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| author | Sarawgi, Utkarsh |
| author2 | Maes, Pattie |
| author_facet | Maes, Pattie Sarawgi, Utkarsh |
| author_sort | Sarawgi, Utkarsh |
| collection | MIT |
| description | Common neurodegenerative disorders such as Alzheimer's dementia and Parkinson's disease are increasingly recognised as leading causes of death and disability with debilitating symptoms such as progressive cognitive decline, communication breakdown, motor dysfunction and accompanying psychiatric disorders. However, factors such as unavailability of efficient and cost-effective assessments for conclusive diagnosis, time-consuming test protocols, poor prognostic capabilities, and inadequate treatment options with accompanying side effects are all barriers to progress in providing faster and more effective intervention to individuals living with these life-altering disorders. In this thesis, we take a step towards using digital health and machine learning to improve diagnostic and prognostic capabilities and to address remote care via telemedicine in Alzheimer's dementia and Parkinson's disease. Our goal is to provide more cost-effective, non-invasive, and scalable technologies for risk stratification of Alzheimer's dementia using speech. We also aim to monitor drug response and disease progression for Parkinson's disease via telemedicine, allowing real time symptom tracking through wearables alongside a patient's treatment status, which will help facilitate remote care and dynamic and adaptive treatment plans. In addition to addressing the challenges in diagnosis and treatment of neurodegenerative disorders, we further propose a novel uncertainty aware boosting technique for multi-modal ensembling and evaluate it on healthcare tasks related to Alzheimer's dementia and Parkinson's disease. This presents manifold benefits, such as reducing the overall entropy of the system, making it more robust to heteroscedasticity, and improving calibration of each of the modalities along with high quality prediction intervals. |
| first_indexed | 2024-09-23T07:55:52Z |
| format | Thesis |
| id | mit-1721.1/140988 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T07:55:52Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1409882022-03-04T03:46:11Z Uncertainty-Aware Ensembling in Multi-Modal AI and its Applications in Digital Health for Neurodegenerative Disorders Sarawgi, Utkarsh Maes, Pattie Program in Media Arts and Sciences (Massachusetts Institute of Technology) Common neurodegenerative disorders such as Alzheimer's dementia and Parkinson's disease are increasingly recognised as leading causes of death and disability with debilitating symptoms such as progressive cognitive decline, communication breakdown, motor dysfunction and accompanying psychiatric disorders. However, factors such as unavailability of efficient and cost-effective assessments for conclusive diagnosis, time-consuming test protocols, poor prognostic capabilities, and inadequate treatment options with accompanying side effects are all barriers to progress in providing faster and more effective intervention to individuals living with these life-altering disorders. In this thesis, we take a step towards using digital health and machine learning to improve diagnostic and prognostic capabilities and to address remote care via telemedicine in Alzheimer's dementia and Parkinson's disease. Our goal is to provide more cost-effective, non-invasive, and scalable technologies for risk stratification of Alzheimer's dementia using speech. We also aim to monitor drug response and disease progression for Parkinson's disease via telemedicine, allowing real time symptom tracking through wearables alongside a patient's treatment status, which will help facilitate remote care and dynamic and adaptive treatment plans. In addition to addressing the challenges in diagnosis and treatment of neurodegenerative disorders, we further propose a novel uncertainty aware boosting technique for multi-modal ensembling and evaluate it on healthcare tasks related to Alzheimer's dementia and Parkinson's disease. This presents manifold benefits, such as reducing the overall entropy of the system, making it more robust to heteroscedasticity, and improving calibration of each of the modalities along with high quality prediction intervals. S.M. 2022-03-03T19:28:48Z 2022-03-03T19:28:48Z 2021-06 2022-02-27T16:50:29.363Z Thesis https://hdl.handle.net/1721.1/140988 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Sarawgi, Utkarsh Uncertainty-Aware Ensembling in Multi-Modal AI and its Applications in Digital Health for Neurodegenerative Disorders |
| title | Uncertainty-Aware Ensembling in Multi-Modal AI and its Applications in Digital Health for Neurodegenerative Disorders |
| title_full | Uncertainty-Aware Ensembling in Multi-Modal AI and its Applications in Digital Health for Neurodegenerative Disorders |
| title_fullStr | Uncertainty-Aware Ensembling in Multi-Modal AI and its Applications in Digital Health for Neurodegenerative Disorders |
| title_full_unstemmed | Uncertainty-Aware Ensembling in Multi-Modal AI and its Applications in Digital Health for Neurodegenerative Disorders |
| title_short | Uncertainty-Aware Ensembling in Multi-Modal AI and its Applications in Digital Health for Neurodegenerative Disorders |
| title_sort | uncertainty aware ensembling in multi modal ai and its applications in digital health for neurodegenerative disorders |
| url | https://hdl.handle.net/1721.1/140988 |
| work_keys_str_mv | AT sarawgiutkarsh uncertaintyawareensemblinginmultimodalaianditsapplicationsindigitalhealthforneurodegenerativedisorders |