Leveraging Unlabeled Data in Supervised Learning to Objectively Assess Depression
Depression is the leading cause of disability, affecting over 250 million people worldwide [34]. Major Depressive Disorder, or MDD, is difficult to assess and diagnose due to lack of resources and the personal, private nature of the disease, which has symptoms that can vary greatly on a patient-by-p...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/139306 |
| _version_ | 1826210339011690496 |
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| author | Bhathena, Darian |
| author2 | Picard, Rosalind W. |
| author_facet | Picard, Rosalind W. Bhathena, Darian |
| author_sort | Bhathena, Darian |
| collection | MIT |
| description | Depression is the leading cause of disability, affecting over 250 million people worldwide [34]. Major Depressive Disorder, or MDD, is difficult to assess and diagnose due to lack of resources and the personal, private nature of the disease, which has symptoms that can vary greatly on a patient-by-patient basis [2]. Even so, the current standard methods of assessing depression are subjective and outdated, consisting of surveys and questionnaires first developed 60 years ago [21]. As part of a recent clinical study, data from wearable sensors, smartphones, and surveys were collected from a number of participants, and used to train classical machine learning models aimed at assessing depression. In this thesis, those methods are expanded upon with the intent of improving them, with varied success. Investigations conducted include training a small neural network on the same data, training a Multimodal Autoencoder on additional unlabeled data, and concatenating time features to utilize temporal information. |
| first_indexed | 2024-09-23T14:47:55Z |
| format | Thesis |
| id | mit-1721.1/139306 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:47:55Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1393062022-01-15T03:15:27Z Leveraging Unlabeled Data in Supervised Learning to Objectively Assess Depression Bhathena, Darian Picard, Rosalind W. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Depression is the leading cause of disability, affecting over 250 million people worldwide [34]. Major Depressive Disorder, or MDD, is difficult to assess and diagnose due to lack of resources and the personal, private nature of the disease, which has symptoms that can vary greatly on a patient-by-patient basis [2]. Even so, the current standard methods of assessing depression are subjective and outdated, consisting of surveys and questionnaires first developed 60 years ago [21]. As part of a recent clinical study, data from wearable sensors, smartphones, and surveys were collected from a number of participants, and used to train classical machine learning models aimed at assessing depression. In this thesis, those methods are expanded upon with the intent of improving them, with varied success. Investigations conducted include training a small neural network on the same data, training a Multimodal Autoencoder on additional unlabeled data, and concatenating time features to utilize temporal information. M.Eng. 2022-01-14T15:02:51Z 2022-01-14T15:02:51Z 2021-06 2021-06-17T20:12:55.562Z Thesis https://hdl.handle.net/1721.1/139306 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Bhathena, Darian Leveraging Unlabeled Data in Supervised Learning to Objectively Assess Depression |
| title | Leveraging Unlabeled Data in Supervised Learning to Objectively Assess Depression |
| title_full | Leveraging Unlabeled Data in Supervised Learning to Objectively Assess Depression |
| title_fullStr | Leveraging Unlabeled Data in Supervised Learning to Objectively Assess Depression |
| title_full_unstemmed | Leveraging Unlabeled Data in Supervised Learning to Objectively Assess Depression |
| title_short | Leveraging Unlabeled Data in Supervised Learning to Objectively Assess Depression |
| title_sort | leveraging unlabeled data in supervised learning to objectively assess depression |
| url | https://hdl.handle.net/1721.1/139306 |
| work_keys_str_mv | AT bhathenadarian leveragingunlabeleddatainsupervisedlearningtoobjectivelyassessdepression |