Reliable and Trustworthy AI for Evidence-based Clinical Decision Support in Cancer Care
The integration of cutting-edge AI methods with real-world clinical data has moved from being a novelty to a necessity in oncology. However, the deployment of AI faces challenges, including the complexity of reliably modeling longitudinal Electronic Health Records (EHR) characterized by missing data...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156651 https://orcid.org/0000-0002-0978-9605 |
| _version_ | 1826190822475825152 |
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| author | Moon, Intae |
| author2 | Gusev, Alexander |
| author_facet | Gusev, Alexander Moon, Intae |
| author_sort | Moon, Intae |
| collection | MIT |
| description | The integration of cutting-edge AI methods with real-world clinical data has moved from being a novelty to a necessity in oncology. However, the deployment of AI faces challenges, including the complexity of reliably modeling longitudinal Electronic Health Records (EHR) characterized by missing data and frequent patient drop-outs, patient heterogeneity which leads to disparities in AI performance, and the need for validating AI models' clinical benefits, especially in managing challenging cancer cases. This thesis presents research focused on addressing these challenges: developing a continuous time model-based time-to-event regression framework to improve the prediction of clinically meaningful patient outcomes from irregularly sampled EHR data; utilizing data and algorithm-driven approaches to mitigate AI performance disparity for predicting cancer-associated adverse events across diverse patient demographics; and developing an AI-based decision support tool that integrates genomics and clinical data for evidence-based cancer care, with a focus on improving management of difficult-to-treat cancer cases. This work contributes towards transforming cancer care through reliable and trustworthy AI-driven clinical decision support. |
| first_indexed | 2024-09-23T08:46:13Z |
| format | Thesis |
| id | mit-1721.1/156651 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T08:46:13Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1566512024-09-04T04:00:09Z Reliable and Trustworthy AI for Evidence-based Clinical Decision Support in Cancer Care Moon, Intae Gusev, Alexander Ghassemi, Marzyeh Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science The integration of cutting-edge AI methods with real-world clinical data has moved from being a novelty to a necessity in oncology. However, the deployment of AI faces challenges, including the complexity of reliably modeling longitudinal Electronic Health Records (EHR) characterized by missing data and frequent patient drop-outs, patient heterogeneity which leads to disparities in AI performance, and the need for validating AI models' clinical benefits, especially in managing challenging cancer cases. This thesis presents research focused on addressing these challenges: developing a continuous time model-based time-to-event regression framework to improve the prediction of clinically meaningful patient outcomes from irregularly sampled EHR data; utilizing data and algorithm-driven approaches to mitigate AI performance disparity for predicting cancer-associated adverse events across diverse patient demographics; and developing an AI-based decision support tool that integrates genomics and clinical data for evidence-based cancer care, with a focus on improving management of difficult-to-treat cancer cases. This work contributes towards transforming cancer care through reliable and trustworthy AI-driven clinical decision support. Ph.D. 2024-09-03T21:14:44Z 2024-09-03T21:14:44Z 2024-05 2024-07-10T13:01:49.015Z Thesis https://hdl.handle.net/1721.1/156651 https://orcid.org/0000-0002-0978-9605 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Moon, Intae Reliable and Trustworthy AI for Evidence-based Clinical Decision Support in Cancer Care |
| title | Reliable and Trustworthy AI for Evidence-based Clinical Decision Support in Cancer Care |
| title_full | Reliable and Trustworthy AI for Evidence-based Clinical Decision Support in Cancer Care |
| title_fullStr | Reliable and Trustworthy AI for Evidence-based Clinical Decision Support in Cancer Care |
| title_full_unstemmed | Reliable and Trustworthy AI for Evidence-based Clinical Decision Support in Cancer Care |
| title_short | Reliable and Trustworthy AI for Evidence-based Clinical Decision Support in Cancer Care |
| title_sort | reliable and trustworthy ai for evidence based clinical decision support in cancer care |
| url | https://hdl.handle.net/1721.1/156651 https://orcid.org/0000-0002-0978-9605 |
| work_keys_str_mv | AT moonintae reliableandtrustworthyaiforevidencebasedclinicaldecisionsupportincancercare |