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 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.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Moon, Intae

Advisors dc:contributor.advisor

• Gusev, Alexander
• Ghassemi, Marzyeh

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156651

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156651