The past decade has witnessed rapid advances at the intersection of machine learning and medicine. Owing to the tremendous amount of digitized hospital data, machine learning is poised to bring innovation to the traditional healthcare workflow. Though machine learning models have strong predictive power, it is challenging to translate a research project into a clinical tool partly due to the lack of a rigorous validation framework. In this dissertation, I presented a range of machine learning models that were trained to classify Alzheimer’s disease - a condition with an insidious onset - using routinely collected clinical data. In addition to reporting the model performance, I discussed several considerations, including feature selection, data harmonization, effect of confounding variables, diagnostic scope, model interpretability and validation, which are critical to the design, development, and validation of machine learning models. From the methodological standpoint, I presented a multidisciplinary collaboration in which medical domain knowledge which was obtained from experts and tissue examinations was tightly integrated with the interpretable outcomes derived from our machine learning frameworks. I demonstrated that the model, which generalized well on multiple independent cohorts, achieved diagnostic performance on par with a group of medical professionals. The interpretable analysis of our model showed that its underlying decision logic corresponds with expert ratings and neuropathological findings. Taken together, this work presented a machine learning system for classification of Alzheimer’s disease, marking an important milestone towards a translatable clinical application in the future.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/46975 |
| Date | 20 September 2023 |
| Creators | Qiu, Shangran |
| Contributors | Kolachalama, Vijaya B. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-NonCommercial-ShareAlike 4.0 International, http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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