Novel Machine Learning Algorithms for Personalized Medicine and Insurance
Over the past decades, analytics have provided the promise of revolutionizing healthcare, providing more effective, patient-centered, and personalized care. As an increasing amount of data is being collected, computational performance is improved, and new algorithms are developed, machine learning h...
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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/1721.1/139482 https://orcid.org/0000-0003-3737-4826 |
| _version_ | 1826205415662157824 |
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| author | Orfanoudaki, Agni |
| author2 | Bertsimas, Dimitris |
| author_facet | Bertsimas, Dimitris Orfanoudaki, Agni |
| author_sort | Orfanoudaki, Agni |
| collection | MIT |
| description | Over the past decades, analytics have provided the promise of revolutionizing healthcare, providing more effective, patient-centered, and personalized care. As an increasing amount of data is being collected, computational performance is improved, and new algorithms are developed, machine learning has been viewed as the key analytical tool that will advance healthcare delivery. Nevertheless, until recently, despite the enthusiasm about the potential of “big data”, only a few examples have impacted the current clinical practice. This thesis presents a combination of predictive and prescriptive methodologies that will empower the transition to personalized medicine.
We propose new machine learning algorithms to address major data imperfections like missing values, censored observations, and unobserved counterfactuals. Leveraging a wide variety of data sources, including health and claims records, longitudinal studies, and unstructured medical reports, we demonstrate the potential benefit of analytics in the context of cardiovascular and cerebrovascular diseases. To propel the adoption of these methodologies, we lay the foundations in the area of algorithmic insurance, proposing a quantitative framework to estimate the litigation risk of machine learning models. This work emphasizes interpretability and the design of models that facilitate clinician engagement and integration into the healthcare system.
Part I introduces data-driven algorithms for missing data imputation, clustering, and survival analysis that lie at the intersection of machine learning and optimization. Part II highlights the potential of prescriptive and predictive analytics in the medical field. We develop a new framework for personalized prescriptions and apply it for the treatment of coronary artery disease. Part II also presents predictive models that could support the early diagnosis and improve the management of stroke patients. Finally, Part III proposes a novel risk evaluation methodology that will enable healthcare institutions to manage the risk exposure resulting from the implementation of analytical decision tools. |
| first_indexed | 2024-09-23T13:12:20Z |
| format | Thesis |
| id | mit-1721.1/139482 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:12:20Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1394822022-01-15T03:28:22Z Novel Machine Learning Algorithms for Personalized Medicine and Insurance Orfanoudaki, Agni Bertsimas, Dimitris Massachusetts Institute of Technology. Operations Research Center Over the past decades, analytics have provided the promise of revolutionizing healthcare, providing more effective, patient-centered, and personalized care. As an increasing amount of data is being collected, computational performance is improved, and new algorithms are developed, machine learning has been viewed as the key analytical tool that will advance healthcare delivery. Nevertheless, until recently, despite the enthusiasm about the potential of “big data”, only a few examples have impacted the current clinical practice. This thesis presents a combination of predictive and prescriptive methodologies that will empower the transition to personalized medicine. We propose new machine learning algorithms to address major data imperfections like missing values, censored observations, and unobserved counterfactuals. Leveraging a wide variety of data sources, including health and claims records, longitudinal studies, and unstructured medical reports, we demonstrate the potential benefit of analytics in the context of cardiovascular and cerebrovascular diseases. To propel the adoption of these methodologies, we lay the foundations in the area of algorithmic insurance, proposing a quantitative framework to estimate the litigation risk of machine learning models. This work emphasizes interpretability and the design of models that facilitate clinician engagement and integration into the healthcare system. Part I introduces data-driven algorithms for missing data imputation, clustering, and survival analysis that lie at the intersection of machine learning and optimization. Part II highlights the potential of prescriptive and predictive analytics in the medical field. We develop a new framework for personalized prescriptions and apply it for the treatment of coronary artery disease. Part II also presents predictive models that could support the early diagnosis and improve the management of stroke patients. Finally, Part III proposes a novel risk evaluation methodology that will enable healthcare institutions to manage the risk exposure resulting from the implementation of analytical decision tools. Ph.D. 2022-01-14T15:14:40Z 2022-01-14T15:14:40Z 2021-06 2021-07-01T00:43:24.174Z Thesis https://hdl.handle.net/1721.1/139482 https://orcid.org/0000-0003-3737-4826 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Orfanoudaki, Agni Novel Machine Learning Algorithms for Personalized Medicine and Insurance |
| title | Novel Machine Learning Algorithms for Personalized Medicine and Insurance |
| title_full | Novel Machine Learning Algorithms for Personalized Medicine and Insurance |
| title_fullStr | Novel Machine Learning Algorithms for Personalized Medicine and Insurance |
| title_full_unstemmed | Novel Machine Learning Algorithms for Personalized Medicine and Insurance |
| title_short | Novel Machine Learning Algorithms for Personalized Medicine and Insurance |
| title_sort | novel machine learning algorithms for personalized medicine and insurance |
| url | https://hdl.handle.net/1721.1/139482 https://orcid.org/0000-0003-3737-4826 |
| work_keys_str_mv | AT orfanoudakiagni novelmachinelearningalgorithmsforpersonalizedmedicineandinsurance |