Machine learning in healthcare : an investigation into model stability

Current machine learning algorithms, when directly applied to medical data, often fail to provide a good understanding of prognosis. This study provides three pathways to make predictive models stable and usable for healthcare. When tested on heart failure and diabetes patients from a local hospital, this study demonstrated 20% improvement over existing methods.<br /

Personal Health Technology: CPN based Modeling of Coordinated Neighborhood Care Environments (Hubs) and Personal Care Device Ecosystems

Healthcare supported by mobile devices, or “mHealth,” has rapidly emerged as a very broad ecosystem that can empower safer, more affordable, and more comfortable independent living environments and assist residents to age in place with a variety of well-understood chronic diseases. mHealth ecosystems leverage every available type of regulated medical and consumer-grade Patient Care Devices (or PCDs). mHealth technologies can also support innovative care and reimbursement models like the Patient-Centered Medical Home (PCMH) and Accountable Care Organizations (ACOs). Although consumer-grade PCDs are becoming ubiquitous, they typically do not provide a large variety of integrated system options for care coordination beyond single individuals. Understanding how to safely implement and use those devices to support heterogeneous mixes of patients, illnesses, devices, medications, and situations in neighborhood contexts is still a case-by-case challenge. By utilizing a well-formalized Colored Petri Nets (CPNs) based approach, this paper provides a proof-of-concept simulation framework for modeling and designing coordinated community care hubs

Early detection of medical deterioration of patients with diabetes by using machine learning

Diabetes is a growing healthcare problem in the world, which affects over 400 million adults. In collaboration with Haukeland University Hospital, we look at medical records from real patients diagnosed with diabetes. The study uses machine learning to predict if a given patient has a high risk of experiencing medical deterioration. Further, the thesis goes through the data cleaning necessary to provide such predictions. The first approach managed to identify 79\% of high-risk patients. If the model classifies a patient to have a high risk of mortality, the model had an accuracy of 18\%. In the second approach, we removed the last four weeks before mortality happens, and the model was able to identify 49\% of the patients with high risk. If the model classifies a patient to have a high risk of mortality, the model had an accuracy of 12\%.Masteroppgave i Programutvikling samarbeid med HVLPROG399MAMN-PRO