Machine learning and algorithms applied to ethnographic and biomedical cancer dataCase studies from Ireland, Finland and Spain

Resumen

Technology has seen an increased presence in the healthcare field for many years now. The last decade especially has seen a boom due to the progress of machine learning techniques and algorithms as well as the digitalization of healthcare records. These records are of different formats, such as text data, images, video, etc. and each requires specific ways to preprocess and analyze it. This thesis tackles important health issues faced in our society through ethnographic and biomedical data analysis using statistical analysis, machine learning and deep learning. The thesis is comprised of three case studies conducted in Ireland, Finland, and Spain, and each follows a different methodology and analysis approach. The first study deals with care pathways, their implementation in the last 20 years around the world, and the Beacon Hospital study. Understanding what factors influence care pathways allow a more person-centered care approach and the redesign of care processes. Four main tasks have been achieved in this study: a literature review of cancer care pathway implementation, an ethnographic study with breast and prostate cancer patients at Beacon Hospital about their perspective on care pathways, creation of two datasets with information coming from electronic health records and one-on-one interviews, and an analysis of the data through statistical analysis to identify the factors influencing care pathways for these two cancer diseases in a hospital setting. The second study is about the use of electronic health records to predict cancer patient survivability employing various machine learning algorithms. A collaboration with a regional hospital in Finland helped to achieve this task. Two steps were taken to predict survivability. The first one was to select the most relevant variables through various feature selection algorithms, and the second one was to perform survival prediction using nine machine learning algorithms. The third and the last study is about colorectal polyps detection using deep learning to prevent colorectal cancer from forming or progressing. The tasks performed to complete it follow a comprehensive review of the published scientific research related to colorectal polyp detection, classification, segmentation, localization, and the implementation of combined convolutional neural networks and autoencoders model to detect colorectal polyps without image preprocessing. All three case studies are accepted for publication in high-impact journals; two are already published online, one is currently in press.