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Back [PhD thesis] Machine learning to support exploring and exploiting real-world clinical longitudinal data

[PhD thesis] Machine learning to support exploring and exploiting real-world clinical longitudinal data

Author: Mariana Nogueira

Supervisors: Bart Bijnens, Gemma Piella Fenoy, Mathieu de Craene

Following-up on patient evolution by reacquiring the same measurements over time (longitudinal data) is a crucial component in clinical care dynamics, as it creates opportunity for timely decision making in preventing adverse outcome. It is thus important that clinicians have proper longitudinal analysis tools at their service. Nonetheless, most traditional longitudinal analysis tools have limited applicability if data are (1) not highly standardized or (2) very heterogeneous (e.g. images, signal, continuous and categorical variables) and/or high-dimensional. These limitations are extremely relevant, as both scenarios are prevalent in routine clinical practice. The aim of this thesis is the development of tools that facilitate the integration and interpretation of complex and nonstandardized longitudinal clinical data. Specifically, we explore approaches based on unsupervised dimensionality reduction, which allow the integration of complex longitudinal data and their representation as low-dimensional yet clinically interpretable trajectories. We showcase the potential of the proposed approach in the contexts of two specific clinical problems with different scopes and challenges: (1) nonstandardized stress echocardiography and (2) labour monitoring and decision making. In the first application, the proposed approach proved to help in the identification of normal and abnormal patterns in cardiac response to stress and in the understanding of the underlying pathophysiological mechanisms, in a context of nonstandardized longitudinal data collection involving heterogeneous data streams. In the second application, we showed how the proposed approach could be used as the central concept of a personalized labour monitoring and decision support system, outperforming the current reference labour monitoring and decision support tool. Overall, we believe that this thesis validates unsupervised dimensionality reduction as a promising approach to the analysis of complex and nonstandardized clinical longitudinal data.

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Thesis carried out in the context of the CardioFunXion Marie Curie Industrial Network coordinated by DTIC-UPF, with the participation of Philips France, and additionally supported by the MdM program