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Dr. Menger: Vincent's successful defense

posted Oct 3, 2019, 2:06 AM by Marco Spruit
Yesterday, on Oct 2, Vincent Menger quite successfully defended his dissertation "Knowledge Discovery in Clinical Psychiatry: Learning from electronic health records" in the Academiegebouw. This is another solid reference work for Utrecht University's Applied Data Science focus area research, especially related to Natural Language Processing applications and foundational research as envisioned by the Special Interest Group (SIG) Text Mining. Here are some key bits from this work which investigates the following overarching research question: "How can data from Electronic Health Records provide relevant insights for psychiatric care?"

In the first three research chapters of this work, he identifies key technical, organizational and ethical challenges related to knowledge discovery in EHRs. He introduces the CRISP-IDM process, where the I stands for Interactive, as a process model for collaboration based on data visualization. He introduces the Capable Reuse of EHR Data (CARED) framework, aiming to support health care institutions to design such infrastructure. He develops and validates the De-identification Method for Dutch Medical Text (DEDUCE), which aims to automatically remove information that can identify a patient from free text.

In the second part of this research, Vincent focuses on applying knowledge discovery techniques to EHR data to obtain new insights with potential to improve care. First he looks at violence risk assessment, by using two clinical datasets to train models that can assess violence risk based on clinical text, and then perform a rigorous evaluation of their accuracy and generalizability. Finally, he turns to identifying psychiatric patient subgroups, and investigate how unsupervised learning can find robust and accurate stratifications of patients using cluster ensembles.

The two parts of this dissertation combined show that learning from EHRs, after addressing key challenges related to the nature of data, is a new and interesting approach with clear potential for improving psychiatric health care.