Health Data Science special interest group
The SIG Health data science is one of the special interest groups linked to the Data Science Research Programme and SAILs, the university wide AI programme. Group leaders are Wessel Kraaij, Marco Spruit and Marta Fiocco.
- Wessel Kraaij
Health research, medical practice and consequently the whole population is increasingly affected by digitization, data science and AI. The possibilities for improving health outcomes on the individual, group and population level are vast, since more data becomes available and is increasingly being combined for improved risk detection, diagnosis, treatment and etiological research. Our group is concerned with analysing structured and unstructured data sources (real world data, routine care data, environmental data) for extracting new knowledge or prediction of health outcomes, by e.g. designing digital biomarkers and update /calibrate published models (the evidence base).
The group organizes monthly meetings (currently virtual) with a speaker or reading group. Membership is open for researchers from all faculties, but the group's focus is on method development for data science challenges in the health science domain.
29-7 2021, 13.15: webinar with Dr. Justin Dauwels (TU Delft)
Speaker: Dr. Justin Dauwels (TU Delft)
Title: Machine Learning Methods to Predict Symptoms of Schizophrenia and Depression Patients from Behavioral Cues
Can automated analysis of audio-visual signals predict the severity of negative, cognitive, and general psychiatric symptoms of schizophrenia and depression and differentiate patients from healthy controls? In this observational study, we extracted a comprehensive set of audio-visual behavioral cues from interview recordings of 103 schizophrenia and 50 depression patients, and 75 healthy participants. We developed machine learning models that, by leveraging these audio-visual behavioral cues, are able to detect overall and specific expression-related negative, cognitive, and general psychiatric symptoms at a balanced accuracy (BAC) of at least 75%, and to distinguish schizophrenia and depression patients from healthy subjects (BAC > 82%). These results suggest that machine learning models leveraging audio-visual characteristics can help diagnose, assess, and monitor schizophrenia and depression patients with negative, cognitive, and general psychiatric symptoms.
24-6 2021, 16:00: webinar Pablo Mosteiro UU
Speaker: Pablo Mosteiro (UU)
Title: Towards improving psychiatric treatment with natural language processing
Abstract: In this talk, I will use the problem of assessing violence risk in an inpatient psychiatric institution to outline the challenges associated with using natural language processing to improve psychiatric treatment. I will then mention some of the strategies currently being used to tackle those challenges, and the work currently being done to implement those strategies.
15-4 2021, 16:00: webinar Saskia Koldijk UMCU : Study with Empatica wearables
Detecting physiological arousal in children using a wearable (Saskia Koldijk, UMC Utrecht, PsyData)
Aggression is one of the main causes of psychiatric admission, and manifests itself in different disorders. Coping with aggression is of importance for children themselves, as well as for staff. We aim to deploy wearables in clinical practice to support emotion regulation. In our research we asked 25 children from the psychiatry ward to wear an Empatica wearable for 5 days. Observations of behavior, especially aggressive incidents were made. Currently we are analyzing the relation between measured physiology over time and observed aggression. We consider to use multilevel modeling, but are also interested in discussing alternative analysis approaches with the SIG members.