Smart alarms in a hospital setting
Data in various domains is two-dimensional: there are two data types and the values of interest
September 1, 2020
9:19 am
DOCTORAL RESEARCH PROJECT
Michela Venturini
This PhD project consists of two main parts. First, the candidate will carry out fundamental research on the intersection between interaction learning and survival analysis. In particular, interaction learning – also called pairwise learning or network mining – is situated in the machine learning domain. It deals with predicting or clustering interactions between two sets of objects (e.g. users and products). Survival analysis is rooted in statistics and deals with predicting the time until an event occurs. Recently survival analysis has entered the machine learning field. The research group has acquired expertise in both domains, however the combination of interaction learning and survival analysis is novel. The candidate will develop new algorithms for prediction and clustering in this context. Second, the developed algorithms will be applied in the context of developing smart alarms in an intensive care unit. Given the current overload of alarms, the candidate will contribute to reduce alarm fatigue in clinical staff and alarm anxiety are sleeping disturbances in patients. For this purpose, the candidate will work on real data from and in close collaboration with the local hospital. Besides doing research, the candidate is expected to participate in educational activities (participate in seminars, be involved in teaching, …).
- Faculty of Medicine
- Doctoral Programme in Biomedical Sciences (Leuven)
Machine Learning & AI
Michela Venturini
Contact information
Michela Venturini is a PHD student on the FWO project ‘smart alarms in a hospital setting’.
involved projects
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