BPI cluster meeting 30Oct’18

This week, October 30, 12:30-13:30h, in Pav. K16, we have our cluster meeting. Paulo will give a talk on “Remaining Useful Lifetime (RUL) prediction across different domains”. Below you can find the abstract of the talk.

Remaining Useful Lifetime (RUL) prediction across different domains:

X-ray tubes are critical components in scanners used for radiography procedures. Tubes are composed of tungsten filaments which are heated up to produce x-rays images. However, such filaments are subject to wear-out given intensive usage. Such behaviour can lead to an early failure of the filaments causing disruptions and incurring high maintenance costs. Currently, there are two types of scanner generators in operation; the newest generator can log enough sensor information for the construction of a Physics Model capable of estimating the RUL. However, older versions do not store crucial sensor data to enable a prediction. Inspired by this use case, we evaluate a transfer learning method to predict the RUL for an equipment when there is a shift in the input distribution among different data sources. We test the method on a benchmark data with different failure modes and unlogged RUL information.


BPI cluster meeting 16Oct’18

This week  Daniel Kapitan from Mediquest company will give a talk during our BPI cluster meeting. Timing is between 12:30-13:15 on 16th October 2018 in  room Pav. K.16.

The topic of the talk will be related to Healthcare and the title is

Project Nightingale: Machine learning as a catalyst for outcome-oriented care

Project Nightingale applies machine learning within the context of value-based healthcare. Given the need for better insight into outcomes, and the potential of predictive analytics for clinical decision support, results from pilot projects are presented, where:

* Compounded outcome measures relevant for shared-decision making are defined, using existing data dictionairies (ICHOM)

* Imbalanced learning is applied to identify high-risk patients prior to an intervention

* Results of machine learning are related to existing epidemiological research

Ultimately, the aim is to promote the use and understanding of machine learning in outcome-oriented healthcare.