Abstract
Due to an ever-increasing amount of data generated
in healthcare each day, healthcare professionals are
more and more challenged with information. Predictive
models based on machine learning algorithms can help
to quickly identify patterns in clinical data. Requirements
for data driven decision support systems for health and
care (DS4H) are similar in many ways to applications in
other domains. However, there are also various challenges
which are specific to health and care settings. The present
paper describes a) healthcare specific requirements for
DS4H and b) how they were addressed in our Predictive
Analytics Toolset for Health and care (PATH). PATH
supports the following process: objective definition, data
cleaning and pre-processing, feature engineering, evaluation,
result visualization, interpretation and validation
and deployment. The current state of the toolset already
allows the user to switch between the various involved
levels, i. e. raw data (ECG), pre-processed data (averaged
heartbeat), extracted features (QT time), built models (to
classify the ECG into a certain rhythm abnormality class)
and outcome evaluation (e. g. a false positive case) and to
assess the relevance of a given feature in the currently evaluated
model as a whole and for the individual decision.
This allows us to gain insights as a basis for improvements
in the various steps from raw data to decisions.
Originalsprache | Englisch |
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Seiten (von - bis) | 183-194 |
Seitenumfang | 12 |
Fachzeitschrift | it - Information Technology (Verlag De Gruyter, Oldenburg) |
Volume | 60 |
Issue | 4 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2018 |
Research Field
- Exploration of Digital Health
Schlagwörter
- Clinical decision support
- Machine learning
- Predictive modelling
- Feature engineering