A temporal abstraction framework for classifying clinical temporal data.
The increasing availability of complex temporal clinical records collected today has prompted the development of new methods that extend classical machine learning and data mining approaches to time series data. In this work, we develop a new framework for classifying the patient's time-series data based on temporal abstractions. The proposed STF-Mine algorithm automatically mines discriminative temporal abstraction patterns from the data and uses them to learn a classification model. We apply our approach to predict HPF4 test orders from electronic patient health records. This test is often prescribed when the patient is at the risk of Heparin induced thrombocytopenia (HIT). Our results demonstrate the benefit of our approach in learning accurate time series classifiers, a key step in the development of intelligent clinical monitoring systems.
Batal
Iyad
I
Department of Computer Science, University of Pittsburgh, PA, USA.
Sacchi
Lucia
L
Bellazzi
Riccardo
R
Hauskrecht
Milos
M
D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records / Q000145:classification*; D006493:Heparin / Q000009:adverse effects; D006801:Humans; D051741:Kruppel-Like Transcription Factors / Q000097:blood; D012097:Repressor Proteins / Q000097:blood; D013921:Thrombocytopenia / Q000139:chemically induced / Q000175:diagnosis; D013995:Time
D051741:Kruppel-Like Transcription Factors; D012097:Repressor Proteins; C115768:ZNF85 protein, human; D006493:Heparin