A temporal abstraction framework for classifying clinical temporal data.

Iyad Batal, Lucia Sacchi, Riccardo Bellazzi, Milos Hauskrecht

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)29-33
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Publication statusPublished - 2009

ASJC Scopus subject areas

  • Medicine(all)


Dive into the research topics of 'A temporal abstraction framework for classifying clinical temporal data.'. Together they form a unique fingerprint.

Cite this