Human computer interaction to learn scenarios from icu multivariate time series
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Human/Computer Interaction to Learn Scenarios from ICU Multivariate Time Series. Guyet T. (1) , Garbay C. (1) and Dojat M. (2). (1) TIMC Laboratory , SIC Team Domaine de la Merci 38706 La Tronche, France. (2) INSERM/UJF U594 CHU Pavillon B - BP 317 - 38043 Grenoble, France.

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Human/Computer Interaction to Learn Scenarios from ICU Multivariate Time Series

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Human computer interaction to learn scenarios from icu multivariate time series

Human/Computer Interaction to Learn Scenarios from ICU Multivariate Time Series

Guyet T.(1), Garbay C.(1) and Dojat M.(2)

(1) TIMC Laboratory, SIC Team

Domaine de la Merci 38706 La Tronche, France

(2) INSERM/UJF U594

CHU Pavillon B - BP 317 - 38043 Grenoble, France

[email protected] and http://www-timc.imag.fr/Thomas.Guyet/


Icu data analysis issue

ICU data analysis issue

Learning abstract scenarios from raw data (ICU)

  • Clinician have difficulties to formalize useful knowledge

    • Lack of knowledge

  • Exploration of data is difficult

    • To point out relevant information

    • To define events signatures

    • To find relations between events (causal, temporal)


Workbench approach

Workbench approach

The computerized system and the clinician interact to deeply explore data

  • Autonomy:

    • Each partner reasons using its own world of knowledge/concepts.

  • Interaction:

    • Mutual understanding: example driven interaction ensures the equivalence between clinician concepts and system concepts (Rooting principle)

    • Progressively builds scenarios


Multi agent system

Multi Agent System

3 abstraction levels = 3 types of agents:

  • Segmentation Agents

    • Reactive agents

    • Each agent represents a segment

    • Interaction between agents places the frontiers

  • Classification Agents

    • 1 agent for each type of time series

    • Build classes of segments (relevant events)

  • Learning Agents

    • 1 agent to explain occurrences of a segment class

    • Build scenarios (relation identification between relevant events)

Segmentation Example

C

D

A

B

Hierarchical Classification of Segments

Resultant Symbolic Time Series


Preliminary results

Preliminary Results

  • System experimentation on real data

    • 4 physiological signals (SpO2, Heart rate, Total expired volume, Respiratory rate)

    • 3 preprocessing methods (sliding window means, trends, stability symbolic values)

    • SpO2 annotations

  • MAS is partially implemented

    • First experiments to validate the system approach

    • Test of segmentation, classification and learning methods: should be improved

  • Interaction with clinicians should be implemented


Poster

Poster


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