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Machine Learning and Decision Support in Critical Care.

Alistair E W Johnson1, Mohammad M Ghassemi1, Shamim Nemati2

  • 1Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Boston, USA.

Proceedings of the IEEE. Institute of Electrical and Electronics Engineers
|October 22, 2016
PubMed
Summary
This summary is machine-generated.

This review discusses challenges in using clinical data for precision medicine. It covers data issues like compartmentalization, corruption, and complexity, proposing solutions for better patient care and prediction.

Keywords:
Critical carefeature extractionmachine learningsignal processing

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Area of Science:

  • Biomedical Informatics
  • Health Data Science
  • Critical Care Medicine

Background:

  • Clinical data management systems primarily serve documentation, not algorithmic development.
  • Growing interest in secondary use of medical records and Big Data analytics for healthcare.
  • Current limitations in clinical databases hinder the transition to precision medicine.

Purpose of the Study:

  • To review challenges in collecting and preprocessing critical care data.
  • To explore applications for improving clinical data utilization.
  • To bridge the gap between current data practices and precision medicine.

Main Methods:

  • Analysis of data compartmentalization, corruption, and complexity in critical care.
  • Review of applications for data modernization and patient tracking.
  • Exploration of incorporating multimodal data sources (genomic, free text).

Main Results:

  • Identified key challenges: compartmentalization, corruption, and complexity of critical care data.
  • Highlighted applications like acuity scoring modernization, online patient tracking, and personalized risk assessment.
  • Emphasized the need for improved data preprocessing for advanced analytics.

Conclusions:

  • Addressing data challenges is crucial for advancing precision medicine.
  • Modernizing data collection and preprocessing enables enhanced clinical decision support.
  • Integrating diverse data sources will improve patient monitoring and predictive capabilities.