Machine learning and decision support in critical care

Journal article

Clinical data management systems typically provide caregiver teams with useful information, derived from large, sometimes highly heterogeneous, data sources that are often changing dynamically. Over the last decade there has been a significant surge in interest in using these data sources, from simply reusing the standard clinical databases for event prediction or decision support, to including dynamic and patient-specific information into clinical monitoring and prediction problems. However, in most cases, commercial clinical databases have been designed to document clinical activity for reporting, liability, and billing reasons, rather than for developing new algorithms. With increasing excitement surrounding “secondary use of medical records” and “Big Data” analytics, it is important to understand the limitations of current databases and what needs to change in order to enter an era of “precision medicine.” This review article covers many of the issues involved in the collection and pre-processing of critical care data. The three challenges in critical care are considered: compartmentalization, corruption, and complexity. A range of applications addressing these issues are covered, including the modernization of static acuity scoring; online patient tracking; personalized prediction and risk assessment; artifact detection; state estimation; and incorporation of multimodal data sources such as genomic and free text data.

Authors: Johnson, A; Ghassemi, M; Nemati, S; Niehaus, K; Clifton, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Journal: Proceedings of the IEEE
• Volume: 104
• Issue: 2
• Pages: 444-466
• Publication date: 2016-01-25
• Acceptance date: 2015-11-16
• DOI: 10.1109/JPROC.2015.2501978
• ISSN: 0018-9219
• Keywords: signal processing; critical care; machine learning; feature extraction