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Surg Infect (Larchmt). 2019 Oct;20(7):546-554. doi: 10.1089/sur.2019.150. Epub 2019 Aug 27.

Artificial Intelligence Methods for Surgical Site Infection: Impacts on Detection, Monitoring, and Decision Making.

Author information

1
Industrial and Systems Engineering, University of Washington, Seattle, Washington.
2
Center of Data Science and Information Quality, School of Management, Xi'an Jiantong University, Xi'an, China.
3
Department of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle, Washington.
4
Department of Surgery, Medical University of South Carolina, Charleston, South Carolina.
5
Department of Computer Science and Engineering, Texas A&M University, College Station, Texas.
6
Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas.

Abstract

Background: There has been tremendous growth in the amount of new surgical site infection (SSI) data generated. Key challenges exist in understanding the data for robust clinical decision-support. Limitations of traditional methodologies to handle these data led to the emergence of artificial intelligence (AI). This article emphasizes the capabilities of AI to identify patterns of SSI data. Method: Artificial intelligence comprises various subfields that present potential solutions to identify patterns of SSI data. Discussions on opportunities, challenges, and limitations of applying these methods to derive accurate SSI prediction are provided. Results: Four main challenges in dealing with SSI data were defined: (1) complexities in using SSI data, (2) disease knowledge, (3) decision support, and (4) heterogeneity. The implications of some of the recent advances in AI methods to optimize clinical effectiveness were discussed. Conclusions: Artificial intelligence has the potential to provide insight in detecting and decision-support of SSI. As we turn SSI data into intelligence about the disease, we increase the possibility of improving surgical practice with the promise of a future optimized for the highest quality patient care.

KEYWORDS:

artificial intelligence; decision support; deep learning; natural language processing; surgical site infection

PMID:
31453753
DOI:
10.1089/sur.2019.150
[Indexed for MEDLINE]

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