Machine learning applications in critical care
Saudi Crit Care J.
Mohammed Al Dhoayan1, Huda Alghamdi2, Yaseen M Arabi3
1 Department of Health Informatics, CPHHI, King Saud Bin Abdulaziz University for Health Sciences; Data and Business Intelligence Management Department, ISID, King Abdulaziz Medical City, Riyadh, Saudi Arabia
2 Data and Business Intelligence Management Department, ISID, King Abdulaziz Medical City, Riyadh, Saudi Arabia
3 College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center; Department of Intensive Care, King Abdulaziz Medical City, Riyadh, Saudi Arabia
Year of Publication:
The use of machine learning (ML) applications in the intensive care units (ICUs) has surged over the last two decades. This is the result of the digital transformation that many health-care organizations have implemented. Data that are generated in the process of intensive care have more volume, velocity, and value than data generated in any other general hospital’s department. This characteristic of ICUs makes them attractive environments for developing models that require rich dataset. ML has been used to develop clinical decision support system (CDSS) that could make informative decisions without requiring prior in-depth knowledge about the roots of the disease or common characteristics of the patients. The adoption of ML-based CDSS in ICUs is continuously increasing as ML algorithms achieve high levels of accuracy in descriptive, diagnostic, predictive, and prescriptive decisions. This article reviews some of the applications of ML in ICUs. This article will show examples of how ML was used for outcome predictions, such as predicting mortality and readmission. Examples in this article also include using ML for diagnostic and image recognition purposes. This review will discuss the use of ML for monitoring ICU patients, whether monitoring their physical safety with artificial intelligence vision detection algorithms, monitoring their continuous bedside measurements, or monitoring the administration and dosage of their medications. All these examples show that ML-based CDSS are on the path for a journey full of innovative and creative solutions that will increase the quality, efficiency, and effectiveness of critical care.