Machine learning for early prediction of circulatory failure in the intensive care unit

by   Stephanie L. Hyland, et al.

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient years of data. This automatic system predicts 90.0 events (prevalence 3.1 resulting in an area under the receiver operating characteristic curve of 94.0 and area under the precision-recall curve of 63.0 validated in a large independent patient cohort.



There are no comments yet.



Early prediction of respiratory failure in the intensive care unit

The development of respiratory failure is common among patients in inten...

Predicting Antimicrobial Resistance in the Intensive Care Unit

Antimicrobial resistance (AMR) is a risk for patients and a burden for t...

A Machine Learning Early Warning System: Multicenter Validation in Brazilian Hospitals

Early recognition of clinical deterioration is one of the main steps for...

AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units

Clinical practice in intensive care units (ICUs) requires early warnings...

Between a ROC and a Hard Place: Using prevalence plots to understand the likely real world performance of biomarkers in the clinic

The Receiver Operating Characteristic (ROC) curve and the Area Under the...

Mind the Performance Gap: Examining Dataset Shift During Prospective Validation

Once integrated into clinical care, patient risk stratification models m...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.