JMIR Publications

ABSTRACT

Background:

It is especially necessary to pay attention to the critically ill patients in ICU(Intensive Care Units) real time. Scoring systems are mostly used in the risk prediction of mortality, while usually they are not so precise and real-time with the clinical data simply weighted, and it is also time-consuming for clinical staff.

Objective:

We would like to fuse all the medical data together and predict the real-time mortality of ICU patients by machine learning method, which would be valuable and significant. Besides, we want to explore predicting the mortality by noninvasive data to lessen the pain of patients.

Methods:

In this paper, we established 5 models to predict mortality real-time based on different features. Based on monitoring data, examination data and scoring data, we structured the feature engineering. 5 Real-time Mortality prediction models were RMM(Monitoring features), RMA(APACHE and monitoring features), RMS(SOFA and monitoring features), RMME(Monitoring and Examination features) and RM(all features from monitoring, examination data and scoring data). Then, we compared the performance of all models and put more focus on the noninvasive method RMM.

Results:

After extensive experiments, the performance of RMME was superior to that of other 4 models. With the scoring features included, the model showed worse performance. And, RMM only based on monitoring features performed better than that of RMA and RMS. Therefore, it is meaningful and practicable to predict mortality by the noninvasive way, which could reduce the extra physical damage to patients like drawing blood. Moreover, we explored the top 9 features relevant with the real-time mortality prediction. Top 9 features were "ABP (mmHg) invasive mean pressure", "Heart rate", "ABP (mmHg) invasive systolic pressure", "Oxygen concentration", "SPO2", "Balance of inflow and outflow", "Total input", "ABP (mmHg) invasive diastolic pressure" and "NBP-average pressure", which could be paid more focus on during the general clinical work.

Conclusions:

This research could be helpful in real-time mortality prediction of ICU patients, especially by the noninvasive method. It is meaningful and friendly to patients, which is of strong practical significance.