As another Clinical Informatics “window into the future” – a window into the future.
These authors used three years of electronic health record data to derive a predictive Bayesian network for patient status. Its scope: home, hospitalized, or dead. There are many simple models for predicting such things, but this one is interesting because it attempts to utilize multiple patient features, vital signs, and laboratory results in a continuously updating algorithm. Ultimately, their model was capable of predicting outcomes up through one week from the initial hospitalization event.
Some fun tidbits:
- What mattered most on Day 1? Neutrophils, Hct, and Lactate.
- As time goes by, the network thinks knowing whether you’re on the Ward at Day 3 is prognostic.
- By Day 5, variables like a simple count of the total number of tests received, the presence of cancer, and albumin levels start to gain importance.
Their Bayesian prediction network was best at predicting death, with an average accuracy of 93% and an AUROC of 0.84. Similarly, the prediction engine was most accurate on Day 1, with an average accuracy for each outcome of 86% and an AUROC of 0.83. Overall, for the entire week and all three outcomes, the AUROC was 0.82.
What was also quite interesting was the model, while also predicting outcomes during the index hospitalization, also detected readmission events within the time period scope. The authors provide a few validation examples as demonstrations, and include a patient whose probability of hospitalization was trending upwards at the time of discharge – and subsequently was readmitted.
Minority Report, medicine style.
“Real-time prediction of mortality, readmission, and length of stay using electronic health record data”
http://www.ncbi.nlm.nih.gov/pubmed/26374704