Name
Title | ||
|---|---|---|
Risk Prediction Of Delirium In Hospitalized Patients Using Machine Learning: An Implementation And Prospective Evaluation Study |
Abstract | ||
|---|---|---|
Objective: Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest-based algorithm to identify hospitalized patients at high risk for delirium, and evaluated its performance in a clinical setting.Materials and Methods: Delirium was predicted at admission and recalculated on the evening of admission. The defined prediction outcome was a delirium coded for the recent hospital stay. During 7 months of prospective evaluation, 5530 predictions were analyzed. In addition, 119 predictions for internal medicine patients were compared with ratings of clinical experts in a blinded and nonblinded setting.Results: During clinical application, the algorithm achieved a sensitivity of 74.1% and a specificity of 82.2%. Discrimination on prospective data (area under the receiver-operating characteristic curve = 0.86) was as good as in the test dataset, but calibration was poor. The predictions correlated strongly with delirium risk perceived by experts in the blinded (r = 0.81) and nonblinded ( r = 0.62) settings. A major advantage of our setting was the timely prediction without additional data entry.Discussion: The implemented machine learning algorithm achieved a stable performance predicting delirium in high agreement with expert ratings, but improvement of calibration is needed. Future research should evaluate the acceptance of implemented machine learning algorithms by health professionals.Conclusions: Our study provides new insights into the implementation process of a machine learning algorithm into a clinical workflow and demonstrates its predictive power for delirium. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1093/jamia/ocaa113 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Keywords | DocType | Volume |
Machine learning, prospective studies, delirium, electronic health records, clinical decision support | Journal | 27 |
Issue | ISSN | Citations |
9 | 1067-5027 | 1 |
PageRank | References | Authors |
0.39 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefanie Jauk | 1 | 1 | 0.72 |
| Diether Kramer | 2 | 1 | 2.41 |
| Birgit Großauer | 3 | 1 | 0.39 |
| Susanne Rienmüller | 4 | 1 | 0.39 |
| Alexander Avian | 5 | 1 | 1.06 |
| Andrea Berghold | 6 | 1 | 1.06 |
| W Leodolter | 7 | 7 | 4.64 |
| Stefan Schulz | 8 | 1092 | 127.03 |