Background: Rational use of internal resources of hospitals including bed fund turnover is important objective in high-tech medicine. Machine learning technologies can improve neurosurgical care and contribute to patient-oriented approach.
Objective: To evaluate the quality of AI-guided predicting the length of hospital-stay in a neurosurgical hospital based on the text data of electronic medical records in comparison with expectations of patients and physicians.
Material And Methods: AI-guided prediction was based on analysis of unstructured text records of the electronic medical history (preoperative examination and surgical protocol). Predictive models were learned on the data of the Burdenko Neurosurgery Center accumulated for the period 2000-2017 (90.688 cases). Model testing was performed on 111 completed neurosurgical cases in a prospective study. We compared the accuracy of prediction models compared to expectations of patients and physicians regarding hospital-stay.
Results: The median absolute error of machine prediction in the final test was 2.00 days. This value was comparable with the doctor's prediction error.
Conclusion: This study demonstrated the possibility of using unstructured textual data to predict the length of hospital-stay in a neurosurgical hospital.
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| http://dx.doi.org/10.17116/neiro20228606143 | DOI Listing |
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