Currently submitted to: JMIR Medical Informatics
Date Submitted: Nov 7, 2019
Open Peer Review Period: Nov 7, 2019 - Nov 14, 2019
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Predicting Inpatient Falls using Natural Language Processing of Nursing Records Obtained from Japanese Electronic Medical Records: A Case-Control Study
Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for healthcare organizations. However, existing methods for predicting falls are laborious and costly.
The objective of the study is to verify that hospital inpatient falls can be predicted through the analysis of a single input, that is, unstructured nursing records (NRs) obtained from Japanese electronic medical records (EMRs), using a natural language processing (NLP) algorithm and machine learning.
The NRs of 335 fallers and 408 non-fallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning dataset and test dataset. The former dataset was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from non-fallers to construct a model for predicting falls. Then the latter dataset was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis.
The prediction of falls using the test dataset was good and showed high accuracy, with the area under the ROC curve, sensitivity, specificity, and odds ratio of 0.834 + 0.005, 0.769 + 0.013, 0.785 + 0.020, and 12.27 + 1.11 (mean + standard deviation of five independent experiments), respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from NRs.
We successfully established that falls among hospital inpatients can be predicted by analyzing NRs using an NLP algorithm and machine learning. Hence, it may be possible to develop a fall risk monitoring system that analyzes NRs on a daily basis that alerts healthcare professionals when the fall risk of an inpatient is increased.
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