University of Pittsburgh

Predicting Falls in the Nursing Home - A Comparison of a Long Short Term Memory (LSTM) Recurrent Neural Network with Other Methods

TBA
Date: 
Friday, September 13, 2019 - 12:30pm - 1:30pm

Abstract: Fall events are one of the most common and dangerous adverse events that occur in the nursing home (NH) setting. The mean incidence of falls is estimated to be 1.7 falls per bed per year, with 10 - 25% resulting in fracture or laceration. A validated model that uses NH data to predict the probability of a NH patient falling in the near future would be a valuable component of an overall safety monitoring system that provides clinicians with informative, patient specific, and actionable alerts. 
The purpose of this study was to develop and validate such a model using patient-specific factors recorded in electronic data available in most of the 15,000 nursing homes in the United States. Specifically, data from drug dispensing and from Long-Term Care Minimum Data Set assessments. Our goal was to develop a model that uses these data to accurately predict the probability of a given patient experiencing a fall up to three months after qualified staff complete a Minimum Data Set assessment for a given patient. In this study we compared a variety of machine learning methods including logistic regression, support vector machines, tree algorithms, and Long Short Term Memory (LSTM) Recurrent Neural Networks. Our hypothesis was that a LSTM model would significantly outperform a broad range of other methods because LSTM networks can to model complex relationships that unfold over time.

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