A method and system for predicting human fall risk based on electronic nursing text data

Abstract

The present invention includes techniques for data processing and particularly relates to a method and system for predicting human fall risk based on electronic nursing text (ENT) data. The method comprises the following steps: obtaining an ENT data set, pre-processing data in the ENT data set, and constructing a Morse Fall Scale (MFS) dictionary with the pre-processed data; extracting features from the ENT data of patients to be predicted with a natural language processing technology; analyzing the extracted text features by the MFS dictionary to obtain a data set of risk factors; training a decision tree algorithm by using the data set of risk factors to obtain a prediction result of the patient's fall risk; clustering and precisely nursing the patients according to the prediction result. The invention constructs the MFS dictionary through the electronic health records to obtain fall risk factors of the patients, and iteratively predicts their fall risks according to the risk factors, thereby improving the prediction efficiency.

Dependencies are inferred from the claim text.