Optimization of patient throughput and wait time in emergency departments (ED) is an important task for hospital systems. For that reason, Emergency Severity Index (ESI) system for patient triage was introduced to help guide manual estimation of acuity levels, which is used by nurses to rank the patients and organize hospital resources. However, despite improvements that it brought to managing medical resources, such triage system greatly depends on nurse's subjective judgment and is thus prone to human errors. Here, we propose a novel deep model based on the word attention mechanism designed for predicting a number of resources an ED patient would need. Our approach incorporates routinely available continuous and nominal (structured) data with medical text (unstructured) data, including patient's chief complaint, past medical history, medication list, and nurse assessment collected for 338,500 ED visits over three years in a large urban hospital. Using both structured and unstructured data, the proposed approach achieves the AUC of @math for the task of identifying resource intensive patients (binary classification), and the accuracy of @math for predicting exact category of number of resources (multi-class classification task), giving an estimated lift over nurses' performance by 16 in accuracy. Furthermore, the attention mechanism of the proposed model provides interpretability by assigning attention scores for nurses' notes which is crucial for decision making and implementation of such approaches in the real systems working on human health.
To automatically learn features from text data @cite_23 proposed to use the bi-RNN algorithm for detection of medical events based on texts from medical records. In their experiments it was shown that bi-RNN significantly outperforms existing tools for medical text analysis. Approaches for automatic feature extraction are more convenient, do not require handcrafted features and have shown to yield representations of highest quality. Our approach builds on the results of @cite_23 as we use the bi-RNN as one of the building blocks of our model which aims to improve waiting room patient disposition.
@cite_23
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Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs. It has important applications in health informatics including pharmacovigilance and drug surveillance. The state of the art supervised machine learning models in this domain are based on Conditional Random Fields (CRFs) with features calculated from fixed context windows. In this application, we explored recurrent neural network frameworks and show that they significantly outperformed the CRF models.