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At IBM Research, we are using deep learning to explore new solutions for a range of health care challenges. One such challenge is emergency room (ER) overcrowding, which can lead to long wait times for treatment. Overcrowding results in part from people visiting the ER for non-emergency conditions rather than relying on primary physicians. Patients who use the ER for non-emergency situations are more likely to return to the ER multiple times (Poole et al. 2016), further contributing to overcrowding. Identifying those patients who are likely to return to the ER may enable hospitals to intervene to ensure access to necessary care outside the ER and potentially alleviate overcrowding.
A neural network model
My team at IBM Research-China took on this challenge. We developed a novel deep learning model to predict how many times a person will visit the ER based on information from his or her electronic health records (EHRs). The model is based on a typical recurrent neural network, but unlike traditional machine learning methods, it exhibits dynamic temporal behavior based on EHR information and has a complex structure to better model the correlation between ER visits and other patient data (Figure 1). We used the model to make precise predictions of whether and how many times a person will visit the ER and found that it outperformed other common techniques. For example, precision of our model was 6.59 percent greater than a typical logistic regression model in predicting whether a person will visit the ER and >90 percent greater in predicting number of ER visits compared with linear regression model. Our model also had approximately 2 percent greater precision than the popular XGboost model in predicting number of ER visits.
By better predicting how many times a person will visit the ER, we hope that this deep learning strategy might enable hospitals to establish, prioritize, and target interventions to ensure that patients have access to the care they require outside an ER setting.
Figure 1. Proposed Model
Sharing our work
These results along with five other papers from the IBM Research team in China have been accepted by Medical Informatics Europe 2018, a premier medical informatics conference taking place this week in Gothenberg, Sweden. The other papers involve analysis of real-world evidence on treatment-subgroup interactions, detection of anomalies in the utilization of medical supplies, use of deep learning and other machine learning technologies to answer questions from patients, and prediction of in-hospital major adverse cardiac events using a generalized linear model. Details of all six accepted papers are listed below. Our collaborators on these projects represent top hospitals (Fuwai Hospital and Anzhen Hospitals) and top pharmaceutical companies (Pfizer). By working with the best partners with the best data on the most challenging real-world problems, we can generate world-class research results in China.
Find more information about what’s happening in our China Research Lab
IBM Research-China accepted papers at MIE 2018
Title: Using Machine Learning Approaches for Emergency Room Visit Prediction based on Electronic Health Record Data
Authors: Zhi QIAO, Ning SUN, Xiang LI, Eryu XIA, Shiwan ZHAO, Yong QIN
Title: Using Model-Based Recursive Partitioning for Treatment-Subgroup Interactions Detection in Real-World Data: A Myocardial Infarction Case Study
Authors: Tiange Chen, Xiang Li, Jingang Yang, Jingyi Hu, Meilin Xu, Yong Qin, Yuejin Yang
Title: Clinical Similarity based Framework for Hospital Medical Supplies Utilization Anomaly Detection: A Case Study
Authors: Ning SUN, Meilin XU, Mingzhi CAI, Xudong MA, Yong QIN
Title: Detecting and Discretizing Nonlinear Continuous Predictors for Generalized Linear Model – The Acute Coronary Syndromes Case Study
Authors: Yingxue Li, Tiange Chen, Xiang Li, Xin Du, Yuan Ni, Zhiqing Kang, Jian Li, Jian Sheng, Jing Sun, Qing Zhao, Yong Qin
Title: A System for Patient Educational Question and Answering in Chinese
Authors: Keqiang Wang, Guoyu Tang, Yuan Ni, Xinli Fan, Liling Wang, Yanling Shi, Chengming Gu, Yong Qin
Title: Fine-Tuning Neural Patient Question Retrieval Model with Generative Adversarial Networks
Authors: Guoyu Tang, Yuan Ni, Keqiang Wang, Yong Qin