Major Research Products
Information retrieval from electronic health records using dynamic and multi-collaborative filtering
Xia Ning, Ziwei Fan, Evan Burgun, Zhiyun Ren, and Titus Schleyer. Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering. Plos One, April 2021. minor revision.
Abstract: Due to the rapid growth of information available about individual patients, most physicians suffer from information overload and inefficiencies when they review patient information in health information technology systems. In this paper, we present a novel hybrid dynamic and multi-collaborative filtering method to improve information retrieval from electronic health records. This method recommends relevant information from electronic health records to physicians during patient visits. It models information search dynamics using a Markov model. It also leverages the key idea of collaborative filtering, originating from Recommender Systems, for prioritizing information based on various similarities among physicians, patients and information items. We tested this new method using electronic health record data from the Indiana Network for Patient Care, a large, inter-organizational clinical data repository maintained by the Indiana Health Information Exchange. Our experimental results demonstrated that, for top-5 recommendations, our method was able to correctly predict the information in which physicians were interested in 46.7% of all test cases. For top-1 recommendations, the corresponding figure was 24.7%. In addition, the new method was 22.3% better than the conventional Markov model for top-1 recommendations.
Abstract: With increasing and extensive use of electronic health records (EHR), clinicians are often challenged in retrieving relevant patient information efficiently and effectively to arrive at a diagnosis. While using the search function built into an EHR can be more useful than browsing in a voluminous patient record, it is cumbersome and repetitive to search for the same or similar information on similar patients. To address this challenge, there is a critical need to build effective recommender systems that can recommend search terms to clinicians accurately. In this study, we developed a hybrid collaborative filtering model to recommend search terms for a specific patient to a clinician. The model draws on information from patients’ clinical encounters and the searches that were performed during them. To generate recommendations, the model uses search terms which are (1) frequently co-occurring with the ICD codes recorded for the patient and (2) highly relevant to the most recent search terms. In one variation of the model (Hybrid Collaborative Filtering Method for Healthcare, or HCFMH), we use only the most recent ICD codes assigned to the patient, and in the other (Co-occurrence Pattern based HCFMH, or cpHCFMH), all ICD codes. We have conducted comprehensive experiments to evaluate the proposed model. These experiments demonstrate that our model outperforms state-of-the-art baseline methods for top-N search term recommendation on different data sets.