Sungrim Moon
Mayo Clinic
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Publication
Featured researches published by Sungrim Moon.
Journal of Biomedical Informatics | 2018
Yanshan Wang; Liwei Wang; Majid Rastegar-Mojarad; Sungrim Moon; Feichen Shen; Naveed Afzal; Sijia Liu; Yuqun Zeng; Saeed Mehrabi; Sunghwan Sohn; Hongfang Liu
BACKGROUND With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research. One critical component used to facilitate the secondary use of EHR data is the information extraction (IE) task, which automatically extracts and encodes clinical information from text. OBJECTIVES In this literature review, we present a review of recent published research on clinical information extraction (IE) applications. METHODS A literature search was conducted for articles published from January 2009 to September 2016 based on Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and ACM Digital Library. RESULTS A total of 1917 publications were identified for title and abstract screening. Of these publications, 263 articles were selected and discussed in this review in terms of publication venues and data sources, clinical IE tools, methods, and applications in the areas of disease- and drug-related studies, and clinical workflow optimizations. CONCLUSIONS Clinical IE has been used for a wide range of applications, however, there is a considerable gap between clinical studies using EHR data and studies using clinical IE. This study enabled us to gain a more concrete understanding of the gap and to provide potential solutions to bridge this gap.
BMC Medical Informatics and Decision Making | 2017
Yukun Chen; Thomas A. Lask; Qiaozhu Mei; Qingxia Chen; Sungrim Moon; Jingqi Wang; Ky Nguyen; Tolulola Dawodu; Trevor Cohen; Joshua C. Denny; Hua Xu
BackgroundActive learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models. However, very few studies have investigated AL in a real-life setting in medical domain.MethodsIn this study, we developed the first AL-enabled annotation system for clinical named entity recognition (NER) with a novel AL algorithm. Besides the simulation study to evaluate the novel AL algorithm, we further conducted user studies with two nurses using this system to assess the performance of AL in real world annotation processes for building clinical NER models.ResultsThe simulation results show that the novel AL algorithm outperformed traditional AL algorithm and random sampling. However, the user study tells a different story that AL methods did not always perform better than random sampling for different users.ConclusionsWe found that the increased information content of actively selected sentences is strongly offset by the increased time required to annotate them. Moreover, the annotation time was not considered in the querying algorithms. Our future work includes developing better AL algorithms with the estimation of annotation time and evaluating the system with larger number of users.
bioinformatics and biomedicine | 2017
Sungrim Moon; Sijia Liu; Paul Kingsbury; David Chen; Yanshan Wang; Feichen Shen; Rajeev Chaudhry; Hongfang Liu
One of the promises of “meaningful use” of Electronic Health Records (EHRs) is to facilitate digital information exchange between healthcare providers through continuity of care documents. Despite such promise, outside medical records (OMRs) of referral patients including clinical notes, lab test results or diagnostic test reports are frequently provided through fax or print out. Moreover, it is not clear how much information in those OMRs is utilized when providing care at the early stage. In this study, we collected clinical concepts automatically from OMRs through optical character recognition (OCR) technology and then performed a quantitative analysis of concepts presented in OMRs and concepts captured in clinical notes at Mayo Clinic. We also investigated information from OMRs not captured in initial consultant notes but presented over subsequent consultant notes. We identified 12.93% of concepts from OMRs were identified in clinical documents within three months. Among those overlapping concepts, 26.74% of them were not captured in initial consultant notes. Our study presents that clinical information from OMRs is important for patient care. Also, the delayed presence of information in clinical notes may indicate important information from OMRs is not fully utilized earlier in the care.
Journal of Biomedical Informatics | 2018
Stephen T. Wu; Sijia Liu; Sunghwan Sohn; Sungrim Moon; Chung-Il Wi; Young J. Juhn; Hongfang Liu
Sequences of events have often been modeled with computational techniques, but typical preprocessing steps and problem settings do not explicitly address the ramifications of timestamped events. Clinical data, such as is found in electronic health records (EHRs), typically comes with timestamp information. In this work, we define event sequences and their properties: synchronicity, evenness, and co-cardinality; we then show how asynchronous, uneven, and multi-cardinal problem settings can support explicit accountings of relative time. Our evaluation uses the temporally sensitive clinical use case of pediatric asthma, which is a chronic disease with symptoms (and lack thereof) evolving over time. We show several approaches to explicitly incorporating relative time into a recurrent neural network (RNN) model that improve the overall classification of patients into those with no asthma, those with persistent asthma, those in long-term remission, and those who have experienced relapse. We also compare and contrast these results with those in an inpatient intensive care setting.
Frontiers in Pharmacology | 2018
Liwei Wang; Majid Rastegar-Mojarad; Zhi Liang Ji; Sijia Liu; Ke Liu; Sungrim Moon; Feichen Shen; Yanshan Wang; Lixia Yao; John M. Davis; Hongfang Liu
Multiple data sources are preferred in adverse drug event (ADEs) surveillance owing to inadequacies of single source. However, analytic methods to monitor potential ADEs after prolonged drug exposure are still lacking. In this study we propose a method aiming to screen potential ADEs by combining FDA Adverse Event Reporting System (FAERS) and Electronic Medical Record (EMR). The proposed method uses natural language processing (NLP) techniques to extract treatment outcome information captured in unstructured text and adopts case-crossover design in EMR. Performances were evaluated using two ADE knowledge bases: Adverse Drug Reaction Classification System (ADReCS) and SIDER. We tested our method in ADE signal detection of conventional disease-modifying antirheumatic drugs (DMARDs) in rheumatoid arthritis patients. Findings showed that recall greatly increased when combining FAERS with EMR compared with FAERS alone and EMR alone, especially for flexible mapping strategy. Precision (FAERS + EMR) in detecting ADEs improved using ADReCS as gold standard compared with SIDER. In addition, signals detected from EMR have considerably overlapped with signals detected from FAERS or ADE knowledge bases, implying the importance of EMR for pharmacovigilance. ADE signals detected from EMR and/or FAERS but not in existing knowledge bases provide hypothesis for future study.
The Journal of Allergy and Clinical Immunology | 2018
Chung-Il Wi; So-Hyun Lee; Sungrim Moon; Hee Yun Seol; Sunghwan Sohn; Euijung Ryu; Hongfang Liu; Young J. Juhn
Journal of the American College of Cardiology | 2018
Sungrim Moon; Sujith Samudrala; Sijia Liu; Jane L. Shellum; Steve R. Ommen; Rick A. Nishimura; Hongfang Liu; Adelaide M. Arruda-Olson
The Journal of Allergy and Clinical Immunology | 2017
Euijung Ryu; Chung-Il Wi; Sunghwan Sohn; Sungrim Moon; Hongfang Liu; Joy A. Green; Kathy D. Ihrke; Rhonda G. Unterborn; Philip H. Wheeler; Young J. Juhn
European Respiratory Journal | 2017
Young J. Juhn; Chung-Il Wi; Hee Yun Seol; Sunghwan Sohn; Sungrim Moon; Euijung Ryu; Richard S. Pendegraft; Miguel Park; Hongfang Liu
AMIA | 2017
Sungrim Moon; Donna M. Ihrke; Yuqun Zeng; Hongfang Liu