Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department
Louise Deléger,Holly Brodzinski,Haijun Zhai,Qi Li,Todd Lingren,Eric S. Kirkendall,Evaline A. Alessandrini,Imre Solti +7 more
TLDR
Automated appendicitis risk categorization based on EHR content, including information from clinical notes, shows comparable performance to physician chart reviewers as measured by their inter-annotator agreement and represents a promising new approach for computerized decision support to promote application of evidence-based medicine at the point of care.About:
This article is published in Journal of the American Medical Informatics Association.The article was published on 2013-12-01 and is currently open access. It has received 46 citations till now.read more
Citations
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Clinical information extraction applications: A literature review.
Yanshan Wang,Liwei Wang,Majid Rastegar-Mojarad,Sungrim Moon,Feichen Shen,Naveed Afzal,Sijia Liu,Yuqun Zeng,Saeed Mehrabi,Sunghwan Sohn,Hongfang Liu +10 more
TL;DR: There is a considerable gap between clinical studies using EHR data and studies using clinical IE, so a more concrete understanding of the gap is gained and potential solutions to bridge this gap are provided.
Journal ArticleDOI
Big data in medicine is driving big changes.
TL;DR: Current research that takes advantage of "Big Data" in health and biomedical informatics applications is summarized, highlighting ongoing development of powerful new methods for turning that large-scale, and often complex, data into information that provides new insights into human health, in a range of different areas.
Journal ArticleDOI
Increasing the efficiency of trial-patient matching: automated clinical trial eligibility Pre-screening for pediatric oncology patients
Yizhao Ni,Jordan Wright,John P. Perentesis,Todd Lingren,Louise Deléger,Megan Kaiser,Isaac S. Kohane,Imre Solti +7 more
TL;DR: An automated ES algorithm to identify patients who meet core eligibility characteristics of an oncology clinical trial could dramatically increase the trial screening efficiency of oncologists and enable participation of small practices, which are often left out from trial enrollment.
Applications of Machine Learning Approaches in Emergency Medicine; a Review Article
Negin Shafaf,Hamed Malek +1 more
TL;DR: In this paper, studies conducted in the recent years on using artificial intelligence in emergency medicine have been collected and assessed and accuracy and results of the algorithms have been briefly evaluated by mentioning machine learning techniques and used datasets.
References
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Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
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Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care
Basit Chaudhry,Jerome K. Wang,Shinyi Wu,Margaret Maglione,Walter Mojica,Elizabeth Roth,Sally C. Morton,Paul G. Shekelle +7 more
TL;DR: This work systematically review evidence on the costs and benefits associated with use of health information technology and to identify gaps in the literature in order to provide organizations, policymakers, clinicians, and consumers an understanding of the effect ofhealth information technology on clinical care.
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Estimated Risks of Radiation-Induced Fatal Cancer from Pediatric CT
TL;DR: It is suggested that pediatric CT will result in significantly increased lifetime radiation risk over adult CT, both because of the increased dose per milliampere-second, and the increased lifetime risk per unit dose.
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Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications
Guergana Savova,James J. Masanz,Philip V. Ogren,Jiaping Zheng,Sunghwan Sohn,Karin C Kipper-Schuler,Christopher G. Chute +6 more
TL;DR: The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text, and its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations.