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Relevant bibliographies by topics / EHR data mining

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Journal articles

Academic literature on the topic 'EHR data mining'

Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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Journal articles on the topic "EHR data mining"

1

Khanal, Rajesh. "The Role of Open Standard Electronic Health Record in Medical Data Mining." European Journal of Business Management and Research 2, no. 2 (2017): 1–7. http://dx.doi.org/10.24018/ejbmr.2017.2.2.9.

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Electronic Health Record (EHR) has received significant attention of all the health service provider in the world. EHR contains electronic information of all the patient information such as demographics, medical history, family medical history, lab tests and results, and prescribed drug. There is not any consistency in type of the EHR software implemented by the hosting organization. So, the EHR is currently vendor dependent and is not transferrable to another health service provider. The open standard electronic health record makes it public available to both vendor and patient. It can furthe

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2

Sarwar, Tabinda, Sattar Seifollahi, Jeffrey Chan, et al. "The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges." ACM Computing Surveys 55, no. 2 (2023): 1–40. http://dx.doi.org/10.1145/3490234.

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The primary objective of implementing Electronic Health Records (EHRs) is to improve the management of patients’ health-related information. However, these records have also been extensively used for the secondary purpose of clinical research and to improve healthcare practice. EHRs provide a rich set of information that includes demographics, medical history, medications, laboratory test results, and diagnosis. Data mining and analytics techniques have extensively exploited EHR information to study patient cohorts for various clinical and research applications, such as phenotype extraction, p

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Sundermann, Alexander J., James K. Miller, Jane W. Marsh, et al. "Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks." Infection Control & Hospital Epidemiology 40, no. 3 (2019): 314–19. http://dx.doi.org/10.1017/ice.2018.343.

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AbstractBackground:Identifying routes of transmission among hospitalized patients during a healthcare-associated outbreak can be tedious, particularly among patients with complex hospital stays and multiple exposures. Data mining of the electronic health record (EHR) has the potential to rapidly identify common exposures among patients suspected of being part of an outbreak.Methods:We retrospectively analyzed 9 hospital outbreaks that occurred during 2011–2016 and that had previously been characterized both according to transmission route and by molecular characterization of the bacterial isol

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4

Grando, M. Adela, Vaishak Vellore, Benjamin J. Duncan, et al. "Study of EHR-mediated workflows using ethnography and process mining methods." Health Informatics Journal 27, no. 2 (2021): 146045822110082. http://dx.doi.org/10.1177/14604582211008210.

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Rapid ethnography and data mining approaches have been used individually to study clinical workflows, but have seldom been used together to overcome the limitations inherent in either type of method. For rapid ethnography, how reliable are the findings drawn from small samples? For data mining, how accurate are the discoveries drawn from automatic analysis of big data, when compared with observable data? This paper explores the combined use of rapid ethnography and process mining, aka ethno-mining, to study and compare metrics of a typical clinical documentation task, vital signs charting. The

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Lee, Wu, Yuliang Shi, Hongfeng Sun, et al. "MSIPA: Multi-Scale Interval Pattern-Aware Network for ICU Transfer Prediction." ACM Transactions on Knowledge Discovery from Data 16, no. 1 (2022): 1–17. http://dx.doi.org/10.1145/3458284.

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Accurate prediction of patients’ ICU transfer events is of great significance for improving ICU treatment efficiency. ICU transition prediction task based on Electronic Health Records (EHR) is a temporal mining task like many other health informatics mining tasks. In the EHR-based temporal mining task, existing approaches are usually unable to mine and exploit patterns used to improve model performance. This article proposes a network based on Interval Pattern-Aware, Multi-Scale Interval Pattern-Aware (MSIPA) network. MSIPA mines different interval patterns in temporal EHR data according to th

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Liang, Chen, Sharon Weissman, Bankole Olatosi, Eric G. Poon, Michael E. Yarrington, and Xiaoming Li. "Curating a knowledge base for individuals with coinfection of HIV and SARS-CoV-2: a study protocol of EHR-based data mining and clinical implementation." BMJ Open 12, no. 9 (2022): e067204. http://dx.doi.org/10.1136/bmjopen-2022-067204.

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IntroductionDespite a higher risk of severe COVID-19 disease in individuals with HIV, the interactions between SARS-CoV-2 and HIV infections remain unclear. To delineate these interactions, multicentre Electronic Health Records (EHR) hold existing promise to provide full-spectrum and longitudinal clinical data, demographics and sociobehavioural data at individual level. Presently, a comprehensive EHR-based cohort for the HIV/SARS-CoV-2 coinfection has not been established; EHR integration and data mining methods tailored for studying the coinfection are urgently needed yet remain underdevelope

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7

Madhavan, Ramesh, Chi Tang, Pratik Bhattacharya, Fadi Delly, and Maysaa M. Basha. "Evaluation of Documentation Patterns of Trainees and Supervising Physicians Using Data Mining." Journal of Graduate Medical Education 6, no. 3 (2014): 577–80. http://dx.doi.org/10.4300/jgme-d-13-00267.1.

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Abstract Background The electronic health record (EHR) includes a rich data set that may offer opportunities for data mining and natural language processing to answer questions about quality of care, key aspects of resident education, or attributes of the residents' learning environment. Objective We used data obtained from the EHR to report on inpatient documentation practices of residents and attending physicians at a large academic medical center. Methods We conducted a retrospective observational study of deidentified patient notes entered over 7 consecutive months by a multispecialty univ

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8

Ross, M. K., Wei Wei, and L. Ohno-Machado. "“Big Data” and the Electronic Health Record." Yearbook of Medical Informatics 23, no. 01 (2014): 97–104. http://dx.doi.org/10.15265/iy-2014-0003.

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Summary Objectives: Implementation of Electronic Health Record (EHR) systems continues to expand. The massive number of patient encounters results in high amounts of stored data. Transforming clinical data into knowledge to improve patient care has been the goal of biomedical informatics professionals for many decades, and this work is now increasingly recognized outside our field. In reviewing the literature for the past three years, we focus on “big data” in the context of EHR systems and we report on some examples of how secondary use of data has been put into practice. Methods: We searched

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Patel, J., Z. Siddiqui, A. Krishnan, and T. Thyvalikakath. "Leveraging Electronic Dental Record Data to Classify Patients Based on Their Smoking Intensity." Methods of Information in Medicine 57, no. 05/06 (2018): 253–60. http://dx.doi.org/10.1055/s-0039-1681088.

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Background Smoking is an established risk factor for oral diseases and, therefore, dental clinicians routinely assess and record their patients' detailed smoking status. Researchers have successfully extracted smoking history from electronic health records (EHRs) using text mining methods. However, they could not retrieve patients' smoking intensity due to its limited availability in the EHR. The presence of detailed smoking information in the electronic dental record (EDR) often under a separate section allows retrieving this information with less preprocessing. Objective To determine patient

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Hernandez-Boussard, Tina, Suzanne Tamang, James D. Brooks, Douglas W. Blayney, and Nigam Shah. "Measurement of urinary incontinence after prostate surgery from data-mining electronic health records (EHR)." Journal of Clinical Oncology 32, no. 15_suppl (2014): 6612. http://dx.doi.org/10.1200/jco.2014.32.15_suppl.6612.

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