Relevant bibliographies by topics / EHR data mining

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

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"

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Khanal, Rajesh. "The Role of Open Standard Electronic Health Record in Medical Data Mining." European Journal of Business Management and Research 2, no. 2 (April 25, 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 further aid in creating a universal EHR database for medical data mining. Mining the EHR helps in developing the best standard of care and clinical practice. The following paper proposes a universal EHR database and medical data mining. The benefits and challenges of implementing a database system is also discussed in the paper. The following paper will also analyze the different application areas of the EHR data mining.
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Sarwar, Tabinda, Sattar Seifollahi, Jeffrey Chan, Xiuzhen Zhang, Vural Aksakalli, Irene Hudson, Karin Verspoor, and Lawrence Cavedon. "The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges." ACM Computing Surveys 55, no. 2 (March 31, 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, precision medicine, intervention evaluation, disease prediction, detection, and progression. But the presence of diverse data types and associated characteristics poses many challenges to the use of EHR data. In this article, we provide an overview of information found in EHR systems and their characteristics that could be utilized for secondary applications. We first discuss the different types of data stored in EHRs, followed by the data transformations necessary for data analysis and mining. Later, we discuss the data quality issues and characteristics of the EHRs along with the relevant methods used to address them. Moreover, this survey also highlights the usage of various data types for different applications. Hence, this article can serve as a primer for researchers to understand the use of EHRs for data mining and analytics purposes.
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Sundermann, Alexander J., James K. Miller, Jane W. Marsh, Melissa I. Saul, Kathleen A. Shutt, Marissa Pacey, Mustapha M. Mustapha, et al. "Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks." Infection Control & Hospital Epidemiology 40, no. 3 (February 18, 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 isolates. We determined (1) the ability of data mining of the EHR to identify the correct route of transmission, (2) how early the correct route was identified during the timeline of the outbreak, and (3) how many cases in the outbreaks could have been prevented had the system been running in real time.Results:Correct routes were identified for all outbreaks at the second patient, except for one outbreak involving >1 transmission route that was detected at the eighth patient. Up to 40 or 34 infections (78% or 66% of possible preventable infections, respectively) could have been prevented if data mining had been implemented in real time, assuming the initiation of an effective intervention within 7 or 14 days of identification of the transmission route, respectively.Conclusions:Data mining of the EHR was accurate for identifying routes of transmission among patients who were part of the outbreak. Prospective validation of this approach using routine whole-genome sequencing and data mining of the EHR for both outbreak detection and route attribution is ongoing.
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Grando, M. Adela, Vaishak Vellore, Benjamin J. Duncan, David R. Kaufman, Stephanie K. Furniss, Bradley N. Doebbeling, Karl A. Poterack, Timothy Miksch, and Richard A. Helmers. "Study of EHR-mediated workflows using ethnography and process mining methods." Health Informatics Journal 27, no. 2 (April 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 task was performed with different electronic health records (EHRs) used in three different hospital sites. The individual methods revealed substantial discrepancies in task duration between sites. Specifically, means of 159.6(78.55), 38.2(34.9), and 431.3(283.04) seconds were captured with rapid ethnography. When process mining was used, means of 518.6(3,808), 345.5(660.6), and 119.74(210.3) seconds were found. When ethno-mining was applied instead, outliers could be identified, explained and removed. Without outliers, mean task duration was similar between sites (78.1(66.7), 72.5(78.5), and 71.7(75) seconds). Results from this work suggest that integrating rapid ethnography and data mining into a single process may provide more meaningful results than a siloed approach when studying of workflow.
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Lee, Wu, Yuliang Shi, Hongfeng Sun, Lin Cheng, Kun Zhang, Xinjun Wang, and Zhiyong Chen. "MSIPA: Multi-Scale Interval Pattern-Aware Network for ICU Transfer Prediction." ACM Transactions on Knowledge Discovery from Data 16, no. 1 (February 28, 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 the short, medium, and long intervals. MSIPA utilizes the Scaled Dot-Product Attention mechanism to query the contexts corresponding to the three scale patterns. Furthermore, Transformer will use all three types of contextual information simultaneously for ICU transfer prediction. Extensive experiments on real-world data demonstrate that an MSIPA network outperforms state-of-the-art methods.
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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 (September 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 underdeveloped.Methods and analysisThe overarching goal of this exploratory/developmental study is to establish an EHR-based cohort for individuals with HIV/SARS-CoV-2 coinfection and perform large-scale EHR-based data mining to examine the interactions between HIV and SARS-CoV-2 infections and systematically identify and validate factors contributing to the severe clinical course of the coinfection. We will use a nationwide EHR database in the USA, namely, National COVID Cohort Collaborative (N3C). Ultimately, collected clinical evidence will be implemented and used to pilot test a clinical decision support prototype to assist providers in screening and referral of at-risk patients in real-world clinics.Ethics and disseminationThe study was approved by the institutional review boards at the University of South Carolina (Pro00121828) as non-human subject study. Study findings will be presented at academic conferences and published in peer-reviewed journals. This study will disseminate urgently needed clinical evidence for guiding clinical practice for individuals with the coinfection at Prisma Health, a healthcare system in collaboration.
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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 (September 1, 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 university physician group at an urban hospital. A novel automated data mining technology was used to extract patient note–related variables. Results A sample of 26 802 consecutive patient notes was analyzed using the data mining and modeling tool Healthcare Smartgrid. Residents entered most of the notes (33%, 8178 of 24 787) between noon and 4 pm and 31% (7718 of 24 787) of notes between 8 am and noon. Attending physicians placed notes about teaching attestations within 24 hours in only 73% (17 843 of 24 443) of the records. Surgical residents were more likely to place notes before noon (P < .001). Nonsurgical faculty were more likely to provide attestation of resident notes within 24 hours (P < .001). Conclusions Data related to patient note entry was successfully used to objectively measure current work flow of resident physicians and their supervising faculty, and the findings have implications for physician oversight of residents' clinical work. We were able to demonstrate the utility of a data mining model as an assessment tool in graduate medical education.
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Ross, M. K., Wei Wei, and L. Ohno-Machado. "“Big Data” and the Electronic Health Record." Yearbook of Medical Informatics 23, no. 01 (August 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 PubMed database for articles from January 1, 2011 to November 1, 2013. We initiated the search with keywords related to “big data” and EHR. We identified relevant articles and additional keywords from the retrieved articles were added. Based on the new keywords, more articles were retrieved and we manually narrowed down the set utilizing predefined inclusion and exclusion criteria. Results: Our final review includes articles categorized into the themes of data mining (pharmacovigilance, phenotyping, natural language processing), data application and integration (clinical decision support, personal monitoring, social media), and privacy and security. Conclusion: The increasing adoption of EHR systems worldwide makes it possible to capture large amounts of clinical data. There is an increasing number of articles addressing the theme of “big data”, and the concepts associated with these articles vary. The next step is to transform healthcare big data into actionable knowledge.
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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 (November 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 patients' detailed smoking status based on smoking intensity from the EDR. Methods First, the authors created a reference standard of 3,296 unique patients’ smoking histories from the EDR that classified patients based on their smoking intensity. Next, they trained three machine learning classifiers (support vector machine, random forest, and naïve Bayes) using the training set (2,176) and evaluated performances on test set (1,120) using precision (P), recall (R), and F-measure (F). Finally, they applied the best classifier to classify smoking status from an additional 3,114 patients’ smoking histories. Results Support vector machine performed best to classify patients into smokers, nonsmokers, and unknowns (P, R, F: 98%); intermittent smoker (P: 95%, R: 98%, F: 96%); past smoker (P, R, F: 89%); light smoker (P, R, F: 87%); smokers with unknown intensity (P: 76%, R: 86%, F: 81%), and intermediate smoker (P: 90%, R: 88%, F: 89%). It performed moderately to differentiate heavy smokers (P: 90%, R: 44%, F: 60%). EDR could be a valuable source for obtaining patients’ detailed smoking information. Conclusion EDR data could serve as a valuable source for obtaining patients' detailed smoking information based on their smoking intensity that may not be readily available in the EHR.
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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 (May 20, 2014): 6612. http://dx.doi.org/10.1200/jco.2014.32.15_suppl.6612.

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Dissertations / Theses on the topic "EHR data mining"

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Liu, Larry Young. "Interplay Between Traumatic Brain Injury and Intimate Partner Violence: A Data-Driven Approach Utilizing Electronic Health Records." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1502886892588355.

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Abar, Orhan. "Rule Mining and Sequential Pattern Based Predictive Modeling with EMR Data." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/85.

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Electronic medical record (EMR) data is collected on a daily basis at hospitals and other healthcare facilities to track patients’ health situations including conditions, treatments (medications, procedures), diagnostics (labs) and associated healthcare operations. Besides being useful for individual patient care and hospital operations (e.g., billing, triaging), EMRs can also be exploited for secondary data analyses to glean discriminative patterns that hold across patient cohorts for different phenotypes. These patterns in turn can yield high level insights into disease progression with interventional potential. In this dissertation, using a large scale realistic EMR dataset of over one million patients visiting University of Kentucky healthcare facilities, we explore data mining and machine learning methods for association rule (AR) mining and predictive modeling with mood and anxiety disorders as use-cases. Our first work involves analysis of existing quantitative measures of rule interestingness to assess how they align with a practicing psychiatrist’s sense of novelty/surprise corresponding to ARs identified from EMRs. Our second effort involves mining causal ARs with depression and anxiety disorders as target conditions through matching methods accounting for computationally identified confounding attributes. Our final effort involves efficient implementation (via GPUs) and application of contrast pattern mining to predictive modeling for mental conditions using various representational methods and recurrent neural networks. Overall, we demonstrate the effectiveness of rule mining methods in secondary analyses of EMR data for identifying causal associations and building predictive models for diseases.
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Miranda, Claudio de Souza. "Uso de Data Mining e do ECR para incremento da competitividade de pequenas e médias empresas: um estudo multicaso sobre varejo e indústria alimentícia." Universidade de São Paulo, 2002. http://www.teses.usp.br/teses/disponiveis/18/18140/tde-13052016-091750/.

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O setor supermercadista sofreu grandes alterações nos últimos anos, principalmente com o avanço das tecnologias, a competição, a concentração e algumas insuficiências em seus processos. Estes e outros fatores favoreceram ao surgimento do movimento de ECR (Resposta de Consumidor Eficiente) que procura criar um relacionamento mais forte entre indústria e varejo através de novas visões para suas estratégias operacionais. A evolução das tecnologias de informação permitiram ao setor varejista gerar uma maior volume de dados a partir, principalmente, de seus check-outs. Entretanto, estes dados nem sempre são armazenados de forma correta ou utilizados de forma a se aproveitar a plenitude das informações neles contidas. O processo de transformar os dados em informação e conhecimento vem evoluindo constantemente. Uma das atuais metodologias de trabalhar dados é o Data Mining ou Mineração de Dados, que pode ser descrito como sendo uma variedade de ferramentas e estratégias que processam dados aumentando a utilidade destes em bancos de dados. Este trabalho analisa através de um estudo multicaso exploratório na região de Ribeirão Preto, no interior de São Paulo, a avaliação da capacidade do uso da tecnologia Data Mining para o fortalecimento do movimento ECR, principalmente em pequenos e médios varejistas e indústrias alimentícias, no sentido de oferecer a estes um diferencial de negociação para formação de alianças estratégias.
The Supermarket sector suffered great changes in the last years, mainly with the development of technologies, competition, concentration and some inefficiency in its processes. These and other factors had emerged the sprouting of ECR (Efficient Consumer Response) that creates a stronger relationship between the industry and the retail, through new vision for its operational strategies. The information technology evolution allowed the retail sector to generate a lager volume of data from, mainly, its checkouts. However, this data is not always stored in a correct form or used in a way to be completely profited. The process to transform the data in information and knowledge is developing constantly. One of the actual methodologies to work the data is the Data Mining, which can be described as a variety of tools and strategies that process data, maximizing the utility of these data base. This report analyzes through an exploratory multicase study in the Ribeirão Preto region, in the interior of São Paulo state, the capacity evaluation of Data Mining technology for the strengthening of ECR, especially in small and medium retail and food industries, in the direction to offer a differential of negotiation for the formation of strategic alliances.
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Mei, Lin. "Data mining approach for automated inquiry handling and risk prediction." 2005. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=362346&T=F.

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Raouf, Abida. "A data mining framework for AVM treatment planning in radiosurgery." 2007. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=452847&T=F.

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Torabi, Keivan. "Data mining methods for quantitative in-line image monitoring in polymer extrusion." 2005. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=232711&T=F.

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Kawasme, Luay. "Context-aware information systems and their application to health care." Thesis, 2008. http://hdl.handle.net/1828/1219.

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This thesis explores the field of context-aware information systems (CAIS). We present an approach called Compose, Learn, and Discover (CLD) to incorporate CAIS into the user daily workflow. The CLD approach is self-adjusting. It enables users to personalise the information views for different situations. The CAIS learns about the usage of the information views and recalls the right view in the right situation. We illustrate the CLD approach through an application in the health care field using the Clinical Document Architecture (CDA). In order to realise the CLD approach, we introduce Semantic Composition as a new paradigm to personalise information views. Semantic Composition leverages the type information in the domain model to simplify the user-interface composition process. We also introduce a pattern discovery mechanism that leverages data-mining algorithms to discover correlations between user information needs and different situations.
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Book chapters on the topic "EHR data mining"

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Li, Tianhao, Najia Yin, Penghao Gao, Dengfeng Li, and Wei Lu. "An Interpretable Conditional Augmentation Classification Approach for Imbalanced EHRs Mortality Prediction." In Data Mining and Big Data, 408–22. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-9297-1_29.

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Singh, Sachin, Pravin Vajirkar, and Yugyung Lee. "Context-Based Data Mining Using Ontologies." In Conceptual Modeling - ER 2003, 405–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39648-2_32.

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Pokharel, Suresh, Guido Zuccon, and Yu Li. "Representing EHRs with Temporal Tree and Sequential Pattern Mining for Similarity Computing." In Advanced Data Mining and Applications, 220–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65390-3_18.

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Scheepers-Hoeks, Anne-Marie, Floor Klijn, Carolien van der Linden, Rene Grouls, Eric Ackerman, Niels Minderman, Jan Bergmans, and Erik Korsten. "Clinical Decision Support Systems for ‘Making It Easy to Do It Right’." In Data Mining, 1461–71. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2455-9.ch076.

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Medical guidelines and best practises are used in medicine to increase the quality of the health-care delivery system. To support implementation and application of these guidelines, clinical decision support systems (CDSS) have been developed. These systems are defined as ‘Computer-based information systems used to integrate clinical and patient information and provide support for decision-making in patient care’ (MeSH) These are integrated with so-called Electronic Health Records (EHR), which have been developed by companies and National Governmental Institutes, and are used to register and present the patient medical data. The integration of an EHR with CDSS modules will revolutionize the way medicine will be practiced. In pediatrics, as well as geriatrics, such systems might prove to be even more needed. The development, use, and maintenance of CDSS in a hospital are complex and far from trivial. This chapter focuses on several aspects and challenges of EHR’s and CDSS-modules in daily clinical practice in the hospital.
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Jonnagaddala, Jitendra, Hong-Jie Dai, Pradeep Ray, and Siaw-Teng Liaw. "Mining Electronic Health Records to Guide and Support Clinical Decision Support Systems." In Healthcare Ethics and Training, 184–201. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2237-9.ch008.

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Clinical decision support systems require well-designed electronic health record (EHR) systems and vice versa. The data stored or captured in EHRs are diverse and include demographics, billing, medications, and laboratory reports; and can be categorized as structured, semi-structured and unstructured data. Various data and text mining techniques have been used to extract these data from EHRs for use in decision support, quality improvement and research. Mining EHRs has been used to identify cohorts, correlated phenotypes in genome-wide association studies, disease correlations and risk factors, drug-drug interactions, and to improve health services. However, mining EHR data is a challenge with many issues and barriers. The aim of this chapter is to discuss how data and text mining techniques may guide and support the building of improved clinical decision support systems.
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Jonnagaddala, Jitendra, Hong-Jie Dai, Pradeep Ray, and Siaw-Teng Liaw. "Mining Electronic Health Records to Guide and Support Clinical Decision Support Systems." In Improving Health Management through Clinical Decision Support Systems, 252–69. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9432-3.ch012.

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Clinical decision support systems require well-designed electronic health record (EHR) systems and vice versa. The data stored or captured in EHRs are diverse and include demographics, billing, medications, and laboratory reports; and can be categorized as structured, semi-structured and unstructured data. Various data and text mining techniques have been used to extract these data from EHRs for use in decision support, quality improvement and research. Mining EHRs has been used to identify cohorts, correlated phenotypes in genome-wide association studies, disease correlations and risk factors, drug-drug interactions, and to improve health services. However, mining EHR data is a challenge with many issues and barriers. The aim of this chapter is to discuss how data and text mining techniques may guide and support the building of improved clinical decision support systems.
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U, Vignesh, and Parvathi R. "Biological Big Data Analysis and Visualization." In Biotechnology, 653–65. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8903-7.ch026.

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The chapter deals with the big data in biology. The largest collection of biological data maintenance paves the way for big data analytics and big data mining due to its inefficiency in finding noisy and voluminous data from normal database management systems. This provides the domains such as bioinformatics, image informatics, clinical informatics, public health informatics, etc. for big data analytics to achieve better results with higher efficiency and accuracy in clustering, classification and association mining. The complexity measures of the health care data leads to EHR (Evidence-based HealthcaRe) technology for maintenance. EHR includes major challenges such as patient details in structured and unstructured format, medical image data mining, genome analysis and patient communications analysis through sensors – biomarkers, etc. The big biological data have many complications in their data management and maintenance especially after completing the latest genome sequencing technology, next generation sequencing which provides large data in zettabyte size.
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U, Vignesh, and Parvathi R. "Biological Big Data Analysis and Visualization." In Modern Technologies for Big Data Classification and Clustering, 244–59. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2805-0.ch010.

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The chapter deals with the big data in biology. The largest collection of biological data maintenance paves the way for big data analytics and big data mining due to its inefficiency in finding noisy and voluminous data from normal database management systems. This provides the domains such as bioinformatics, image informatics, clinical informatics, public health informatics, etc. for big data analytics to achieve better results with higher efficiency and accuracy in clustering, classification and association mining. The complexity measures of the health care data leads to EHR (Evidence-based HealthcaRe) technology for maintenance. EHR includes major challenges such as patient details in structured and unstructured format, medical image data mining, genome analysis and patient communications analysis through sensors – biomarkers, etc. The big biological data have many complications in their data management and maintenance especially after completing the latest genome sequencing technology, next generation sequencing which provides large data in zettabyte size.
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Kusuma, Guntur P., Angelina P. Kurniati, Eric Rojas, Ciarán D. McInerney, Chris P. Gale, and Owen A. Johnson. "Process Mining of Disease Trajectories: A Literature Review." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210200.

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Disease trajectories model patterns of disease over time and can be mined by extracting diagnosis codes from electronic health records (EHR). Process mining provides a mature set of methods and tools that has been used to mine care pathways using event data from EHRs and could be applied to disease trajectories. This paper presents a literature review on process mining related to mining disease trajectories using EHRs. Our review identified 156 papers of potential interest but only four papers which directly applied process mining to disease trajectory modelling. These four papers are presented in detail covering data source, size, selection criteria, selections of the process mining algorithms, trajectory definition strategies, model visualisations, and the methods of evaluation. The literature review lays the foundations for further research leveraging the established benefits of process mining for the emerging data mining of disease trajectories.
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Sathiyabhama B., Rajeswari K. C., Reenadevi R., and Arul Murugan R. "Preserving Data Privacy in Electronic Health Records Using Blockchain Technology." In Transforming Businesses With Bitcoin Mining and Blockchain Applications, 195–206. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0186-3.ch011.

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Technology is a boon to mankind in this fast-growing era. The advancement in technology is the predominant factor for the sophisticated way of living of the people. In spite of technology, revolution happens across the world, and mankind still suffers due to various health issues. Healthcare industries take immense measures to improve the quality of life. An enormous volume of digital data is being handled every day in the healthcare industry. There arises a need for the intervention of technology in the healthcare industry to be taken to a greater extent. The prime duty of any healthcare industry is to store and maintain those data in the form of electronic health records (EHR) in a secured manner.
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Conference papers on the topic "EHR data mining"

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Zhang, Jiancheng, Xiao Yang, Hefeng Meng, Zhiqiang Lin, Yonghui Xu, and Lizhen Cui. "A Survey on Knowledge Enhanced EHR Data Mining." In ICCSE '21: 5th International Conference on Crowd Science and Engineering. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3503181.3503202.

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Hirano, Shoji, and Shusaku Tsumoto. "Visualization of Patient Distributions in a Hospital Based on the Clinical Actions Stored in EHR." In 2014 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE, 2014. http://dx.doi.org/10.1109/icdmw.2014.185.

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Chakraborty, Prithwish, and Faisal Farooq. "A Robust Framework for Accelerated Outcome-driven Risk Factor Identification from EHR." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3330718.

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Keloth, Vipina K., Shuxin Zhou, Luke Lindemann, Gai Elhanan, Andrew J. Einstein, James Geller, and Yehoshua Perl. "Mining Concepts for a COVID Interface Terminology for Annotation of EHRs." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377981.

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Riesener, Michael, Christian Dolle, Alexander Menges, and Gunther Schuh. "Time-driven Activity-based Costing Using Process Data Mining." In 2021 IEEE Technology & Engineering Management Conference - Europe (TEMSCON-EUR). IEEE, 2021. http://dx.doi.org/10.1109/temscon-eur52034.2021.9488654.

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Keloth, Vipina K., Shuxin Zhou, Andrew J. Einstein, Gai Elhanan, Yan Chen, James Geller, and Yehoshua Perl. "Generating Training Data for Concept-Mining for an ‘Interface Terminology’ Annotating Cardiology EHRs." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313435.

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7

Mehlstaubl, Jan, Felix Braun, and Kristin Paetzold. "Data Mining in Product Portfolio and Variety Management – Literature Review on Use Cases and Research Potentials." In 2021 IEEE Technology & Engineering Management Conference - Europe (TEMSCON-EUR). IEEE, 2021. http://dx.doi.org/10.1109/temscon-eur52034.2021.9488575.

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Soret, Ignacio, Carmen De Pablos, and Jose Luis Montes. "Efficient Consumer Response (ECR) Practices as Responsible for the Creation of Knowledge and Sustainable Competitive Advantages in the Grocery Industry." In InSITE 2008: Informing Science + IT Education Conference. Informing Science Institute, 2008. http://dx.doi.org/10.28945/3269.

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This paper presents a model to measure and to explain knowledge and sustainable competitive advantages generation within the Efficient Consumer Response (ECR) framework. Some specific goals are: a) identification, selection and validation of intellectual capital and of sustainable competitive advantages, b) study of what we name associate concepts: facilitators, implantation drivers and critical success factors, c) develop a validation of a methodology for the measurement model and of the indicators adaptation degree, meeting the demand of related companies and consultants. Results show that individual improvement, work conditions, management style, learning improvement, education, management by objectives and work environment influence directly the human capital increase. Data mining techniques, generation of manuals of procedures and processes, and continuous improvement can be evidenced for a structural capital increase. Increase of relational capital is in direct relationship with the creation and improving of standard procedures for clients, their satisfaction, management by categories, and loyalty programs. To conclude, the implementation of ECR practices generates and increases the intellectual capital, or knowledge, in the organizations by positively promoting the generation of sustainable competitive advantages.
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Tompkins, Brandon T., Hoseok Song, and Timothy J. Jacobs. "Particulate Matter Emissions From Late Injection High EGR Low Temperature Diesel Combustion." In ASME 2011 Internal Combustion Engine Division Fall Technical Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/icef2011-60067.

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Low temperature combustion (LTC) is an advanced mode of combustion that has attained much attention due to ever increasing emission standards. LTC simultaneously reduces soot and nitric oxide (NO) emissions by having combustion take place at, for example bulk gas temperatures below 1200K (as observed in this study) so that soot and NO formation is substantially reduced. Soot is typically considered a building block for particulate matter (PM); both PM and NO are heavily regulated emissions by government agencies due to their potential effects on human and environmental health. Although LTC is believed to substantially reduce soot, it is not clear what is the end effect on PM. Because PM is composed of other agents, such as condensed liquid and solid hydrocarbons, there could potentially be non-negligible emission of PM from LTC combustion. This study will compare the gravimetric-based PM data from 3 different modes of combustion in a direct injection diesel engine; specifically: conventional combustion, combustion with high exhaust gas recirculation (EGR) at conventional injection timing, and combustion with high EGR and late injection timing (all other control parameters are the same, including fuel flow rate and engine speed). The objective of this study is to quantify PM emissions of LTC and assess potential differences relative to the soot concentration (the latter as assessed by a smokemeter). PM is gravimetrically measured using a mini-dilution tunnel. Further, chemical analysis of the collected PM is analyzed by an independent laboratory to develop an understanding of the constituent species composing conventional and LTC PM. PM results show that there are differences among the three modes of combustion. The PM differs in appearance as well as composition, and due to the change in appearance FSN may not correlate with PM when running LTC modes of combustion.
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Zhang, Danhua Leslie, Xiaodong Shi, Chunyan Qi, Jianfei Zhan, Xue Han, and Denis Klemin. "Formation Characterization and Production Forecast of Tight Sandstone Formations in Daqing Oilfield Through Digital Rock Technology." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206055-ms.

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Abstract With the decline of conventional resources, the tight oil reserves in the Daqing oilfield are becoming increasingly important, but measuring relative permeability and determining production forecasts through laboratory core flow tests for tight formations are both difficult and time consuming. Results of laboratory testing are questionable due to the very small pore volume and low permeability of the reservoir rock, and there are challenges in controlling critical parameters during the special core analysis (SCAL) tests. In this paper, the protocol and workflow of a digital rock study for tight sandstones of the Daqing oilfield are discussed. The workflow includes 1) using a combination of various imaging methods to build rock models that are representative of reservoir rocks, 2) constructing digital fluid models of reservoir fluids and injectants, 3) applying laboratory measured wettability index data to define rock-fluid interaction in digital rock models, 4) performing pore-scale modelling to accelerate reservoir characterization and reduce the uncertainty, and 5) performing digital enhanced oil recovery (EOR) tests to analyze potential benefits of different scenarios. The target formations are tight (0.01 to 5 md range) sandstones that have a combination of large grain sizes juxtaposed against small pore openings which makes it challenging to select an appropriate set of imaging tools. To overcome the wide range of pore and grain scales, the imaging tools selected for the study included high resolution microCT imaging on core plugs and mini-plugs sampled from original plugs, overview scanning electron microscopy (SEM) imaging, mineralogical mapping, and high-resolution SEM imaging on the mini-plugs. High resolution digital rock models were constructed and subsequently upscaled to the plug level to differentiate bedding and other features could be differentiated. Permeability and porosity of digital rock models were benchmarked against laboratory measurements to confirm representativeness. The laboratory measured Amott-Harvey wettability index of restored core plugs was honored and applied to the digital rock models. Digital fluid models were built using the fluid PVT data. A Direct HydroDynamic (DHD) pore-scale flow simulator based on density functional hydrodynamics was used to model multiphase flow in the digital experiments. Capillary pressure, water-oil, surfactant solution-oil, and CO2-oil relative permeability were computed, as well as primary depletion followed with three-cycle CO2 huff-n-puff, and primary depletion followed with three-cycle surfactant solution huff-n-puff processes. Recovery factors were obtained for each method and resulting values were compared to establish most effective field development scenarios. The workflow developed in this paper provides fast and reliable means of obtaining critical data for field development design. Capillary pressure and relative permeability data obtained through digital experiments provide key input parameters for reservoir simulation; production scenario forecasts help evaluate various EOR methods. Digital simulations allow the different scenarios to be run on identical rock samples numerous times, which is not possible in the laboratory.
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