Knowledge Management, Data Mining, and Text Mining in Medical Informatics

Chen, Hsinchun; Fuller, Sherrilynne S.; Friedman, Carol; Hersh, William R.

doi:10.1007/0-387-25739-x_1

Cited by 51 publications

(33 citation statements)

References 100 publications

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“…Moreover, healthcare analytics is defined as a set of computer-based methods, processes, and workflows for transforming raw health data into meaningful insights, new discoveries, and knowledge that can inform more effective decision-making [23]. Data mining in healthcare has traditionally been linked to knowledge management, reflecting a managerial approach to the discovery, collection, analysis, sharing, and use of knowledge [52][53]. Thus, the DAD and ADT are designed to enable hospitals and health authorities to apply knowledge derived from data recording patient numbers, health outcomes, length of stay (LOS), and so forth, to the evaluation and improvement of hospital and healthcare system performance.…”

Section: Conceptual Frameworkmentioning

confidence: 99%

Operational Efficiencies and Simulated Performance of Big Data Analytics Platform over Billions of Patient Records of a Hospital System

Chrimes¹,

Moa²,

Kuo³

et al. 2017

Adv. sci. technol. eng. syst. j.

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Section: Conceptual Frameworkmentioning

confidence: 99%

Operational Efficiencies and Simulated Performance of Big Data Analytics Platform over Billions of Patient Records of a Hospital System

Chrimes¹,

Moa²,

Kuo³

et al. 2017

Adv. sci. technol. eng. syst. j.

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“…-Text mining, or text analytics, is the process that extracts relevant information from (big amount of ) text expressed in the form of natural language (Chen et al 2005). It is employed in healthcare, for example, in order to discover new knowledge patterns or hypotheses from the large amount of existing and new literature in biomedicine (Yandell and Majoros 2002).…”

Section: Related Workmentioning

confidence: 99%

An ontological knowledge and multiple abstraction level decision support system in healthcare

2014

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The rationalization of the healthcare processes and organizations is a task of fundamental importance to grant both the quality and the standardization of healthcare services, and the minimization of costs. Clinical Practice Guidelines (CPGs) are one of the major tools that have been introduced to achieve such a challenging task. CPGs are widely used to provide decision support to physicians, supplying them with evidence-based predictive and prescriptive information about patients' status and treatments, but usually on individual pathologies. This sets up the urgent need for developing decision support methodologies to assist physicians and healthcare managers in the detection of interactions between guidelines, to help them to devise appropriate patterns of treatment for comorbid patients (i.e., patients affected by multiple diseases). We identify different levels of abstractions in the analysis of interactions, based on both the hierarchical organization of clinical guidelines (in which composite actions are refined into their components) and the hierarchy of drug categories. We then propose a general methodology (data/knowledge structures and reasoning algorithms operating on them) supporting user-driven and flexible interaction detection over the multiple levels of abstraction. Finally, we discuss the impact of the adoption of computerized clinical guidelines in general, and of our methodology in particular, for patients (quality of the received healthcare services), physicians (decision support and quality of provided services), and healthcare managers and organizations (quality and optimization of provided services).

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“…When a child shows a great disparity between those areas of functioning in which she or he does well and those in which considerable difficu lty is experienced, this child is described as having a learning disability [4,44]. When a LD is suspected based on parent and/or teacher observations, a formal evaluation of the child is necessary.…”

Section: Learning Disabilitymentioning

confidence: 99%

“…This has led to a growing interest in the development of tools capable in the automatic extraction of knowledge fro m data. Diverse fields such as marketing, customer relationship management, engineering, medicine, crime analysis, expert pred iction, web mining and mobile computing besides others utilize data mining [4]. A majority of areas related to medical services such as prediction of effectiveness of surgical procedures, med ical tests, medicat ion and the discovery of relationship among clinical and d iagnosis data also make use of data mining methodologies [5].…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Kannan¹,

Julie²

2013

IJISA

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Abstract-Learn ing Disability (LD) is a classification including several d isorders in wh ich a ch ild has difficulty in learning in a typical manner, usually caused by an unknown factor or factors . LD affects about 15% of children enrolled in schools. The prediction of learning disability is a comp licated task since the identification of LD fro m diverse features or signs is a co mplicated problem. There is no cure for learning disabilit ies and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some t ime. The aim of this paper is to develop a new algorith m for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the perfo rmance of fuzzy and neuro fu zzy classifiers with specific emphasis on prediction of learning d isabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorith m, viz. the correlation based new algorith m for imputing the missing values and Principal Co mponent Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in predict ion system and is capable of improving the performance of a classifier.

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Knowledge Management, Data Mining, and Text Mining in Medical Informatics

Cited by 51 publications

References 100 publications

Operational Efficiencies and Simulated Performance of Big Data Analytics Platform over Billions of Patient Records of a Hospital System

Operational Efficiencies and Simulated Performance of Big Data Analytics Platform over Billions of Patient Records of a Hospital System

An ontological knowledge and multiple abstraction level decision support system in healthcare

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

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