Data Mining in Health-Care: Issues and a Research Agenda

While data mining has become a much-lauded tool in business and related fields, its role in the healthcare arena is still being explored. Currently, most applications of data mining in healthcare can be categorized into two areas: decision support for clinical practice, and policy planning/decision making. However, it is challenging to find empirical literature in this area since a substantial amount of existing work in data mining for health care is conceptual in nature. In this paper, we review the challenges that limit the progress made in this area and present considerations for the future of data mining in healthcare

Data Mining in Health Care Sector: Literature Notes

© 2019 ACM. A standout amongst the most essential strides of the knowledge discovery in database KDD is data mining. Data mining is defined as a basic advance during the time spent learning discovery in databases in which understanding strategies are utilized in order to pattern discovery. Due to the huge amount of data available within the healthcare systems, data mining is important for the healthcare sector in the clinical and diagnosis diseases. However, data mining and healthcare organizations have developed some of dependable early discovery frameworks and different healthcare related frameworks from the clinical treatment and analysis information. The main motivation of this paper is to give a survey of data extraction in health care. In addition, the benefits and obstacles of the use of data extraction strategies in health care and therapeutic information have been thought

Approaches to creating anonymous patient database

Health care providers, health plans and health care clearinghouses collect patient medical data derived from their normal operations every day. These patient data can greatly benefit the health care organization if data mining techniques are applied upon these data sets. However, individual identifiable patient information needs to be protected in accordance with Health Insurance Portability and Accountability Act (HIPAA), and the quality of patient data also needs to be ensured in order for data mining tasks achieve accurate results. This thesis describes a patient data transformation system which transforms patient data into high quality and anonymous patient records that is suitable for data mining purposes.;This document discusses the underlying technologies, features implemented in the prototype, and the methodologies used in developing the software. The prototype emphasizes the patient privacy and quality of the patient data as well as software scalability and portability. Preliminary experience of its use is presented. A performance analysis of the system\u27s behavior has also been done

Analysis of Heart Disease using in Data Mining Tools Orange and Weka

Health care is an inevitable task to be done in human life Health concern business has become a notable field in the wide spread area of medical science Health care industry contains large amount of data and hidden information Effective decisions are made with this hidden information by applying patient however with data mining these tests could be reduced But there is a lack of analyzing tool according to provide effective test outcomes together with the hidden information so and such system is developed using data mining algorithms for classifying the data and to detect the heart diseases Data mining acts so a solution by many healthcare problems Na ve Bayes SVM Random Forest KNN algorithm is one such data mining method which serves with the diagnosis regarding heart diseases patient This paper analyzes few parameters and predicts heart diseases thereby suggests a heart diseases prediction system HDPS based total on the data mining approache

Prediction and Decision Making in Health Care using Data Mining

Tendency for data mining application in healthcare today is great, because healthcare sector is rich with information, and data mining is becoming a necessity. Healthcare organizations produce and collect large volumes of information on daily basis. Use of information technologies allows automatization of processes for extraction of data that help to get interesting knowledge and regularities, which means the elimination of manual tasks and easier extraction of data directly from electronic records, transferring onto secure electronic system of medical records which will save lives and reduce the cost of the healthcare services, as well and early discovery of contagious diseases with the advanced collection of data. Data mining can enable healthcare organizations to predict trends in the patient conditions and their behaviors, which is accomplished by data analysis from different perspectives and discovering connections and relations from seemingly unrelated information. Raw data from healthcare organizations are voluminous and heterogeneous. They need to be collected and stored in the organized forms, and their integration enables forming of hospital information system. Healthcare data mining provides countless possibilities for hidden pattern investigation from these data sets. These patterns can be used by physicians to determine diagnoses, prognoses and treatments for patients in healthcare organizations.DOI: http://dx.doi.org/10.11591/ijphs.v1i2.138

A Data Quality Framework for Process Mining of Electronic Health Record Data

Reliable research demands data of known quality. This can be very challenging for electronic health record (EHR) based research where data quality issues can be complex and often unknown. Emerging technologies such as process mining can reveal insights into how to improve care pathways but only if technological advances are matched by strategies and methods to improve data quality. The aim of this work was to develop a care pathway data quality framework (CP-DQF) to identify, manage and mitigate EHR data quality in the context of process mining, using dental EHRs as an example. Objectives: To: 1) Design a framework implementable within our e-health record research environments; 2) Scale it to further dimensions and sources; 3) Run code to mark the data; 4) Mitigate issues and provide an audit trail. Methods: We reviewed the existing literature covering data quality frameworks for process mining and for data mining of EHRs and constructed a unified data quality framework that met the requirements of both. We applied the framework to a practical case study mining primary care dental pathways from an EHR covering 41 dental clinics and 231,760 patients in the Republic of Ireland. Results: Applying the framework helped identify many potential data quality issues and mark-up every data point affected. This enabled systematic assessment of the data quality issues relevant to mining care pathways. Conclusion: The complexity of data quality in an EHR-data research environment was addressed through a re-usable and comprehensible framework that met the needs of our case study. This structured approach saved time and brought rigor to the management and mitigation of data quality issues. The resulting metadata is being used within cohort selection, experiment and process mining software so that our research with this data is based on data of known quality. Our framework is a useful starting point for process mining researchers to address EHR data quality concerns

Can the US Minimum Data Set Be Used for Predicting Admissions to Acute Care Facilities?

This paper is intended to give an overview of Knowledge Discovery in Large Datasets (KDD) and data mining applications in healthcare particularly as related to the Minimum Data Set, a resident assessment tool which is used in US long-term care facilities. The US Health Care Finance Administration, which mandates the use of this tool, has accumulated massive warehouses of MDS data. The pressure in healthcare to increase efficiency and effectiveness while improving patient outcomes requires that we find new ways to harness these vast resources. The intent of this preliminary study design paper is to discuss the development of an approach which utilizes the MDS, in conjunction with KDD and classification algorithms, in an attempt to predict admission from a long-term care facility to an acute care facility. The use of acute care services by long term care residents is a negative outcome, potentially avoidable, and expensive. The value of the MDS warehouse can be realized by the use of the stored data in ways that can improve patient outcomes and avoid the use of expensive acute care services. This study, when completed, will test whether the MDS warehouse can be used to describe patient outcomes and possibly be of predictive value