Data Mining in Health Care
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This document provides an overview of data mining applications in healthcare. It discusses how electronic health records have increased the amount of patient data available and how healthcare organizations are now using data mining and predictive analytics to optimize efficiency and quality. The document outlines several common uses of data mining in healthcare, such as predictive medicine, fraud detection, and measuring treatment effectiveness. It also describes some common data mining algorithms like decision trees and neural networks that are applied in healthcare. Finally, the document discusses future opportunities for data mining in healthcare like improved data sharing and more integrated web mining tools.
![15
Cont.…
Uses Of Data Mining in Healthcare :
1) Diagnosis and prediction of diseases :
When it comes to social insurance businesses, conclusion and anticipation of ailments is imperative,it
is a standout amongst the most imperative motivation behind utilizing information digging for social
insurance. Utilization of information digging for human services has helped specialist's to enhance
the wellbeing administrations gave by them. One can't sit idle and cash by picking some off base
treatment for a patient, which can likewise hurt patient's wellbeing.
2) Ranking of various hospitals :
Data mining strategies are utilized to think about every one of the points of interest of different
healing centers keeping in mind the end goal to rank them [19]. Associations rank different healing
centers based on their ability to deal with patients with genuine disease, i.e., healing centers with a
higher rank are more reasonable for taking care of high– hazard patients, as it is their most elevated
need though this isn't the situation in bring down positioned clinics since they don't considerably
consider the hazard factor.](https://image.slidesharecdn.com/160010116021-53-t01oep-191210093006/85/Data-Mining-in-Health-Care-15-320.jpg)
![17
Cont.…
5) Better quality services provided to patients :
With the headway in innovation, we as of now have voluminous information put away in digitized
shape. Information mining when connected on this enormous therapeutic information can help us in
removing a significant number of the fascinating obscure examples.
6) Infection control in hospitals :
Doctor's facility diseases influences a huge number of patients consistently and the quantity of
contaminations which are sedate safe is extremely high [22]. Investigation for contamination is done
through information mining to distinguish some unpredictable examples in the information of
disease control [15]. For contamination control, these examples are additionally examined by a
proficient individual. Such a reconnaissance framework, to the point that utilizations information
digging strategies for finding obscure examples in disease control information was actualized at the
University of Alabama.](https://image.slidesharecdn.com/160010116021-53-t01oep-191210093006/85/Data-Mining-in-Health-Care-17-320.jpg)
![18
Cont.…
7) Reduction in insurance fraud and abuse :
Human services guarantor develops a model to distinguish abnormal examples of cases by patients,
doctors, clinics, and so on [25]. In 1998, Texas Medicaid Fraud and Abuse Detection System spared
million dollars by distinguishing misrepresentation and mishandle through information mining
procedures.
8) Proper hospital resources management :
Administration of doctor's facility assets is an imperative assignment in human services businesses.
Information digging develops a model for overseeing healing facility assets. Gathering Health
Cooperative uses information mining and gives administrations to clinics at a lower cost [27]. Blue
Cross oversees illnesses proficiently by diminishing the cost and enhancing the yields with the
assistance of information mining.
9) Medical device industry :
Without therapeutic gadgets, social insurance industry couldn't exist. Portable interchanges and
economical remote bio-sensors are the most critical part of versatile medicinal services applications
which gives a protected strategy to concentrate imperative indications of patient.](https://image.slidesharecdn.com/160010116021-53-t01oep-191210093006/85/Data-Mining-in-Health-Care-18-320.jpg)
Data Mining in Health Care
- 1. A. D. Patel Institute Of Technology Data Mining And Business Intelligence (2170715): A. Y. 2019-20 Data Mining In Health Care Prepared By : Dhruv V. Shah (160010116053) B.E. (IT) Sem - VII Guided By : Prof. Ravi D. Patel (Dept Of IT , ADIT) Department Of Information Technology A.D. Patel Institute Of Technology (ADIT) New Vallabh Vidyanagar , Anand , Gujarat 1
- 2. Outline Introduction Literature Review Problem Methods Final Conclusion References 2
- 3. Electronic health records (EHR) are common among healthcare facilities in 2019. With increased access to a large amount of patient data, healthcare providers are now focused on optimizing the efficiency and quality of their organizations use of data mining. Since the 1990s, businesses have used data mining for things like credit scoring and fraud detection. Today, healthcare organizations are seeking similar benefits from data mining and predictive analytics. In healthcare, data mining has proven effective in areas such as predictive medicine, customer relationship management, detection of fraud and abuse, management of healthcare and measuring the effectiveness of certain treatments. The purpose of data mining, whether it’s being used in healthcare or business, is to identify useful and understandable patterns by analyzing large sets of data. These data patterns help predict industry or information trends etc.In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiencies, improve patient quality of life, and perhaps most importantly, save the lives of more patients. 3 Introduction
- 4. Cont.… 4 Necessity Of Data Mining in Healthcare : With the help of data mining we can improve the public health, the health care of the system users, reduce costs, save time and money.For preparing Health Care Information System (HMIS) reports for usage of hospital capacities such as number of occupied and vacant beds, number of patient vs doctors and nurses etc. Combining data mining and geographical information system (GIS) we can discover disease clusters towards specific location which leads to better policy making to detect and manage disease outbreaks. Healthcare Business Lifecycle : 1) Health-Care Business Understanding: This Phase concentrates on understanding health- care project objective and various requirements. 2) Medical-Data Understanding : This phase starts with collection of health care data from Laboratories, Operation Theater, Blood Bank, drug store, Therapy Modules etc, and also focuses on understanding of patient’s data to discover knowledge out of it to generate HMIS repots.
- 5. Cont.… 5 3) Medical-Data Preparation: This phase constructs final data set to feed into modelling tools and it is iterative process. Here various database artifacts such as attribute, table,records are selected as well as transformed and cleaned for modelling tools. 4) Modelling : This phase apply various modelling techniques such as Naive Bays, Artificial neural network, decision tree, time series algorithm, clustering algorithm, sequence clustering algorithm etc to generate optimal values. 5) Evaluation : In this stage thorough evaluation and reviewing the model to check whether applied algorithms discovers proper hidden pattern. And this stage also checks forfast accessing on mined data. 6) Deployment : In this phase most of the time customer carry out the deployment wizard with the help of analyst to generate HMIS reports. For generating useful knowledge out of data i.e. reports we can repeat data mining process.
- 6. 6 What is Data Mining in Healthcare ? - By David Crockett and Brian Eliason Abstract: Data mining holds great potential for the healthcare industry to enable health systems to systematically use data and analytics to identify inefficiencies and best practices that improve care and reduce costs. Some experts believe the opportunities to improve care and reduce costs concurrently could apply to as much as 30% of overall healthcare spending. This could be a win/win overall. But due to the complexity of healthcare and a slower rate of technology adoption, our industry lags behind these others in implementing effective data mining and analytic strategies. Key point: Many industries successfully use data mining. It helps the retail industry model customer response. It helps banks predict customer profitability. It serves similar use cases in telecom,manufacturing, the automotive industry, higher education, life sciences, and more. However, data mining in healthcare today remains, for the most part, an academic exercise with only a few pragmatic success stories. Academicians are using data-mining approaches like decision trees, clusters, neural networks, and time series to publish research. Healthcare, however, has always been slow to incorporate the latest research into everyday practice. Literature Review
- 7. Cont.… A Survey on Medical Data by using Data Mining Techniques – Anusha N, Rajshree and Srikanth Bhat K Abstract: Data mining is a vital region of research and is practically utilized as a part of various areas like back, clinical research, instruction, human services and so on. Data mining is an imperative zone of research and is even-mindedly utilized as a part of various areas like fund, clinical research, training, social insurance and so forth. Truth be told, the assignment of data extraction from the medicinal information is a testing attempt and it is a perplex ingerrand. The principle intention of this audit paper is to give a survey of data mining in the domain of medicinal services. In fact, the task of knowledge extraction from the medical data is a challenging endeavor and it is a complex task. The main motive of this review paper is to give a review of data mining in the purview of healthcare. Moreover, intertwining and interrelation of previous researches have been presented in a novel manner. Furthermore, merits and demerits of frequently used data mining techniques in the domain of health care and medical data have been compared. The use of different data mining tasks in health care is also discussed. An analytical approach regarding the uniqueness of medical data in health care is also presented. 7
- 8. Cont.… 8 Data Mining Issues and Challenges in Healthcare Domian - Dr. C. Sunil Kumar, Dr. Govardhan and Sunil Srinivas Abstract: Data mining (DM) has become important tool in business and related areas and its task in the healthcare field is still being explored. Currently, most applications of DM in healthcare can be classified into two areas: decision support (DS) for clinical practice, and policy development. However, it is difficult to find experimental literature in this area since a considerable amount of existing work in DM for healthcare is theoretical in nature.
- 9. Future Perception In Public Health Care Using Data Mining – Dr. Surendra Kumar Yadav, Nitesh Dugar and Aditi Jain Abstract: Healthcare information is diverse in scope and huge in content and its volume is so vast that traditional / routine analytical methods reveal very little of the possible conclusions. Modern data mining techniques can be applied to this data to extract otherwise hidden/ unknown facets of knowledge which may be of vital importance to therapeutic, commercial and preventive aspects of healthcare. This research paper provides a survey of mining concepts in health-care, necessity of data-mining in Medicare field, algorithms used and its applications in various health care domains. 9 Cont.…
- 10. The question that leading warehouse practitioners are asking themselves is this: how do we narrow the adoption time from the bench (research) to the bedside (pragmatic quality improvement) and affect outcomes? The Three Systems Approach: The most effective strategy for taking data mining beyond the realm of academic research is the three systems approach.Implementing all three systems is the key to driving real-world improvement with any analytics initiative in healthcare. Unfortunately, very few healthcare organizations implement all three of these systems. The three systems are : 1) The analytics system : This system includes the technology and the expertise to gather data, make sense of it and standardize measurements. Aggregating clinical, financial,patient satisfaction, and other data into an enterprise data warehouse (EDW) is the foundational piece of this system. 10 Problem Methods
- 11. Cont…. 11 2) The best practice system : The best practice system involves standardizing knowledge work systematically applying evidence-based best practices to care delivery. Researchers make significant findings each year about clinical best practices, but, as I mentioned previously, it takes years for these findings to be incorporated into clinical practice. A strong best practice system enables organizations to put the latest medical evidence into practice quickly. 3) The Adoption system : This system involves driving change management through new organizational structures. In particular, it involves implementing team structures that will enable consistent, enterprise-wide adoption of best practices. This system is by no means easy to implement. It requires real organizational change to drive adoption of best practices throughout an organization. If a data mining initiative doesn’t involve all three of these systems,the chances are good that it will remain a purely academic exercise and never leave the laboratory of published papers.Implementing all three enables a healthcare organization to pragmatically apply data mining to everyday clinical practice.
- 12. Cont.… 12 Future Issues Improved Data Sharing Among Agencies : Several institutions such are overcoming privacy problems that limit information sharing by blocking out significant patient identification information such as Social Security Numbers (SSNs). For example, HCFA has established a data availability link on their web site and support data exchange for research purposes. The challenge will be to overcome propriety conditions imposed by private institutions. Researchers may want to develop contractual relationships with such institutions which may limit the publication of explicit findings but will provide the opportunity to work with real data instances.Finally, researchers will increasingly find public information becoming available on the web. Integrated Web Mining Tools : Text Mining (TM) has recently come into focus for mining on the web. However, the web includes a large amount of non-text based data that may need to be considered in the future, especially as online telemedicine begins to prosper. Another feature that is gaining important reputation is the automation of billing transactions via the Internet. This change will provide a great opportunity to use DM techniques to detect fraudulent online transactions.
- 13. DW Standardization : While uniform DW standards may take a while to appear, there needs to be a bridge to help facilitate mining of data from various sources. The development of inter-agency, flexible standards may mitigate the need for extensive cleaning tools. DW Compression : As DWs continue to grow, the difficulties for mining a massive DS will continue. A process of compression without compromising data quality would diminish some of those issues. Scaling a wider range of accessible tools for large DSs is met with a number of difficulties including idea, data and computational difficulty, and storage requirements. This is particularly a worry if mining is going to continue to be a driving force behind desktop decision support. 13
- 14. 14 Cont.… Common Data Mining Algorithm in Health-care Domain : Decision Tree Algorithm : Decision tree is a flow chart like tree structure which represents rules, where each node denotes a test on an attribute value, branch denotes test output, and leaves represent classes. It recursively partitions a unsupervised learning applications. Neural networks have high acceptance ability for noisy data and high accuracy and are preferable in data mining. Artificial Neural Networks : Neural network is a parallel processing networ which is based on biological neural network and features of neurons (computing elements), generated with simulating the image intuitive thinking of humans. Neuron receives a number of input signals and performs a simple operation on this set of inputs. The output of each neuron is fanned out to the input of other neuron.It can be used to model complex relationships between inputs and outputs or to find patterns in data. It uses the idea of nonlinear mapping, parallel processing and neural network structure to express the associated knowledge of inputs and outputs.
- 15. 15 Cont.… Uses Of Data Mining in Healthcare : 1) Diagnosis and prediction of diseases : When it comes to social insurance businesses, conclusion and anticipation of ailments is imperative,it is a standout amongst the most imperative motivation behind utilizing information digging for social insurance. Utilization of information digging for human services has helped specialist's to enhance the wellbeing administrations gave by them. One can't sit idle and cash by picking some off base treatment for a patient, which can likewise hurt patient's wellbeing. 2) Ranking of various hospitals : Data mining strategies are utilized to think about every one of the points of interest of different healing centers keeping in mind the end goal to rank them [19]. Associations rank different healing centers based on their ability to deal with patients with genuine disease, i.e., healing centers with a higher rank are more reasonable for taking care of high– hazard patients, as it is their most elevated need though this isn't the situation in bring down positioned clinics since they don't considerably consider the hazard factor.
- 16. 16 Cont.… 3) Better treatment techniques : With the assistance of information mining procedures, both the specialist and patient can pick the best treatment choice by looking at among all the treatment systems. They can choose the best treatment systems both as far as adequacy and cost. Through information mining they can likewise discover the reactions of different medications and in this way diminishes hazard to patients. 4) Effective treatments : By contrasting components like causes, indications, symptoms, and cost of medications information mining is utilized to break down the adequacy of medicines. For instance, one can look at the consequences of medications of various patients which were experiencing a similar sickness yet were treated with various medications. Along these lines, we can discover which treatment is compelling regarding the patient's wellbeing and cost.
- 17. 17 Cont.… 5) Better quality services provided to patients : With the headway in innovation, we as of now have voluminous information put away in digitized shape. Information mining when connected on this enormous therapeutic information can help us in removing a significant number of the fascinating obscure examples. 6) Infection control in hospitals : Doctor's facility diseases influences a huge number of patients consistently and the quantity of contaminations which are sedate safe is extremely high [22]. Investigation for contamination is done through information mining to distinguish some unpredictable examples in the information of disease control [15]. For contamination control, these examples are additionally examined by a proficient individual. Such a reconnaissance framework, to the point that utilizations information digging strategies for finding obscure examples in disease control information was actualized at the University of Alabama.
- 18. 18 Cont.… 7) Reduction in insurance fraud and abuse : Human services guarantor develops a model to distinguish abnormal examples of cases by patients, doctors, clinics, and so on [25]. In 1998, Texas Medicaid Fraud and Abuse Detection System spared million dollars by distinguishing misrepresentation and mishandle through information mining procedures. 8) Proper hospital resources management : Administration of doctor's facility assets is an imperative assignment in human services businesses. Information digging develops a model for overseeing healing facility assets. Gathering Health Cooperative uses information mining and gives administrations to clinics at a lower cost [27]. Blue Cross oversees illnesses proficiently by diminishing the cost and enhancing the yields with the assistance of information mining. 9) Medical device industry : Without therapeutic gadgets, social insurance industry couldn't exist. Portable interchanges and economical remote bio-sensors are the most critical part of versatile medicinal services applications which gives a protected strategy to concentrate imperative indications of patient.
- 19. 19 Examples in Healthcare Data Mining One of the most prominent examples of data mining use in health care is detection and prevention of fraud and abuse. In this area, data mining techniques involve establishing normal patterns, identifying unusual patterns of medical claims by health care providers (clinics, doctors, labs, etc). On May 17, 2013, the Department of Health and Human Services (HHS) issued the final rule "State Medicaid Fraud Control Units; Data Mining" which permits Federal financial participation in the cost of data mining can be covered, if certain criteria are satisfied. MFCUs must submit data mining applications to the Office of Inspector General for approval. The Centres for Medicare & Medicaid Services (CMS) also updated data mining rules to enrich patient care. This rule makes identifiable data files (IDFs) available to certain stakeholders as allowed by federal laws and regulations and CMS policy. Aiding hospital management is another data mining task in healthcare. Here the data mining tools can identify and track chronic disease states and high-risk patients, develop appropriate treatment schemes, and reduce the number of hospital admissions and claims. A Survey of Health Care Prediction Using Data Mining cites the Arkansas Data Network data mining initiative as an example of an organization that is developing better diagnosis and treatment protocols. The facility analyzes readmission and resource utilization data and compares its data with current scientific literature to “determine the best treatment options, thus using evidence to support medical care and streamlining the process”.
- 20. Cont.… 20 Measuring treatment effectiveness is another application of data mining in healthcare. This application involves comparing and contrasting symptoms, causes and courses of treatment to find the most effective course of action for a certain illness or condition. Data mining tools compare symptoms, causes, treatments and negative effects, identify the side effects of a particular treatment, and analyse which decision would be most effective. This application of data mining can help providers develop smart methodologies for treatment, best standards, and care practices. For example, a research paper published in International Journal of Scientific & Engineering Research explores a case of data mining used by United HealthCare. This facility has mined its treatment record data to find ways to deliver better medicine at a lower cost.
- 21. 21 Applications Anaesthesia Analysis of Drug Side effect Cervical Cancer Tumours Health-care Image Processing Classification of Blood Cells To analyse ECG Wave Forms To classify Retina Damage
- 22. Empirical research in DM for healthcare is limited. Several factors, most importantly those related to data quality and availability, have limited research in this locale. In our evaluation, addressing these two issues will give significant drive to research in this arena. In particular, healthcare institutions and governing bodies need to establish strong data quality standards before the environment can be conducive to productive research. Secondly, a positive partnership must be established among institutions maintaining DW. Different assignments and applications identified with data mining are broke down inside the domain of human services associations. Maybe, there is no single information mining strategy which can give reliable comes about for a wide range of social insurance information. In fact, the execution of strategies shifts from one dataset to other dataset. For compelling use of these procedures in medicinal services space, there is a need to upgrade and secure wellbeing information sharing among different gatherings. 22 Final Conclusion
- 23. 23 What is Data Mining in Healthcare ? - By David Crockett and Brian Eliason A Survey on Medical Data by using Data Mining Techniques – Anusha N, Rajshree and Srikanth Bhat K Data Mining Issues and Challenges in Healthcare Domian - Dr. C. Sunil Kumar, Dr. Govardhan and Sunil Srinivas Future Perception In Public Health Care Using Data Mining – Dr. Surendra Kumar Yadav, Nitesh Dugar and Aditi Jain https://downloads.healthcatalyst.com/wp-content/uploads/2014/05/Healthcare-Data- Mining.pdf https://archer-soft.com/en/blog/data-mining-healthcare References
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