Data mining concepts in healthcare

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1 Data mining concepts in healthcare Güney Gürsel Department of Medical Informatics, Gülhane Military Medical Academy, Ankara, TURKEY ABSTRACT Data mining has great contributions to the healthcare such as support for effective treatment, healthcare management, customer relation management, fraud and abuse detection and decision making. The common data mining methods used in healthcare are Artificial Neural Network, Decision trees, Genetic Algorithms, Nearest neighbor method, Logistic regression, Fuzzy logic, Fuzzy based Neural Networks, Bayesian Networks and Support Vector Machines. Because of the complexity and toughness of medical domain, data mining is not an easy task to accomplish. There exist serious challenges. The use of Data mining in healthcare informatics and challenges will be examined in this study. This is a descriptive study that examines the concepts, issues related and techniques used in data mining in healthcare data. INTRODUCTION From primary care institutions to big healthcare centers, every healthcare organization uses an information system. These healthcare information systems (HCIS) store, process and retrieve healthcare data. Healthcare data is very valuable in today s world. By the help of rapidly developing healthcare informatics, there are efforts to use the valuable data stored electronically in HCIS databases to improve healthcare. Healthcare giving staff expects more than e-recording the data from HCIS. Besides using healthcare data for care giving, healthcare centers and academic centers use these data for education and research. Research in medical area is not limited to healthcare development such as developing new healing techniques and drugs, but also there are healthcare informatics fields such as structured data entry, constructing longitudinal patient data, image processing etc. We know that when compared to human brain, computers are well suited to making rapid calculations and detecting hidden facts, facilitating decision networks that support near limitless complexity. Any research area, dealing with huge and valuable data,such as medical domain, requires creative techniques supported with computers and computer systems to utilize it. Data mining techniques are good examples for these required creative techniques. This is a descriptive study that examines the concepts, issues related and techniques used in data mining in healthcare data. MEDICAL DATA MINING A. Contribution of the Data Mining to the Healthcare Healthcare service is costly. Especially for the inpatients, the cost increases exponentially. Food consumed, bed occupied and related services for lodging, surgery preparation laboratory tests, radiology tests, surgery, post-surgery care, postsurgery control laboratory tests... Although human life and health is invaluable, healthcare institutions are professional plants which are expected to make profit to survive. In addition, insurance companies or bodies paying the healthcare service fees of patients, put pressure on these healthcare institutions to decrease the costs. By the way, the toughest thing is the quality of the service must increase in spite of the decreased cost. Otherwise the attractiveness of the healthcare institution decreases, which results in losing the customers, patients. American Medical Association (AMA, 2010) states that USA spent 2 trillion dollars in 2008 for healthcare, which stands for %16.2 of gross domestic product of USA. The highest ranked countries according to the health expenditure,within34 countries of Organization for Economic Cooperation and Development (OECD)are given in Table 1 (Speights, 2013). Table 1. Countries with the highest healthcare expenditure in OECD Rank Country Percentage in Gross National Product (%) $ per Person Spent Life Expectancy (years) 1 USA , Netherlands 12 5, Germany ,338 80,5 4 France ,974 81,3 5 Switzerland , Canada , Denmark , As seen in Table 1. the expenditures are huge. These expenditures are not compatible with the life expectancy, USA is the first to spend but near last to have life expectancy, whereas Switzerland is almost last to spend but first to have life expectancy. 32

2 Although the discussion about what is data, what is information and what is knowledge is took placed long ago and these terms are clear in today s world, it will be useful to mention briefly in this study to avoid confusion. Data is the raw form and it has no meaning by itself. Wikipedia (2013) explains the relation between these three concepts in simple and understandable way. Difference between these three terms, lie in the level of abstraction. Data is the first level, the raw and meaningless form. With interpreting, the data becomes information. Utilization of information brings the knowledge. Returning back to our subject, almost all healthcare institutions use an information system. These systems collect and generate data. These data is not only about patient but also about medical devices, medical resources, in short about every item related to healthcare. The healthcare data at hand is growing bigger and bigger as the time passes. The anonymous saying (it is everywhere but no sign about who is the owner) Drowning in data, starving for information/knowledge summarizes the situation. We need data mining to extract information and knowledge from these huge data in healthcare, to use this information and knowledge for decreasing the costs and increasing the quality. Data mining help healthcare get (Hays, 2012); Question based answers Anomaly based discovery New Knowledge discovery Informed decisions Probability measures Predictive modeling Decision support Improved health Personalized medicine One of the most value adding contributions of data mining to the healthcare is in reimbursement field. Medicare, the national social insurance program administered by US government, declared that it wouldn t reimburse the healthcare organization for medical errors in This would lead serious cuts in the healthcare giving organizations. Reduction of medical errors, by means of data mining, not only gives way to more qualified service, but also elimination of the fund cuts. In addition to elimination of fund cuts, Data mining also enables organizations to have accurate claims to the reimbursement companies. By the use of the information and knowledge discovered, underestimation or over billing risks will definitely lower. B. Data mining techniques used in healthcare informatics The data mining techniques/methods and data mining tasks may be a point of confusion in the literature. To avoid misunderstanding, in this study, classification, clustering, rule association, description and visualization are called as data mining tasks. The techniques/methods used accomplishing these tasks such as Artificial Neural Networks, Decision Tree, Logistic Regression etc. will be called as data mining techniques/methods. Healthcare is an endless area for the data mining. So it is hard to tell any technique that is not used in. In this section of the study, the most common techniques will be examined and explained briefly. The most common task used in medical data mining is the classification (Chen, Fuller, Friedman &Hersh, 2005 ). The common methods/techniques of data mining are; Artificial Neural Network (ANN), Decision trees (DTs), Genetic Algorithms (GAs), Nearest neighbor method, Logistic regression, Fuzzy logic, Fuzzy based Neural Networks (Neuro-Fuzzy) Bayesian Networks, Support Vector Machines. In the literature review of Kolçeand Frasheri (2012) for the data mining techniques used in the diagnosis and prognosis of diseases, DTs, ANNs and Bayesian algorithms appeared to be the most well-performing algorithms for diagnosis. For prognosis ANNs, Bayesian algorithms, DTs and Fuzzy algorithms came out to be the most well-performing algorithms respectively. In the same study, literature showed that, in diagnosis of Cancer Diseases ANNs, in Heart Diseases Bayesian algorithms and in other diseases DTs, in prognosis of Cancer Diseases again ANNs, in Heart Diseases ANNs and Bayesian algorithms are the most well performing techniques. C. Temporal data mining in healthcare informatics Time is a critical dimension in healthcare knowledge extraction, because hidden patterns do not exist throughout the whole time period of healthcare monitorization, but only in some time intervals with recurrent and periodic nature (Henriques, Pina,&Antunes). Temporal data mining (TDM) can be defined as the attempt to search for interesting correlations and patterns in temporal databases (Bruno&Garza, 2012). TDM tries to discover qualitative and quantitative patterns in temporal databases or in 33

3 datasets of discrete-valued time series (Lin, Orgun, & Williams, 2002). It has two popular threads (Lin, Orgun, & Williams, 2002): In the purpose of finding fully or partially similar patterns in temporal databases(similarity) In the purpose of finding fully or partially periodic patterns in temporal databases(periodicity) Temporal Abstraction is the approach of dealing with time-related data in medical research in which methods give the opportunity to make abstract definition of temporal data by extracting the relevant features (Takabayashi, Ho, Yokoi, Nguyen, Kawasaki, Le, Suziki & Yokosuka, 2007). It is the transformation of time-stamped data into interval based representation. Temporal Abstraction has two phases: First one is extracting time-stamped data, second one is extracting temporal specific relationships between these extracted data (Takabayashi, Ho, Yokoi, Nguyen, Kawasaki, Le, Suziki& Yokosuka, 2007). Batal, Valizadegan, Cooper, andhauskrecht (2012), has divided the clinical variable as medical variables (that uses the abstraction as on medication and of medication) and lab variables having two types of abstractions, trend abstraction (decreasing, steady, increasing) and value abstraction (e.g. very low, low, high etc.). State sequence representation is theattempt to represent the variables as a series of state intervals where the state intervals are ordered according to the start times. After abstracting the variables, every patient in the mined database is represented as state sequence (Batal, Valizadegan, Cooper, & Hauskrecht, 2012) To describe the relations between pairs of state intervals, Allen s (1984) temporal logic is used in TDM not in medicine but in general. Figure 1.shows Allen s temporal logic. Figure 1.Allen s temporal logic (Batal, Valizadegan, Cooper, &Hauskrecht 2012). One of the most attractive applications of TDM is the extraction of temporal rules from data (Laboratory for Biomedical Informatics, 2013). In temporal rules the consequent is related to the antecedent of the rule with a kind of temporal relationship, unlike association rules; in addition, a temporal rule suggests a cause-effect association between the antecedent and the consequent of the rule (Laboratory for Biomedical Informatics, 2013). For example, by the help of TDM, a temporal rule extracted may be, in many diabetic patients, the presence of hyperglycemia overlaps with the absence of glycosuria (Patel, Hsu, & Lee, 2008). Another example can be, Low sys-tolic blood pressure AND Low diastolic blood pressure frequently occur before high heart rate frequency (Concaro, Sacchi,Cerra, Fratino, &Bellazzi, 2011). D. Distributed data mining in healthcare informatics Healthcare organizations are (and should be) distributed to facilitate easy access of public. In most countries, there is a chain of healthcare that is; the patient visits the primary care health institutions first, if he/she can t solve the problem, then secondary and third step health institutions are visited. This type of healthcare organization brings some problems for healthcare informatics such as standardization of healthcare data, healthcare language problem (called as medical coding systems), and communication of the healthcare institutions, collection of patient data scattered to different healthcare institutions etc. This issue is a challenge to healthcare data mining and will be mentioned in the following sections. There should be a distributed possibility to enable mining scattered healthcare databases. Figure 2 gives the idea of distributed data mining (DDM). 34

4 FIGURE 2. Distributed data miningvs. Central mining (Park, &Kargupta, 2003). DDM has two possibilities: To process and analyze the data in the distributed centers separately, or transfer all data to a center and analyze all centrally. The latter option is somehow requires huge resources for the healthcare. There are efforts to have a central database for the patient data, but all these efforts have the architecture to have summary data in the center and link them to the original source to avoid the requirement of huge resources and budget. Mining in this central architecture will be nonsense. Than the first option is more suitable for healthcare There are efforts to solve DDM in healthcare using agents. The terms agant-based DDM, DDM using intelligent mobile agents etc. are available in the literature. In such approaches, the idea is deploying a data mining agent to the site, and getting analysis from the agent to the centre. There is no standard way to accomplish such approaches and it is not easy to perform. There are some studies in the literature having the same idea but proposing different designs. E. Challenges for the use of data mining in healthcare informatics In every Introduction to Medical Informatics lecture, the complexity and toughness of the medical environment is emphasized. This complexity and toughness is a challenge to every information technology tool and application as well as data mining. In this section of the study, the challenges specific to healthcare data mining will be examined. Heterogeneous Sources and Forms of the Data In the previous section, scattered nature of the healthcare institutions is mentioned. This scattered structure is a challenge to the healthcare data mining applications, distributed applications are needed to handle this challenge. The wide range of medical domain also fuels the heterogeneity. There are many different departments of medical domain such as, clinics, laboratories, radiology, nuclear medicine etc. All these departments may have different databases or have a common database if they are the part of the same institution. In either way, they have different type and format of data to mine. For example radiology data have images; laboratory data have number type results whereas clinical data have texts. This form heterogeneity is also another challenge for healthcare data mining. Data Heterogeneity may cause sampling, selection, and spectrum bias (Kwiatkowska, Atkins, Ayas, &Ryan, 2007). Disharmony Between Medical and Computer Communities This challenge is related to the complexity and toughness of the medical domain. It is not a specific healthcare data mining challenge, but a challenge for all information technology applications. Medical users have tight schedule in dealing with care giving and are not volunteer to use any information technology that costs them additional time. Computer staff urges medical users to enter structured data to have them in computer processable form, while medical users want to write down their needed notes to papers because of the time consumed for dealing with computers. In addition to this disharmony, the problems of accessibility in hospital exist such as, placing any hardware to sterile areas, entrance of technical staff to these areas for maintenance and fixing. Legal and Ethical Issues Privacy and security issues, their protection with the law, are an item of another challenge that healthcare data mining has to deal with. These issues will be examined in the next section thoroughly. Ciosand Moore (2002) describethese challenges as follows: Data ownership: Ownership of patient data is not clear.whether the patients, the physicians, the laboratories, or the insurance companies own the data collected from patients. The main regulation about the ownership of the data is the Health Insurance Accountability and Portability Act (HIPAA) Privacy Rule. Fear of lawsuits: In medical communities, particularly in the Unites States, malpractice and other costly lawsuits is amatter of fear that constitutes a challenge to the application ofhealthcare data mining. 35

5 Privacy issues: Protecting patient privacy and doctor-patient confidentially constitutes another set of challenges to healthcare data mining. Administrators and researchers should pay utmost attention to privacy and security issues. Non-standard Data This subject deserves being mentioned separately, because of its being big challenge for healthcare data mining although it is mentioned in the heterogeneity of data part. Doctor s interpretations about clinical data are written in unstructured form, which makes data mining difficult to perform (Milovic & Milovic, 2012).Even the high experienced specialists of the same area cannot agree on common terms that indicate the status of the patient. Different names are used to describe the same disease different grammatical structures are used to explain the relations between medical entities (Milovic & Milovic, 2012). Although there are considerable efforts to constitute nomenclatures and classifications for structured medical data entry such as International Classification of Diseases (ICD), Systematized Nomenclature of Medicine (SNOMED), International Classification of Nursing Practices (ICNP), still there is enormous size of non-standard data in medical domain. For this reason, data mining researchers has great efforts for text mining applications in healthcare. Noisy Data In the previous parts, the tight schedule of healthcare staff is mentioned. This tightness brings many handicaps. Missing, corrupted, inconsistent data is some outcomes. In the time of care giving, the user does not think or care about the usefulness of the data he is entering and he miswrite (if it is obligatory field) or leave it blank. This missing, corrupted, inconsistent data is called as noisy in short. Noisy data is a big challenge for data mining. Surely there are data cleaning techniques in data mining to handle noisy data, but it causes a bias anyway no matter how well it is handled. Domain Participation Healthcare data mining has different perspectives when compared to data mining in other fields. In the previous section complexity and toughness of the medical field is emphasized. This complexity and toughness needs domain expertise to perform successful data mining applications. To have both medical and data mining expertise is almost impossible, so cooperation and team work is needed for healthcare data mining. This can be possible by either having a domain expert in the research team or convincing medical staff for cooperation. Dynamism of the Medical Domain With the help of the technology, online scientific databases and increasing research funds, medical knowledge is developing rapidly. Healthcare data mining is like a shooter to hit a moving target (Anne, 2011). Healthcare informatics fields of data mining Medical domain is so huge and complex, so applications fields of data mining is not an easy task to list. The Figure of healthcare data need can give an idea about this impossibility. To give an idea about the contribution of data mining to the healthcare, the fundamental fields of data mining applications will be examined in this section. Hospital Infection Control Literature says nosocomial infections effect two millions of patients in the USA in each year in 2001 (Gaynes, Richards, Edwards, Emori, Horan, Alonso-Echanove, Fridkin, Peavy&Tolson, 2001). Association rules can be drawn from culture and patient data obtained from the laboratory database and infection committee can review these rules periodically (Obenshain, 2004). Standardized Reporting Hospitals report many incidents and epidemic diseases to the government. Cluster and association analyses can show how risk factors are reported with the use of International Classification of Diseases and by reconstructing patient profiles (Obenshain, 2004). 36

6 Figure 3. Need of healthcare data according to the purposes. Identifying High-Risk Patients Data mining applications can report patients that have high risks. This can aid caregivers to take some precautions and save lives (Obenshain, 2004). Outlier Detection Outlier Detection, can be also named as Anomaly detection, provides opportunity for identifying rare events in large datasets (Jacob&Ramani, 2012). Detection of extreme observations may prevent incorrect data and human error while at the same time presence of Outliers could lead to novel insights in clinical knowledge discovery (Jacob&Ramani, 2012). Fraud and Abuse Detection Healthcare is prone to fraud and abuse, because of the huge amount of money it creates. Especially medical insurance companies employ data mining applications to reduce their losses. Treatment Effectiveness Data mining can help about which courses of action prove effective, by comparing and contrasting causes, symptoms, and courses of treatments (Koh& Tan, 2011). Data mining also helps evolve standardized treatments for specific diseases (Koh& Tan, 2011). Also applications to facilitate early diagnosis, non-invasive diagnosis, determination of adverse drug effects can be made. Customer Relationship Management Data mining applications can help healthcare to determine the preferences, usage patterns, current and future needs of individuals,whether a patient is likely to comply with prescribed treatment or whether preventive care is likely to produce a significant reduction in future utilization (Koh& Tan, 2011). Reduction of Medical Errors Medical errors are important issue in healthcare. One of the most important application fields of data mining is medical errors. By means of decision support facility, it can help reduce medical errors. Examples of Data mining applications in healthcare After giving the key concepts and fields of data mining in healthcare, some examples of data mining applications will be given in this section.one can find hundreds of application examples and case studies designed and used for healthcare. In this section a few examples will be given. This case study is excerpted from Naddar. Hill Physicians Medical Group s company, PriMEd Management, was using their transactional system for processing the claims (Verma& Harper, 2001). This activity was slowing down their daily works by slowing the system they use. By constructing a data warehouse in 1996 and building data mining applications on it, the corporation did not only handle the problem of slowing, but they also handled ths issues: Analysis of physician compensation, Physician profiling, Utilization reporting, 37

7 Disease state management reporting, Contract viability analysis Lin, Orgun & Williams (2002), applied a temporal data mining application for the elderly diabetic patients to the Australian Medicare (the Australian Government s health care system) data. They used hybrid method, combination of wavelet analysis and the statistical distribution of the values. They found some periodic patterns and similar patterns. Centers for Medicare and Medicaid Services (CMS), is a federal agency within the United States Department of Health and Human Services (DHHS) that administers the Medicare program, has a data mining application in broader scope for abuse and fraud detection called as Medicaid Fraud & Abuse Detection System. The results are striking. In less than one year, 1400 suspected operations are detected and investigated, 2.2 million $ recovered. Customer Potential Management (CPM) Corporation developed a system based on data mining called as Consumer Healthcare Utilization Index (CHUI), to help healthcare organizations determine who might get sick and need certain services (Direct Marketing, 2001). This system is in the claim to identify the best audience for a healthcare organization's programs and products. By using data mining techniques, actual behavior and individual demographics of patient, CHUI provides smaller, more relevant and more precise bits of data to forecast healthcare needs(direct Marketing, 2001). United HealthCare has a data mining application to cut the costs and improve the delivery of healthcare (which is the main objective for healthcare organizations). This application gave physician the clinical profiles of their practice patterns which is comparable with other physicians and industry standards. FUTURE RESEARCH DIRECTIONS Healthcare domain is dynamic and we named this issue as a challenge to the data mining. It is also a forcing motivation to the data mining applications in healthcare. This dynamism gives way to new horizons and more data mining applications will be employed to discover new patterns and associations. In the view of the subjects examined in this study, future data mining studies seem to take place, not limited to but in considerable weight, in distributed data mining applications and text mining algorithms. CONCLUSION Complexity and toughness of the medical domain makes use of information technologies mandatory. Its scattered nature, text loaded data forms, having the subject of human life, and the pressure to decrease costs push healthcare managers to use data effectively and convert it to the information and knowledge. Accomplishment of these tasks is possible with the effective use of data mining. REFERENCES Allen, J. F. (1984). 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