Converting Raw Medical Data Into Informative Data Through Data-Mining Tools

Health care facilities, physicians, health care personnel and most importantly patients will definitely benefit from the data mining health care information. This paper will discuss different ways data mining health care information will be beneficial to health care facilities, health care personnel and patients alike and also the risk of data mining health care data.

The process of collection of data can be done in many different ways and the present paper will review it in the next section, but the fact that the data is going to be used to grade a system suggests that the health care system finally has a way of reviewing, analyzing and using the data in a useful way.

Data mining is defined as the process of data selection and studying and building models using massive data stores to disclose previously unidentified patterns in databases (Koh and Tan, 2005, p. 64). Koh and Tan have found financial institutions, marketers, manufactures and so has many other agencies have used data mining. Data mining has been of great use by various organizations. For example, data mining has been useful to detect fraudulent credit card transactions (Koh and Tan, 2005, p. 64). Koh and Tan stated, “In healthcare, data mining is becoming increasingly popular, if not increasingly essential” (Koh and Tan, 2005, p.64). In healthcare there have been reports that data mining has been successful in detecting fraud and abuse in healthcare claim (Koh and Tan, 2005, p.65). There are many factors in healthcare that have driven the use of data mining applications. One of the factors that have driven healthcare to use the data mining applications will be the medical insurance fraud and abuse. All organizations currently involved in the healthcare industry can profit from the data mining applications. For example, data mining is able to help

In healthcare organization data mining plays the most leading role in the research area. Data mining plays a vital role in various fields of technology. In healthcare industry we gather more information regarding patients, diseases, hospital resource, electronic patient’s records, diagnosis methods, etc., by using health care in data mining it is easy to classify or group the patients having the same disease so that it helps to treat them effectively. In this paper I have reviewed about data mining application in health care and data mining challenges in health care.

The healthcare industry has undergone many changes within the last few years. The healthcare industry is constantly evolving with newest technologic advancements. The majority of the technology advancements are deeply interrelated with their overall mission of quality improvement. In Ms. Epstein opinion the health information technology field has evolved drastically since her entering this industry over last decade. In Charity Hospital was large device, which housed all the medical records in the basement of the hospital. The interface of the data centers was not accessible. The next major change was from having a few limited computers to multiple technology machines. Now the database can even cloud computing medical information. Cloud computing

The next step in the database development effort is to select one process or a set of related processes for further analysis and improvement

Data generated throughout the perioperative experience can be used to analyze patient care in a process-based approach that can help identify opportunities for improvement (St. Jacques & Minear, 2008). Reports generated from warehouse data collected throughout the perioperative experience enables identification of key process steps, productivity, procedure cost comparison, trends of surgical site infections, among others (Jacques & Minear, 2008). The ability of the computer software to generate countless categories of data allows healthcare providers to support an optimal perioperative process (Jacques & Minear, 2008). Utilization of data reports generated through the data warehouse will facilitate ongoing process improvement and fine tuning of a complex

Without properly cleaning, transforming and structuring the data prior to the analysis, one cannot expect to find meaningful knowledge. In a KDD process, the preprocessing step represents at least 60% of the entire process for about two thirds of the Data Mining experts responding to the survey .

Abstract— Data mining, also popularly known as Knowledge Discovery in Database, refers to extracting or “mining" knowledge from large amounts of data. Data mining techniques are used to operate on large volumes of data to discover hidden patterns and relationships helpful in decision making. Many application areas such as medical, research, stock market, weather forecasting, business strategies…etc data mining is very much helpful to gain the hidden and useful information. Nowadays the universities also have been started to use the data mining in-order to achieve highest quality in teaching. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in

Data mining prediction model works on the process of identifying the patterns based on the historical information to predict the new incoming data sets. This prediction modelling is much useful in the case of decision making process in the business models. On the other way, Descriptive model describes the data in an efficient way by means of grouping the data by using clustering; association rules principles of data mining.

At the conclusion of the requirements phase, the team has agreed upon the needs of the business and what must be required of the new system. The next step in designing and implementing the new system is determining the processes that will be applied to the data and graphically representing them in a model using structured analysis techniques. Process models are used to identify and document the portion of system requirements that relates to data. Processes are the logical rules that are applied to transform the data into meaningful information. During this phase, data flow diagrams are required to show how the data will move through the system. The systems data flow and data stores are then documented in the data dictionary, which is used

Data mining: Choosing appropriate data mining algorithm is the main task in this step. After getting decided with what kind of data to be used among classification, regression, clustering etc we need to consider two important steps like: Prediction and description.

data is also Growing. It has resulted large amount of data stock in databases , depot and other repositories . therefore the Data mining comes into model to explore and analyses the databases to extract the interesting and previously obscure patterns and rules well-known as association rule mining

The Clinical Laboratory in the Inpatient setting has evolved over the years. Electronic data and electronic medical records’ demand require medical documentation to be processed fast and accurate daily. I selected the Clinical Laboratory Department because the amount of data that is processed in these departments is impressive. I covered three main points in my report: (i) an overview of the department, (ii) the role of Health Informatics in the Clinical Laboratory, and (iii) the impact of the advances in Health Informatics in the Clinical Laboratory Department.

Data mining is the process of discovering patterns, trends, correlations from large amounts of data stored electronically in repositories, using statistical methods, mathematical formulas, and pattern recognition technologies (Sharma n.d.). The main idea is to analyze data from different perspectives and discover useful trends, patterns and associations. As discussed in the previous chapter, the healthcare organizations are producing massive amounts of electronic medical records, which are impossible to process using traditional technologies (e.g., Microsoft excel). Therefore data mining is becoming very popular in this field as it can be used to identify the presence of chronic disease, detect the cause of the disease, analyze the effectiveness of treatment methods, predict different medical events, identify the side effects of the drugs, and so on. Kidney diseases such as CKD or AKI require immediate detection and medical attention based on the patient’s clinical condition, medical history, medication history and some demographical factors. From the literature survey, we discovered a good number of studies and tools that used data mining methods such as clustering, association, and classification to improve the decision-making ability of the healthcare providers regarding kidney disease. In the subsequence sections in this chapter, we present an overview of the data mining methods and discuss how they have been used in existing literature.