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Data Mining Applications in Healthcare

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IE 525 - Term Project Proposal


Data Mining Applications in Healthcare

Group members:

Hazal Ercan

Elifnas Ertekin

Supervised by:

Kemal Kılıç

November, 21, 2016

Fall 2016-2017

Sabancı University


In this project, data mining applications in health care systems will be analyzed. Deliverables of this project will be a wide research about data mining applications in health care therefore forty or more literature reviews will be investigated. While reviewing these forty papers, the correlation end relation between the papers will be noted. According to these connections between them, possible taxonomies will be detected. Throughout the project, two reports will be written. In the first report, sufficient inferences will be involved, while in the review paper, evaluations will be made and possible taxonomies of related literatures reviews will be determined. Also by preparing this project, important information about writing scientific report will be acquired.

1 Introduction

Today, almost every human-based endeavor stores large amount of data which creates overload information and leads to “demand for new, powerful tools for turning data into useful, task-oriented knowledge” (Springer). In order to extract meaningful patterns and sufficient information, data mining which is a new research area come into existence. Data mining includes various fields such as optimization, operations research, database management and statistics.

Process of discovering knowledge with the use of data mining can be ordered as respectively; defining objective, preparing data, mining knowledge and interpreting the results. After specifying a certain scientific objective, relational data for that objective should be required. It is not always true that more data will increase the accuracy of the result. Sometimes big data sets may contain irrelevant information or correlations between data sets may exist. Therefore data cleaning and feature selection is important in the context of preparing data. Mining the knowledge is bounded with model types. Correlation analysis, clustering and association rules are preferred for descriptive models, on the other hand categorization, classification and regression are utilized for predictive models. Also different techniques which are being used in data mining result from various representations of different kind of problems, different scales of data and different technical requirements. To sum up, it would be true to say that data mining is a “non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” (M. Durairaj, V. Ranjani).

Nowadays, most popular applications in data mining are health care management, energy systems, manufacturing, scheduling, social networks, virtual reality and online gaming, interactive machine learning and zoning in operations management.

1.1 Definition and Scope

As mentioned above, one of the major areas that data mining is utilized, is healthcare. Healthcare data generates detailed information about resources of hospital, records of patients, diagnosis of disease, treatment and prevention of disease etc. Since healthcare storage rises with a continuous increase, it is possible to say that, as time went on healthcare data becomes more complex. While healthcare industry contains that amount of complex data, its possibility of being analyzed with traditional methods to extract useful information from it, decreases. Therefore, data mining is a beneficial way to transform collected data into profitable information. It is possible to identify the data mining usage in health sector by dividing it into five categories, which are: research & development, pricing, new business models and clinical operations. Since each of these categories’ background is too complicated to deal with, on this project the category, which mainly focused, is clinical operations. Data mining applications in clinical operations healthcare can be aligned as, fraud detection, comparative effectiveness research, disease management, case management, remote patient monitoring, transparency about medical data and patient profiling.

In this project, comparative effectiveness research and remote patient monitoring will be the applications that will focused on.

Comparative effectiveness research is a research that searches for the best treatment that will applied to a particular patient. CER analyzes the comprehensive data that belongs to the patient and according to its results, it determines the most effective treatment out of different interventions.

Remote patient monitoring is used for collecting data from chronically ill patients. It aims to improve future drug treatments by determining if the patients are doing what they told to do or not.

1.2 Project Objective

The proposed outcome of this project will be writing a report and review paper. Sufficient inferences, evaluations and possible taxonomies will be involved. Anticipated completion date is 9th of January.

2 Project Planning

2.1 Organizational Structure

Provide the following information in plain text. Do not just give a table that contains these items:

[pic 1]        Describe the responsibilities/ job allocation of each of the team members.

[pic 2]        Indicate how much time per week each team member will spend. Every team member should spend a reasonable time for the project course. Consider your other courses and make a guess on how much time per week you will be able to have for PROJ.102.

[pic 3]        Give your periodic meeting schedule including both the meetings with the supervisors and the other meetings that you will have with your teammates.


 2.2 Time and Resource Plan

Provide a Gantt chart prepared by using MS Excel or a similar tool. Please see the sample Gantt chart provided on the Proj102 web site.

Note that your Gantt chart must include all the work packages, and its structure should reflect the work breakdown structure you give in the previous sections.

[pic 4]  The information in the graph should demonstrate that you will finish the project    within the established time.

[pic 5]        There should be no conflict between the total time requirement of the different work stages and the total time that will be spent by team members.


3 References

The American Psychological Association [APA] has established guidelines and a documentation system called an “author-date style” for writing in both social and physical sciences. The Council of Science Editors [CSE, formerly CBE] has established guidelines for writers in engineering and natural sciences. The CSE approves two documentation formats, the “citation-sequence style”, which requires the listing of the sources in the order they appear in the text by numbers and the “author-date style” similar to APA. Since APA is more broadly used in the undergraduate and CSE in graduate studies, you are required to use the APA style.


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