Download healthcare knowledge management using data mining techniques

Advances in Computational Research
ISSN: 0975-3273 & E-ISSN: 0975-9085, Volume 7, Issue 1, 2015, pp.-274-278.
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HEALTHCARE KNOWLEDGE MANAGEMENT USING DATA MINING TECHNIQUES
MAHAMUNE M.1*, INGLE S.2, DEO P.3 AND CHOWHAN S.1
1School
of Computational Sciences, S. R. T. M. University, Nanded - 431606, MS, India.
of Computer Science & Engineering, Shreeyash College of Engineering & Technology, Aurangabad - 431 005, MS, India.
3Department of Commerce and Management, S. B. E. S. College, Aurangabad - 431 001, MS, India.
*Corresponding Author: Email- mohnish.mahamune@gmail.com
2Department
Received: December 18, 2014; Revised: January 05, 2015; Accepted: January 15, 2015
Abstract- Knowledge Management (KM) in any organization in integration with information technology supports system strategies with business strategies to attain the vision and mission very precisely. In today’s era knowledge management is crucial in all aspects of organization;
Hence knowledge discovery from massive database is today’s need. Knowledge Discovery in Databases (KDD) helps organizations turn their
data collection into valuable information. Organizations that take advantage of KDD will find that they can lower the healthcare costs while
improving healthcare quality by using fast and better clinical decision making. The data collected by healthcare organization might be structured or unstructured. Hence it is essential to use some technology to gain knowledge from massive databases which will increase the access
to knowledge for the people working in organization as well as competitiveness among organizations. Though the healthcare sector relies
heavily on knowledge and evidence based medicine is expected to be implemented in daily healthcare activities; the quality of care relies on
the exchanging the knowledge between the organization. The Data Mining comes up against major scientific and technological type of tool for
knowledge discovery and is also considered as significant field in Knowledge Management. This paper explores the Data Mining techniques
which found suitable for discovering the hidden knowledge from the healthcare massive database and their applications which supports the
KM process in healthcare sector, its advantages, disadvantages and challenges.
Keywords- Knowledge Management, Data Mining Applications, Knowledge Discovery in Healthcare, Knowledge Discovery in Databases
Citation: Mahamune M., et al (2015) Healthcare Knowledge Management using Data Mining Techniques. Advances in Computational Research, ISSN: 0975-3273 & E-ISSN: 0975-9085, Volume 7, Issue 1, pp.-274-278.
Copyright: Copyright©2015 Mahamune M., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
Introduction
In today’s information era, organizations want to seek competitive
advantage. This gives rise to Knowledge Management (KM). Many
organizations collected and stored huge amount of data however
they are unable to discover valuable information hidden in the data
by transforming these data into valuable and useful knowledge [1].
The information technology plays a vital role in Knowledge Management to create, share, integrate and distribute the knowledge; it’s
actually a process of data usage [2]. In today’s era health organizations collecting vast amount of data which would benefit them by
application of KDD tools and techniques. These techniques are
helpful for discovering the valuable knowledge from large datasets
for decision making [3]. Data Mining can be use as tool; Data Mining is an essential part of KM [2]. The study of data mining has an
objective of finding meaningful knowledge from huge datasets. Data
mining becoming popular in healthcare field because it fulfils the
need of efficient analytical methodology for detecting unknown and
valuable information in data. The data mining techniques provide
several benefits in health industry like detection diseases cause,
medical treatment methods identification, constructing drug recommendation systems, individuals health profile development,
healthcare research policies, even detection of fraud in health insur-
ance etc [4]. For decision making regarding patient health is difficult
due to vast and complex data. El-Sappagh, et al [22] stated that the
clinical decisions are often made based on doctor’s intuition and
experience rather than on the knowledge rich data hidden in the
database. This practice leads to unwanted biases and excessive
medical costs which affects the quality of service provided to patients [5,21]. The data may consist of patients health reports, medical claims, drug prescriptions, etc. hence there is a need of powerful tool which can generate important information from complex
data. The data mining technologies can provide benefits to
healthcare by grouping the patients having similar types of diseases
so that the healthcare organizations provide them better treatments
[7]. Data Mining can be useful for KM in two main manners: (i) to
share common knowledge of business intelligence (BI) context
among data miners. (ii) to use data mining as a toll to extend human knowledge [2]. As a part of Data Mining research, this paper
focuses on surveying data mining techniques for Knowledge Management.
Literature Survey
Koh & Tan [8] mainly discusses data mining and its applications
with major areas like Treatment effectiveness, Management of
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Healthcare Knowledge Management using Data Mining Techniques
healthcare, Detection of fraud and abuse, Customer relationship
management [8].
Ranjan [9] presents how data mining discovers and extracts useful
patterns from large data to find observable patterns. This paper
states the ability of Data mining in improving the quality of the decision making process in pharma industry.
Rani, et al [10] discusses mainly examine the potential use of classification based data mining techniques such as Rule Based, Decision tree, Naïve Bayes and Artificial Neural Network to the massive
volume of healthcare data.
Singh & Nagpal [11], presents an experiment based on clustering
data mining technique to discover hidden patterns in the dataset of
liver disorder patients. The system uses the SOM network’s internal
parameters and k-means algorithm for finding out patterns in the
dataset. The research has shown that meaningful results can be
discovered from clustering techniques by letting a domain expert
specify the input constraints to the algorithm.
Santhi, et al [12] proposed a framework where they used the heart
attack prediction data for finding the performance of clustering algorithm. Result shows the performance of classifier algorithm using
prediction accuracy and the visualization of cluster assignments
shows the relation between the error and the attributes.
Apiletti, et al [13] proposed a flexible framework to perform real-time
analysis of physiological data and to evaluate people’s health conditions. Patient or disease-specific models are built by means of data
mining techniques. Models are exploited to perform real time classification of physiological signals and continuously assess a person’s
health conditions. The proposed framework allows both instantaneous evaluation and stream analysis over a sliding time window for
physiological data. But dynamic behavior of the physiological signals is not analyzed also the framework is not suitable for ECG type
of signals.
Knowledge Management
Knowledge will be a major asset if managed properly; it is complex
and fluid concept. It can be either explicit or tacit in nature. Explicit
knowledge can be easily articulated and transferred to others. In
contrast tacit knowledge, which is personal knowledge, residing in
individual’s minds, is very difficult to articulate, codified and communicates [33,34]. The process of knowledge management focuses
on creation, share, dissemination and acquisition; application can
be facilitated by information technology.
 KM technologies can be classified in seven categories:
Data Mining
“Data Mining (DM) is the process of discovering actionable and
meaningful patterns, profiles and trends by sniffing through your
data using pattern recognition technologies such as neural networks, machine learning and genetic algorithms”. DM tool can solve
real complex problems; can search hidden patterns for finding predictive information. Data mining is an essential step in knowledge
discovery in databases (KDD) [4]. The terms of KDD and data mining are different. KDD refers to the overall process of discovering
useful knowledge from data. Data mining refers to discover new
patterns from a wealth of data in databases by focusing on the algorithms to extract useful knowledge [4]. According to Fayyad at al.,
“The knowledge discovery process are structured in various stages
where data selection is the first stage where data is collected from
various sources, then in second stage the selected data is preprocessed, in third stage data is transformed in specific format for further processing, in fourth stage suitable data mining technique is
applied on the data for extracting valuable information and at last
evaluation of data for gaining Knowledge [Fig-1], [15, 16].
Fig. 1- Data Mining and the KDD Process
Source: Fayyad, et al [15]
The CRISP-DM (Cross Industry Standard Process for Data Mining)
is a framework for carrying out Data Mining activities which are
divided into six different phases. The first phase understand the
business activities, second phase collect the data required for business activities and analyzed, third phase pre-processed it, in fourth
phase modeling is done, fifth phase evaluates the model, and in last
sixth phase model deployed. McGregor et al., proposed extended
CRISPDM framework clinical care through integrating the temporal
and multidimensional aspects. This model supports the process
mining in critical care [Fig-2], [7,17].
 KM Framework
 Knowledge-Based System (KBS)
 Data Mining
 Information and Communication Technology
 Artificial Intelligence (AI)/ Expert Systems (ES)
 Database Technology (DT)]
 Modelling
Knowledge Management in Healthcare
Knowledge Management is also known as “the right information to
the right people at the right time in the right form” [14]. In healthcare
there are lot of restrictions for information and knowledge sharing. It
results inefficient use or loss of resources in the organization.
Fig. 2- CRISP-TDM Model
At present all the public and private organizations are producing
enormous amount of data which are very difficult to manage. Hence
there are need of powerful tool for analyzing and producing useful
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Mahamune M., Ingle S., Deo P. and Chowhan S.
information from it. This information found valuable for understanding and making decisions for the cause of diseases and cost effective treatments to patients.
Tomar, et al [7], had proposed the concept of Data Mining in the
field of healthcare which offers novel information regarding
healthcare which in turn helpful for making administrative as well as
medical decision such as estimation of medical staff, decision regarding health insurance policy, selection of treatments, disease
prediction etc [7,18-21]. The various Data Mining techniques such
as classification, clustering and association are found useful for
healthcare organization to increase their ability for making decision
regarding patient health.
Data Mining Techniques
Data mining techniques can be classified in six main functions:
Classification: Finding models that analyze and classify a data
item into several predefined classes.
Regression: Mapping a data item to a real-valued prediction variable.
Clustering: Identifying a finite set of categories or clusters to describe the data.
Dependency Modeling (Association Rule Learning): Finding a
model which describe significant dependencies between variables.
Deviation Detection (Anomaly Detection): Discovers the most
significant for a subset of data.
Summarization
Finding a compact description for a subset of data. Data mining has
two primary objectives; prediction and description. Prediction involves using some variables in data set in order to predict unknown
values of other relevant variables (e.g. classification, regression,
and anomaly detection) Description involves finding human understandable patterns and trends in the data (e.g. clustering, association rule learning, and summarization) [5]. The extraction of hidden
predictive information from large datasets is a powerful new technology with great potential to help companies focus on the most
important information in their data warehouses. Data mining tools
predict future trends and behaviors, allowing business to make
proactive, knowledge driven decisions. DM techniques can be implemented rapidly on existing software and hardware platforms to
enhance the value of existing information resources, and can be
integrated with new products and systems as they are brought online. The most commonly used DM techniques in data mining are:
Artificial Neural Networks: A nonlinear predictive model that
learns through training and resembles biological neural networks in
structure.
Decision Trees: Tree-shaped structures that represent sets of
decision. These decisions generate rules for the classification of a
dataset.
Generic Algorithms: They are optimization techniques that use
process such as genetics combination, mutation, and natural selection in a design based on concepts of evolution.
Rule Induction: The extraction of useful if-then rules from data
based on statistical significance.
Regression Methods: This tries to identify the best linear pattern in
order to predict the value of one characteristic we are studying in
relation to another.
Data Mining Process Model in Healthcare
Knowledge discovery is a continuous process, El-Sappagh, et al
[22] proposed that as other processes KDD also has its own environment and phases that runs under certain constraints. In the
healthcare environment, the clinical database can be queried directly using SQL [22]. A Data Warehouse (DW) can be created to integrate data from many sources and enhance data quality. Analysts
can apply OLAP tools on the DW. Data warehouse is not enough
for data analysis. Data mining is required to discover hidden patterns in DW. KDD is an iterative or cyclic process that involves a
number of stages. Although the specific techniques may vary from
project to project, the basic process is the same for all KDD problems [22].
Fig. 3- Decision Making Environment;
Source: El-Sappagh, et al [22]
KDD process helps in analysis of some aspects that might be forgotten or neglected. The following figure shows basic phases of
KDD process [23].
Fig. 4- KDD Phases
Selection Phase targets database, Noise, inconsistency, incompleteness solved by preprocessing. Then the data transformed to
DM tasks, where DM techniques are used to generate a set of patterns. From these patterns only interesting and useful patterns are
evaluated/ interpret. These patterns represent the discovered
knowledge [22].
Applications of Data Mining for Knowledge Management in
Healthcare Data mining provides several benefits to healthcare
organizations. It can be applied for detection and diagnosis with
high performance of many diseases like cancer [24], diabetes [25],
[26]. Kidney [27], heart diseases [22], etc.
Following are the several applications of Data Mining in healthcare:
Treatment Effectiveness
Data mining applications found can develop to evaluate the effectiveness of medical treatments. Data mining can deliver an analysis
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Healthcare Knowledge Management using Data Mining Techniques
of which course of action proves effective by comparing and contrasting causes, symptoms, and courses of treatments.
she is a woman. This type of domain knowledge needs to be collected and added to the data mining system [22].
Effective Management of Hospital Resource
It is possible by using data mining techniques to find out complications of the patient disease priorities and they get better treatment
in timely and accurate manner. Healthcare organization offers patients to online access their medical information, online fill the prescription form and allow safe exchanging of treatment details with
the doctors.
Conclusion
Knowledge Management as a concept is very attractive, many organizations are associated with. As we continue our fight for improving quality service using every possible way, Knowledge Management is one of them. As more and more data collected by the
healthcare organization and required more powerful tool to interpret
the data into valuable knowledge through which the healthcare
organizations will earn extra benefit, such as it acts as key driver of
organizations efficiency, maximizing organization’s potential, competitive advantage, managing intellectual capital. Since we find that
the applications of data mining brings a lot of advantages like optimization of resources, better treatment, policy planning, etc. in this
paper we also have discussed the data mining tasks and techniques can be integrated into KM and enhance the KM process with
better knowledge. It is clear that the data mining techniques will
have a major impact on the practice of KM, and will present significance challenges for future knowledge and information research.
Knowledge Management is not a short term quick fix. It is a long
term commitment to changing the culture of healthcare industry to
become more collaborative, more transparent, and more proactive.
Hospital Ranking
Different data mining techniques are used to analyze the various
hospital details in order to determine their ranks [28]. Ranking of
hospitals determines by analyzing how the hospitals handles the
high and low risk priorities patients.
Smarter Treatment Techniques
Doctors and patients can easily compare various treatment techniques. They can analyze the effectiveness of available treatments
and find out which one is better and cost effective. The side effects,
hazard of particular treatment, and developing smart methodologies
are also possible by the data mining techniques.
Improved Patient Care
In order to analyze the hospitals massive data, a predictive model is
constructed using data mining that discover valuable information
from it and make decision to improve healthcare quality [7]. Data
mining helps the healthcare providers to identify the present and
future requirements of patients and their preferences to enhance
their satisfaction levels.
Health Policy Planning
Data mining play an important role for making effective policy of
healthcare in order to improve the health quality as well as reducing
the cost for health services.
Data Mining Challenges in Healthcare
Knowledge Management using data mining faces many challenges
such as [29,30]. Need for very high accurate algorithms.
For Active Data Mining
An automated data mining technique is needed on each data updating. And for embedding the discovered knowledge to healthcare
information system.
Every time various data mining techniques are used and compare
for most appropriate.
For the modeling and model evaluation process previously gained
knowledge must be taken in to account for evaluation phases [31].
Managing complex data, like mining knowledge from graphs or
charts and non relational data such as text and images requires
multi-relational data mining.
KDD systems today work with relational data bases. But now it
needs to work with Object-oriented and multimedia database.
In distributed data mining, mining across multiple heterogeneous
data sources must be possible.
Collecting the default knowledge is a big challenge for example if a
person is pregnant, then the system must by default understand the
Conflicts of Interest: None declared.
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