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Brain Computer Interaction Disease Prediction using Machine Learning 143
https://t.me/med1917
the mental and emotional state throughout sessions. Internal non-stationary causes
include fatigue and concentration levels as well. Noise and non-stationary causes
are major challenges for BCI technology. They involve unnecessary signals
triggered by changes in electrode placement as well as noise from the
environment. The acquired signals represent a combination of moving objects,
such as electrical activity generated by signals created by eye movements and
skeletal muscles electromyogram (EMG) and blinking Electrooculogram (EOG),
making it difficult to discern the underlying pattern.
Small Training Sets
The training procedure is dominated by usability problems and limited training
sets. Although the subjects find the training sessions to be time-consuming and
exhausting, they provide the user with the requisite experience to cope with the
device and learn to monitor his or her neurophysiologic signals. As a result,
developing a BCI requires a significant balance between the technical difficulty of
understanding the brain's signals and training requirements for effective usage.
CONCLUSION
Brain-computer interface is a cutting-edge technology that uses brain impulses to
control an external device, bypassing the regular neuromuscular system, to
complete a task. BCI technology is a relatively new innovation in neuroscience
that has drawn researchers from a variety of fields, including entertainment and
gaming, security, and marketing. Invasive and non-invasive recording equipment
are classified into two groups. Invasive surgery is frequently required for essential
paralyzed conditions due to its higher accuracy rates obtained either
geographically or temporally. Besides its advantages over the invasive category,
which we have previously discussed, the non-invasive category has also been
widely adopted in a variety of applications. Other concerns and obstacles that
arise as a result of using brain signals have also been examined as the result of the
answers provided by algorithms at BCI system processing. The interaction
possibilities of new technologies are fully appreciated by HCI researchers today.
We also look at the different challenges that must be overcome in order for BCI to
be a successful and widely adopted technology. The paper's primary goal is to
bring this new emergent technology to the forefront. The BCI research
community hopes to work more closely together in the future and accepts that
other research areas and society in general will value and direct BCI research,
than other medical applications.
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146 Disease Prediction using Machine Learning, 2024, 146-158
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CHAPTER 9
Mining Standardized EHR Data: Exploration,
Issues, and Solution
Shivani Batra
1
KIET Group of Institutions, Delhi-NCR, Ghaziabad, Uttar Pradesh, India
2
GD Goenka University, Gurugram, India
Abstract: Medical database is among the most crucial databases in terms of their
applicability to human life. Many researchers are in search of knowledge that is
abstracted within the data. Data mining is popular in today's world as it gives access to
knowledge that is otherwise unavailable. The concealed knowledge which is offered as
a result of it can help the individual to make better decisions. Data mining tools in
health have great potential. These solutions may be divided into four categories:
therapeutic efficacy assessment, patient care, customer service, and embezzlement
monitoring. The authors discovered that giving decision assistance in the medical
sector with an emphasis on electronic health records (EHRs) can save lives. Though
offering decision assistance in EHRs using data mining is valuable, it needs
consistency. As a result, the authors intend to use data mining methods on standardised
EHRs to create a decision support system. This paper presents the state-of-the-art data
mining approaches and their application in the healthcare sector. It provides an
integrated summary and a comparison detail of the existing literature. This chapter
surveys several issues that need to be handled before employing data mining on EHRs
and further proposes a solution for dealing with these problems. The problems such as
multiple origins, multiple formats, missing data, distinguished users, data granularity,
flexibility, and sparseness need immediate attention from researchers. Resolving these
problems is important to build an efficient standardized EHRs database.
1,*
, Vinay Kumar1, Neha Kohli2 and Vaishali Arya
2
Keywords: Data Mining, EAV Model, Health Data, Standardized EHRs,
Sparseness.
INTRODUCTION
Data mining (DM) is the method of choosing, examining, and modelling massive
datasets in order to uncover interesting patterns or correlations that offer the
expert a valid and meaningful conclusion [1 - 3].
*
Corresponding author Shivani Batra: KIET Group of Institutions, Delhi-NCR, Ghaziabad, Uttar Pradesh, India;
E-mail: ms.shivani.batra@gmail.com
Geeta Rani, Vijaypal Singh Dhaka & Pradeep Kumar Tiwari (Eds.)
All rights reserved-© 2024 Bentham Science Publishers
Mining Standardized EHR Data Disease Prediction using Machine Learning 147
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DM is the process of extracting useful information from massive datasets
maintained in a variety of places, including data stores, spreadsheets, and cloud
platforms. This information is useful in a variety of fields, including corporate
strategy, biomedical investigation, and policy decisions. DM is a crucial aspect of
information extraction since it examines massive amounts of facts and provides us
with previously unrecognized, concealed, and usable information. DM has been
successfully employed in a variety of industries, including weather forecasting,
healthcare, transit, education, finance, and governance. When employed in a given
business, DM offers several benefits such as prediction, clinical diagnosis, and
categorization. Aside from such benefits, DM has its own drawbacks, such as the
potential for confidentiality breaches. For example, if the miner has access to all
of the data's details, he may exploit some of the data's secret information.
Although DM may be used in a variety of fields, its application in the health
industry has the potential to assist humanity. Data includes a lot of information
that might be concealed. Many academics are working to uncover these
previously unknown sections of medical information [4 - 6]. Any platform's most
precious asset is data. In the health sector, a lack of vital information can be
dangerous to health. The discipline of DM is rapidly expanding, but applying it to
medical records is much more difficult than applying it to other data due to the
existence of several unique traits.
The remainder of the paper is laid out as follows. Section 2 delves into the
complexities of the medical industry, with a focus on EHRs. Section 3 focuses on
how DM can be used in EHRs. Section 4 looks at the issues of applying DM to
EHRs, and Section 5 offers a remedy. Sections 6 and 7 respectively show related
work and the conclusion.
COMPLEXITY IN EHRS
Paper-based patient data have a number of drawbacks, including restoration,
productivity, and integrity. EHRs address all of the drawbacks of paper-based
health data and make them available to users at a single click.
An Integrated Care EHR [7] is defined as: “a repository of information regarding
the health of a subject of care in computer processable form, stored and
transmitted securely, and accessible by multiple authorized users. It has a
commonly agreed logical information model which is independent of EHR
systems. Its primary purpose is the support of continuing, efficient and quality
integrated healthcare and it contains information which is retrospective,
concurrent and prospective”.
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The importance of standardisation for health clients cannot be overstated.
Examining the available standards in the health industry is crucial. Various
standard bodies are attempting to make interoperable EHRs a reality. Prominent
organizations include Health Level 7 (HL7) [8], European Committee of
Standardization Technical Committee 251 (CEN TC251) [9], International
Standard Organization (ISO) [10, 11], and openEHR [12].
Medical advances are a data-intensive discipline that accumulates massive
amounts of complicated and varied information. The health domain is very vast.
Considering the complexity of the EHR domain, we identified that it consists of
EHR_patients, EHR_concepts, EHR_relationships, terminology concepts and
terminology relationships. There are a lot of EHR concepts in a clinical concept.
Medical terminologies are a useful tool for standardising taxonomy and
incorporating interpretation into EHRs [13]. Professionals frequently use jargon
and a plethora of identifiers to define diagnoses. Doctors specialise in a variety of
fields. For medical terms, there are several standardised labelling standards. Each
specifies its own collection of terminology as well as the connections between
them. The SNOMED-CT (Systemized Nomenclature of Medicine-Clinical Terms)
coding system, for example, covers about 300,000 medical notions and 7 million
connections. Amongst the most significant application fields for DM is healthcare
[14]. Apart from having a complex structure, medical data is very sensitive in
terms of ethical and legal issues. Besides all of this, a key issue is the occurrence
of missing values in the data, which might cause a researcher to receive erroneous
findings.
IMPLEMENTING DM ON EHRS
In today's technological age, the volume of health data is growing. Manually
processing such a big volume of data is difficult and might result in numerous
mistakes. Furthermore, it is likely that a regular person will not be able to acquire
hidden knowledge. DM is critical in giving solutions to all of these issues. Many
research works are going on to mine electronic health records such as finding the
effects of drugs, detecting health insurance fraud, and finding patterns in
symptoms to predict future diseases of patients.
All research works which are going on or have already had done provide a good
accuracy measure but lack standardization in terms of the local schema used by
the researchers. Standardization is necessary since it gives individuals perfect
control over any asset, regardless of the criterion. Because of the wide range of
storage and retrieval systems, standardisation is critical in the health sector. To
construct the data structure, each company has its own set of regulations. This
study's major aim is to discover gaps in the use of DM techniques to standardised
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EHRs, as well as potential solutions to these gaps. Our main focus is
standardization because if it is achieved, the benefits of the standardized EHRs
will be provided worldwide. DM can be used to predict the future or to find the
interrelationship among observed or unobserved variables. As a result, DM
algorithms are divided into three groups based on the roles:
1. Classification: The technique of anticipating the category of a new item from a
collection of preset classes is known as classification [15]. The data is separated
into a testing set, and a training set. The entire framework is designed by using a
training set, and its correctness is determined by verifying the categories to which
the test set of data samples are classified.
2. Clustering: Clustering [15] is a strategy for unlabeled data in which the
categories are undefined. It operates by estimating the distance and similarities
between the two items. Items will correspond to the same category if the
resemblance metric is in the predefined threshold; alternatively, they would not.
Clustering is a term used to describe a procedure in which items belonging to the
same category are grouped with each other to establish a cluster.
3. Association: The goal of association [15] is to figure out how distinct data
properties are associated with one another. One of the most well-known
association techniques is the Apriori method [15]. Finding fascinating trends
necessitates two parameters: support and confidence. The effectiveness of
association methods is the criterion for assessment, not correctness since they
locate each conceivable relationship among the characteristics of the data.
The type of DM work to be undertaken is determined by the result desired. During
the present investigation of implementing DM on EHRs databases, the author
examined several DM methodologies based on various criteria necessary. The
comparison of the DM techniques categories is shown in Table 1.
Table 1. Comparison of various data mining algorithm types.
Characteristic Classification Clustering Association
Modelling With Supervision Without Supervision Without Supervision
Forecasting Predictive Descriptive Descriptive
Output Predicting Class Output
Performance Measure Accuracy Accuracy Efficiency
Efficiency Dependency Training Data Precision Adopted Threshold Value Support and Confidence
Segregating Unlabeled
Classes
Recognizing Features’
Correlation
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Next, the information gathered by using a DM algorithm may be important for
delivering effective decision assistance. The procedure is demonstrated in Fig. (1).
Decisions making in healthcare is directly related to people's lives. Thus, it is
mandatory to minimize human errors and biases in decision-making. Humans are
capable of making mistakes, but machines are more dependable. It will be highly
advantageous for everyone to build a system that can assist in decision-making.
The details of the problems identified and the proposed solution as demonstrated
in Fig. (1) are presented in Section 4 and 5 respectively by the authors.
Fig. (1). Application of data mining to EHRs.
All parties such as physicians, corporate leaders, and sufferers engaged in the
medical field may tremendously benefit from DM applications [1]. DM
techniques used on EHRs will aid in the retrieval of previously unknown
information. However, due to the intricacy of health data and the existence of
distinctive attributes in health data, mining health records is more difficult than
mining other sets of data. Data mining can provide decision support in many areas
but nothing else can be more useful if we can take the right decision about human
life at the right time. Mining health records is undoubtedly significant, although
it's not as simple as mining any other sort of data due to the existence of certain
distinctive traits described by Cios and Moore [16] as well as difficulties outlined
by us in Section 4. Medical data is very special in terms of its contents [13].
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Due to the sheer peculiarity of health data, Cios and Moore [14] offer a full
overview of all difficulties (heterogeneity, regulatory and political issues,
analytical bounds, and protected privilege) that a DM researcher must be aware of
before beginning his investigation. These are described in detail below:
1. Heterogeneity: Medical data might include scans, unstructured and semistructured data, visual information, audible data, infographics, and figures. Due to
this nature of data, high-capacity storage is required. Moreover new mining tools
are also required to analyze this type of data so that it can be interpreted easily by
the user.
2. Regulatory and Political Issues: Dealing with people to gather medical data
necessitates dealing with a variety of difficulties, including information privacy,
the threat of litigation, safety and confidentiality of data, anticipated benefits, and
administration.
3. Analytical Boundaries: Statistics deal with different types of numbers whereas
data mining deals with different types of data. Applying statistics operation
requires the medical data to be numeric.
4. Protected Privilege: Medicine is an important part of everyone’s life. Special
attention has to be given while dealing with mining medical data since the result
of carelessness can be very hazardous. All ethics should be maintained by the
person who is accessing the data for any legal purpose and no access rights should
be given for illegal purposes.
CHALLENGES IN MINING STANDARDIZED EHRS
Based on a thorough examination of DM in the health sector, the present study
looked into the following issues when implementing DM on EHR data.
1. Multiple Origins: Every organization holds information on an individual basis.
When implemented in the associative data of all organizations, DM techniques
would be beneficial. As a result, data ought to be present in a single location,
however, data protection would be a major concern in such a situation.
2. Multiple Formats: Each institution has its own data structures for storing
patient information. As a result, data must be collected using a single standard
format so that DM tools may be used to uncover information. Also, there is a need
to develop automatic tools for converting this heterogeneous medical data to a
uniform format.
3. Missing Data: There are times when data has missing values or is
contaminated by noise. When DM technologies are used on inaccurate data, the
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outcomes will be wrong as well. For example, a particular hospital is capturing
information by blood tests, X-rays, as well as CT scans. But, another hospital does
not provide a facility for CT scan. This leads to a null value in place of a CT scan
for some of the patients. Such null values must be handled before decisionmaking.
4. Distinguished Users: Users are divided into three categories: professional,
semi-skilled, and beginner. A professional user such as a physician frequently
requests all the information and reasoning to interpret things. A semi-skilled
operator such as a nurse is primarily interested in the service's entire information.
The average beginner such as a sufferer is simply interested in the outcomes. In
this way, the desired outcome is different for different groups of people.
5. Data Granularity: The amount of granularity is determined by the user's
requirements. Varying levels of granularity may be required by professional,
semi-skilled, or beginner-level clients. Whenever a client checks his vital sign
record, he may need to determine if his heartbeat is regular, excessive, or lower,
but if a professional checks his heart rate records, he needs to examine the details
rate at which his heart is beating.
6. Flexibility: The healthcare era is rapidly approaching. As a result, modern
treatment ideas or diagnosis terminology are established, or established health
conceptions are coupled with novel diagnostic terminology. To address this issue,
we need flexible data structures that can support these changes without disrupting
the entire system.
7. Sparseness: Several times, health data is gathered using surveys that ask for
personal details that the individual may not wish to share. In this case, the feature
containing the specific value has a null value. Thus, this leads to storage wastage.
Several agencies, including HL7, openEHR, and ISO13606, are constantly
working to fix these challenges, but we discovered that DM on standardized
EHRs has received very little investigation.
SOLUTION FOR MINING STANDARDIZED EHRS DATABASE
Traditionally databases are stored under a relational model which maintains one
column per attribute but the relational model is not able to handle the problems
defined in the previous section; hence a new data model is required for storing
EHR such that a space is utilized in a very efficient way. The EAV model is
capable enough to store EHR data in a very space-efficient way. The EAV model
[17] facilitates data storage in a column-oriented storage with triplets in every
tuple corresponding to Entity, Attribute, and Value. The 'Entity' component
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