The document proposes a methodology for optimal extraction of integrated clinical informatics data from various web sources to support precision medicine. It involves collecting 4D medical data through unique IDs, extracting text from different file formats, integrating data through SQL queries, and verifying/validating the integrated data. The methodology was tested on a COVID-19 dataset and achieved a 76% success rate in reducing active cases through precision treatment approaches enabled by optimal data extraction and integration from clinical informatics sources.

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International Journal of Advanced Research in Engineering and Technology (IJARET)
Volume 11, Issue 4, April 2020, pp. 595-602, Article ID: IJARET_11_04_057
Available online at https://iaeme.com/Home/issue/IJARET?Volume=11&Issue=4
ISSN Print: 0976-6480 and ISSN Online: 0976-6499
DOI: https://doi.org/10.34218/IJARET.12.4.2021.057
© IAEME Publication Scopus Indexed
OPTIMAL EXTRACTION OF INTEGRATED
WEB INTERACTIVE CLINICAL INFORMATICS
TOWARDS PRECISION MEDICINE
Richards Hadlee R
Research Scholar, Department of Computer Science, St.Xavier's College,
Tirunelveli, Tamil Nadu, India
Victor S.P
Associate Professor, Department of Computer Science,
Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India
ABSTRACT
Precision medicine is the recent gift in our era to understand and handle the medical
complexities along with the prescribed solution. The web interactive clinical informatics
provides the collected medical data for any particular medical issues from anywhere
among the world in an efficient manner. In this advanced digital communication, since
the world is well connected the need from the medical domain is the extraction of
medical information towards precise medicine. The process of extracting the predicted
precision medicine state is a tedious one which requires the complex heterogeneous
data collection from different medical resources with proper verifications and
validations along with the efficient integrated data for the precise medicine is a tedious
one to implement. This paper proposed the optimal extraction of integrated web
interactive clinical informatics towards precision medicine with evaluation techniques
using data mining strategies by frequent monitoring and evaluation for individual
patient condition. In future this paper will be extended with artificial intelligence
analysis of implementation through genetic algorithm to attain an intelligent precision
medicinal system.
Key words: Precision, Medicine, Interaction, Clinical informatics, Integration.
Cite this Article: Richards Hadlee R, and Victor S.P, Optimal Extraction of Integrated
Web Interactive Clinical Informatics towards Precision Medicine, International Journal
of Advanced Research in Engineering and Technology, 11(4), 2020, pp. 595-602.
https://iaeme.com/Home/issue/IJARET?Volume=11&Issue=4
1. INTRODUCTION
Data integration is the process of bringing data from disparate sources together to provide users
with a unified view. The premise of data integration is to make data more freely available and

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easier to consume and process by systems and users[1]. Data integration done right can reduce
IT costs, free-up resources, improve data quality, and foster innovation all without sweeping
changes to existing applications or data structures[2].
Web Data Interactions are generated when a user creates an interaction between any analytic
content and other analytic content (query to query) [3]. Data interactions involve changing the
target query with input from the source item. By default, all interactions between queries are
assumed to involve data interactivity - which will be enabled unless verbosely turned off[4,6].
Clinical informatics is the study of information technology and how it can be applied to the
healthcare field. It includes the study and practice of an information-based approach to
healthcare delivery in which data must be structured in a certain way to be effectively retrieved
and used in a report or evaluation[5,7]. Clinical informatics can be applied in a range of
healthcare settings including hospitals, physician’s practices, and the military and other
locations[9].
Precision medicine is a medical model that proposes the customization of healthcare, with
medical decisions, treatments, practices, or products being tailored to a subgroup of patients,
instead of an one‐drug‐fits‐all model[8].
Figure 1 Clinical Informatics Overview
1.1. Problem Statement
The methodology of extracting the integrated precision medicine state is a difficult one which
requires the data mining based extraction procedural approach with collected treatment
information’s incorporated by proper verification and validation for further medical treatment
improvements.
The objective of this work is to perform the optimal Web data extraction from Web data
Informatics by patient interaction and integration from several clinical informatics web
resources.
2. PROPOSED METHODOLOGY
The proposed methodology focuses on implementing the optimal extraction of integrated
clinical informatics from different web data informatics with proper verification and validation.
The data collection, integration methodology for optimal data extraction with proper
verification and validation in the field of information technology plays the important role in

3. Richards Hadlee R, and Victor S.P
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precision medicine for the optimal gain in the clinical informatics improvements system. The
following figure-1 shows the proposed methodology structure,
Optimal data extraction for integrated Clinical informatics interactions in
Precision Medicine
Figure 2 Proposed Web Extraction of integrated Clinical informatics system for Precision medicine.
3. IMPLEMENTATION
3.1. Medical Data Interaction
Medical data interaction is a proposed 4-Dimensional data from each medical resources
uniquely represented by 24 character data id such that,
3 alpha numeric characters for Gender and Human part,
3 alpha numeric characters for disease code,
2 alpha numeric characters for country,
3 alpha numeric characters for hospital code,
3 alpha numeric characters for Medical expert handler ID,
7 alpha numeric characters for Patient ID,
For example MLKSS8INXXXDDDabcd123
Where
MLK represents Male with Left Kidney issues

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SS8 represents Stone Size of 8 mm
IN represents India
XXX represents the Hospital code
DDD represents Doctors code
Abcd123 represents the patient ID.
The following table-1 illustrates the 4-Dimensional Medical data Resource.
Table 1 Proposed 4-Dimensional Medical data resource of interactions.
Medical data Id: MLKSS8INXXXDDDabcd123
Hospital data interaction
Text/scanned data files
Patient data interaction
Text/audio/video data
Lab data interaction
Text/Graphics/Opinion formats
Medical expert interaction
Text/audio/video/Graphics
3.2. Web Data Extraction
The extraction of multimedia information’s other than texts are through normal file transfers
but it is different for the text information extractions due to the non-uniform editors. The java
program to extract universal medical data code or web medical text data extraction for any text
editors used for storage by different web medical resources are as follows,
import java.io.*;
import java.util.List;
import org.apache.poi.xwpf.usermodel.XWPFDocument;
import org.apache.poi.xwpf.usermodel.XWPFParagraph;
public class Medataext {
public static void main(String[] args) throws Exception
{
String path = System.getProperty("medical1.dir");
path = path + File.separator + "medicalfile1.ext";
FileInputStream fin = new FileInputStream(path);
XWPFDocument document = new XWPFDocument(fin);
List<XWPFParagraph> paragraphs
= document.getParagraphs();
for (XWPFParagraph para : paragraphs) {
System.out.println(para.getText() + "n");
}
document.close();
}
}
3.3. Clinical Informatics Integration
The following SQL query integrates the table results of different medical information’s from
various resources.
SELECT *
FROM

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name_of_table_one
RIGHT OUTER JOIN
name_of_table_two
ON
name_of_table_one.name_column_one = name_of_table_two.name_column_one
For converting all the integrated data into the word processing tool by having the Word
installed on the server, and has configured SQL Server to allow OLE Automation. With the
external trusted connection, and the execution query in the current database as
Execute spExportToword @Query Text='My Query',
@documentFile='C:foobar.doc',@Tableformat='Colourful 1'
The following table represents the final output.
Table 2 The final output
IntMed DataFCVDT3INXXXDDDabcd123FCVDT3ZEYYYEEEefgh456 FCVDT3GRZZZFFFijkl789
Symptoms Fever, Cough, Body pain Fever, Sore throat, Pain Fever, Cough, Head ache
Medicines Crocin, paxlovid Calpol, Gilead Ibuprofin, molnupiravir
Curing span 16 days 18 days 11 days
3.4. Verification and Validation of Integrated data extraction for Precision
Medicine
The verification completion is done through the matching of first 6 characters for the same
gender with similar disease issues medical data extraction with proper data integration.
Final Pattern p = Pattern. Compile ();
For (final String s : new String[] {"FCVDT3"})
{
final Matcher m = p.matcher(s);
if (m.matches()) {
System.out.println("Verified Pass with: " + s);
} else {
System.out.println("Failed Verification with: " + s);
}
}
The validation completion is done through the uniqueness of last 7 characters with at least
one character difference for the different patients count towards the same gender with similar
disease issues medical data extraction with proper data integration.
String Flast7;
if (str == null || str.length() < 7) {
Flast7 = str;
} else {
Flast7 = str.substring(str.length() - 7);
}// similar for the second name Slast7
System.out.println("Flast7.equalsIgnoreCase(Slast7))
Output: true for same/ false for different

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3.5. Precision Medicine Efficiency
The optimal data integrated collection are put together and implemented with the efficient
treatment for the similar diseases. The proper monitor and evaluation system produced the
following results in table-3.
Table 3 Sample Precision medicine solution for Health improvements values.
Sl.No Health issues Normal
isolated
approach
in days
Optimal data extraction based precision
medicine approach in days
1 Covid 18 9
2 Kidney Stones 12 5
3 Gallbladder malfunction 10 6
4 Color blindness. 7 2
5 Type-1 Diabetes 30 7
The following figure shows the performance of the optimal data extraction for the integrated
clinical informatics towards precise medicine.
Figure 3 Sample Precision medicine solution for Health improvements values
4. RESULTS AND DISCUSSION
The results of Covid-19 Omicron Variant in India is used for the implementation of the
proposed schema with respect to a minimal restricted data collection of 50 cases within a
fortnight in table-4 is as follows,
Table 4 Proposed Model results for covid-19 Clinical informatics using precision medicine
Category Covid-19 recovery Covid-19 active count
Total Cases 0 50
Optimal data integration
results from Tamilnadu
state
12 38
Optimal data integration
results from other states
18 20

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Optimal data integration
results from Asia
10 10
Optimal data integration
results from Overseas.
10 0
The following figure shows the results obtained for the optimal implementation of web data
integrated clinical informatics for precision medicine is as follows.
Figure 4 Proposed Model results for covid-19 in Clinical informatics for precision medicine
The final results show the 76% success due to the 38 count reduction based on shared
optimal extraction of integrated clinical informatics for the precision medicine.
5. CONCLUSION
The web data collection of medical data resources are of heterogeneous nature in every user
aspects throughout the world. The process of organizing the data extraction from different level
of medical information’s is useful in solving the medical issues and improves the healthier
environment. This research paper initially focuses on the collection of web medical data
interactions from the hospitals, patients, doctors and labs in the form of different data types.
The data integration plays the important role with proper matching of patients information’s is
the secondary objective of this research work. The final part of the research verifies and
validates the uniqueness for identifying the similar patient cases with the optimal data extraction
for the precision medicine. Our proposed methodology provides 76% success rate for the
medical support efficiency.in future this research will be extended with the implementation of
artificial intelligence for the effective automated precision medicinal system.
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