=Paper=
{{Paper
|id=Vol-2544/shortpaper4
|storemode=property
|title=Adequacy of Personal Medical Profiles Data in Medical Information Decision-Making Support System
|pdfUrl=https://ceur-ws.org/Vol-2544/shortpaper4.pdf
|volume=Vol-2544
|authors=Solomiia Fedushko
|dblpUrl=https://dblp.org/rec/conf/irehi/Fedushko18
}}
==Adequacy of Personal Medical Profiles Data in Medical Information Decision-Making Support System==
https://ceur-ws.org/Vol-2544/shortpaper4.pdf
Adequacy of Personal Medical Profiles Data in Medical
Information Decision-Making Support System
Solomiia Fedushko [0000-0001-7548-5856]
Lviv Polytechnic National University, Lviv, Ukraine
solomiia.s.fedushko@lpnu.ua
Abstract. The paper describes the verifying methods of medical specialty from
user profile of online community for health-related advices. To avoid critical
situations with the proliferation of unverified and inaccurate information in
medical online community, it is necessary to develop a comprehensive software
solution for verifying the user medical specialty of online community for
health-related advices. The algorithm for forming the information profile of a
medical online community user is designed. The scheme systems of formation
of indicators of user specialization in the profession based on a training sample
is presented. The method of forming the user information profile of online
community for health-related advices by computer-linguistic analysis of the in-
formation content is suggested. The system of indicators based on a training
sample of users in medical online communities is formed. The matrix of medi-
cal specialties indicators and method of determining weight coefficients these
indicators is investigated. The proposed method of verifying the medical spe-
cialty from user profile is tested in online medical community.
Keywords: Personal Medical Profiles, Decision-Making Support System, Med-
ical Information, Profiles Data, Health-Related Advices.
1 Research Aims
developing community health workers
improving quality of e-health systems
simplifying the patients identification in various medical organizations
collecting, processing and analyzing Big medical data from various sources
working-time reduction of medical staff
detecting non-valid accounts and accounts with incorrect or stale medical data
consolidating all medical personal data with high level adequacy
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons Li-
cense Attribution 4.0 International (CC BY 4.0) IREHI 2018 : 2nd IEEE International Rural
and Elderly Health Informatics Conference
2
2 Problems Solving
To develop an effective methods and means of determining the level of data ade-
quacy of personal medical profiles.
Fig. 1. E-health systems categories
3 Models of personal medical profiles
Proposed method is carried out in order to identify the user personal data in the
online community and verify the authenticity of the personal data specified by the
user.
The information track of the web user:
InfTrack Pi Content Pi , PersonalData Pi (1)
The components of an information track are: Content(Pi) created by a member of the
online community, and personal data – PersonalData(Pi).
Content Pi Thread Pi , Poll Pi , Post Pi (2)
j 1
NiUThead
Thread Pi Thread j Pi is set of online community discussions;
j 1 is set of online community polls;
NiUPoll
Poll Pi Poll j Pi
3
j 1 is set of online community posts.
NiUPost
Post Pi Post j Pi
Computer-linguistic analysis is made only for the personal data that the user of the
online community has specified in user account.
The most prioritized data for forming the data profile of the patients in the online
community is the mandatory information about the online community user, less im-
portant – important data. Personal data distribution of online community user account
to blocks is as follows:
BasicInfo Pi , EduInfo Pi , InterestsInfo Pi ,
PersonalData Pi (3)
WorkInfo Pi , ContactInfo Pi , FotoInfo Pi ,
Formal description of the online community member account:
BasicInfo(Pi) is block of basic personal information of the online community user.
Name Pi ,NickName Pi ,Age Pi ,
BasicInfo Pi = (4)
Gender Pi , Region Pi ,Lang Pi
where Name(Pi) is full name; NickName(Pi) is nick name; Gender(Pi)is gender;
Age(Pi) is age; Lang P Lang P Ni is plural of languages signed by a user;
Lang
i j i j 1
Region Pi = Regionk Pi i
N Region
NiLang is number of language; is set of regions with
k=1
which a user is associated; NiRegion is set of regions.
EduInfo(Pi) is information block about education.
EduInfo Pi EduLevel Pi , Specialization Pi (5)
where EduLevel(Pi) is level of education received, Specialization(Pi) is specialty.
WorkInfo(Pi) is block of data about the work of online community user.
WorkInfo Pi Company Pi , Position Pi (6)
where Company(Pi ) is institution where works, Position(Pi) is a position taken by a
member of the online community in that institution.
ContactInfo(Pi) is contact information for the member of the online community.
ContacInfo Pi Email Pi , SocialNets Pi ,Website (7)
where Email(Pi) is main email address, SocialNets P SocialNets P Ni
(Up )
is
i j i j 1
plurality of pages in social networks, N iUp is the number of pages in social networks;
Website is website.
FotoInfo(Pi) is graphic information block.
4
FotoInfo Pi Avatar Pi ,Userbar Pi , Foto Pi (8)
where Avatar P Avatar P Ni
Ava
is set of avatars, N iAva is number of avatars,
i j i j 1
N iFoto is a number of photos, Userbar Pi Userbark Pi i
Userbar
N
is set of graphic
k 1
signatures, NiUserbar is number of signatures, Foto Pi Fotom Pi Ni
Foto
is set of photos.
m 1
InterestsInfo(Pi) is a block of information about the hobby and interests of the
online community user.
InerestsInfo Pi Byline Pi , Activity Pi , Quot Pi , Biography Pi
(9)
where Byline P Byline P Ni
Byline Byline
is number of signatures, N i is number of
i j i j 1
signatures; Activity Pi Activityk Pi Ni
Act
is a set of favorite lessons and phrases,
k 1
N iAct is number of lessons, phrases, Quot Pi Quotl Pi Ni
Quot
is a plurality of
l 1
quotations, N iQuot is number of quotations, Biography(Pi) is a biography.
Information about contacts and websites where the web user displays communica-
tive activity is placed in the ContactInfo(Pi) block. Each account blocks contain in-
formation from three groups of personal data of the online user. Preferably, in the
BasicInfo(Pi) block, compulsory data is placed, without this data registration in the
online community is not possible.
4 Method of determining the personal medical profiles
data adequacy level
The concept of the personal data adequacy of the patient profile of the medical
clinics is presented to compare the personal data of the online communities informa-
tional profile with the medical information system data. The information profile in the
web communities is engendered by the method of computer-linguistic analysis of the
user information track.
Determining the personal data adequacy of the account to the real information of
system user consists in the implementation of the main stages of the algorithm of
determining the of personal data adequacy of the account.
5
Fig. 2. Block diagram of the algorithm for determining the personal data adequacy of account
The difference between 1 and jk Value, P is the distance between the reference
value of the personal characteristics and the value of the personal data of the atomic
k-th user is determined by the adequacy of the personal data of the k-th user profile.
j Value, P 1 j Value, P
k k (10)
j Value, P is distance to each possible value of the personal data of the atom-
k
ic k-th user of web community:
*w
N_Ind PrCh,k
PrCh,Vc PrCh,P 2
μj Value,P =1-
k
i=1
Indi,j -Indi,j PrCh
i
(11)
where k 1 N _Vl Pr Ch,Vc . Moreover, jk Value, P 0,1 .
j Value, P max , then the degree of probability of personal characteristics of a
k
particular user in the online community to this user personal data is high.
The proposed method of vectorization consists in transforming the data into a vec-
tor form, which will enable to determine the degree of similarity between the values
of personal characteristics. The value of a similarity measure between the value of
personal characteristics and the control vector indicates the importance of member-
ship by the online community to a certain value characteristics.
6
5 Approbation of results in the practice
The results of data level adequacy analysis of personal medical profiles of Ukraini-
an medical centers comparing to patients online information tracks.
Fig. 3. Results of medical profiles data adequacy analysis of Ukrainian medical information
systems
The indicator of the effectiveness of the developed methods of data verification of
personal medical profiles is determined in equation (12).
N VerPD , NVerPD ¹N LAdequacy
APD
(12)
Efficiency=
-N LAdequacy
VerPD APD
N
N(LAdequacy)APD is number of personal medical profiles with low account data
adequacy, NVerPD is the total number of verified personal medical profiles.
Based on equation (12) the results of data verification level of personal medical
profiles, investigated profiles are classified according of data verification of personal
medical profiles (21% of all investigated accounts contained high level of data verifi-
cation, 41% of all investigated accounts contained average level of data verification,
38% of all investigated accounts contained low level of data verification). These re-
sults are presented graphically in Fig. 3.
7
Fig. 4. Results of analysis of level of data verification of personal medical profiles
The results show that 23% of the patients (total of 4708 person) provided reliable
information in their accounts. 28% of members updated their credentials in the ac-
counts. 4% of all personal medical accounts are blocked.
TOOLS FOR DETERMINING
VERIFIER OF PERSONAL THE DATA ADEQUACY OF
DATA OF WEB- PERSONAL MEDICAL ANALYZER OF PERSONAL
COMMUNITY USER PROFILES IN MIDMS MEDICAL PROFILES DATA
ANALYSIS OF PERSONAL DATA AND INFORMATION MIDMS MANAGEMENT
PROFILE BUILDING OF WEB-COMMUNITY USERS
CCOMPONENT
OMPONENT OFOF
CCOMPONENT
OMPONENT OF OF SETS
SETS CCOMPONENT
OMPONENT OFOF CCHECKING
HECKING DDATA
ATA OF
OF
FFORMATION
ORMATION OFOF BBUILDING
UILDING IINFORMATION
NFORMATION PPERSONAL M
ERSONAL MEDICAL
EDICAL
IINDICATORS
NDICATORS PPROFILE
ROFILE PPROFILE
ROFILE
CCOMPONENT CCOMPONENT CCOMPONENT
OMPONENT OFOF
OMPONENT OF
OF OMPONENT OFOF M ISSING D
MISSING DATA
ATA
IINFORMATION
NFORMATION TTRACK
RACK PPERSONAL
ERSONAL DDATA
ATA IIMPUTATION OF M
MPUTATION OF MEDICAL
EDICAL
FFORMATION
ORMATION VVALIDATION
ALIDATION PPROFILES
ROFILES
WEB-USERS SDCH
INDICATORS INFORMATION MARKERS MEDICAL
TRACKS PROFILE DATA MISSING DATA
GLOSSARY
CONTENT PERSONAL DATA
SUBSYSTEM OF INFORMATION CONTENT EXTRACTION
INFORMATION SYSTEM OF
MULTI-COMPUTER MEDICAL INFORMATION
MONITORING WEB-COMMUNITY DECISION-MAKING
USERS SUPPORT SYSTEM USERS
Fig. 5. Software Complex of Determining the Data Adequacy of Personal Medical Profiles
Medical Information Decision-Making Support System
8
Conclusion
the suggested method of medical information decision-making support sys-
tem have been tested in 5 medical information systems.
the efficient in detecting non-valid accounts and accounts with incorrect or
fake data.
the given methods simplifies work of medical staff on analysis of patients’
personal data and reduces check times.
References
1. Fedushko S., Shakhovska N., Syerov Yu. Verifying the medical specialty from user profile
of online community for health-related advices. CEUR Workshop Proceedings. Vol. 2255:
Proceedings of the 1st International workshop on informatics & Data-driven medicine
(IDDM 2018) Lviv, Ukraine, November 28–30, 2018. P. 301–310.
2. Babichev, S., Korobchynskyi, M., Mieshkov, S., Korchomnyi, O.: An Effectiveness Eval-
uation of Information Technology of Gene Expression Profiles Processing for Gene Net-
works Reconstruction. IJISA. Vol.10, No.7, 1-10 (2018).
3. Boyko, N., Pylypiv, O., Peleshchak, Y., Kryvenchuk, Y., Campos, J. Automated document
analysis for quick personal health record creation. Proceedings of the 2nd International
Workshop on Informatics & Data-Driven Medicine (IDDM 2019) Lviv, Ukraine, Novem-
ber 11-13, 2019. Vol 2488. http://ceur-ws.org/Vol-2488/paper18.pdf
4. Fedushko S. Adequacy of Personal Medical Profiles Data in Medical Information Deci-
sionMaking Support System. IREHI 2018. http://ieee-rural-elderly-health.com/2018/wp-
content/uploads/2018/12/IREHI-Programm-1.pdf
5. Gnatyuk S., Kinzeryavyy V., Sapozhnik T., Sopilko I., Seilova N., Hrytsak A. (2020)
Modern Method and Software Tool for Guaranteed Data Deletion in Advanced Big Data
Systems. In: Hu Z., Petoukhov S., He M. (eds) Advances in Artificial Systems for Medi-
cine and Education II. AIMEE2018 2018. Advances in Intelligent Systems and Compu-
ting, vol 902. Springer, Cham. pp 581-590.