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 information content is suggested. The system of indicators based on a training sample of users in medical online communities is formed. The matrix of medical specialties indicators and method of determining weight coefficients these indicators is investigated. The proposed method of verifying the medical specialty from user profile is tested in online medical community.
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
Problems Solving
To develop an effective methods and means of determining the level of data adequacy of personal medical profiles.
The components of an information track are: Content(Pi) created by a member of the online community, and personal data – PersonalData(Pi).
j1
is set of online community discussions;
Poll Pi Poll j Pi i
j1
is set of online community polls;
(
Post Pi Post j Pi i is set of online community posts.
j1
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 important – important data. Personal data distribution of online community user account to blocks is as follows:
PersonalData Pi
BasicInfo Pi , EduInfo Pi , InterestsInfo Pi ,
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.
BasicInfo Pi =
Name Pi ,NickName Pi ,Age Pi ,
Gender Pi , Region Pi ,Lang Pi
(
FotoInfo(Pi) is graphic information block. where Name(Pi) is full name; NickName(Pi) is nick name; Gender(Pi)is gender;
N Lang is plural of languages signed by a user;
j1 NiLang is number of language; Region Pi =Regionk Pi kN=iR1egion is set of regions with which a user is associated; NiRegion is set of regions.
EduInfo(Pi) is information block about education.
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 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.
FotoInfo Pi Avatar Pi ,Userbar Pi , Foto Pi where Avatar Pi Avatarj Pi NjiA1va is set of avatars, NiAva is number of avatars, NUserbar is set of graphic
signatures, NiUserbar is number of signatures, Foto Pi Fotom Pi mNiF1oto is set of photos.
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 where Byline Pi Byline j Pi NjiB1yline is number of signatures, NiByline is number of
N Act is a set of favorite lessons and phrases, signatures; Activity Pi Activityk Pi ki1 NiAct is number of lessons, phrases, Quot Pi Quotl Pi lNi1Quot is a plurality of quotations, NiQuot is number of quotations, Biography(Pi) is a biography.
Information about contacts and websites where the web user displays communicative activity is placed in the ContactInfo(Pi) block. Each account blocks contain information 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 informational 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. (8) (9)
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.
jk Value, P 1 jk Value, P jk Value, P is distance to each possible value of the personal data of the atomic k-th user of web community: μjk Value,P =1
N_IndPrCh,k i=1 Indi,j PrCh,Vc -Indi,j *wiPrCh PrCh,P 2 where k 1N _Vl Pr Ch,Vc . Moreover, jk Value, P 0,1 .
jk Value, P max , then the degree of probability of personal characteristics of a 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 vector 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 membership by the online community to a certain value characteristics. (10) (11)
Approbation of results in the practice
The results of data level adequacy analysis of personal medical profiles of Ukrainian medical centers comparing to patients online information tracks.
The indicator of the effectiveness of the developed methods of data verification of personal medical profiles is determined in equation (12).
Efficiency=
NVerPD NVerPD -N LAdequacy APD , NVerPD¹NLAdequacy APD (12)
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 verification, 41% of all investigated accounts contained average level of data verification, 38% of all investigated accounts contained low level of data verification). These results are presented graphically in Fig. 3. 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 accounts. 4% of all personal medical accounts are blocked.
VERIFIER OF PERSONAL
DATA OF WEBCOMMUNITY USER
TOOLS FOR DETERMINING THE DATA ADEQUACY OF
PERSONAL MEDICAL
PROFILES IN MIDMS ANALYSIS OF PERSONAL DATA AND INFORMATION
PROFILE BUILDING OF WEB-COMMUNITY USERS COMPONENT OF SETS
FORMATION OF
INDICATORS
COMPONENT OF INFORMATION TRACK
FORMATION
COMPONENT OF BUILDING INFORMATION
PROFILE COMPONENT OF PERSONAL DATA
VALIDATION INDICATORS
WEB-USERS INFORMATION
TRACKS
SDCH MARKERS GLOSSARY
ANALYZER OF PERSONAL MEDICAL PROFILES DATA MIDMS MANAGEMENT
COMPONENT OF CHECKING DATA OF PERSONAL MEDICAL
PROFILE COMPONENT OF
MISSING DATA IMPUTATION OF MEDICAL
PROFILES
MEDICAL PROFILE DATA
MISSING DATA INFORMATION SYSTEM OF
MULTI-COMPUTER
MONITORING Conclusion the suggested method of medical information decision-making support system 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.