=Paper=
{{Paper
|id=Vol-2485/paper71
|storemode=property
|title=Methods for the Intelligent Analysis of Biomedical Data
|pdfUrl=https://ceur-ws.org/Vol-2485/paper71.pdf
|volume=Vol-2485
|authors=Emilia Geger,Aleksandr Podvesovskii,Sergey Kuzmin,Viktoria Tolstenok
}}
==Methods for the Intelligent Analysis of Biomedical Data==
https://ceur-ws.org/Vol-2485/paper71.pdf
Methods for the Intelligent Analysis of Biomedical Data
E.V. Geger1, A.G. Podvesovskii2, S.A. Kuzmin2, V.P. Tolstenok2
emiliya_geger@mail.ru|apodv@tu-bryansk.ru|wolv3333@mail.ru|tolstenok21@yandex.ru
1
Bryansk Clinicodiagnostic Center, Bryansk, Russia
2
Bryansk State Technical University, Bryansk, Russia
The paper discusses methodology of cleaning and analysis of small semi-structured samples of biomedical data. This methodology
is aimed at statistical evaluation of harmful production factor correlation with workers’ laboratory test data. As a result of the analysis
and interpretation of the data, a deviation from the norm is observed according to some indicators of a clinical blood test in individuals
whose occupational activity is associated with harmful factors. Conclusions are drawn about the need for further research in the group
of people whose work is related to harmful production factors. It is necessary to employ intelligent methods for analyzing possible health
risks and their negative consequences in order to make management decisions. The presented assessment methodology can be used to
create an occupational health and safety information system.
Keywords: risk assessment, data analysis, harmful working conditions, statistical methods, data cleaning, model ensembles,
Kohonen self-organizing maps.
New analytical technologies can not only increase the
1. Introduction efficiency of medical institutions but also make it possible to
solve such health problems as identifying diagnoses, medical
In the modern world, the necessity often arises for auxiliary
errors, associative connection of diagnoses with results of
methods of preliminary disease detection at early stages. This
laboratory tests and much more.
problem is especially characteristic of people whose labor
The objective of the present research has been to assess
activity is associated with the constant impact of harmful
relationship between occupational morbidity and harmful
working conditions [6, 10].
production factors. So the experimental sample consisted of
In turn, increased level of exposure to harmful substances
individuals whose occupational activities were associated with
and related industrial hazards significantly increase the
harmful and dangerous working conditions. Another objective
likelihood of developing occupational diseases and the risk of
was development of a new methodology focused on processing
injury [8].
and analyzing small semi-structured samples of biomedical data.
Thus, occupational risk management is a complex of
organizational and technical measures which should be based on 2. Existing Solutions
reliable results of data analysis [9].
For the tasks of occupational risk assessment, it is necessary Among many proposed methods and tools for working with
to use medical data analysis methods that correspond to these small samples, it is advisable to apply ensemble data analysis
tasks. Choice of methods affects the construction of theoretical methods in combination with the basic classifier – Kohonen self-
biomedical models and characteristics of experimental studies [7, organizing maps.
17]. An ensemble of models is a combination of several learning
However, many experts note that biomedical data are often algorithms that, working together, help to build a model more
unsuitable for processing using traditional software not only efficient and accurate than any of the models built using a
because of their volume but also because of the variety of data separate algorithm. That is, to find a solution for one problem or
types and speed at which they must be analyzed [1, 3, 15]. to prove a hypothesis, not one but several models are used.
Therefore, a system for the intelligent analysis of medical data is Besides, the overall operating result matters and not that of a
required, which could aggregate and analyze heterogeneous single separate model.
information coming from different sources: electronic medical Formerly, researchers faced the problem of combining
records, data from monitoring sensors, ultrasound and X-ray accuracy, simplicity and ease of interpretation in one method. A
apparatus and other devices [4]. simple method could be used with a relatively easy interpretation
If we consider real biomedical data, they have a number of but coming short of accuracy or vice versa, a complex but
specific features that make them unstructured: presence of accurate method could be chosen that would be difficult to
various data corruptions, such as omissions, extreme values, interpret. Ensembles of models have become a solution to this
manual entry errors, incorrect information, high dimensionality problem as a universal way of improving the accuracy of
and heterogeneity, a large number of noisy and duplicate data. methods.
This leads to the unsuitability of most of the data or the entire Ensemble learning refers to the training of a finite set of basic
sample for existing analysis algorithms [16]. classifiers with the subsequent combination of their forecasting
Analysts believe that today, to solve many tasks of the results into a single forecast of an aggregated classifier. Thus, an
healthcare system, it is necessary to go in the direction of aggregated classifier will give a more accurate result.
structuring information and focusing on work with small samples The goal of combining models is to improve (enhance) the
[5]. solution provided by a separate model. It is assumed that a single
Particular attention should be paid to the so-called small model will never be able to achieve the efficiency that the
samples, the volume of which is about 100-200 records. This, in ensemble will provide.
turn, is very difficult and makes the use of existing methods of Self-organizing maps are a special type of artificial neural
data processing and analysis ineffective. network allowing for non-linear regression and projection of
The main obstacle to the manual analysis of small samples is multidimensional data onto a two-dimensional plane with
the inability of the analyst to notice hidden patterns in presented preservation of distances in their original data space [2]. This
data, while special analytical algorithms detect existing patterns approach can be applied in various fields, including biomedical
with much greater efficiency. The main task of the analyst, in this data processing.
case, is to interpret the results of these algorithms and filter out
false and trivial patterns.
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
3. Proposed Theoretical Solution features of this model, in addition to the fact that it does not
allow meaningless data loss, are:
To solve the research problems of working environment risk 1. Correction of empty values.
assessment, we formed two groups: 2. Deletion of entries containing no information.
Group I included individuals whose labor activity was 3. Recalculation of deviations by scores.
associated with exposure to harmful production factors. 4. Recovery of missing scores by deviations.
Group II consisted of individuals whose professional activity 5. Reassignment of diagnoses by ICD codes.
lacked harmful production factor. 6. Assigning a unique number to each patient.
The studies were carried out in the laboratory of Bryansk 7. Increasing the number of records suitable for analysis
Clinical Diagnostic Center; the results were reflected in the without generating synthetic patients.
medical information system "MAIS DC". 8. Download and comparison with ICD certified directory
The list of occupational diseases was determined in of codes and their descriptions.
accordance with the Order of the Ministry of Health and Social 9. Uploading data that has been damaged and
Development of the Russian Federation No. 417n dated April 27, subsequently corrected for additional possible analysis.
2012 “On the Approval of the List of Occupational Diseases”. 10. Converting source data into a form suitable for
Those working in harmful labor conditions are at risk for diseases intelligent analysis methods.
associated with exposure to occupational physical factors [12]. At the second stage, development and construction of a
The studies were carried out in accordance with the Order of model ensemble takes place. To simplify the development
the Ministry of Health of the Russian Federation dated April 12, process, the following algorithm must be followed:
2011 No. 302n (as amended on February 06, 2018) “On the 1. Select a base model.
Approval of Lists of Harmful and (or) Hazardous Occupational An ensemble can consist of classifiers of one type (e.g., only
Factors and Kinds of Work that Require Mandatory Preliminary of decision trees or only of neural networks) or of classifiers of
and Periodical Medical Examinations (Surveys), and the various types (decision trees, neural networks, regression
Procedure for Conducting Mandatory Preliminary and Periodical models, etc.).
Medical Examinations (Surveys)” [13]. 2. Define the approach to using the learning set.
To assess the possible risk of diseases caused by harmful This can be resampling (several subsamples are extracted
occupational factors, the values of clinical blood test scores were from the original learning set, each of which is used to train one
used as a source of information. The experiment was carried out of the ensemble models) or the use of one learning set to train all
in compliance with the ethical principles of biomedical research ensemble classifiers.
and in accordance with the Federal Law of the Russian 3. Select a method for combining results.
Federation No. 152 "On Personal Data" [14]. Three methods are usually used: voting (the class is selected
For morbidity analysis, the "International Statistical that has been produced by a simple majority of ensemble
Classification of Diseases and Related Health Problems" of the models), weighted voting (the result is delivered taking into
tenth revision (ICD-10) was used [11]. account weights set for ensemble models) and averaging (the
To solve the problem, a methodology was proposed output of the entire ensemble is defined as the simple average
consisting of the following stages: value of outputs of all models; in weighted averaging, the outputs
1. Data cleaning. of all models are multiplied by the corresponding weights).
2. Model ensemble development. As a result of applying this algorithm, a basic ensemble of
3. Result interpretation. models has been obtained (Fig. 1), which can be further modified
To carry out the first step, a special model has been designed and complexified.
and developed, the task of which is to clean the source data and
convert unstructured data into ordered ones.
The resulting model can be roughly divided into five parts.
Data import. It includes setting field names and labels,
excluding empty fields, setting data types.
Data preprocessing. It is a submodel that generates two sets
of data at the output – intact data, containing no errors, and
corrupted data, which are input to the next submodel.
Data cleaning. This submodel receives damaged data as
input and, after appropriate processing, provides output of
cleaned data that does not contain initial errors. In particular, this
submodel sets correct deviation values. In addition to the cleared
data, the output of the submodel contains data sets with incorrect
values in deviations and in scores.
Data merge. In this part, aggregation of initially complete
and cleared data takes place. In addition to this, using a code
written in the JavaScript programming language, patient’s Fig. 1. Structure of the developed model ensemble
unique identifier is generated and all diagnoses are divided into
several records, so that three records containing one diagnosis are The data that has undergone preliminary cleaning at the first
obtained from one record containing three diagnoses. This, in stage will be the input data.
turn, allows for data structuring. The converted data can be used At the third stage, the results obtained during the ensemble
when applying transaction analysis algorithm. Due to the errors application are interpreted. In most cases, for their qualitative
in the diagnosis name, it was decided to download an excel file interpretation, it is necessary to contact specialists in the field of
containing codes and names of 12257 diagnoses of the medicine.
international classification of diseases of the tenth revision [11]. For automatic analysis and visualization, ready-made
In the resulting data set, the initial field “Diagnosis Name” analytical platform Deductor Studio was used, and, for
has been replaced by the name of the diagnosis from this file. preprocessing and data cleaning, Loginom software products
Data export. It creates a file with the extension .txt, suitable developed by BaseGroupe Labs [18, 19] were used.
for uploading to analytical platforms and further analysis.
Thanks to the preprocessing of the initial sample, it is Also, this age group is characterized by significant positive
possible either to level out completely or to minimize the errors deviations from the norm in terms of hemoglobin, red blood
related to the human factor, which makes it possible to speak cells, and erythrocyte sedimentation rate.
about sufficient correctness of the medical research. The increase in red blood cells is especially noticeable in the
Unfortunately, small semi-structured samples cannot be called group of individuals whose work is associated with harmful
sufficiently representative. However, the identified patterns may occupational factors. Erythrocytosis could be caused by external
be of interest to experts or for further verification. factors.
Survey results may indicate the presence of pathological
4. Proposed Practical Solution processes in the body. Additional studies are needed to diagnose
the disease that has caused high content of red blood cells.
Laboratory test analysis has been carried out in workers
The methodology for analyzing small semi-structured
whose occupation is related to harmful factors (Group I) and a samples of biomedical data considered in the article can be used
control group (Group II).
with adequate efficiency as an integral part of the risk analysis of
Two small samples have been formed: the first sample
diseases related to harmful occupational factors. Application of
includes 100 records; the second one includes 207 records for
the proposed method is demonstrated on specific actual data,
each of the scores taken into account for each group. A total of 9
collection and consolidation of which has been carried out using
scores of the general blood test were selected.
medical automated information system. This provides reliable
Data on the primary disease incidence in workers from
evaluation of harmful occupational factor effects on working
Groups I and II have been analyzed. The results of diagnoses
population’s health indicators.
processing are presented.
The research results will help to define and evaluate
The results of the study made it possible to identify the
occupational risk factors that increase the likelihood of disease
diagnoses that most often occurred in Group I and Group II:
development and to draft proposals for the prevention of harmful
E78.0 – Pure hypercholesterolemia;
effects of occupational factors on working citizens’ health.
G90 – Disorder of autonomic nervous system, unspecified;
H52.4 – Presbyopia; 5. Conclusion
I10 – Essential [primary] hypertension.
The cross-table as an interactive tool for data representation The article discusses a methodology tested on specific actual
and analytical processing has allowed creating a pivot table data, which allows pre-processing, cleaning and analysis of small
which represents data on quantitative composition of diseases for biomedical data samples.
each group. As a result of the analysis, there have been revealed no
The use of Kohonen self-organizing maps helped to identify statistically significant difference in blood indices in individuals
differences and patterns between the studied groups. from groups I and II.
Examples of Kohonen self-organizing maps used are Also, the study of the diagnoses of the primary disease
presented in Fig. 2. incidence in both groups has not revealed statistically significant
differences between the groups.
However, the established significant deviations from the
normal content of red blood cells and eosinophils in the blood of
individuals from Group I may indicate the presence of certain
pathological processes in the body, in particular, autoimmune
processes, identification of which requires additional research.
In the future work perspective, it is advisable to conduct a
research on these clinical blood test results using the traditional
analysis of variance method regardless of normal intervals. This
will allow for comparing the results obtained. Hereupon, it will
be possible to draw final conclusions about the influence of
specific occupational factors on the development of pathological
processes.
The results obtained help to increase the efficiency of
detecting patterns in biomedical databases and are of interest for
the construction of intelligent systems designed to analyze and
assess human health.
6. Acknowledgements
The reported study was funded by RFBR, project number 19-
07-00844.
Fig.2. An example of data processing results using 7. Литература
Kohonen self-organizing maps [1] Bruce McCormick (2014) Update in Anaesthesia.
World Federation of Societies of Anaesthesiologists. 466 p.
It should be noted that in both groups at least 24% of [2] Kohonen T. The Self-Organizing Map // Proceeding of
employees were people aged 54 and over. In this regard, it has the IEEE. 1990. Vol. 78. P. 1464-1480.
been suggested that these diseases may be associated primarily [3] Manyika J., Chui M., Brown B. et al. (2011) Big Data:
with age-related changes, for example, age-related decrease in The Next Frontier for Innovation, Competition, and Productivity
the accommodative ability of the eye associated with the natural / McKinsey Global Institute.
process of aging of the lens, a prolonged and persistent blood [4] Baranov A.A., Namazova-Baranova L.S., Smirnova
pressure increase, blood cholesterol increase. I.V. et al. Methods and Tools for Complex Intelligent Analysis
of Medical Data. Trudy ISA RAN. Vol. 65. 2.2015. pp. 81-93.
[5] Barriers and Prospects for Digital Transformation: Big
Data Management Issues in the Healthcare Industry [Online]. –
Available: http://www.medlinks.ru/article.php?sid=83028
(Accessed: July 23, 2019).
[6] Geger E.V., Fedorenko S.I. Information Support of
Decision-Making when Assessing the Risk for Occupational
Morbidity Based on Analysis of Binary Samples // Proceedings
of Southwest State University. Control, Computer Engineering,
Information Science. Medical Instruments Engineering, no. 2
(27). 2018. pp. 101-107.
[7] Healthcare will Show the Largest Increase in Data
Generation by 2025 [Online]. – Available:
http://apcmed.ru/news/news-all/zdravookhranenie-pokazhet-
naibolshiy-rost-v-generatsii-dannykh-k-2025-godu/ (Accessed:
July 23, 2019).
[8] Izmerov N.F., Actualization of Occupational
Morbidity Issues // Health Care of the Russian Federation
(Zdravookhraneniye Rossiyskoy Federatsii.), no. 2. 2013. pp. 14-
17.
[9] Ismailova L.N. Effective Management of Production
Risks. // Economy and Business: Theory and Practice. 2016. No.
5. pp. 77-79.
[10] Kostenko N.A. Working Conditions and Occupational
Morbidity as a Basis for Risk Management of Workers' Health:
abstract of cand. med. sci. diss. M., 2015. 21 p.
[11] International Classification of Diseases of the tenth
revision (ICD-10) [Online]. – Available: https://mkb-10.com
(Accessed: July 20, 2019).
[12] The Order of the Ministry of Health and Social
Development of the Russian Federation No. 417n dated
27/04/2012 “On the Approval of the List of Occupational
Diseases”. [Online]. – Available:
http://base.garant.ru/70177874/ (дата обращения 23.05.2019).
[13] The Order of the Ministry of Health of the Russian
Federation dated 12/04/2011 No. 302n (as amended on
06/02/2018) “On the Approval of Lists of Harmful and (or)
Hazardous Occupational Factors and Kinds of Work that Require
Mandatory Preliminary and Periodical Medical Examinations
(Surveys), and the Procedure for Conducting Mandatory
Preliminary and Periodical Medical Examinations (Surveys)”.
[Online]. – Available: http://base.garant.ru/12191202/
(Accessed: May 21, 2019).
[14] The Federal Law dated 27/07/2006 No. 152-FZ (as
amended on 29/07/2017) "On Personal Data". [Online]. –
Available: http://base.garant.ru/5635295/ (Accessed: May 21,
2019).
[15] Tsvetkova L.A., Cherchenko O.V. Big Data
Technology in Medicine and Healthcare in Russia and the World
// Information technologies for the Physician, 2016. No. 3. pp.
60-73.
[16] Tsygankova, I.A. Method of Intelligent Processing of
Biomedical Data [Text] / I.A. Tsygankova // Software Products
and Systems. – 2009. –no. 3. – pp. 120-123.
[17] Choporov O.N., Razinkin K.A. Optimization Model of
Choice of the Initial Plan of Control Actions for Medical
Information Systems / Control Systems and Information
Technology. 2011. Vol. 46. No. 4.1. P. 185-187.
[18] BaseGroup. Data Analysis Technologies [Online] //
Available: https://basegroup.ru/ (Accessed: May 20, 2019).
[19] Loginom [Online] // Available: https://loginom.ru/
(Accessed: May 20, 2019).