=Paper=
{{Paper
|id=Vol-2753/paper7
|storemode=property
|title=Collaborative Deterministic and Stochastic Decision-Making Models in Health Care
|pdfUrl=https://ceur-ws.org/Vol-2753/paper5.pdf
|volume=Vol-2753
|authors=Tetiana Shmelova,Abdel-Badeeh M. Salem,Volodymyr Smolanka,Oleksandr Sechko
|dblpUrl=https://dblp.org/rec/conf/iddm/ShmelovaSSS20
}}
==Collaborative Deterministic and Stochastic Decision-Making Models in Health Care==
https://ceur-ws.org/Vol-2753/paper5.pdf
Collaborative Deterministic and Stochastic Decision-Making
Models in Health Care
Tetiana Shmelovaa, Abdel-Badeeh M. Salemb, Volodymyr Smolankac, and Oleksandr Sechkod
a
National Aviation University, Liubomyra Huzara ave. 1, Kyiv, 03058, Ukraine
b
Ain Shams University, Faculty of Computer and Information Sciences, Abbassia 11566, Cairo Egypt
c
Uzhhorod National University, Narodna Square, 3, Uzhgorod, 88000, Ukraine
d
Uzhhorod National University, Narodna Square, 3, Uzhgorod, 88000, Ukraine
Abstract
The authors presented smart decision-making models for effective interaction of doctors and
patients, doctors of various qualifications, and the choice of joint effective solutions in the
health care system such as Non-Stochastic (Deterministic) and Stochastic (Decision Making in
Risk and Uncertainty). There presenting intelligent Algorithms of the integration of
Deterministic and Stochastic Decision-Making models and Finding the optimal solution in
Uncertainty for building Collaborative Decision-Making Models for use in Healthcare
systems. The Expert system for the estimation of the priority of patients for the Artificial
Intelligence system developed.
Keywords 1
Artificial Intelligence, Expert Judgment method, Intelligent Decision Making in Risk, Decision
Making in Uncertainty, Deterministic Model, Health Care, Collaborative Models, Medical
Informatics.
1. Introduction
The interaction of doctors of various qualifications and the patient in the health care system is an
urgent issue, both now and before. Patient-physician interaction is a potentially important factor in
optimal communication during consultations as well as before treatment and during treatment,
compliance, and follow-up care. Many authors share their experience of using integration models and
solutions between doctors of different qualifications, between doctor and patient, between young and
experienced doctors.
The model of interaction between doctors and patients in the health care system is understood as a
product of analytical design, which is based on functional, behavioral, communicative, sociocultural
aspects [1].
The collaborative decision making (CDM) has advantages in different organization systems,
including in Aviation systems, where operator’s decision making (DM) in difficult situations [2; 3; 4].
A properly managed solution can improve group results of decisions [4; 5].
The modern development of society involves taking into account the influence of sociocultural
values on the patient's condition. For example in aviation, investigation of the evolution of the aviation
system in the direction of sociotechnical system SHEL (1972) [8; 9; 10], show that the SCHELL model
since 2004 complements the interface associated with the culture of human-operator and CRM (Crew
resource management), "SCHELL model and CRM" - Software (procedures), Culture (culture),
Hardware (machines), Environment, Liveware, Liveware (humans) [11; 12; 13; 17]. At the heart of the
SHEL model is man as the most important component of the system. It is very important to take into
account culture (C), the interaction between people (L-L), collaborative decisions (CDM), consistency
IDDM’2020: 3rd International Conference on Informatics & Data-Driven Medicine, November 19–25, 2020, Växjö, Sweden
EMAIL: shmelova@ukr.net (T.Shmelova); abmsalem@yahoo.com (Abdel-Badeeh M. Salem); vsmolanka@gmail.com (V.Smolanka),
aleksander.sechko@gmail.com (O.Sechko)
ORCID: 0000-0002-9737-6906 (T.Shmelova); 0000-0001-5013-4339 (A-B.Salem); 0000-0001-7296-8297 (V.Smolanka); 0000-0002-4136-
5511 (O.Sechko)
©️ 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
in the team of humans, the balance between cost and efficiency in rational solutions, the use of AI
systems to solve and help a person in difficult situations. These additions to the conceptual model SHEL
are considered in the evolution of the human factor models. Now the most relevant is the use of CDM
models, especially in critical situations [3; 13].
The world's leading airlines have developed programs to prepare flight crew for air defense, which
complement each other and integration. Aeronautical Decision Making (ADM) is a systematic approach
that pilots must consistently use to select the best strategy according to the circumstances. Each decision
can significantly increase or decrease the risk of successful completion of the flight and is based on the
pilot's ability to make informed and timely decisions. Nowadays importantly CDM by all operational
partners such as pilots of manned and unmanned aircraft, air traffic control services, airports, airlines,
and ground operators on the basis of shared information on the flight process and ground handling of
aircraft in an airport [3; 11; 12; 13].
International civil aviation organization (ICAO) constantly develops and improves, based on the
evaluation of the risks and the proactive approach, which to oriented on operator. A modern approach,
founded on the characteristics (performance-based approach – PBA), based on the next three principles
monitoring [3; 12; 13]:
1. The main accent on desired/necessary results
2. DM, oriented on desired/necessary results
3. Using facts and data while DM
Herein the principle “using facts and data while DM” admits that tasks shall comply with the widely
known in Western management criteria SMART [9]:
• Specific
• Measurable
• Achievable
• Relevant
• Timebound
The concept of ICAO assumes provision collaborative СDМ between all operational partners [11;
12]. Implementation of the CDM requires the use of a modern information environment based on the
concepts of System Wide Information Management (SWIM). Such a level of accuracy of task
determination may be achieved only using the new methodology includes the process of Integration
Stochastic and Non-Stochastic Uncertainty Models for Network Planning models in Conflict Situations
[12; 13].
Nowadays, ICAO extended the existing and defined new approaches to improve the practical and
sustainable implementation of preventive aviation security measures based on modern advances in
information technology and Artificial Intelligence (AI). AI technologies have expanded considerably
with successful applications in many areas and for support of humans in difficult situations, of cause.
The approaches in patient-physician interaction a patient-centered presented, improving interactions
between young and experienced healthcare providers are confirmed by statistics data [6; 7].
The authors' proposal introduces in medicine the methodology of CDM for improving patient states
that are used in aviation [5; 6] with the application of integrated models of DM and AI methods. The
approaches in patient-physician interaction a patient-centered presented, improving interactions
between young and experienced healthcare providers are confirmed by statistics data [6; 7].
The purpose of the publication:
• Analysis multi-DM in certainty using Network Planning Models for all participants of a process
• Working-out of smart models DM in Certainty, in Risk, and Uncertainty for the search of the
optimal solution
• Integration of models DM in Certainty, in Risk, and Uncertainty
• To develop an Expert system (ES) as a knowledge-based system for estimation of priority of
patients using Expert Judgment Method (EJM)
2. Collaborative Decision-Making Models
The CDM an uninterrupted process of presenting information and individual decision-maker by
various interacting participants (in patient-physician and physician-physician interaction too), as well
as providing synchronization of decisions taken by participants and the exchange of information
between them. It is important to ensure the possibility of making a joint, integrated solution with
partners at an acceptable level of efficiency. This is achieved by completeness and accuracy of available
information. Solutions planning should provide using DM different models such as deterministic
models; stochastic and non-stochastic of uncertainty models; the Markov and reflexion models. After
analysis of the situation needs synthesis (aggregation) of stochastic models for the correction of the
deterministic model. Algorithm of the integration of DM models for participants of a complex process
(operators, patient-medic, medic-medic, etc.) was obtained.
2.1. Intelligent Algorithm of the integration of Deterministic and Uncertainty
Decision Making models
Algorithm where indicated the analysis of the actions of operators with the aid of the Network
Planning methods, DM in Risk and Uncertainty, EJM and obtaining of simple models with
unambiguous decisions and ordered actions of patients and medics.
Intelligent Algorithm of the integration of Deterministic and Uncertainty DM models:
1. Deterministic models with ambiguous decisions and ordering actions of patients and medics.
Deterministic models:
• Building a deterministic DM model with a lot of problems, and ambiguous decisions in specific
stages using the Network Planning method - complex certainty DM model (Figure 1)
• Decomposition of main technology (problem/procedure/situation/conflict situation) on
procedures
• Flowchart of performance technology procedures (procedure/situation/conflict situation) /
ordering of procedures
• Determination of the times of operating procedures using the EJM (according to experimental,
statistics, experience, skills data too)
• Structural-timing table of operational procedures and time on the procedures in main
technology (procedure/situation/conflict situation)
• Network graph of main technology (problem/procedure/situation/conflict situation) and
obtaining main critical time of performance all proceeding.
2. Optimization of schedule/plan of performance of main technology
(problem/procedure/situation/conflict situation):
• Identification of difficult points where several alternative solutions and in next using the
effective method of DM. In the presence of a large amount of statistical data and probabilities are
used DM methods in Risk. In the absence of a large amount of statistical data and probabilities, are
used DM methods in conditions of Uncertainty.
• Analysis each part of main technology using assessment by DM in Stochastic Uncertainty (DM
in Risk) and Non-stochastic Uncertainty (DM in Uncertainty) methods.
3. Stochastic models for the determination of uncertainty moments, such as DM in Risk and DM
in Uncertainty (Figure 1).
4. Deterministic models with unambiguous decisions and ordered actions of patients and medics
- – integrated simplified certainty DM model (Figure 1).
5. Construction of a Decision Support System (DSS) for the attending physician, which are used:
• Model base (DM and CDM models)
• Database (specific treatment conditions)
• Knowledge base (expert assessments of specialized specialists)
6. DSS maintenance using statistical, expert and experimental data.
7. Building an AI system for intellectual assistance and support to the physician in difficult
situations with Big Data.
Figure 1: The integration certainty, risk and uncertainty DM models: a - complex certainty DM model;
b - risk and uncertainty DM models; c – integrated simplified certainty DM model
In aviation, with the aim to optimize the solutions (pre-flight preparation, Air Traffic Control) the
automated systems of preparation information and DSS were created [3; 13]. In the recent documents,
ICAO defined new approaches - application of AI models the organization of CDM by all aviation
operators using collaborative DM models (CDMM) based on general information on the flight process
and features of the situation [11; 12; 14].
In the process of analysis and synthesis of DM models in situations makes sense to simplify complex
models and solutions. So, for example, stochastic and non-stochastic of uncertainty, neural, the Markov,
and GERT (Graphical Evaluation and Review Technique) - models, reflexion models, dynamic models
may be integrated into deterministic models. The models for decision and predicting the situation using
CDMM [14]. For the formation (modeling) of DM, operator has the property such as the ability to apply
different levels of DM complexity depending on the factors that influence the DM [15].
The selection task of an optimal solution using the method of DM under uncertain-ty was obtained
by means of the criteria of DM under uncertainty: Wald, Laplace, Savage, Hurwicz [13]. Each of the
criteria has a set of differences in application. The main difference is the different levels of uncertainty
of problem, types of situation (often, rare, first time), and complexity of care situation. For instance, the
Laplace criterion is grounded on more optimistic assumptions (same situations what were); the Wald
criterion is grounded on more pessimistic assumptions is used to find the optimal solution for the first
time. The coefficient of optimism-pessimism is used in the Hurwicz criterion that can be used in
different approaches from the most optimistic to the most pessimistic value. The Savage criterion is
used in after situations for re-calculation decisions minimizes the losses.
2.2. Collaborative Deterministic and Stochastic Decision-Making Models
As known, the environmental conditions (natural, social, communication, financial) determine the
reactions of humans, while the reaction of the latter, in its turn, changes the conditions themselves
development of situation. For example, the systemic analysis has been carried out as well as the
formalization of the factors which affect DM by operators in the Air Navigation System (individual-
psychological, psycho-physiological and social-psychological) in the normal, difficulty, emergency
situations [3; 8]. The impact of individual-psychological and socio-psychological factors on the
professional activities of operators during the conflict situation and development from normal to
catastrophic has been studied. On the basis of the reflexive theory of bipolar choice, the expected risks
of DM have been studied and the influence of the external environment, previous experience and
intention of the operator have been identified [3]. It is very important to create highly intelligent
collaborative DM systems for people who are involved in solving one problem and influence the
development of the situation.
In research are presented DM models for humans who taking part in solution important single
problems in Health Care. There are such as medics, operators, physician, medics of different
qualifications, and patients, of cause too. The authors have experience in building decision-making
models in air navigation systems especially in emergency situations for operators (pilots of manned and
unmanned aircraft, air traffic controllers, engineers, flight dispatch) [3; 4; 13; 14]. There are obtained
the deterministic and stochastic models of DM for operators of the air navigation system with using of
collaborative solutions of different operators. Were obtained the integrated models with using
deterministic and stochastic DM models such as DM in certainty, risk and uncertainty, Markov chains;
stochastic models type GERT’s (Graphical Evaluation and Review Technique) network; neural network
models; fuzzy logic models; reflexive models of bipolar choice; models of diagnostics of emotional
state deformation in the activity of operators; graphical-analytical models of situation development;
graphical-analytical models of decision-making for ANS operators [3; 13].
In Health Care, for example, the search for an optimal solution for effective medical care/treatment
of patient A with alternative ways (A1; A2; A3) of finding the patient in a simple situation B: in a hospital,
day hospital, outpatient treatment, home treatment. Such options for finding patients with mild diseases
are used now when the COVID-19 pandemic in the world, in many hospitals, need a reserve for patients
with coronavirus disease [16].
Effective decision rules for patient care are determined using a deterministic model built using
network planning methods. However, the deterministic model of DM, taking into account all the
circumstances, turns out to be very complex (Figure 2), there are many suggestions from doctors, from
patients with the construct wishes. The deterministic model of the DM in a situation where are many
suggestions from participants (subjective factors) and external factors influence decisions (objective
factors) are present in Figure 2. To simplify the deterministic model, it is proposed to resolve multi-
alternative situations (S) using DM methods under conditions of risk and uncertainty.
Figure 2: The deterministic model of DM in situation with many suggestions from participants of
process
For example, the DM matrix with some alternative decisions and objective-subjective factors
influencing DM is presented in Table 1. DM matrix consists of the next components:
1. Set of alternative actions:
{A}= {А1, А2, …Аі, …, Аn},
where
А1 – finding the patient in hospital; А2 – finding the patient in day hospital; А3 – finding the patient
in outpatient treatment
2. Set of factors:
{λ} = (F1; F2)
where
F1 - objective factors: λ1 - comfort of finding; λ2 - service cost; λ3 - remoteness; λ4 - medical
indications, etc.
F2 - subjective factors: λ5 – physicians’ opinion; λ6 - doctor opinion; λ7 – patient-doctor opinion.
3. Set of outcomes {U} – results / outcomes of expert’s evaluations.
Table 1
The matrix of DM in Uncertainty
F1 - objective factors F2 - subjective factors
A comfort cost remoteness indications physician doctor patient
Alternative λ1 λ2 λ3 λ4 λ5 λ6 λ7
actions
In hospital А1 5 6 10 10 8 5 2
In day А2 6 7 7 7 7 6 7
hospital
In outpatient А3 7 8 6 4 8 6 8
treatment
In home А4 8 8 5 2 6 7 9
treatment
2.2.1. Intelligent Algorithm of finding of optimal solution in Uncertainty
Decision-Making:
1. Formation of a multiplicity of alternative decisions {A}:
{А} = {А1, А2, …Аі, …, Аn},
where
А1 – finding the patient in hospital;
А2 – finding the patient in day hospital;
А3 – finding the patient in outpatient treatment;
А4 – finding the patient in home treatment;
2. Formation of factors {λ}, that influence on selection of best solution:
{λ} =λ1, λ2 …, λj, …, λm,
where
F1 - objective factors:
λ1 - comfort of finding;
λ2 - service cost;
λ3 - remoteness;
λ4 - medical indications;
F2 - subjective factors:
λ5 – physicians opinion;
λ6 - doctor opinion;
λ7 - patient-doctor opinion.
3. Formation of possible consequences {U} that influence on selection:
{U} = U11, U12, …, Uij, …, Unm,
where
Uij - is defined according to the evaluation scale / regulatory data (F1) and opinions of participant
(F2).
4. Estimation of factors that influence the selection of optimal solutions is realized with the help
systems of the preferences of participants of the process. The opinions processed using AI methods
if need. Coordination of opinions using EJM obtained [3; 12; 13; 17]. The graphical presentation
Multi-Factor estimation and DM in a complex situation in Health Care in the Figure 3.
Results of estimations 12
10
8
6
4
2
0
λ1 λ2 λ3 λ4 λ5 λ6 λ7
comfort cost remoteness indications physician doctor patient
F1 - objective factors F2 - subjective factors
In hospital А1 In day hospital А2 In outpatient treatment А3 In home treatment А4
Figure 3: Graphical presentation Multi-Factor estimation and DM in complex situation in Health Care
5. Formation of decision matrix (Table 1) M=|| Мi ||.
6. Obtaining the optimal solution in the case of complex situations using methods of DM under
uncertainty: Wald, Laplace, Savage, Hurwicz. Wald's (maximin)criterion is used if a rare case:
𝐴∗ = 𝑚𝑎𝑥 {𝑚𝑖𝑛𝑢𝑖𝑗 (𝐴𝑖 , 𝜆𝑗 )}.
𝐴𝑖 𝜆𝑗
Laplace's criterion is a case that is often encountered:
1
𝐴∗ = 𝑚𝑎𝑥 { ∑𝑛𝑗=1 𝑢𝑖𝑗 (𝐴𝑖 , 𝜆𝑗 )}. 𝑚
𝐴𝑖
The optimal solutions using Wald, Laplace methods of DM presented in the Table 2.
Table 2
The matrix of DM with optimal solutions of DM in Uncertainty using Wald, Laplace methods
F1 - objective factors F2 - subjective factors Solutions
A comfort cost remoteness indications physician doctor patient Wald Laplace
Alternative λ1 λ2 λ3 λ4 λ5 λ6 λ7 maximin max
actions
In hospital А1 5 6 10 10 8 5 2 2 6,57
In day А2 6 7 7 7 7 6 7 6 6,71
hospital
In А3 7 8 6 4 8 6 8 4 6,71
outpatient
treatment
In home А4 8 8 5 2 6 7 9 2 6,43
treatment
The Hurwitz criterion is used for decisions with varying degrees of optimism using the optimism-
pessimism coefficient α:
𝐴∗ = 𝑚𝑎𝑥 {𝛼𝑚𝑎𝑥 𝑢𝑖𝑗 (𝐴𝑖 , 𝜆𝑗 ) + (1 − 𝛼)𝑚𝑖𝑛𝑢𝑖𝑗 (𝐴𝑖 , 𝜆𝑗 )},
𝐴𝑖 𝜆𝑗 𝜆𝑗
where
– is an optimism index (0 ≤ ≤ 1), α =0.5.
The optimal solutions using Hurwitz method of DM with different level of optimism - pessimism
presented in the Table 3.
Table 3
The matrix of DM with optimal solutions of DM in Uncertainty using Hurwitz methods
F1 - objective factors F2 - subjective factors Solutions
A comfort cost remoteness indications physician doctor patient Hurwitz
Alternative λ1 λ2 λ3 λ4 λ5 λ6 λ7 α =0,5 α =1
actions
In hospital А1 5 6 10 10 8 5 2 6 10
In day А2 6 7 7 7 7 6 7 6,5 7
hospital
In outpatient А3 7 8 6 4 8 6 8 6 8
treatment
In home А4 8 8 5 2 6 7 9 5,5 9
treatment
The Savage criterion (minimax regret criterion) is used to recalculate whether the decision was made
correctly using the additional loss matrix rij:
𝐴∗ = 𝑚𝑖𝑛𝑚𝑎𝑥𝑟𝑖𝑗 (𝐴𝑖 , 𝜆𝑗 ),
𝜆𝑗 𝐴𝑖
where loss matrix:
𝑟𝑖𝑗 (𝐴𝑖 , 𝜆𝑗 ) = 𝛥 = 𝑚𝑎𝑥 𝑢𝑖𝑗 (𝐴𝑖 , 𝜆𝑗 ) − 𝑢𝑖𝑗 (𝐴𝑖 , 𝜆𝑗 ).
𝐴𝑖 𝜆𝑘
The following solutions were obtained: if a rare disease (Wald criterion) is the best solution - А2 =
max Аj = 6 (А2 – finding the patient in day hospital), with mild disease, often occurring (Laplace
criterion), the optimal solution is - А2 = max Аj = 6,71 and А3 = max Аj = 6,71 ( А2 - finding the patient
in day hospital and А3 – finding the patient in outpatient treatment). Taking into account different
degrees of optimism (Hurwitz criterion), for α = 0,5, rationalism, optimal solution А2 = max Аj = 6 (А2
– finding the patient in day hospital) and for α = 1, optimistic assurance for recovery, optimal solution
А1 = max Аj = 10 (А1 – finding the patient in hospital).
There is a different approach to finding a solution. For rational CDM, each person involved in DM
has analyzed are considering the current situation and then they together make joint decisions. For
example, each participant of the process solved the first matrix of decisions, where alternative solutions
are the condition of treatment such as in the hospital, in day hospital, in outpatient treatment, in-home
treatment ({А}). The factors, influence the effectiveness of the condition of treatment for patients:
comfort, service cost, remoteness, medical indications (F1 - objective factors {λ}). The output data of
individual solutions from the first matrixes are included in the initial data for the second matrix. In
second CDM matrix determines the optimal solution by all participants of the process of cure (F2 -
subjective factors {λ} – the opinions of the participants about the process of treatment).
3. What is next
In the future authors are planning to predict real-cared results using an artificial intelligence system.
The machine learning algorithm based on supervised learning and performs a regression technique that
finds out a linear relationship between x (input) and y (output). The input variable x data features of
care and treatment, the output variable y predicts real-cared results of cure [13; 15; 18; 19] (Figure 4).
Figure 4: The intellectual connection between input variable x (data features of care and treatment),
the output variable y (prediction real-cared results of cure)
The function of the minimization of the difference between the predicted values and ground truth
measures the error difference. This function is also known as the Mean Squared Error (MSE) function
[13; 15]. Using the MSE function may change the values coefficients of regression such that the MSE
value settles at the minima. For updating the coefficients method of gradient descent used. The learning
rate is very important because a smaller learning rate could get closer to the minima of error but takes
more time to reach the minima. A larger learning rate converges sooner but there is a chance that MSE
function could overshoot the minima. To create a new AI system, need Big data and CDM of
participants of the modeling process.
4. Conclusion
The CDM an uninterrupted process of presenting information and individual DM by various
interacting participants, as well as providing synchronization of decisions taken by participants and the
exchange of information between them. It is important to ensure the possibility of making a joint,
integrated solution with partners at an acceptable level of efficiency. This is achieved by completeness
and accuracy of available information. Solutions planning should provide using DM different models
such as deterministic models; stochastic and non-stochastic models. After analysis of the situation needs
synthesis of stochastic models for the correction of deterministic model.
The authors' proposal introduces in medicine the methodology of CDM for improving patient states
that are used in aviation with the application of smart integrated models of DM and AI methods and
techniques. An intelligent algorithm of the integration of DM models for participants of a complex
process (operators, patient-medic, medic-medic, etc.) was obtained. Algorithm of the actions of
participants in complex situations for obtaining simple models with unambiguous decisions and ordered
actions of patients and medics presented.
The example of the service situation of patients in Healthcare, the search for an optimal solution for
effective medical care/treatment of patients presented.
The obtained DM models can be applied in the DSS of physicians to serve patients in future medical
AI-based systems.
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