The problem of providing computer support for decision making in medicine is relevant due to the increasing information load on the doctor, the development of computer technologies. When making medical decisions, there is a lack of time, high dynamics of the course of diseases, a high cost of medical error, etc. This paper describes basic design principles of the first version of next-generation decision support system for providing personalized patients care based on patients' clinical and treatment data with the use of authors synergetic collective decision-making model and the methods of hybrid computational intelligence which allows us to increase significantly the quality of the results of solutions to complex medical problems in information variety and heterogeneity as well as to enhance decision-making by reducing losses from erroneous and irrelevant to the problem complexity individual solutions. The result of the implementation should be breakthrough successes in solving many epidemiological, diagnostic, therapeutic, prophylactic, social and economic problems.
In work [
The synergetic model [
Perhaps this is due to the knowledge and experience of experts solving certain
parts of a complex problem in medicine [
Hybrid computational intelligence (HCI) [
Joint consideration of three mentioned complex concepts opens the way to a
promising engineering technology (the basics of which are presented in this article)
in the field of creating applied intellectual decision-making systems in medicine of
the new generation able to integrate diverse knowledge models (in the following –
the heterogeneous model field [
According to the law of requisite variety [
Advantages of SEG are focused on the implementation of ideas that are not
feasible for individual decision-making, because the individual decision maker (DM) has
no opportunity to go beyond his immediate activities. Professional duties in SEG are
distributed in accordance with the abilities and competencies of performers,
depending on the activity complexity. Synergetic effect in SEG is achieved by "the group
compensation of individual disabilities [
SEG simulation and the resulting synergy are encouraged to implement using
HIMAS [
The functional structure shown in Fig. 2. can be used in the design of HIMAS for
a wide range of applied problems, because:
(1) a general multi-agent model of reality is used;
(2) a list of solver agents covers five classes of basic methods of artificial
intelligence used in HIS [
(3) an order of agent interaction is determined by the subject domain model
[
Let us consider the purpose of agents of the HIMAS functional structure shown in
(see Fig1):
(1) the interface agent requests input data and returns the result;
(2) the decision-making agent distributes the problem specification to the
findingsolution agents and determines the order of their interaction;
(3) the finding-solution agents perform generation and evaluation of solutions based on
the subject domain knowledge.
(4) the proxy agent monitors the capabilities of registered agents of intellectual
technologies (solvers). Agents refer to proxy agent to find out which of the solvers can help
in solving the subproblem set before them;
(5) the solvers together with the converter agent implement the hybrid component of
HIMAS, combining diverse knowledge, simulating the multilingual nature of the
solution of complex applied problem-system;
(6) the subject domain model is the semantic network [
The central element of HIMAS shown in (see Fig.1) is a subsystem of "Hybrid
computational intelligence (Decision-making agent, see Fig. 1)". The concept of the
hybrid computational intelligence occurs when the decision maker in the Decision
Support System (DSS) breaks up a single whole (see Fig. 1), i.e. the initial problem
into its constituent parts, and entrusts the solution of subproblems to the experts. In the
fig. 3 the model of the problem has a two-level representation: at the macrolevel - the
problem as a whole and its properties; at the microlevel - a system of subproblems
(light circles) and a coordinating problem (dark circle). A complex problem is a
problem-system [
Macro- and microlevels are connected by reduction relations. If we consider the
DSS model and the problem-system model together in (see Fig. 3), then a
correspondence shown by dotted horizontal lines appears. DM solves the problem entirely and a
coordinating problem, while the experts solve sub-problems grouped in the area of
homogeneous parameters of the system-problem [
Fig. 4. shows the synergetic model of hybrid computational intelligence [
Experts who solve parts of the general problem-system (Fig. 3), create the parent
models (see Fig. 4,a): Model1P ,..., ModelкP , ..., Model Pf ,..., ModelNP , and the variety of
team work structures used in DSS (sequential, simultaneous, formation of subgroups,
coalitions, etc.), i.e. the usage procedure of parent models for joint reasoning,
determines the variety of descendant models: Model1П , ..., ModelNП . These models relate to
the general problem-system. The descendant models are the hybrids obtained on a
heterogeneous model field [
Fig. 4, b shows another pattern: the expert's sensation that none of the known prototype methods are suitable for solving the subproblem. This sensation arises when the expert is not satisfied with certain aspects of the methods. For example, one of them leads to models with intolerable error, but with good computational capabilities, and the other gives the opposite picture. The expert starts to consider the tool for solving the problem at different detail levels.
Fig. 5 shows a two-level representation of methods: at the macrolevel — the method as a whole; at the microlevel - the method decomposition using the "language — model — procedure" triad in combination with instrumental heterogeneity (decomposition of the procedure component into separate parts-grains).
At the macrolevel, the expert assesses the capabilities of the method as a single whole, and at the microlevel the method is represented as an entity consisting of separate parts. Macro- and microlevel aspects of the method are interdependent, their cause-effect relationships exist. It is important to know which modifications of the method parts at the microlevel lead to its desired properties at the macrolevel. And vice versa, how to combine and modify the method parts to obtain the desired properties.
Fig. 5 shows the classification of the cause-effect relationships of macrolevel properties of simulation methods and features of their microlevel construction. It uses the component parts of the method: "model", "description language", "solution procedure". Model — is representations (conceptual description of the subject domain), within which the method "works". Change of representations is a qualitative leap of the method properties from one class to another. The description language — is an alternative means to record a model, the form of its existence. The procedure — is an ordered set of actions (calculations) for finding solutions on the model. In a variety of methods of performing actions, a variety of their properties at the macrolevel is hidden.
Decomposition of the procedure component allows us to create sets of typical tools — grains, from which a tool for solving the problem is built during the hybridization.
This approach to the representation of the method allowed us to introduce the
evolutionary model "the world of simulation methods" [
In (see Fig. 4,b) the methods are depicted on the microlevel, what is shown by the variety of their parts with different hatching. In order to achieve the desired macrolevel properties (hybridization chain), it is possible to combine the parts and obtain the descendant methods: Method1П ,..., MethodNП . The descendant methods are hybrids obtained by combining according to the instrumental feature and inheriting the positive aspects of the reasoning methods used by the experts. We call the hybridization shown in (see Fig. 4,b), a fine-grained hybridization.
In [
The point of the direction "from the method" is that each method of a limited set should be considered at macro- and microlevel. The macrolevel (method phenotype) is a method entirely as a complex entity, a system. Microlevel (method genotype) is a set of grains "model", "description language", "solution procedure" or grains of a more detailed level as components of the decision procedure. Macro- and microlevel representations of the problem are interrelated and should be considered in unity. Hybridization "from the method" requires: 1) research and extraction of knowledge about the possibilities of methods; 2) research and extraction of knowledge about macrolevel and microlevel representations of methods; 3) research and extraction of knowledge about the interdependencies of macro- and microlevel method representations. 3
To approbate the proposed solutions, the diagnostics problem of arterial hypertension
(AH) was chosen - one of the most widespread diseases of the cardiovascular system.
It is established that 20-30% of the world’s adult population suffer from arterial
hypertension (WHO data). To study diagnostics problem of arterial hypertension (DPAH)
the method of mixed reduction [
The method choice for computer-aided solution of diagnostics subproblems and
analysis of instrumental heterogeneity of the hypertension diagnostics problems is
made on three-quadrant matrix data model that contains "method–characteristics",
"problem–characteristics" and "problem–method" knowledge [
Laboratory experiments with the HIMAS-AH prototype (a hybrid intellectual
multi-agent system for arterial hypertension diagnostics developed within the grant of the
Russian Foundation for Basic Research (RFBR project No. 16-07-00272)) produced
the following results:
1. With the use of HIMAS-AH, the total time of the examination and result processing
is approximately 30 seconds, which is seven times less than the diagnostics time when
experienced doctors work together with a nurse;
2. A standard deviation in the diagnosis, based on the use of HIMAS-AH, was f =
0,0837, i.e. HIMAS-AH gives a reliable diagnosis in 92% of cases. Thus, the use of
HIMAS-AH on the basis of fine-grained hybridization (see Fig. 4. b) gives better
results than with a homogeneous approach, for example, the well-known "Diagnosis"
project [
The tradition of complex problem solving by expert teams led by the DM, has old
roots: military councils, collegium of Ministry, all kinds of meetings, briefings,
concilia, think tanks etc. [