Inference engine the
Systems of the Dr.Watson Type.
S.I.
Goldberg
MediSpectra, Inc. 100 Inman Street ,Cambridge, MA 02139
Saveli@aol.com
The systems of decision making support (DSS) are a popular and quickly developing
ranch of artificial intelligence. In a number of regions the application of
decision support systems give a great economic effect [ 1 ]. It is natural that
the value of the effect depends
in many respects on the accuracy relation of
DSS conclusion to a man's ability to decide the problem, task complexity
by himself. DSS are applied most successfully when its ability considerably
surpasses the man's capacities. This happens either when it is possible to create
adequate formal mathematical models, or when DSS are constructed on the basis of
experts' knowledge considerably more qualified than potential users. Lately, DSS
have been widely applied for the opposite flank as well, and namely for
the users inventing new knowledge (invention machines) [ 2 ]. Besides, DSS in
principle cannot make correct final conclusions, but create conditions for the
activation of an inventor's work. It is natural that there is an intermediate situation as
well when the conclusion accuracy of DSS and a user are
comparable
between themselves. Usually these are creative, but relatively standard and badly
formalized tasks, which have to be decided every day by specially trained experts
intended for this. To such kind of tasks are related many of the problems of high
level specialists in medicine, business, criminology and so on. There are
known numerous attempts of DSS' creation for such specialists. However , more
than not the similar systems are used
for teaching or , at the best , for a
posterior checking by a specialist’s conclusion . For example, in medicine, as
shown in [3] with a very great number of completed DSS , there are utterly
few used in real practical work.
There are offered various ways of DSS' creation really used for specialists:
application
of
self-learning
DSS [4 ]: integration of DSS with other
informational technologies [5]; maximum use of an interactive regime in
DSS
[6]; intensification of organizing and administrative measures when
implementing DSS [7]. Considering all the recommendations absolutely
correct, nevertheless it is thought that the problem of unsuccessful use of
DSS is in the very nature of the given tasks. Most likely it will be
impossible in the foreseeable future to make conclusions of DSS surpassing
specialists' conclusions in accuracy. Absence of formal theories, a great
percentage of subjective information, the trend of results in time do not
allow to construct precise mathematical models and the experts' conclusions (at
least what presents them in a computer) differ a little from
professionals' judgments. In conditions when the accuracy of DSS' conclusions is
close or even inferior to the accuracy of a specialist's conclusions, the specialist's
knowledge of DSS’ conclusions ,even formally, does not improve the accuracy of
his conclusions. And from the point of view of psychologists such a prompt
can even lead to the so-called effect of locking [8], when the specialist fully
loses control of the situation. The reasonings are question able us well that the
situation of constant competition with DSS - conclusions highness activity and
responsibility of specialists. In [ 13 ] Yates explained that the competition situation
of a person constantly solving complex tasks can lead to worsening the decision
quality at the expense of developing mentality stereotypization. Moreover, a
prompt at the consideration moment can lead to undesirable consequences
even if their accuracy surpasses the accuracy of specialists. Mistakes in conclusions
of specialists are often not the result of the absence of logical thinking or
ignorance of the actual material but of a tendentious selection of the
material for analysis. Gathering and filtration
the
material
take
place
already under the influence of obviously or subconsciously selected
hypothesis. And physicians, criminalists and businessperson know very well this
phenomenon. If on the basic of such data, DSS make a decision favorable for
the user, this will increase his misconception even more, though it may
reduce
his responsibility if only morally. Observing DSS' actions the
specialist quickly enough learns with understanding in what situation DSS
takes this or that decision and sometimes quite subconsciously he adjusts the
data for DSS' response necessary for him. The author often observed
this effect in the actual work of DSS created by him both on the basis of
pattern recognition methods [9], and fully expert systems [10].
On the
other hand, cases are known when less-skilled specialists being in the process
of discussing some material help their more qualified colleagues to
make correct decisions.
The best known example of such
sort is the joint
activity of a famous
detective Sherlock Holmes and his friend
Dr.Watson. Moreover in his
professional activity Holmes considerably more often turns to the comparatively
poor detective Dr.Watson for aid than to his brother - a recognized authority Microft
Holmes (an ideal expert system). Namely this story of interrelations of Holmes and
Dr.Watson was an example for the creation of DSS' new class systems of the
Dr.Watson Type. The main ideas of the systems were worked out by the author
together with Prof. Meshalkin [11], [12]. A way out of the situation is seen in the
refusal to present a decision to a user accepted by DSS, but using a mechanism
of decision making for creation of an informational environment which would
promote a correct decision making by the user. It must be noted that giving up an
already accepted decision is not our invention. As early as Platon in his treatise
“Taetet” indicated that Socrates had helped his disciples to make correct decisions
solely with the help of leading questions.
The article covers the present notion on such systems on the basis of the first
experience of their creation, and the inference engine for the systems of the
Dr.Watson Type. The second paragraph gives definition of the systems of the
Dr.Watson Type and a scheme of principle of the decision support algorithm
in Dr.Watson Type Systems (WTS). The third paragraph describes a concrete
appendix of the algorithm in WTS for an intensive therapy department physician, and
briefly considers other methods of decision making support in WTS' ideology.
The forth paragraph considers the experience of WTS' creation and application
for an intensive therapy physician. The fifth paragraph is conclusion and
summary.
2.
Dr. Watson Type System.
Ways to stimulate thinking is one of the directions of
practical psychology.
Common regulations are known well enough [14], [15].
This is necessity:
1) to present all the necessary information for decision making;
2) to create a conflict situation in the informational space for stimulating the user;
3) to direct the user's reflections towards a correct decision;
4) to provide a positive mind-set to the solution of a task;
5) to provide a motivation for continuing the work at a task.
The methods of such a kind are actively applied for
organization of
a
creative
process
by
psychologists, specialists of
system
analysis,
invention machines a distinctive feature of Dr. Watson systems is the presence of
inference engine in them.
Though the decision themselves are not offered, namely on the basis of making
decision algorithms and results of their work,
WTS create the above indicated
conditions (W-technologies). Another specific feature is self-tuning of the system to
a concrete specialist solving this problem, but not to a problem which is being solved
(as it is done in invention machines).
Let’s name as a Dr. Watson Type System the decision making support system
a) possessing the conclusion algorithm, but using it for creating the conditions 1-5,
but not for presenting a decision
b) having an adaptation mechanism DSS to a concrete user.
Below is given an example of the algorithm realizing W-technology of making
decision for the system of Dr.Watson.
Let specialist  consider situation and according to information ( 
- whole possible information about ) make decision i( ii=1,..., n all the possible decisions).
The rest of the information about  can be taken in the form of answers to
questions. Each question is put by the set of J 
 answers to the question.
There are the thresholds D and the making decision algorithm F, which for each
decision i calculates the membership function f ( i , X ) . If f ( i , X ) D (
i ) ,
then F takes the
meaning i.
Action
of
WTS:
In the beginning , D ( ) is formed taking into consideration a probability
error of the specialist M in determining the decision i ( the first sort error ) D (  ,
M , 1) 
D( ).
If
f (  , X ) 
D(,
M , 1) , then WTS displays itself in no way.
If
f ( , X )  D (  ,
M , 1) , then M
receives additional
information in the form of indicating elements from X , which do not increase the
value f (  , X ) .
Of the set \ X there is selected an admissible ( not contradictive X ) element
j increasing the value f (  , X & j ) in the greatest manner and the question J
containing j is put to a
specialist. In case if in \ X admissible elements
increasing f (  , X )
are not left then there must be a question with an admissible element j
which
mostly increases assurance in the alternative hypothesis
D ( k ) --f (  k, X & j ) =
min
(D ( i ) --f (  i, X &
l))
iil \ X
If the answer j to the question J increases f (  , X & j ) , then
D(
 , M , 2 ) decreases in comparison with
D (  , M , 1) .
If the answer j to the question J does not increase f (  , X & j ) , then
D (  , M , 2) = D (  , M , 1). After the answer to the
question, WTS
continues its work with new set of thresholds and so on until the decisions M and
F are concerted between themselves or M stops work with WTS.
Let us consider WTS' influence on the accuracy of making decision by a specialist.
Let us assume that in situation it is required to make decision  .
G ( M , X ( A) ) - decision of specialist M on information X (A) ;
P (G ( M , X ( A) ) =  ) - probability of making M decision  ;
p ( F ( X(A)) = ) - probability of making F decision  ;
If G ( M , X ( A) ) = F ( X(A)) , then even with P >> p probability of the error
( M - WTS ) decreases and makes:
( 1 - P (G ( M , X ( A) ) = )  ( 1 - p ( F ( X(A)) =  ) ) < 1 - P (G ( M , X ( A)) =
)
If G ( M , X ( A) )  F ( X(A)) , but as a result of the above indicated
procedure there is an increase of M knowledge about the situation A, then
in this case as well there is a decrease of probability of the error ( M -WTS ). It
is clear that the assumption that the accuracy of making decision increases with the
increase of information about a situation, is sufficiently strong and all the problems
connected with WTS' creation consist in making the assumption a real one.
Formally, the expert systems constructed on the principle of adverse conclusion begin
their work from a conclusion analysis of the user as well, and, in the preocess of the
work, ask the user questions. However, these questions serve for the control of the
conclusion from the point of expert systems and because they are not fastened
neither to a diagnostic process nor to a concrete user like in the Dr.WTS. Moreover,
these queries are not similar to an indirect leading information which is proposed in
the below described subsystems
“ Effectiveness” and “ Adequacy” of the system for a physician of the intensive
therapy department.
3. W- technologies in DSS for a physician of the
intensive therapy department.
The System of supporting decision making of a therapeutic department physician of the
in-hospital ("System Analysis Guide" or "SAGe") was supposed that the physician cures
several patients simultaneously, but immediate medical procedures are carried out
by medical nurses or doctor's assistants. They perform information entry into the
system as well. As a result of the program activity there appears an analog of a
case history. Before creating the system there was held a questioning of 8
physicians-intensivists with no less than two years experience of work with DINAR.2
[16] . They were offered a list of 25 possible functions of the hypothetical ideal
system. Each function was to be evaluated by a 5 point-grading scale as to the
usefulness for a physician. The most useful functions were recognized : treatment
adequacy assessment (point sum = 40); treatment efficiency assessment ( 39 points
) ; and state dynamics assessment ( 39 points ) in distinction for example
from treatment selection (30 points) and diagnostics (27 points). Namely these 3
problems were considered as central ones in the system being under construction,
though the system had to help
in solving other intellectual problems, such as
diagnostics, state development prognoses and so on. Necessity to solve such complex,
numerous and versatile tasks has made the choice of Dr.WTS' approach not only
desirable, but forced as well.
The SAGe is written on the language C++ for a 486SX- compatible personal computer
and consists of 5 subsystems : "Diagnostics", "Treatment Effectiveness", "Treatment
Adequacy", "Integral assessment of patients in the department" and "Information import".
3.1
Diagnostics.
A task of the given subsystem is help in selection of
a diagnostic
hypothesis about
pathologic
syndromes and assessment of a patient state
severity. For assessment of state severity a special scale [17] was used.
For helping selection of leading pathologic syndromes there has
the algorithm of W-technology realization (proposed above).
been
realized
As f ( i , X ) was considered:

r (i , x )
x  X
Where -- 
hypothesis i .
<
r (i , x )
< +  the sign weight x in relation to
In case if the first kind error for M when determining
then
If e ( , M) < 0.1 then D (  ,
 2 ) .
If e (  , M)  0.1 then D (  ,

is
the
e ( , M)
M , 1) = D (  ) + D (  )  ( 0.1 - e (  , M)
M,1)=D( ) .
In the work process of the algorithm, if there is T-step, when receiving the information
j.
f (  , X & j ) >> f (  , X ) , then
D (  , M , T +1 ) = D (  , M , T ) + (f (  , X & j ) - f (  , X ) ).
After putting the two questions, which are not dependent upon the physician's actions,
SAGe's diagnostic algorithm stops its work and doesn't inform about its conclusions.
An important role in the diagnostic subsystem is played by information visualization
with the help of special circles painted in a special manner. The graphic image,
original for every case and reflecting all the collected information, will better help to
assess the specific situation, to remember a like case, to determine contradictions and
empty spaces in information.
3.2
Treatment efficiency.
The task of the given system is help in selecting treatment and determining its
efficiency. For the assessment of the treatment effectiveness there was used a
multidimensional evaluation of an organism reaction on the current therapy. Changes
of integral assessment of the patient’s state severity cannot be the only index of this
notion . As a treatment effectiveness can be considered avoiding of complications or
stabilization of the patient’s state when decreasing intensity of the treatment or
improving separate concrete indexes. On the other hand, when there is a common
improvement of the patient’s state, an unfavorable dynamics of separate indexes can turn
out with irreparable problems in future.
In this connection for effectiveness assessment the SAGe gives the physician a state
severity dynamics and changes of character and intensity of leading pathological
syndromes in comparison with dynamics of type and intensity of a treatment.
Description of the treatment is considered in conformity with [18]. At will the
physician can compare these curves with changes of any quantitative and qualitative
signs. On its part, the SAGe, by itself, offers dynamics of signs, which from "its point of
view", needs attention when selecting a treatment.
The five groups of such signs are considered:
1 group - indexes reflecting a lesion of an organism system not affected by a disease by
this moment;
2 group - indexes with a paradoxical dynamics ( for example, indexes majority increases,
but a meaning of the given index decreases);
3 group - indexes with the most severe dynamics;
4 group - the most threatening indexes;
5 group - indexes earlier studied by the user in this situation.
For each of the group indexes , the SAGe gives a rank from 1 to 7 depending upon its
potential importance. Irrespective of the groups the user is offered 4 quantitative and 2
qualitative signs with the most great ranks for demonstration. If in this case the number
of the signs with the same rank is too great, then preferences are formed according to the
group numbers.
An important role in achieving the subsystem's purposes is played by the
requirement to the physician
to make independently a disease development
prognosis in response to the treatment administered by him. In case of
discrepancy of prognosis with reality, the physician must find explanations of
the discrepancy,
causes for state aggravation and causes for
treatment
inefficiency, and indicate evidently the causes in the SAGe. Experience of using similar
questions in DINAR.2
revealed
their great stimulating effect for
comprehension by the physician of his own actions [17], and SAGe itself, tries
to assess the treatment efficiency. However, its conclusion is very shifted towards the
negative response and therefore a priori it can't be directly used by the physician.
3.3 Treatment adequacy.
The purpose of the given subsystem is help in determining adequacy of an
administered treatment. We suppose that the adequacy is a harmony between a
treatment and an organism. It is not only to treatment effectiveness. The effectiveness can
be attained by an excessive therapy. In a certain sense, an adequate therapy for an
organism. However, necessary for us therapeutic resources can be absent or be limited.
With the limitations being accounted, the most adequate therapy can be ineffective. But
at least the adequate therapy should not be blind. Selecting the most adequate therapy
out of possible ones, the physician should understand to what consequences this therapy
can lead and what cause of the present state of the organism is.
On the presentation and analysis of the physician’s reflections about the situation with a
patient there, the system of supporting making decision in ‘Adequacy’ has been
constructed. A special cognitive graphics is used. The whole course of treatment
and pathologic process development and mainly the physician’s reflections dynamics is
represented in the form of the growth of the flower. Each element of this picture
responds to a certain side of this process: the root is anamnesis, the stem is the
curve of changing the state severity, the branches are unrealized prognoses, the
floscule is the final state of physiological parameters (an analog of the circle
from "Diagnostics") , the
leaves
are
leading
pathological syndromes,
props and strings are the rendered treatment , the atmospheric phenomena are reasons for
the aggravation of the state , the roughness of the soil and problems with props and
strings are reasons for inefficiency and inadequacy and so on.
Difference in time of the reflections (for example, an assessment of treatment
adequacy occurs in time of treatment administration, and an assessment of its
efficiency - in the subsequent moment of time), control of the reflections by the
pathologic process course, necessity of forecast the future generate difficulties of
coordination and direct contradictions in the reflections. The view and number of these
reflections give the physician assessment of his understanding the treatment course. In its
turn, SAGe tries to assess the degree of controllability of the therapeutic process.
According to its
index the background intensity of the cognitive picture of
“Adequacy” is selected.. By disorders
of naturalness in drawing pictures, SAGe
reflects contradictions in the physician's reflections about reasons for prognosis
discrepancy, reasons for state aggravations, reasons for
inefficiency and
inadequacy . For example, a treatment was recognized by a physician as adequate and
the prognosis is supposed as a state improvement, however the state has been gone bad.
Direct logical contradictions are obviously indicated, such as for example : the cause of
the treatment inefficiency is its wrong use and the cause of an aggravation is the wrong
administration of the treatment.
3.4 . Integral assessment of patients in the department.
The given subsystem starts and completes the work with patients. Display
screen with the system action presents the department plan with its wards and
medical beds. Each bed is a window able to reflect interesting information about a
patient on it. Using information from other subsystems we get data on patient with
leading severity of state, unfavorable state dynamics, financial expenses on
treatment, medical complexity of a case etc.
The physician has a possibility to receive integral characteristics and
reports on an individual patient or on all patient treated or on patients from the
archive. In the subsystem the physician indicates what data about the state and
treatment of a patient and with what regularity must be entered by a doctor's assistant
on each concrete patient in subsystem of assessment of diagnostics and treatment.
4. SAGe as an example of Dr.Watson system.
When SAGe was
system have been
subsystems.
under construction the basic principles of
Dr.Watson
formulated. The principles determined creating each of the
Creation of conflict situations in the " Diagnostics" is provided with revealing
information which doesn't go into a diagnostically hypothesis of a physician; in
the “Efficiency"- with a disparity between prognosis and reality,
in the
"Adequacy" - with showing contradictions in the physician's reflections.
Attempts to change the direction of the physician's reflections occur in
the "Diagnostics" with the help of directing questions; in the "Efficiency" with showing signs demanding an attention; in the "Adequacy" - with showing
possible mistakes in previous assessment of treatment adequacy. In all the
subsystems algorithms make their choice of signs, necessary for stimulation of the
physician, on the basis of decisions of final tasks of the subsystem accepted by
SAGe. Algorithms developed for SAGe possess an apparatus of self-tuning for a
concrete user though now in the Working
Version, this part is absent.
Visualization of information in each of the subsystems is organized in such a way to
stimulate the physician's intellectual activity.
Stimulating motivation for work with the program is necessary for every computer
system, though for the Dr.Watson type system it is especially important. So, as to
SAGe a specialist must turn to the system of decision making support for help in the
most complex question, while the system makes some vague hints instead of giving
actual answers. We hope that in due course this will be natural for a physician,
however at the start, a mighty stimulating motive for such an activity is
necessary. Such a stimulating motive for the system as a whole is the entire first
subsystem "Integral assessment" and namely: making the case history of a
concrete patient and a case history archive, a possibility
integral information and a scope of all patients at once.
to
receive
an
For separate subsystems as a motivation can be considered:
- for the "Diagnostics" - obtaining a standard assessment of a patient's state severity
necessary for a physician;
- for the "Efficiency" -a possibility of comparison of changes of various parameters,
and evaluation of the therapy invasiveness;
- for the "Adequacy" - creating an archive of the physician's own reflections and his
problems arising from treatment of the patient.
Nevertheless the physician can ignore one or some subsystems.
Information about his activity entered into SAGe, is accessible only for himself. The
input of SAGe is protected with a parole, and the entry of information into SAGe,
with the help of the physician's assistant, takes place in a special subsystem of
input.
The first results of work reveal a common favorable attitude to SAGe.
Among the intellectual subsystems, the greatest success belongs to the
"Treatment efficiency", then
the "Diagnostics", and
finally the "Treatment
adequacy". However, during the work there appears greater interest in the two
last subsystems. For a while these assessments and dynamics of
their changes
correspond to our prognoses.
However an exhaustive analysis of work with SAGe is still ahead. The task of the
following stage of SAGe elaboration is a realization of algorithms W-technologies in
complete amount
with
their
self-tuning
for
users and constructing a
communicational subsystem . The communicational subsystem must connect SAGe
with informational flows in hospitals, with available informational and reference
systems.
5.
Summary.
The systems of Dr.Watson Type are not, of course, a
all the problems of making decision systems.
universal solution of
Moreover, if in a concrete WTS a sufficiently strong direct motivation for using this
system is not made, indirect help in decision making can turn out to be
absolutely unclaimed. However, a look at the problem under solution through the user
solving this problem can turn out to be very fruitful. Even in a standard approach to
decision support it is relevant to use statistics of the user's work.
Taking into account subjective distortions of information will help to make a correct
decision and, besides, knowledge
of his own conclusions indicates a possible
direction of the subconscious misrepresentation [19].
On the other hand, in the systems of Dr.Watson Type there can be a direct supposition
of a decision as a stimulation of the user's reflections. It is only important here that
this decision should not be perceived as a guidance to action. It can be provided,
for
example,
with
an
intended hyper diagnostics as in the subsystem
"Treatment efficiency" of the system - SAGe. The central idea of WTS sufficiently
broadly interpreted.
WTS know a genuine decision, however they use this knowledge only for
creating optimum conditions for independent creativity of the user. On the basis of
this idea, there can be constructed not only the systems of decision making support.
There are already concrete projects of creation of teaching games, special
systems for working out collective decision, organizing business-like games.
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