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AN INTELLIGENT SYSTEM TO HELP EXPERT USERS : APPLICATION TO

DRUG PRESCRIPTION

S. FRENOT*, F. LAFOREST*, A. FLORY**.*PhD student, **Professor

Laboratoire dIngnierie des Systmes dInformation (L.I.S.I.)Bt 401,INSA -20 av. A. Einstein, F-69621 Villeurbanne Cedex (France)

ABSTRACT

Conventional expert systems aim to replace the domain expert to help inexperienced users.This paper introduces an intelligent interface designed to help rather than replace expert usersin their decision making process. The system, based on incremental concept formation, learnsthe way the expert user works and gives him solutions he had already made in similar cases.Such a system is useful for expert-dependant decision domains, like medicine. Thus, weapplied our system to physicians prescription practice.

KEY WORDS

Artificial intelligence, artificial learning, incremental concept formation, expert-dependentdecision domain, drug prescription.

1 - INTRODUCTION

Artificial intelligence researchers have mainly studied systems usually called Expert Systems.They are programs that use knowledge and inference procedures to solve problems that aredifficult enough to require significant human expertise to solve. The knowledge needed toperform at such a level and the inference procedures used, can be thought of as a model of thebest practitioners of the field ([HAR 85]). These systems are mostly useful for non expertusers, who cannot infer solutions by themselves.We also have to consider systems which would be used by experts. A classical expert systemis not adapted for users who know how to make a decision according to their own point ofview, but the tune to take a decision can be long. For this reason, we describe an intelligentinterface that could learn the expert users processes and suggest solutions fastly. Such asystem would greatly help the user : it simplifies the user-machine dialogue and preventsfrom repetitive thought and wasting time. The system we propose does not decide accordingto predefined rules and knowledge. It learns by itself the expert users processes in anincremental way and then suggests the decisions he had already made. The system, thereforeis expert-dependent : installed on two different experts workstations, it does not give thesame solutions on the two, but answers each one according to the respective skill of each user.We can consider three generations of intelligent systems :

- The Expert Systems, which, according to domain rules given by the system engineer,provide universal answers (Fig. 1-a). They have many drawbacks which [Li 92] presentsclearly : unmaintainability, untestability, and thus unreliability because of their poor structure.

- A more elaborate system observes the user to process rules and, knowing metarulesgiven by the system engineer, processes solutions according to the users method (Fig. 1-b).

- A completely automated system which generates by itself rules, metarules andsolutions by examining the user. This type of system is not currently realistic. The existanceof such systems would mean that intelligence has been brought to computers (Fig. 1-c).


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Intelligent System

Solutions

Meta Rules

user

engineer

(b)

Rules

engineer

Data Base

Expert System

Solutionsuser

Automated System

Solutionsuser

Data

Base

(c)(a)

Data BaseRules Rules Meta Rules

Fig.1 : Three generations of Intelligent SystemsThe system we defined is a system of the second type : an artificial learning system.

2 - ARTIFICIAL LEARNING

2.1 - Basic Concepts

An artificial learning system can be defined by the following concepts ([LAM 91]) :

- the learner is a computerized system,- the system learns by acquiring knowledge, or by improving its dexterity.

A learning strategy can be defined as a composition of actions such as inference,representation transformations, experiments...We want to build a learning system which requires the minimum of basic knowledges andwhich has to run in a non-supervised and a personalized way. Among all the existing learningmethods (by exemple, by analogy, by explanation ...) the one which suits the best is theincremental concept formation.

2.2 - Incremental Concept Formation

This theory assumes, that human beings acquire concepts that organise their observations, anduse them to classify future experiences. That process may occur without any tutor and despiteincomplete information. Concept learning is incremental and can represent complex realworld experiences. In incremental concept formation systems, the computer is the learner. Itobserves a succession of objects and builds, from these examples, some concept hierarchiesthat summarize and organize its experiences.

The main ideas for incremental concept formation are ([GEN 89]) :- concept hierarchy : concepts are organized in a set of nodes ordered by generality (is-a

links). Each node corresponds to a concept and its intensional description.- top down classification of instances : this classification begins at the top node and sorts

instances through the concept hierarchy according to the result of tests at each node.- unsupervised learning mechanism : the system classifies and decides by itself theaffectation of an instance to a given class and of the nature of classes, as soon as thetutor has defined metarules for classification (criteria).

- integration of learning and performance in the same incremental process : classificationof a new instance does not extensively reprocess previous instances.

We present below three systems based on incremental concept formation. Each one is animprovement of the previous one. These systems were first aimed at classifying population ofbacteria. Bacteria sharing the same features are stored in the same concept.

EPAM ([FEI 63]) The entities are stored on terminal nodes (leaves). Non terminal nodes testthe entities with criteria stored in the node and send them to another node. The entities are


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routed through the network until they reach a leaf. There is always a branch OTHER, whichmakes the system deterministic. On each terminal node, we find an image which consists in aset of attribute/value pairs. All the population classified in one leaf share the sameattribute/value pairs. The image built through this way allows the definition of a concept.Sorting a new instance through the discrimination network is an iterative process. Twomechanisms are possible : familiarization and discrimination. The former occurs when anentity reaches a leaf and does not differ from its image i.e. all the attributes of the entity havethe same values as the ones stored in the image : EPAM selects a new attribute from the entitywhich is not yet in the image and adds it in the image. The latter occurs when the value of anattribute of the image differs from the entity : the system then rolls back through the networkto find if that attribute has been tested in an upper node ; if not it specialises the leaf andcreates a node (discrimination in depth), else it specialises the node containing the test(discrimination in width) ; in both cases, the new entity is then stored alone in a new leaf, andthe image contains the dissimilar attribute.

UNIMEM ([LEB 83]) sets concepts (attribute/value pairs) at each node of the network. Eachpair has an associated weight (confidence) and each link has a score measuring itspredictiveness. Classification of a new instance corresponds to evaluation of the adequacy ofthe instance to the node and propagation to the nodes children which share the samecharacteristics as the instance.

COBWEB ([FIS 87]) places instances on terminal nodes. All attributes appear at each node.Their values characterize the node. Predictability and predictiveness are associated with eachattribute. New instances are classified in the network by the evaluation at each node of aprobabilistic law (Bayes law).

Choosing one of these systems is not easy. EPAM is easy to implement but it does not specifyhow to choose an attribute for familiarization or discrimination ; UNIMEM is a completesystem, but definition of thresholds is restrictive ; and COBWEB has entirely-definedevaluation functions but all attributes must appear in every node.We do not choose to use COBWEB because our system has to work with incompleteinformation. UNIMEM does not satisfy us : we can not define thresholds for each attribute.Then, we propose to implement an EPAM-like system.

3 - IMPROVEMENTS ON EPAM

The biggest lack in these systems is that they only classify population and do nothing more.We want to classify population and also associated protocols. Each leaf contains an image andthe protocols. These protocols describe the decisions the user made for this concept.We also need a few additional improvements :

- definition of a criterion selection method,- manipulation of numeric values thus introducing intervals,- specification of data structures,- elaboration of a research algorithm in the classification network.

3.1 - Definition of a Criterion Selection Method

Artificial intelligence is interested in methods of knowledge representation whoseelaboration is based on criteria of explanation. ([BOY 91])EPAM defines a set of criteria (attributes) to build its discrimination network. It does not

precise how to choose this or that attribute. Avoiding this drawback, we defined a structure


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that classifies criteria and then determines a selection order. We implemented, at this time, anordered list.

3.2 - Manipulation of Numeric Values, Intervals

EPAM manipulates only symbolic values, as most knowledge-based systems do ([BAS 93]),and equality between two attributes values is a strict equality. Opening the system to numericvalues seems important for many applications. But equality should then take place in terms ofinterval : attribute A belongs to the interval (B,C). This mechanism allows definition andmanipulation of value brackets, which are used in many applications (age, wages,temperature...). Intervals are implemented by means of a minimum and a maximum value.

3.3 - Structures and Algorithms

Structure of a Node or a ProfileWe defined the same structure for nodes and profiles (set of attribute/value pairs describingthe characteristics of an instance). Comparisons are then simple to execute. This commonstructure is composed of lines (as many as needed). We defined 3 line types (see fig.2) :1. The line contains the word TERMINAL. This line type is the characterization of terminal

nodes and is in the first line of such nodes.2. The line contains the word OTHER followed by a node reference. This line type appears

in test nodes only, and indicates the node linked to that test node through an OTHERlink. It is the last line of such nodes.

3. The line contains a criterion followed by a min value, a max value and a parameter. In a testnode, the parameter references the node to go when the criterion value is in accordancewith the min and max values. In a terminal node, the parameter contains the reference tothe instance described by the line and the preceeding tests. If this line appears in aprofile, the criterion value is located in ; and arenot used.

Node or Instance = n lines

1 line = OR

OR

fig. 2 : Data structure

Tests on values depend on the type of the value : if the value is numeric, and determine an interval ; otherwise, contains the value to compare to,and is unused.

Definition of CriteriaCriteria are stored as a list of (attribute/value type) pairs. The expert user defines eachcriterion and determines its place in the list. This list can be modified by adding new criteriaat any time, but no deletion is allowed. Available types are numeric, boolean, and symbolic.

Elaboration of a Searching AlgorithmEPAM has defined a classification algorithm. But, for this classification be useful, the systemshould reuse it, that is to say be able to find information in it, and, for example, present whatit has found to the user. That is why we developed a searching algorithm.


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Our Classification AlgorithmBased on the EPAM theory, our classification algorithm includes the improvements we havedescribed above. This classification algorithm is processed when a new instance is to beclassified. We built another algorithm to enlarge intervals of numeric values.

Search This algorithm searches, in the discrimination network, the leaf corresponding to theprofile presented. It starts at the root node, runs down through the network and returnsthe terminal code corresponding to the entered profile. Then, it returns the found leaf,and the differences between the image stored in this leaf and the profile presented.

Class This algorithm classifies a new instance. If this instance is not different from the mostappropriate leaf found then it calls a familiarisation function else it calls thediscriminiation function.

familiarize This algorithm is used for familiarisation. It adds to the image an attribute/valuepair extracted from the new instance and not yet in the image.

discriminate It creates a new node to take into account the new instance which does not

correspond to any leaf. It uses two functions : one to create a discrimination in depth,the other one to create a discrimination in width.

deepen The leaf which was selected by the search algorithm becomes a node. One branchconnects the previous leaf to this node, another branch is built to connect the newinstance (OTHER).

broaden This algorithm creates a new node in width corresponding to the new instance.

enlargeInterval This algorithm permits to enlarge the brackets of the interval of an attribute.

4 - APPLICATION

We applied our system to a software for electronic medical records at the general

practitioners. One part of this software concerns drug prescription. Drug prescription is aparticular action that all physicians practice, but each with their own prescription habits : twodifferent physicians do not prescribe the same drugs for the same disease. Drug prescription isa typical expert-dependant decision domain. Moreover, for a given physician, prescription isquite a repetitive task (especially for epidemies or chronic pathologies). All these facts showthat an artificial learning system would be interesting in this case.In fact, prescription consists of choosing drugs according to the patients state of health andthe physicians experience. In our system, patients state of health is described usingattributes, and the physicians experience by the classification network. Criteria (a selectionof attributes) could be determined according to a medical consensus. Physicians all agree thatthe 3 most important criteria are pathology, age, and sex. We also use a pharmacological

databank which contains all french drugs : the BCB ([FLO 83]).

Instances classified in terminal nodes correspond to prescriptions. Criteria are attributes ofpatients profiles. The physician first defines the profile of his patient. Then, the systembuilds an instance with selected items of that profile, and processes the search algorithm. Theuser interface proposes to the physician all prescriptions contained in the terminal node found.He can select a prescription among those proposed, or decide to create another one. Theclassification algorithm is then processed to take into account the users choice.Our three criteria for this example are pathology, age, and sex. The root node of the networkcontains one line for each pathology. Each pathology is defined as a boolean attribute. TheOTHER line corresponds to unknown pathologies.

Let us consider as starting point the following tree : a patient who has tuberculosis and is 30should receive prescription P1 :


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RootNode

Node 1 Tub

erculo

sis

Terminal

age 30 X P1

Fig. 3 : Starting state of the example network

Some days later, another 30 year old patient reaches the physician : he also has tuberculosis.The system proposes P1, but the physician does not accept it : he prescribes P2. As there is noconflict between the instance referring to P1 and the patients profile, familiarisation isprocessed on the third criterion : sex. The new network is on fig. 4-a.A third patient comes : he is 3, and also has tuberculosis. The system proposes P1 and P2.The physician decides to create a new prescription P3. As ages are different, the system will

discriminate in depth on age, as shown on Fig. 4-b.A fourth patient comes. She is 15 and has tuberculosis. As she is not 30, the system selectsNode 3 and proposes P3. The physician decides to create a new prescription P4.Discrimination in breadth on age is processed, as shown on Fig. 4-c.

Node 3

Terminal

age 3 X P3

30

RootNode

Node 1

age 30 X Node 2

OTHER Node 3

Tub

erculo

sis

Node 2

Terminal

age 30 X P1

sex M X P2

RootNode

Node 1

Terminal

age 30 X P1

sex M X P2

Tub

erculo

sis

Fig. 4 : Example(a) : Familiarization - (b) : Discrimination in depth - (c) : Discrimination in breadth

Node 3

Terminal

age 3 X P3

30

RootNode

Node 1

age 30 X Node 2

age 15 X Node 4

OTHER Node 3

Tube

rculosis

Node 2

Terminal

age 30 X P1

sex M X P2

Node 4

Terminal

age 15 X P4

15

(a)

(b) (c)

OTHER

OTH

ER

When the system contains enough data, it gives adequate propositions, and then is helpful.We can see that managing intervals and tolerances is essential. If the practitioner had chosento keep the 15 year old patient in node 2, the system has to conclude that node 2 corresponds

to people from 15 to 30. Moreover, we must be able to assimiliate a 30 and a 31 year oldperson, through tolerance on values. Eventhough the choice of an ordered list of criteriaseems to be adequate, we think now that creating a tree should be better : for babies under 1,the system shall try to assimilate weight rather than sex. Furthermore, this tree should be pre-processed according to users clues : each classification in the discrimination network wouldbe followed by the tutors explanations of his choice (choose a proposed instance or create anew one). These explanations would indicate eventual modifications for the system to makeon the criteria tree. Intervention of the tutor goes against the idea of unsurpervision but bringsevident advantages in performance. Pitrat, in [PIT 90], points out that a system mustunderstand the reasons for its success and failures to understand its behaviour and to learn.

5 - CONCLUSION


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We present in this paper an intelligent interface based on incremental concept formation. Thissystem aims at assisting expert users in their daily work, especially in repetitive tasks.Manipulation of numeric values and intervals, definition of a criterion selection method, andelaboration of a research algorithm are the improvements we brought on an EPAM basis.Other improvements would be interesting : elaboration of a criteria tree rather than a criterialist, and definition of a backtracking mechanism which would allow restructuring thediscrimination network ([FOU 93]). This system may also be viewed as an engine whichcould be reversed : after a long learning period it can become a teacher.Drug prescription is quite a good example of a repetitive expert-dependant decision domain,but other domains such as bank loans or automatic programmation should be improved usingthis technique.Such a system gives an active role to the user : he makes his tool progress. The quality of thepropositions is directly linked to the quality of the users work : an optimal use of thesoftware increases good contribution of the intelligent system.Through the conceptual approach, this kind of system can extract the semantics related to aspecific population : their bonds, their behaviors, their insinuated representation...

6 - REFERENCES

[BAS 93] A. Basu : A knowledge representation model for multiuser knowledge-basedsystem, in IEEE TKDE , Vol 5, n 2, april 1993, pp 177-189[BOY 91] G. Boy : Intelligent assistant systems. Knowledge Based Systems, Vol 6, Booseand Gaines Eds, Academic Press Ltd, London, 1991[FEI 63] E.A. Feigenbaum : The simulation of verbal learning behavior, Computer andthought, E.A. Feigenbaum and J. Feldman Eds, Mac Graw Hill, NY, 1963[FIS 87] D. Fisher : Knowledge acquisition via incremental conceptual clustering MachineLearning n 2, 1987, pp. 139-172[FLO 83] A. Flory, C. Paultre, C. Veilleraud :"A relational Databank to aid in the Dispensingof Medicines", Congress edInfo'83, Amsterdam, 1983[FOU 93] J.M. Fouet : Utilisation de mtaconnaissances pour lauto-amlioration dunsystme, Congress AFCET 93, Versailles, 1993, pp. 83-92[GEN 89] J.H. Gennari, P. Langley, D. Fisher : Models of incremental concept formation,in Artificial Intelligence, vol 40, n 1-3, Elsevier Science BV, North Holland, 1989, pp 11-61[HAR 85] P. Harmon, D. King : Expert Systems : Artificial Intelligence in Business, WileyPress Books, New-York, 1985[LAM 91] A. van Lamsweerde: apprentissage artificiel in Approche Logique delIntelligence Artificielle vol IV. Dunod, Paris, 1991, pp 1-113[LEB 83] M. Lebovitz : Generalisation from natural language text, Cognitive Science n 7,1983, pp. 1-40[LI 92] X. Li : Expert systems implementation : rules are bad, objects are good in Int.

journal of software engineering and knowledge engineering, vol 2, n 4, 1992, pp 611-625[PIT 90]J. Pitrat : Mtaconnaissances : futur de lintelligence artificielle. Herms, Paris,1990

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