Computer Methods and Programs in Biomedicine, 37 (1992) 215-229 © 1992 Elsevier Science Publishers B.V. All rights reserved 0169-2607/92/$05.00

215

COMMET 01272 S e c t i o n II. Systems a n d p r o g r a m s

Development of an expert system advisor for anaesthetic control S.G. Greenhow, D.A. L i n k e n s a n d A . J . A s b u r y Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, England, UK and University Department of Anaesthesia Western Infirmary, Dumbarton Road, Glasgow, Scotland, UK

A review is given of numerous approaches which have been taken to provide automated control of depth of anaesthesia. Most of these approaches use a single indicator for anaesthetic state, and do not perform adequately for such a complex system. It is desirable, therefore, to merge a number of qualitative clinical signs and quantitative on-line measurements to provide decision-support for control of anaesthetic depth. The paper describes such an expert system called Resac (Real time Expert System for Advice and Control). Details of the knowledge representation and inference structure are given, together with the method adopted for propagating uncertainty measures, combining fuzzy information and merging quantitative and qualitative indicators. The importance of good human machine interface design is shown via the GEM-based graphics of Resac. Expert systems; Anaesthesia; On-line; Bayesian reasoning

1. Introduction T h e t h r e e m a i n a r e a s of r e s p o n s i b i l i t y for anaesthetists comprise those of depth of anaest h e s i a ( d r u g - i n d u c e d u n c o n s c i o u s n e s s ) , m u s c l e relaxation ( d r u g - i n d u c e d paralysis), a n d analgesia, ( d r u g - a s s i s t e d p a i n relief). This p a p e r is conc e r n e d solely with m e t h o d s for c o m p u t e r - a s s i s t e d c o n t r o l o f a n a e s t h e t i c d e p t h in o p e r a t i n g theatres. A n a e s t h e t i c d e p t h is b o t h difficult to d e f i n e a n d h a r d to m e a s u r e accurately. In p r a c t i c e , t h e a n a e s t h e t i s t has a n u m b e r o f clinical signs a n d o n - l i n e m e a s u r e m e n t s which can be u s e d selectively at any t i m e to d e t e r m i n e the p a t i e n t ' s state. N o t surprisingly, t h e r e f o r e , a n u m b e r o f app r o a c h e s have b e e n r e p o r t e d which a t t e m p t online a u t o m a t e d c o n t r o l of a n a e s t h e s i a . A review

Correspondence: Professor D. A. Linkens, Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, England, UK.

of c o n t r o l of d e p t h o f a n a e s t h e s i a is given by C h i l c o a t [1]. O t h e r m o r e r e c e n t reviews have b e e n c a r r i e d o u t [2-4], the l a t t e r p a p e r giving a w i d e r review o f c o m p u t e r c o n t r o l in b i o m e d i c i n e . In the following sections a review is given of p r e v i o u s a p p r o a c h e s to a u t o m a t e d c o n t r o l for a n a e s t h e t i c d e p t h . This reveals i n a d e q u a c i e s which l e a d to the c o n c e p t of an e x p e r t system advisor a p p r o a c h which is d e s c r i b e d in d e t a i l in the r e m a i n i n g sections o f t h e p a p e r . 1.1. Control via anaesthetic concentration C o n t r o l l i n g ' d e p t h of a n a e s t h e s i a ' by m a n i p u lating the c o n c e n t r a t i o n o f a n a e s t h e t i c a g e n t has b e e n widely used. This i n c l u d e s using M A C ( m e a n a l v e o l a r c o n c e n t r a t i o n , w h e r e 1 M A C is d e f i n e d as ' t h e p o i n t at which 5 0 % of p a t i e n t s move in r e s p o n s e to a surgical incision'), as well as conc e n t r a t i o n s within v a r i o u s b o d y fluids ( n o t e intrav e n o u s a g e n t s will not possess a M A C value). T a t n a l l et al. [5] d e s c r i b e a system c o n t r o l l i n g the e n d - t i d a l a n a e s t h e t i c c o n c e n t r a t i o n with con-

216

trolled respiration for halothane, and halothane with nitrous oxide anaesthesia. The control of alveolar anaesthetic concentration is achieved using a model to describe the uptake of anaesthetic via the lungs. This model takes into account individual patient characteristics (that affect alveolar concentration response). The patient characteristics are determined in the first few breaths. These characteristics are then used for the rest of the operation. This p a p e r only describes off-line analysis of this system. A subsequent p a p e r by Morris et al. [6] describes an on-line clinical trial of this system. Manual override of delivered anaesthetic was supported, and this was used in cases where other clinical signs indicated an inappropriate anaesthetic. A p a p e r by O'Callaghan et al. [7] describes a system controlling the administration of volatile anaesthetic using isoflurane in oxygen anaesthesia. A completely closed-circuit breathing system was employed. The controller was designed to maintain an end-tidal anaesthetic concentration of 1.3 M A C (this value is reckoned to give adequate anaesthesia for 95% of patients, see DeJong and Eger [8]). Uptake of anaesthetic was correlated against various patient parameters such as free fat mass, body mass, surface area, etc. The best correlation occurred between total uptake of isoflurane and surface area, with a correlation coefficient of 0.629 (at P < 0.001). The authors were attempting to find an anthropometric measurement that could be used in a pre-prog r a m m e d approach to anaesthesia, but the correlation coefficients of the measured parameters were all too low. Given the immense patient variability, even just considering the effects of lung disease, no such correlation is likely to exist. Also, correlation is still statistical in nature, so for a certain percentage of a population, the anaesthetic dose supplied on a basis of some anthropometric m e a s u r e m e n t will be incorrect. Ross et al. [9] describe a servo-controlled halothane re-breathing system, using controlled respiration. Generally the controller maintained the alveolar concentration at about the set value (to within 0.1%). No mention was made about the suitability of the alveolar anaesthetic concentration for individual patients.

Chilcoat et al. [10] describe a system to control the 'brain tension'. Since brain tension cannot be measured directly, they predict the brain tension from a model of anaesthetic uptake. Since the brain tension cannot be measured, the arterial tension was used as a means of assessing the system's performance. The system allows an anesthetist to set nitrous oxide and oxygen flow rates as normal, but the halothane was delivered in ' M A C ' units. Closed-loop control was applied, with a non-rebreathing system and controlled respiration. The model requires various parameters such as the body mass, ventilation and cardiac output of the patient, and the halothane blood gas partition coefficient. The control of the inspired tension was carried out with feedback techniques, whilst matching the model to an individual patient was carried out with feedforward techniques (information from monitoring parameters can be fed into the controller to correct for deviations before they actually occur). The p a p e r reports the results of using the system on dogs, where reasonable control was achieved. The authors note that the system requires 'considerable additional preparation and manipulation on the part of the anaesthetist'. The remaining papers mentioned in this section concern the control of intravenous drugs. Schuttler et al. [11] describe a system that uses a computer to control the infusion rate of propofol and alfentanil. The controller attempted to keep the concentration of propofol in the blood to 2.5 p,g/ml, whilst alfentanil concentration varied with the degree of noxious stimulation. The set values could be manually overridden. The controller relied on pharmacokinetic data obtained from previous studies by Schwilden [12]. Tackley et al. [13] have implemented a system to allow computer controlled infusion of propofol. The program was designed to achieve a propofol blood concentration of 3 k~g/ml. The system thereafter should maintain this concentration. Patients were divided into two groups: one allowed spontaneous breathing, whilst the other group were given artificial ventilation. The distribution of propofol was assumed to be described by a linear three compartment model. Rates of uptake and elimination were based on this model,

217 together with clearance via metabolic elimination by the liver. The pharmacokinetic parameters (such as volume of distribution) were refined throughout the study, but there was no attempt at on-line identification of those parameters. The control strategy was pre-programmed.

1.2. Control via electroencephalogram (EEG) Rampil et al. [14] analyzed the E E G with Fourier analysis to generate the power spectra. Experiments were conducted on dogs. They observed that the highest frequency (with a significant energy level) in the spectra for both halothane and enflurane varied with anaesthetic dosage. They called this point the 'Spectral Edge Frequency' (SEF). It was observed to decrease in frequency with increasing dose of anaesthetic (in the therapeutic range). This concept of spectral edge is referred to by Hudson et al. [15] where a study of the 'depth of anaesthesia' with respect to the drug thiopental was carried out. The aim of the study was to detect acute thiopental tolerance. Scott et al. [16] studied the effects of the narcotics fentanyl and alfentanil using spectral edge analysis. Neither attempted to control 'depth of anaesthesia'. The correlation between SEF and anaesthetic dose seems quite good, but is rapidly reduced in standard surgical anaesthesia where factors such as patient variability, drug regimes, and stimulus types are more varied [17]. Schwilden et al. [18] describe a system using feedback control of methohexital anaesthesia. They used a combined pharmacokinetic and pharmacodynamic model for this control, using median frequency E E G power spectrum for the feedback variable, the set point being 2-3 Hz. The study recorded the median time to recovery (where this was defined as opening eyes on command), after stopping delivery of anaesthetic. Schwilden et al. [19] describe an adaptive feedback controller using propofol. The control signal used was the median E E G power frequency (control value being aimed at being 2-3 Hz). A twocompartment model was used to describe the relationship between the drug input and plasma

concentration. A separate function was used to model the relationship between median E E G and concentration. Determination of adequate anaesthesia was largely based on response to verbal commands. The controls of the median E E G power was consistent, but the time to recovery varied widely (from 11 rain up to 38.8 min, mean 17.8 + 8 S.D.). This was largely due to a single patient. Possibly with a larger sample number the SD would go down. Using adaptive feedback techniques fairly good control of the E E G signal (be it spectral analysis, median power etc.) can be achieved, but this does not mean that the control variable is correlated to 'depth of anaesthesia'. Thus control of" E E G seems to work well under carefully managed conditions, but the applicability to routine surgery can only be assessed with far more trials of a similar nature to those by Schwilden.

1.3. Control via cardiocascular system This section describes the attempts to control the depth of anaesthesia via the cardiovascular system (ie usually by controlling arterial blood pressure). Millard et al. [20] describe a self-tuning controller used to control mean arterial pressure (MAP). Determination of the dead time, and patient sensitivity to the volatile anaesthetic agents isoflurane was carried out before the operation proper, by inducing a step response to the anaesthetic. Initially a proportional controller was used, with identification of its parameters being determined via recursive least squares [21]. After this phase the self tuner (based on Clarke and Gawthorp [22]) was used. Oscillations again occurred, but the control seemed to be good, with variations being usually confined to 4-5 mmHg. The oscillations were observed to increase in patients that were deliberately kept to a lighter anaesthetic state (due to surgical procedure). The authors main interest was in the deliberate maintenance of hypotension, where the reduced bleeding is beneficial for certain types of surgery, such as ear nose and throat (ENT) surgery. Gray and Asbury [23] describe a system that

218 controls the systolic arterial pressure (SAP). The system employed closed-loop control. The control algorithm used a proportional-integral controller. The administration of the anaesthetic (which consisted of a alfentanil-methohexitone mixture) was via a syringe driven by a pump (minimum delivery rate being 0.1 m l / h ) . The SAP was measured noninvasively. The patients were artificially ventilated (67% nitrous oxide in oxygen). During an operation, routine examination of the patient for presence of other clinical signs such as lacrimation and sweating was carried out. As noted by the authors, the quality of control achieved was highly variable, ranging from good to fairly poor. Despite this variable quality of control, the patients recovered fairly quickly with the exception of two patients (thus indicating that the system was not strongly overdosing the patients). The absence of clinical signs such as lacrimation and sweating implied that no significant underdosing occurred. As mentioned in the paper, one run was abandoned due to the patient having a very high resistance to anaesthetic. It was discovered later that the patient had not disclosed a drinking problem. Robb et al. [24] describe further work based on the paper by Gray and Asbury [23]. However, the volatile anaesthetic enflurane was used to control SAP, using a vaporizer controller. All the patients were artificially ventilated. The system was considered to 'produce a clinically acceptable anaesthetic state, and a rapid recovery of patients'. However, control was abandoned in one operation due to excessively high enflurane requirements. It was discovered later that the patient had a history of hypertension (after the patient denied any such problem in the pre-operative interview). 1.4. Miscellaneous approaches to control

This section contains miscellaneous approaches to the control of anaesthesia. Evans et al. [25] describe two methods for a closed-loop control of anaesthesia. The first method involves the assessment of a variety of clinical signs via a scoring mechanism. The score

value was used as the output signal. Various signs were used, such as systolic blood pressure, heart rate, sweating and lacrimation. The second method utilizes the correlation between changes in the rate of oesophageal motor activity and depth of anaesthesia. The deeper the anaesthetic, the less pronounced is the oesophageal activity. This activity can be detected b y placing a pressure transducer down the oesophagus (via an oesophageal catheter). As mentioned by the authors, this correlation breaks down in the presence of certain disease states, and use of certain drugs such as smooth muscle relaxants. The output signal in both cases was applied to what was described as a simple control system controlling the delivery of intravenous fentanyl, but details were not given. Schils et al. [26] describe an approach that differs markedly from the previous paper. They describe a halothane controller that used O N / O F F control, i.e. the output can be either ON (4% halothane) or OFF (0% halothane). The system used either MAP or E E G (using the spectral edge frequency) as output variables. These two variables were divided into three states (low, normal, high), giving 9 possible states. Depending upon the state space the relevant controller would be activated, and supplied either 0 or 4% halothane. For instance, if MAP and E E G were both 'low' then the MAP controller would be used (supplying 0% halothane), whilst if MAP was high and E E G low, then E E G would be used (also supplying 0% halothane); the justification for this being that E E G and MAP dynamics differ, and thus control would be determined by the most appropriate output variable. Another advantage cited for this approach was that given a sudden change (e.g. rapid reduction in MAP) the controller would instantly respond by stopping halothane delivery. However, if MAP then returned to a more normal value the system would rapidly restore halothane delivery (until a state change moves the system into a zero delivery mode). As a result the average delivery rate of the system would be normal. This study was conducted on dogs (using a non-rebreathing system, with controlled respiration), and the performance in a real clinical situation is not known. Regard-

219 less of the quality of control, this type of delivery pattern would cause problems of acceptance.

1.5. Expert system approaches to control Only a few papers have been published describing expert systems to control anaesthetic delivery. Other aspects of anaesthetic management are however more fully represented. For instance the Attending system described by Miller [27] examines an anaesthetist's proposed plan for an operation. The program considers various risks associated with different aspects of the proposed plan, and reports those risks, together with a suitable alternative where appropriate. A few underlying medical conditions are known by Attending. Attending has mainly been used for training [281. Vishnoi and Gingrich [29] describe a fuzzy logic controller (for gaseous anaesthesia), which uses both blood pressure and heart rate as output measurements. Appropriate rules in the controller are fired depending upon the patient status (as determined by the outputs). Experiments were conducted using fluothane (halothane) and isoflurane anaesthesia on dogs. The control of the inputs seem good, but the performance in a real clinical situation was not investigated, so the quality of control in such situations is not known. Schecke et al. [30,31] describe a KnowledgeBased system (AES-2) offering Decision Support for monitoring during cardiovascular surgery. The aim was to provide an intelligent alarm system, together with appropriate therapy recommendations. All information that AES-2 required was obtained from an existing program called AIS (described in Klocke et al. [32]), which was designed to monitor the patient and maintain anaesthetic records. Since anaesthetic records (of some form) must be maintained, time spent keeping AIS up to date (for instance logging drugs given), would have to be carried out regardless of whether the information was stored on computer or paper, and thus AES-2 would not interfere with the anaesthetist. AIS automatically logs both manual entries (such as drug administration and special events), and information from on-line sources (such as blood pressure), thus AES-2

would have a fairly detailed picture of the patient. AES-2 builds up a picture of state variables (such as myocadial contractility) and the vital signs. These outputs are used to determine the therapeutic actions taken. Both AES-2 and AIS were designed to be as user friendly as possible, with communication to AIS via a touch sensitive screen, and data being displayed on a series of windows. AES-2 makes use of AIS as its user interface. The anaesthetist is able to interrogate AES-2 to determine its therapeutic recommendations, condition of the state variables and vital parameters. Although AES-2 has been used in clinical situations, the performance was not discussed. A wide variety of control methods have been used to attempt to control the 'depth of anaesthesia', but many approaches such as reliance on MAC, and the pre-programmed systems (using end-tidal anaesthetic etc.) have an incorrect assumption built into the controller, namely the patient's response will be predictable and reproducible. To avoid this pitfall attempts have been made to determine parameters on-line, thus avoiding the use of population-derived parameters. Many of the described systems have only been tried out in a fairly restricted manner, for instance only being used with artificial respiration, an example being the system described by Tatnall et al. [5]. Another aspect recognized by many of the authors is the inability of a single output to safely reflect the true state of 'depth of anaesthesia'. The number of systems using two or more parameters is rapidly increasing, performance of which (over a wide variety of clinical situations) should be substantially better than single output systems. The expert system type controllers should allow safer control (although not necessarily better control of a single output) over traditional controllers, due to 'intelligent' interpretation of vital signs, relative ease of considering numerous outputs, etc. As yet the systems reviewed here have not been tested in real clinical situations. In developing Resac [33,34] the aim was to build an expert system to control the 'depth of anaesthesia', which could handle many routine surgical procedures without the restrictions men-

220 tioned above. Linkens et al. [35,36] briefly describe the performance of Resac whilst handling routine anaesthesia. Note that 'control' is achieved manually with the system advising the anaesthetist as to the anaesthetic state, and required control actions and thus the system is technically an advisor for the anaesthetic state. Resac uses fuzzy logic and Bayesian inference to cope with conflicting and uncertain evidence. As speed of inference was very important, the program was written in the 'C' language rather than an AI language. The Atari ST range of computers was chosen on grounds of speed and cheapness. The anaesthetic knowledge base developed for Resac makes use of multiple clinical signs. On-line information is supplied from a Dinamap 1846 instrument. The Dinamap is frequently used during surgery, and automatically determines: heart rate, and systolic, diastolic and mean arterial pressures.

2. Model representation in Resac The model language comprises: Hypothesis elements (Goals and Assertions) Numerical elements (Objects) Rule elements (Logical, Plausible, Arithmetic) Question elements ( y e s / n o , slider, multiple choice, numeric) Result elements All rules, questions and result elements have a Provided clause which controls whether the element can 'fire'. If the Provided clause evaluates to greater than U N C E R T A I N in value then the element can fire. One or more items of evidence (i.e., Hypothesis or Object elements) can be used to determine the value of the clause. Normal arithmetic operators and a sub-set of the fuzzy logic operators ('and' 'or' 'not') can be used to combine these elements. The only restriction is that the clause returns a value of the correct type. For instance, '(age + 6) >/10' is a valid clause as ' > i ' operator returns a degree of belief, whilst 'age + 6' is not valid as the expression returns a

number. Note: functions are provided that can convert a number into a belief and vice versa. Goals and Assertions have a Relevance value as well as a certainty value. Relevance was introduced as it was found that clinical signs could be rendered either less useful or useless as an indicator of depth of anaesthesia by various conditions. In particular, the use of patient movement as an indicator for depth of anaesthesia becomes irrelevant when the patient is paralysed via muscle relaxant administration. Similarly, any assessment via respiration becomes irrelevant when an artificial ventilator is being used. As a further example, pupil diameter is artificially reduced with the application of opiates, thus Relevance of pupil diameter should be reduced considerably. Relevance can also be used where a short term effect should be ignored, such as temporarily making systolic arterial pressure not relevant when the vascular system is being interfered with due to the surgical procedure. The following subsections describe the types of rules supported by the model language.

2.1. Logical rules The logical rules [37] otherwise known as 'if.. then' rules, allow evidence to be combined using the fuzzy set operators. The template of this type of rule is: if Expression- 1 then H LS L N

Expression-1 follows the same rules as in the Provided clause, thus numeric and non-numeric items can contribute to the value of Expression-1. This value is then used to update the posterior probability of the Hypothesis element H. The LS and LN values are referred to as the rule strength.

2.2. Plausible relation This rule allows multiple items of evidence to contribute using Bayes' theorem to a Hypothesis

221

element. In general it is expressed as: " H depends on

Yl

LSI LNI

Yn

LSn LNn"

Each item of evidence is used to produce an 'effective LS', which is combined with others to determine the posterior probability of H.

2.3. Arithmetic rules

O

(Root)

A

1

B

C

2

E

F---~ G

3

rermlnal

D

I

H

indicates search path taken to prove current ]eve[.

Fig. 1. Example section of inference network, where the Root corresponds to a single goal, and the Terminal part refers to a question.

This rule was primarily designed to handle numeric entities. The template of the rule is: X is Expression-1 This rule can be used to determine the posterior probabilities of Goals and Assertions, but the rule directly assigns the value of Expression-1 to the Goal or Assertion without any modification. Note that if the rule is being used to determine an Object then Expression-1 must return a numeric value. However, if the rule is determining the value of a Goal or Assertion then Expression-1 must return a value that can be assigned to the posterior probability of the Goal or Assertion.

3. Inference process in Resac

'Compilation' of the knowledge base consists of linking the model elements together to produce a network. This network is an acyclic directed graph. A graph structure consists of a set of nodes (each node being a model element), with arcs interlinking them. The direction of travel along an arc is fixed. An acyclic directed graph constrains the structure so that beginning at any arbitrary node, regardless of the path taken thereafter, a return to the initial node is impossible [38]. This structure thus supports either backward or forward chaining (depending upon the imposed direction along the arc), backward chaining being chosen for Resac. Circularities within

the network must be prevented f r o m occurring in the structure as such circularities would never terminate, and would also render the structure non-directed. The top of this graph consists of the Goal or Goals to be evaluated. The bottom or terminal parts of the network are often questions. An example network (with one Goal) is shown in Fig. 1. When more than one node can supply an answer to a node above (such as Nodes B, C and D which will be rules a n d / o r questions, being able to supply an answer to A), these nodes are placed in a list. The nodes representing questions are placed in front of nodes representing rules, thus questions will be the first node that the Provided clause evaluates to TRUE. For Goals and Assertions, this situation is complicated slightly since the Relevance of the element must be determined. Any nodes that contribute to the Relevance of another are also placed in a list. The Relevance of Goals and Assertions is determined before the belief in the Goal or Assertion. Note that if there are no items in the Relevance list, then the Relevance of the Goal of Assertion will evaluate to T R U E . In order for Resac to be able to handle uncertain evidence (such as when the system asks a question like 'How certain are you that the patient is sweating') the concept of certainty is used. Thus the answer from a question will be returned as a certainty factor (CF), which is then mapped

222

into the posterior probability by the following (see Alty and Coombs [39] for further details):

ity of H). This is given by the following equation [39]:

I F ( C ( E I E ' ) > 0)"

If 0 ~> 1)

(5)

223 As in the determination of O(H I - - E ) , P(H I E) can be expressed in terms of odds:

O ( H I E ) =LS × O(H)

(6)

Both LS and LN values are usually obtained from the domain expert. Since some of the equations are being expressed in the odds form, the following two equations show how odds and probabilities can be converted between one form and the other. O P = - O+1

(7)

mination of the Relevance of a Goal or Assertion. In this situation it would be undesirable to set Relevance posterior probability to the Relevance prior probability, since the effect would be to make Relevance go to U N C E R T A I N . A better default in this case is to make the posterior probability go to the maximum value, giving a Relevance of T R U E . Note that for all Relevance values the prior probability is set to 0.5. The effect of relevance on the posterior probability is given by: if

P(H I E') >1P(H)

e(/-/I E ' ) = P(/-/) And the reciprocal function:

+

P 0 = - 1-P

MaxCert

(8)

(lla)

Assuming that contributing evidence is independent [40], a single general updating formula can be used. This uses the effective LS(A') for all contributing terms:

O( H I E~..E') = [ I--I A'~]O( The i'th 'effective

( P( H]E') - P ( H)) × Rel

(9)

if

P(H I E') < P(H)

P(HIE') =P(H) +

( P ( H I E ' ) - P ( H ) ) × Rel MaxCert (11b)

LS' is given by:

A'~ = O( H I Ei)/O( H ) O(HIE~) comes from P(HIE~) and O(H) comes from P(H). Both O(H I E 1) and O(H) are known. Finally, to determine P(HIE'), the value obtained for O(H I E~..E') is converted via the odds to a prob-

where

ability equation. Note that if there is no contributing evidence, then Resac will set the posterior probability to the prior probability. The Hypothesis will then have a certainty value of 'UNCERTAIN'. The next section describes the effect of Relevance on P(HI E'). The determination of Relevance is almost identical to that of determining certainty of a Goal or Assertion. The Relevance of an item is determined before calculating the posterior probability. The only difference concerns the treatment when no evidence contributes to the deter-

where Rel is belief in Relevance (in CF units). Thus if the Hpyothesis's Relevance is totally true (Rel = MaxCert) posterior probability will not be affected. As Relevance becomes less true, the effect will be to reduce posterior probability. The effect of Relevance cannot make the posterior probability go less than the prior probability, hence Relevance values from FALSE to UNC E R T A I N have the same effect on posterior probability (i.e. P(H I E') = P(H)).

4. User interface

The following factors were considered before selecting an interface for the expert system: 1. An anaesthetist might be a poor typist, a n d / o r poor speller. Hence input from the keyboard must be kept to a minimum.

224

Nme :

inicl

RskPatlen~tovlng

, but not directed at stinulus,

These requirements ruled out command line interfaces, including natural language interfaces. Another possibility was touch-sensitive screens, but cost and programming overheads ruled this

~!!~!!~!!~!i!~i!!!!!!!!!!!!i!i!!!~!!!!!~!!!!!!!!!!!!!~ii !!ii!i~!i!i!!!!!!i!!!!!!~i!!~!!!!~ i~i~i~i!!i!i!i!!!!!!!~ii~!!i!!!~!~!!~!!!!!!!!~!!!ii!!!~!!!!!!!i~!!!!!!!!!!!!!!~?~!~F~rn ~Nane I InjectionOfMorphine

U

t|nulus, or coughJn9

Fig. 5. Multiple choice question Form.

Fig. 3. Slider question Form.

2. An operation might be at a critical stage, where updating the system's knowledge might be too time consuming. Again, keyboard input should be minimized, and incorrect or out of date information should be easily modified at a slightly later period in time. 3. Results should be constantly displayed, possibly with graphical display of some information. This would avoid problems of memory recall, and allow more considered thought from the anaesthetist concerning the advice, rather than worrying about recommendations scrolling off the screen. 4. All command choices should be easily accessed, keeping knowledge required to use the system to a minimum.

)

~

~es

approach out. Of the possible window-based interfaces, GEM on the Atari ST range of computers was selected, largely due to the large memory (4 Mbytes), together with the speed and cheapness of these machines. Whilst Resac's inference engine is running, the knowledge base is constantly being re-evaluated. On encountering a question, a suitable question Form is displayed. The different types of question Forms are illustrated in Figs. 3-6. The numeric question Form (Fig. 6) is the only Form that requires the use of the keyboard, all other Forms being driven by the mouse. Resac remembers the answers given to the question and thus will not have to re-ask these questions on subsequent iterations, unless some underlying situation has changed. Thus on the first iteration a large number of questions will be asked, but in subsequent iterations there will be either no or very few questions.

Name :

~

RskDosageHorphlne

~

oue,tion r

~

~

ge of Horphlne ?

#

2 0

Fram :

6

To :

-~99

Enter value : 2gl~,. . . .

Fig. 4. Yes/No question Form.

Fig. 6. Numeric question Form.

~t

225

Desk File

Packet

Edit

Delete

Others Control

open

.......... ~.Results..,,~............... ~......i

.................

on-line data. Sustm started at 15 : 28 Current tire is 15 : ZI Elapsed tire is 9 : Z RESULTS Operation length 58 Hins, TiM renatntng 48 Hins. The sgstolic arterial pressure at 55 ~hg is dangerously lo~, Xarning the belief in the statenent 'the anaesthetic state is too DEEP' is : fairlg true , Suggested dosage of volatile

~

~+~ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

iniesthesic

egont

=

!~

I._~.~

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G Fig. 7. Example of Results window.

At any time during the run, the anaesthetist is able to inform Resac of various events such as alteration in a clinical sign, administration of a

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