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Application of artificial intelligence to pharmacy and medicine.

Artificial intelligence (AI) is a branch of computer science dealing with solving problems using symbolic programming. It has evolved into a problem solving science with applications in business, engineering, and health care. One application of AI is expert system development. An expert system consists of a knowledge base and inference engine, coupled with a user interface. A crucial aspect of expert system development is knowledge acquisition and implementing computable ways to solve problems. There have been several expert systems developed in medicine to assist physicians with medical diagnosis. Recently, several programs focusing on drug therapy have been described. They provide guidance on drug interactions, drug therapy monitoring, and drug formulary selection. There are many aspects of pharmacy that AI can have an impact on and the reader is challenged to consider these possibilities because they may some day become a reality in pharmacy.

Artificial Intelligence↗

Validating the knowledge base of a therapy planning system.

Validation of expert system knowledge bases has proved to be difficult. This paper presents a description of a system called ScriptGen that generates test data for validating the knowledge base of the ONCOCIN cancer therapy planning system. Because of the size and complexity of the ONCOCIN knowledge base, we require tools for automated validation. ScriptGen, which applies techniques developed in testing both traditional software and expert systems, uses a parallel model of the ONCOCIN knowledge base and its own inference engine to generate test cases. We derived the limits of the system from a study that seeded errors into an existing knowledge base.

Antineoplastic Combined Chemotherapy Protocols↗

Diagnosis of selected pulpal pathoses using an expert computer system.

The diagnosis of dental pain that results from a pulpal pathosis may prove to be a confusing and complex issue for both dental students and experienced clinicians alike. A computerized diagnostic expert system, COMENDEX, was developed to aid in the clinical diagnosis of pulpal pathosis and to provide a rapid, accurate second opinion when a human consultant is not readily available. The vast majority of diagnostic expert systems use a single reasoning methodology as their inference mechanism. COMENDEX is a prototype hybrid expert system that combines and exploits the best aspects of both rule-based and statistical reasoning methodologies. The COMENDEX endodontic diagnostic system was tested to determine the validity and accuracy of its diagnoses by the use of a variant of Turing's test and weighted kappa statistic. The promising result obtained from the initial tests suggests that an expert system using this type of hybrid reasoning methodology is well suited to the area of endodontic diagnosis and may prove highly successful if expanded to include other problem areas in oral diagnosis.

Bayes Theorem↗

[Our challenge in Kagoshima University Hospital--development of more useful clinical laboratory].

In 1995, we newly developed a clinical laboratory system equipped with an expert system, which was named HIPOCLATES (Hospital Intelligent POwers of Clinical Laboratory Automation Technology with Expert System). Since this system includes an expert system, we have been able to support diagnostic and therapeutic procedure in clinics and begin developing a "zonal verification method" and "early-stage DIC diagnosis support system". The former is one of the individual quality control methods and we have attempted to develop a test assurance system in HIPOCLATES using this method. The latter system is to diagnosis DIC in the early phase. Here, we introduce the detail of our project using HIPOCLATES. Following the introduction HIPOCLATES, we attempted to develop a highly integrated system in physiological examinations, and succeeded in establishment PLATON (Physiological LAboratory TOtal Network system) in 1997. Thereafter, we planned a graphic reporting system named GALIREO (Graphic Assistant Laboratory Informational REport Operating system). In GALIREO, we attempted to connect the Department of Clinical Laboratory, Ultrasonic waves test room, Fiberscope test room, Department of Pathology and Surgical center by computer network. Developing these computer network system in our clinical laboratory we want to create electronic medical record linking with THINK (HIS in our hospital). We plan to make these expert system and electronic medical record available to clinical staff via a network to realize evidence-based medicine (EBM) and utilize these to support treatment and education.

Clinical Laboratory Information Systems↗

Structural and functional design of WWTP operation decision support system with a case study.

This paper developes the WWTP operation decision support system (ODSS) to simulate the dynamic behavior of the WWTP treatment process based on ASMs (activated sludge models) and WWTP experiential knowledge. The novel structure and functions of ODSS can offer more flexible and general instructions to the WWTP operations. The three independent sub-systems, namely expert system, simulation system and training system, can cooperate to achieve many more functions such as operation alert, fault diagnosis, process simulation and so forth. The expert system based on the dynamic simulation, an essential part of WWTP ODSS, has been proved to be feasible and effective by the implementation at Fang Zhuang WWTP. Our results indicated that the WWTP ODSS has significant potential for improving plant performance and reducing treatment costs by assisting the operators at the decision-making level.

Decision Making↗

Potential effects of decision support systems on the role of the nurse.

Decision support systems, and expert systems in particular, may have profound effects on nursing's role, causing the profession to reexamine the meaning of expertise for nursing and the educational strategies which enhance its development. In addition, expert systems provide support for enhanced scope of practice and greater autonomy, and they present intriguing liability challenges for the profession. Utilization of these sophisticated systems will encourage the trend toward increased information management, and will underscore the importance of nurses' interpersonal communication skills and their qualitative, ethical contributions to clinical decision-making.

Clinical Competence↗

Expert system-controlled image display.

Conventional computer-based medical expert systems deliver advice to physicians as written text. While such advice is useful, it has distinct limitations in a visually oriented discipline such as diagnostic radiology, in which decisions often depend on pattern recognition and appreciation of subtle morphologic features. The authors developed a prototype expert computer system, IMAGE/ICON, which displays groups of images sorted into a series of axes based on different ways in which they may be similar. They may share a common feature, group of features, causes, or clinical setting. IMAGE/ICON may display examples of morphologic variations of a dominant finding or a spectrum of abnormalities seen in an specific disease or group of diseases. The system also assembles a written analysis of key features of a case. Such a tool may be useful as a diagnostic aid or for continuing medical education. It is likely to have particular impact in the form of an intelligent radiologic workstation, as picture archiving and communication systems become available.

Diagnosis, Computer-Assisted↗

Tuning of myoelectric prostheses using fuzzy logic.

This contribution concerns the use of a supervisory expert system based on fuzzy logic for the parameter adjusting of myoelectric prostheses. The prosthesis system is an artificial arm produced by INAIL in Vigorso (Bologna, Italy), which is equipped with an on-board actuation system. The expert system guides patients through an interactive session whose aim is to test the prosthesis functionality and, when necessary, to self-adjust the parameters.

Artificial Limbs↗

[Methodology of a study on insomnia in the general population].

The study of sleep disorders in the general population involves several methodological issues that need to be defined prior to proceeding to the epidemiological study. The rigor of the methodology is an important issue since it will determine the reliability of the data gathered. This paper describes the methodology used in an epidemiological study performed in the French general population using telephone interviews with the help of Sleep-EVAL, an expert system designed for this purpose. The study aimed to investigate the prevalence of insomnia disorders according to the DSM IV classification and the use of psychotropic medications in the general population. The methodological choices for this study were based on several considerations. First, the sample had to be representative of the French population. Second, the study had to be conducted in the shortest period of time. Third, the interviews had to be conducted with respect to a strict standardization and fourth, the realization costs had to be minimal for a maximum of data collected. The telephone interview procedure was chosen over postal and face-to-face interviews because it offered the possibility of conducting all the interviews from the same site. Supervision was easier. It also offered an absolute control in the application of the selection procedure. To draw the sample, a two-stage procedure was adopted. At the first stage, we pulled a random series of telephone numbers in each Nielsen region with respect to the size of the settlement. At the second stage, during the initial telephone contact, a household member was chosen using the Kish selection procedure. This method is based on the utilization of eight tables of selection that allows for the choice of the person to interview in a given household and keeps the representativeness of the sample. This technique is little used in telephone surveys because of its burden and its intrusive nature: the interviewer must collect the age and gender of all eligible subjects, to classify men from the oldest to youngest and then to classify women. However, it is the most rigorous selection method for epidemiological surveys. To reduce the refusal rates and to alleviate the work of interviewers, the Kish method was implanted in the computer software used for this study. For this study, the exclusion criteria were minimal. Only individuals younger than 15 years of age, those with a speech or hearing impairment and those who were too ill to perform the interview were not included. Subjects who refused to participate, those who hung up without speaking to the interviewer and those who hung up before completing at least half of the interview were tabulated as refusal. The participation rate was calculated by dividing the number of completed interviews by the number of eligible participants (completed interviews, refusals and telephone numbers where the interviewer was unable to determine if the individuals met an exclusion criterion). In this study, the participation rate was 80.8% (5 622 completed interviews/6 966 eligible households). The diagnostic tool used for this study was the Sleep-EVAL system, an expert system designed to conduct epidemiological studies in the general population. It is a level 2, non-monotonic system endowed with a causal reasoning able to provide sleep and mental disorders diagnoses according to the DSM IV classification for this study. Subsequent versions of Sleep-EVAL also included the International Classification of Sleep Disorders. System symbolic representation of the classifications was put in a compiled knowledge base. This knowledge base was read and interpreted by the inference engine at the beginning of the interview. During the interview, this interpretation changed as a function of the answers provided by the interviewee and by deductions made from the analysis of information the system already knew. All interviews began with a standard questionnaire about sociodemographic information and sleep habits. From these first answers, the Sleep-EVAL system emitted a series of diagnostic hypotheses that were confirmed or rejected with supplementary questions. The interview ended once all diagnostic possibilities were exhausted. The validity of the Sleep-EVAL system was demonstrated in different studies performed in sleep disorders clinics. There were several advantages in using such a tool to conduct epidemiological surveys. No special skills from the interviewers nor specific knowledge of sleep and mental disorders were required. All the questions were chosen and formulated by the Sleep-EVAL system. The interviewer had simply to read the questions as they appeared on the monitor screen and enter the interviewee's responses by clicking the appropriate answer or typing it on the keyboard. Missing answers were non-existent because there was no possibility of skipping a question or entering inconsistent answers. It also ensured the uniformity of the interviews. Furthermore, it allowed the exploration of infrequent diagnoses. In summary, the methodology used for this study allowed for the investigation of the sleep pathology of the French population in a short period of time: only three months were necessary to complete the 5 622 interviews. The use of a computerized tool greatly facilitated the training of the interviewers and also their work. Furthermore, it ensured a standardized administration of the interviews and the exploration of a broad range of disorders that could hardly be realized with traditional paper-pencil questionnaires.

Adolescent↗

Integrating management and expertise in a computerised system for hypertensive patients.

An expert system has been integrated to the data management system of the ARTEMIS programme for hypertensive patients. The patient database, which has been used since 1975, contains the medical records of about 20,000 patients. Information is interactively entered by physicians, nurses and secretaries on video display units. The computerised medical record has replaced the traditional handwritten medical record. The database management system is used to produce different summary reports (inpatient and outpatient care) and personalized recall letters which are mailed to the patients before their appointments. Suggestions provided by the expert system include additional information to be obtained (complementary patient interrogation, biological or radiological investigations, etc.), possible causes of hypertension, and medical prescriptions. The information base allows the description of both static knowledge (in the form of a semantic network) and dynamic knowledge (in the form of production rules). The inference system sequentially uses a combination of forward and backward chaining and performs both exact and approximate reasoning. The diagnostic performance of the expert system was evaluated in 100 cases of hypertension (50 of essential hypertension and 50 of secondary hypertension. Concordance between the diagnosis proposed by the expert system and the one proposed by the specialist was achieved in 92% of secondary hypertension cases and 88% of essential hypertension cases. It is suggested that the integration of data and knowledge management might enhance the overall acceptance by medical staff of a computerised system, and facilitate the validation of a knowledge base.

Database Management Systems↗

Health-care technology transfer: expert and information systems for developing countries.

Computer-based technologies such as information systems and expert systems have an undoubted contribution to health-care development in developing countries. This paper addresses the appropriateness of these technologies for developing countries, the criteria to be used in selection of the technology to be transferred, and the need for a systematic approach to evaluation. A conceptual model for assessing transferability so as to achieve an effective transfer, has been introduced. This requires not only an attempt to amplify the role of information and expert systems in health-care improvement and for socio-economic development, but the analysis of prior experience in transferring these technologies to developing countries. Following this approach, and using operational research techniques such as the Analytic Hierarchy Process, a subjective assessment model has been described that can systematically guide decision-making about computer-based health-care technology to be transferred to developing countries.

Developing Countries↗

Development and use of a rule-based pathology expert consultation system.

Since histologic sections of pathologically changed tissues (e.g., tumors and inflammations) are highly complex structures, their subjective diagnostic or prognostic interpretation may result in a lack of interobserver and intraobserver agreement. Rule-based expert systems have the advantages that they require precise representation of the problem and can be used quickly and efficiently, even if the user is not acquainted with the special field concerned. Because of these and other advantages, a rule-based "Pathology Expert Consultation System" (PECS) is under development for the assessment of different premalignancies, malignancies and noncancerous conditions. In addition to supporting diagnosis making, the expert system can infer stage, type and grade, can check for inconsistencies in the answers and can predict the prognosis of an individual patient. PECS permits total integration of information contained in database, worksheet and text files with the rules and other control structures inherent in the rule base. The consultation can be interrupted to display a graph; when the user dismisses the graph, the consultation will resume where it left off or will move to another part of the system (if desired). If the user asks why a question is raised, the system will answer with a textual, graphic or combined explanation. The construction and use of the PECS expert system is discussed. Its initial applications to tumors of the lung, breast and endometrium have shown it to have both educational and clinical significance.

Computer Systems↗

An object-based architecture for biomedical expert database systems.

Objects play a major role in both database and artificial intelligence research. In this paper, we present a novel architecture for expert database systems that introduces an object-based interface between relational databases and expert systems. We exploit a semantic model of the database structure to map relations automatically into object templates, where each template can be a complex combination of join and projection operations. Moreover, we arrange the templates into object networks that represent different views of the same database. Separate processes instantiate those templates using data from the base relations, cache the resulting instances in main memory, navigate through a given network's objects, and update the database according to changes made at the object layer. In the context of an immunologic-research application, we demonstrate the capabilities of a prototype implementation of the architecture. The resulting model provides enhanced tools for database structuring and manipulation. In addition, this architecture supports efficient bidirectional communication between database and expert systems through the shared object layer.

Database Management Systems↗

Computer-aided diagnosis and negligence.

The phrase 'expert system' has been widely used to describe computer systems which are capable of performing at or near to the level of an expert. Expert systems may be recognized by their construction or by their performance. Structurally, expert systems usually comprise a knowledge-base, an inference engine and an interface with the user as shown in Figure 1. The knowledge-base contains the raw material of the expert system; the rules and facts representing the expertise. An important part of that knowledge-base usually will be heuristic in nature and this is particularly so with respect to expert systems in medicine. A large amount of a specialist's knowledge is informal and experimential in nature and this heuristic knowledge is often what sets the specialist apart from the general practitioner or indeed sets the latter apart from the medical student. The inference engine is a computer program which attempts to resolve the user's enquiries by operating on and interacting with the knowledge-base. Finally, the interface with the user serves two purposes: first, to make the system relatively easy to use and second, and very importantly, to provide an explanation and justification for the results, advice and suggestions obtained from using the system (Winfield, 1982). There will be other parts to an expert system which are used to refine and modify the knowledge-base. The performance of an expert system is of utmost importance and it is the most fundamental test for whether a computer system falls into this classification. d'Agapeyeff (1984) defines expert systems as being: a) programmed to a significant extent, from an explicit representation of empirical human knowledge; b) readable by those who provided the knowledge and, potentially, by similarly knowledgeable users and managers; c) able to provide explanations of their reasoning on demand; d) quickly alterable with (comparatively) low risk of unwanted side effects.' d'Agapeyeff describes the knowledge-base as being empirical, suggesting that it is informal or heuristic knowledge rather than comprising clear and formal rules as written in textbooks. This does not preclude the inclusion of formal knowledge in the expert system but it is clear that heuristic knowledge occupies a key role in the development of expert systems. Much medical knowledge is heuristic in nature; for example, an experienced doctor might use a rule in diagnosis such as: 'If symptoms A and B are observed then C is plausible but certainly not D'. In the field of medicine, expert systems will enable specialist expertise (a rare commodity) to be available to non-specialists such as general practitioners.(ABSTRACT TRUNCATED AT 400 WORDS)

Diagnosis, Computer-Assisted↗

What kind of expert should a system be?

Human experts are the source of knowledge required to develop computer systems that perform at an expert level. Human beings are not, however, able to reliably express what they know. As a result, experts often develop non-authentic accounts of their own expertise. These accounts, here termed reconstructed methods of reasoning, lead to computer systems that perform at a high level of proficiency but have the disadvantage that they often do not reflect the heuristics and processing constraints of a system user. Reconstructed methods of reasoning are compared with authentic methods derived from the study of expert human behavior. Tests are proposed to establish the authenticity of reasoning methods and examples from medical diagnosis are used to illustrate how authentic methods of reasoning can be incorporated into an expert computer system.

Clinical Competence↗

Modeling all dialogue system participants to generate empathetic responses.

A dialogue system between an expert system and its users is described which combines two recent hypotheses. First, that the dialogue system should explicitly model both the person directly interacting with the dialogue system (the agent) and the person reasoned about by the expert system (the patient) in order to communicate meaningfully with both people. Second, that a dialogue system can model the domain-related beliefs, preferences and concerns of both its users and generate responses empathetic to both. This dialogue system is called SERUM, standing for 'System for Empathetic Responses with User Models.' SERUM generates natural-language responses about attribute values of domain objects, via three transformations. First, the system converts properties of the agent and patient, and domain knowledge, into a pragmatic objective like empathy. Second, SERUM converts the pragmatic objectives into surface structure cues like object emphasis and level of technicality. Finally, SERUM converts the surface structure cues to realize text that is natural, appropriately technical and emotionally empathetic. SERUM is demonstrated in describing tests and treatments for lung disease in AIDS patients, a sensitive domain where empathetic responses may be needed.

Algorithms↗

[An expert consulting system for the epidemiologic physician].

The questions relating to the practical use of the automated expert system for diagnostics, the type of the epidemiological process and giving recommendations for carrying out single purpose prophylactic and antiepidemic measures has been considered herein. The peculiarities of use of the present system with micro-computer has been described.

Epidemiology↗

Real time monitoring and analysis via the medical information bus, Part II.

The complexity of the monitored data available in modern intensive care units suggests that they may be best processed, for presentation to medical staff, by expert system techniques. However, standard expert system shells are ill-fitted to either the basic sequential monitoring tasks of data acquisition and storage, or to handling the temporal considerations inherent in monitoring and in the recognition and processing of sporadic alarm signals. A solution to this dilemma is described: an expert system that has an appropriately designed inference engine and manages data acquisition via a medical information bus (MIB). Use of this MIB standard allows great flexibility as regards the monitoring apparatuses employed; in particular, the connection or disconnection of any apparatus is recognised and triggers the automatic reconfiguration of the network.

Computer Systems↗