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PubMed1993

[Utilization of expert systems in psychiatry].

Are expert systems liable to be used as consultants in psychiatry? Most expert systems deal with an over-restricted part of psychiatry and cannot be a real help in everyday care. Moreover, most of them are not actually validated (the comparison between the system's and the expert's conclusions in a few cases is not enough). Another problem is that they reflect the uncertainties of nosographic problems. Validation of such systems needs the careful checking of the logical structure of the underlying nosography, the fitness of the structure's knowledge base and the fitness of the inference engine. Moreover, the naïve use of the system by untrained clinicians is the best means of validation since it provides real life proof of the ability of expert systems to make diagnoses in unselected cases where the need for a common diagnostic reference is clear (for example, epidemiologic, psychopharmacological ornosographic research). Some of the best known expert systems in the field of psychiatry are reviewed and another expert system, Adinfer, is presented. Developed since 1982, Adinfer is a forward-tracking level O system (in its simplified version for micro-computers). The knowledge base is a translation of the DSM-III-R into production rules. The program has been included in several software packages and used in many clinical studies, both among psychiatrists and physicians. The program has been validated with 1,141 unselected cases, and with 47 physicians: an 83% agreement rate was found between the system's and the physician's diagnoses, taking into account that the clinicians were asked to give their conclusions according to their usual nosography.(ABSTRACT TRUNCATED AT 250 WORDS)

Artificial Intelligence↗

PubMed1990

Workplace--worker--environment. An expert system.

In the expert system described stresses due to the workplace and the life environment are represented by characteristics, which are graded from 0 to 5. The same is true of capabilities of the man. The algorithm looks for the compatibility of one with the other. Examples of characteristics, the function of the certainty factor and of steps of examination depth are shown, with particular reference to the cardiovascular subsystem. The expert system "Workplace--Worker--Environment" is proposed to serve as a basic tool in occupational health care.

Cardiovascular Physiological Phenomena↗

PubMed1996

Otoneurological expert system.

An otoneurological expert system was developed to help collect data and diagnose both central and peripheral diseases causing vertigo. Patient history and otoneurological and other examination results are used in the reasoning process. The case history data can be either mandatory or supportive. Mandatory questions are used to confirm a diagnosis, and conflicting answers are used to reject an unlikely disease. Supportive questions support or suppress a diagnosis, but their presence is not obligatory. The reasoning procedure of the otoneurological expert system scores every question independently for different diagnoses, depending on how well they agree with the symptom entity of a disease. Diagnostic criteria are set for each disease. Graphic displays illustrate the linear and nonlinear correlation between the symptoms and diseases. Emphasis is placed on diminishing the possibility of a wrong decision rather than maximizing the likelihood of reaching only one right decision, so that even rare diseases can be taken into consideration.

Diagnosis, Computer-Assisted↗

PubMed2005

[Applications and progresses of expert system on chromatography].

The expert system on chromatography has achieved great advancement in the past two decades, and is playing a more and more important role in solving analytical problems of complex samples. Research results of expert system on chromatography in authors' group are reviewed with 64 references. A brief introduction of the expert system on chromatography is presented. Applications of the expert system on chromatography are summarized in the fields of petrochemical online analysis, environmental air sample analysis, tumor diagnosis and traditional Chinese medicine analysis. The review followed the scientific foot steps in the authors' group, starting from the development of the expert system on gas chromatography, to the selection of multi-column systems in online industrial gas chromatographs in petrochemical plants, and to the employment of the new techniques in gas chromatography, liquid chromatography and capillary electrophoresis to solve the practical analytical problems in the nation's scientific and economic development.

Chromatography↗

PubMed1989

[Interpretative reading and quality control of an antibiotic sensitivity test using an expert system. Application to the API ATB system and Enterobacteriaceae].

An expert system (cadi-yac), written in Turbo-Prolog and working on IBM PC and Bacanal + (a management software of microbiology laboratory) was used to recognize and correct the phenotype of antibiotic sensibility. The results were there of API ATB system. The knowledge was adapted from two references works. A routine use of the expert system give a correct recognition of enzymatic profile in more than 80% cases for the beta-lactams and more than 98% cases for the aminosides. The mistakes detected by cadi-yac, were often interpreted as deficiency of API system by humans experts. The expert system mistakes (1.5%) were due composites phenotypes.

Anti-Bacterial Agents↗

PubMed1994

Expert systems.

The concept of computerized expert systems is explained, the potential utility of these systems in pharmacy is explored, and strategies and imperatives for implementing them are described. Computerized expert systems attempt a higher level of analysis than traditional computer programs. They can be defined as systems that attempt to make or assist in a decision that is not yet completely and reliably definable in objective terms. Because of the information-intensive nature of pharmacy practice, this field is particularly suited to use of expert systems. Current applications include screening for drug interactions and therapeutic drug monitoring. Expert systems must offer a substantial advantage over human expertise (for example, by quickly analyzing enormous quantities of data); those that perform functions that humans could perform have failed to gain widespread use. An ideal hospital expert system would have access to any data available about a patient's care and would detect critical situations as they occur. Such a system would require pharmacists to shift from a prescription-based orientation to a case-management orientation. Factors to consider in implementing an expert system include linkage among multiple departments, usage options, development strategies, and maintenance requirements. Computerized expert systems hold great potential for application to pharmacy and may influence the pharmacist's role in patient care.

Drug Interactions↗

PubMed1992

[Clinical laboratory and expert system].

Computer-based expert systems are designed to incorporate the knowledge of a human expert such that the computer program will solve problems in a manner similar to the human expert. The systems can be applied fruitfully to many phases of the laboratory testing, including order entry, specimen collection, analysis, result verification, and reporting. Interpretive reporting by means of expert systems is useful for influencing physician's use of laboratory and they offer potential help for clinical decision making. Although much of the early work in the clinical laboratory involved stand-alone expert systems, approaches that integrate more closely with laboratory information system or hospital information system are being used. A short review on the technology of expert system with an emphasis on the roles played by clinical laboratory is made. A trial system dealing with thyroid diseases, based on interpretation of hormonal levels (TSH, Total and Free T3, T4), is described.

Clinical Laboratory Techniques↗

PubMed2002

[Expert systems in gastroenterology].

Expert systems are the software systems developed by the application of the various intelligence, which could successfully compete to peoples-experts, and have the consultant function with the characteristics of the explanations and the advices in some specific domain. These are, in essence, the intelligent information systems, which consists several thousands of the rules from the definite problem field and which are capable to explain their decisions. The knowledge systems are lesser software systems, also developed by means of the technique of the artificial intelligence which are usually less successful in some field of knowledge of the experts. In the paper are presented the basic characteristics three most often applied expert systems in gastroenterology: Quick Medical Reference (QMR), HEPAT, ILIAD.

Diagnosis, Computer-Assisted↗

PubMed1992

Integration of a stand-alone expert system with a hospital information system.

A stand-alone PC expert system for evaluating the appropriateness of inpatient admissions has been integrated with an existing hospital information system. The expert system supports preadmission screening for appropriateness of inpatient admissions. The HIS provides extensive clinical data in a coded electronic form, permitting high-level decision support. The integrated system was developed for a 20 week randomized clinical trial to evaluate the effects of preadmission screening on inappropriate inpatient admissions. Three factors of the integration are considered: programmatic integration of the expert system, seamless presentation of mixed platform applications, and integration of coded data from the stand-alone application into the HIS data structure.

Evaluation Studies as Topic↗

PubMed1989

Expert systems in histopathology. II. Knowledge representation and rule-based systems.

Two aspects of expert systems for use in diagnostic histopathology and cytopathology are examined: knowledge representation and the structure and operation of rule-based systems. Knowledge may be represented, e.g., in semantic networks, frames, multiple contexts and model-based structures; the choice of structure should be matched to the type of information to create an efficient and logically adequate expert system. In a rule-based system, knowledge is represented as "rules," often in the form of "IF (condition)-THEN (conclusion)" rules. The anatomy of such rules and their operation is explored via the use of examples. Uncertainty in rules is briefly addressed, and their processing by the symbolic reasoning of the "inference engine" of the expert system is described, including both "forward-chaining" ("data-driven") operations and "backward-chaining" ("goal-driven") operations.

Algorithms↗

PubMed1988

Development and application of simple expert systems in obstetrics and gynecology.

Expert systems have become increasingly popular in medicine for the support of medical decisions, e.g. diagnosis or treatment. We describe the development and application of 2 simple rule-based expert systems, one used for cycle stimulation in our in vitro fertilization program and the other for preoperative assessment of urinary incontinence. The programs were written using a commercially available expert system shell, are run on a standard personal computer, and are in actual use in our department. Though it is doubtful that simple expert systems can be superior to a human expert we feel that expert systems are very useful in the standardization of protocols and are a valuable teaching instrument.

Decision Making, Computer-Assisted↗

PubMed1991

Using OMIM (On-line Mendelian Inheritance in Man) as an expert system in medical genetics.

Expert systems have been used in Medicine for many years, but they are usually highly sophisticated and not well integrated into day-to-day practice. On the other hand, bibliographic databases such as Medline and others are easily accessible and are widely used. We report here the use of OMIM (On-line Mendelian Inheritance in Man), one of these bibliographic databases, as an expert system in Medical Genetics. The description of 93 syndromes was used as search-key and the diagnoses proposed by OMIM were analyzed to determine whether the correct diagnosis was among them. The proposed diagnoses were automatically ranked by OMIM from the most probable (weight = 100) to the least probable (weight = 1). OMIM suggested a total of 1538 +/- 692.2 diagnoses per search. In order to deal with a reasonable number of proposed diagnoses, we only considered the diagnoses with a weight of 50 or more. With this limit, OMIM proposed a mean of 37.0 +/- 24.6 diagnoses per case. The overall accuracy was 76%. A correct answer with a perfect weight of 100 was proposed in 29% of the case. The diagnostic accuracy of OMIM increased linearly when weights lower than 100 were considered. When the rank alone was analyzed, the accuracy of OMIM increased very rapidly from position 1 to 5 with a subsequent almost linear increase. If one only considered the first five proposed diagnoses, the accuracy of OMIM was just above 50%. This study shows that bibliographic databases are not only restricted to the provision of references but could also be used as expert systems and are therefore of great value to medical geneticists.

Databases, Bibliographic↗

PubMed1993

Competence reasoning--handling ambiguous and imprecise data in the Pro.M.D. expert system shell.

The Pro.M.D. expert system shell (Pohl and Trendelenburg, Methods Inf Med 1988;27:111-117) is a computer program tool for knowledge-based computer interpretation of laboratory data. Competence reasoning deals with more or less vague knowledge. Vague knowledge is knowledge using vague data. In Pro.M.D. terminology, vague data are called 'semi-known'. Examples of semi-known numbers are numeric intervals like [5.2,8.8] (= all numbers between lower limit 5.2 and upper limit 8.8 are possible), or imprecise numbers like N[4,1] (= numbers vary around mean of 4 with standard deviation of 1). Semi-known qualitative data are ambiguous information on alternative values like low or normal (= from three alternatives low or normal are possible and high is impossible) or probability distributions on the alternatives like low in 60% or normal in 30% or high in 10% (= low, normal and high appear with the respective probabilities). Regarding vagueness, data may be divided into known data (unambiguous and precise information), semi-known data (ambiguous or imprecise information) or unknown data (no information). Semi-known data may be further subdivided into probabilistic data, where the probability distribution of domain values is known (e.g. Gaussian distribution) and possibilistic data, where probability distribution of domain values is not known (e.g. intervals). Probabilistic data may be obtained either by completely counting the population's individuals or by estimation from samples. In the latter case sample size is another measure for data vagueness. In the Pro.M.D. expert system shell functions are being implemented for input, output, calculation and testing of semi-known data. Thus, instruments for competence reasoning in Pro.M.D. will help to build up reliable and user friendly knowledge-based systems.

Clinical Laboratory Information Systems↗

PubMed1999

EICO-1: an orthodontist-maintained expert system in clinical orthodontics.

Expert systems are increasingly being used to provide comprehensive interpretative services for diagnosis and treatment planning. Some of these systems are constrained by the complexities of rule-based strategies and a need for knowledge engineers throughout the maintenance phase. A new approach to knowledge acquisition known as Ripple-Down-Rules was used in the development of EICO-1 (Expert Interpretation in Clinical Orthodontics). This expert-maintained system for automating orthodontic reports has a knowledge base of six hundred and eighty rules, and is maintained by an expert trained only in Orthodontics and without the help of knowledge engineers. EICO-1 is the first expert system in Dentistry to use Ripple-Down-Rules. It has potential as an interactive advisory tool and is applicable in a clinical situation.

Artificial Intelligence↗

PubMed1991

Radiology image interpretation system: modified observer performance study of an image interpretation expert system.

Application of computer-based expert systems to diagnostic medical problems has been described in many areas including clinical diagnosis and radiology. Expert systems are computer programs that contain encoded expert knowledge to provide expert advice. A modified observer-performance study was done comparing the efficacy of the Radiology Image Interpretation System (RIIS), an expert system that diagnoses focal bone abnormalities, and radiology residents on a known set of 44 abnormal and 10 normal cases. Modified receiver operating characteristic curves for four inexperienced residents, five experienced residents, and RIIS were generated using the set of known radiographs. The true-positive rates of RIIS and the residents at false-positive rates of 0.05, 0.15, and 0.20 were estimated using the modified receiver operating characteristics curve and were compared using a paired t test. On the average, the RIIS system was less accurate when compared with experienced and inexperienced residents but the difference was only significant for experienced residents at a false-positive rate of 0.05. RIIS performed better than inexperienced residents when RIIS was used by experienced residents but this difference was not significant.

Bone Diseases↗

PubMedDate not supplied

Legal liability in the development and use of medical expert systems.

The application of medical expert systems is likely in some instances to result in patient injury litigation. Such legal action will be based on the same principles now applicable to medical practice and products liability. These principles define legally wrongful acts, product defects, and theories of recovery: negligence, breach of warranty and strict liability. The resolution of such litigation will depend on whether the clinician should have relied on the advice of the expert system when the advice and patient treatment is alleged to have been wrong, as well as the overall quality of the expert system, its functional design, and claims made for it. Clinical engineers who become involved with expert system development, selection or implementation should understand the risk management implications of this technology.

Expert Systems↗

PubMed2000

Improvement in tangential breast planning efficiency using a knowledge-based expert system.

A knowledge-based expert system was developed for the purpose of improving radiotherapy planning efficiency for a standardized, tangential breast technique. Treatment parameters pertaining to 150 previously planned patients were used for correlating the midplane breast contour of a new patient with an appropriate set of tangential beam weights and wedge angles; other treatment parameters including, planning target volume and isocenter, were specified by a radiation oncologist. Treatment plans generated by the expert system approach and a traditional, dosimetric approach were compared and rated prospectively in 45 patients. In addition, planning time was measured for both approaches. A performance rating of 97% was achieved for the expert system, in which an artificial neural network was used to correlate breast contours to treatment parameters, and approximately 30 minutes per patient was saved in treatment planning time. This high performance rating validated various assumptions concerning the expert system: namely, that the resultant dose distribution was not influenced by tangential field width (within the range of 7 to 12 cm), nominal beam energy (6 MV), or wedge type (physical vs. enhanced dynamic). Hence, the knowledge base may be directly transferable to other cancer centers using the same breast technique, and suggests that a global resource of radiotherapy treatment plans as well as planning strategies, categorized by treatment site, stage, and technique, may be viable.

Breast Neoplasms↗

PubMed1992

Expert systems in dentistry. Past performance--future prospects.

Expert systems are knowledge-based computer programs designed to provide assistance in diagnosis and treatment planning. They assist the practitioner in decision making. A search of the literature on expert system design for medical and dental applications was carried out. It showed an increase in the number of articles on this subject. Between 1984 and 1991, 608 articles have been published in medical journals and two in dental journals. Because it is likely that this development will influence dental practice in the future a critical review of medical literature on the topic has also been carried out. A number of general principles are described to give the dental practitioner some insight into how expert systems work. A set of criteria have been formulated from the medical literature which expert systems should meet. These requirements are also applicable to dentistry and may be used to judge dental expert systems. In the last part of the paper the features of several dental expert systems developed in the past decade are described in the light of these criteria. It is concluded that in the future more attention should be paid to the development and evaluation of expert systems in the clinical setting. Only well-designed and properly evaluated expert systems can be expected to earn a place in everyday practice.

Dentistry↗