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PubMedDate not supplied

Are deterministic expert systems for computer-assisted structure elucidation obsolete?

Expert systems for spectroscopic molecular structure elucidation have been developed since the mid-1960s. Algorithms associated with the structure generation process within these systems are deterministic; that is, they are based on graph theory and combinatorial analysis. A series of expert systems utilizing 2D NMR spectra have been described in the literature and are capable of determining the molecular structures of large organic molecules including complex natural products. Recently, an opinion was expressed in the literature that these systems would fail when elucidating structures containing more than 30 heavy atoms. A suggestion was put forward that stochastic algorithms for structure generation would be necessary to overcome this shortcoming. In this article, we describe a comprehensive investigation of the capabilities of the deterministic expert system Structure Elucidator. The results of performing the structure elucidation of 250 complex natural products with this program were studied and generalized. The conclusion is that 2D NMR deterministic expert systems are certainly capable of elucidating large structures (up to about 100 heavy atoms) and can deal with the complexities associated with both poor and contradictory spectral data.

Expert Systems↗

PubMed1996

Modifying an expert system construction to pattern recognition solution.

Medical expert systems are a successful field of applied artificial intelligence. We constructed an otoneurological expert system in our previous research, and in this study we consider its reasoning method. The reasoning process can be described as a modified nearest neighbour solution derived from pattern recognition. The expert system was tested and functions reliably.

Ear↗

PubMedDate not supplied

Evaluating a population-based recruitment approach and a stage-based expert system intervention for smoking cessation.

A stage-matched expert system intervention was evaluated on 4144 smokers in a two-arm randomized control trial with four follow-ups over 24 months. Smokers were recruited by random digit-dial calls, and 80.0% of the eligible smokers were enrolled. Individualized and interactive expert system computer reports were sent at 0, 3, and 6 months. The reports provided feedback on 15 variables relevant for progressing through the stages. The primary outcomes were point prevalence and prolonged abstinence rates. At 24 months, the expert system resulted in 25.6% point prevalence and 12% prolonged abstinence, which were 30% and 56% greater than the control condition. Abstinence rates at each 6-month follow-up were significantly greater in the Expert System (ES) condition than in the comparison condition with the absolute difference increasing at each follow-up. A proactive home-based stage-matched expert system smoking cessation program can produce both high participation rates and relatively high abstinence rates.

Adult↗

PubMedDate not supplied

Monitoring expert system performance using continuous user feedback.

OBJECTIVE: To evaluate the applicability of metrics collected during routine use to monitor the performance of a deployed expert system. METHODS: Two extensive formal evaluations of the GermWatcher (Washington University School of Medicine) expert system were performed approximately six months apart. Deficiencies noted during the first evaluation were corrected via a series of interim changes to the expert system rules, even though the expert system was in routine use. As part of their daily work routine, infection control nurses reviewed expert system output and changed the output results with which they disagreed. The rate of nurse disagreement with expert system output was used as an indirect or surrogate metric of expert system performance between formal evaluations. The results of the second evaluation were used to validate the disagreement rate as an indirect performance measure. Based on continued monitoring of user feedback, expert system changes incorporated after the second formal evaluation have resulted in additional improvements in performance. RESULTS: The rate of nurse disagreement with GermWatcher output decreased consistently after each change to the program. The second formal evaluation confirmed a marked improvement in the program's performance, justifying the use of the nurses' disagreement rate as an indirect performance metric. CONCLUSIONS: Metrics collected during the routine use of the GermWatcher expert system can be used to monitor the performance of the expert system. The impact of improvements to the program can be followed using continuous user feedback without requiring extensive formal evaluations after each modification. When possible, the design of an expert system should incorporate measures of system performance that can be collected and monitored during the routine use of the system.

Expert Systems↗

PubMed1996

A rule-based expert system for laboratory diagnosis of hemoglobin disorders.

OBJECTIVE: To illustrate the utility of a rule-based expert system in diagnosing hemoglobin disorders. DESIGN: A rule-based expert system was developed for diagnosing hemoglobin disorders. This expert system runs on IBM-compatible personal computers and uses a backward-chaining search strategy to draw conclusions. Laboratory data (ie, results of hemoglobin electrophoresis, quantitative measurements of hemoglobin F and hemoglobin A2 levels, and result of a sickle cell screen) are processed by the system using defined rules to obtain a set of differential diagnoses. Additional data, such as hematologic parameters, ethnicity of the patient, and the presence or absence of certain clinical signs and symptoms, aid in making a final diagnosis. The rules in the current version of this expert system include diagnostic criteria for 71 hemoglobin disorders. SETTING: Regional academic medical center. PATIENTS: We tested the system by using 58 survey sample cases offered by the College of American Pathologists during the period of January 1989 through December 1994. MAIN OUTCOME MEASURE: The established diagnosis for a given case must be included in the list of differential diagnoses suggested by the expert system. RESULTS: The expert system included the actual diagnosis as one of the top four differential diagnoses in 90% of the cases, whereas all the laboratories participating in the survey included it in 84% (mean) of the cases. CONCLUSION: We propose that this user-friendly expert system is a potential tool for computer-assisted diagnosis of hemoglobin disorders.

Clinical Laboratory Techniques↗

PubMed1995

"Auctoritas" psychiatric expert system shell.

We present a short description of a complex psychiatric computer expert system, including functions that help the physicians and the hospital staff in the administrative, diagnostic, therapeutic, statistical, and scientific work. There are separate data-storing, health insurance-supporting, or simple advisory programs, but we can not avail a system--in our country--that provides us with all these functions together. Hence the aim of our program is to produce a universal computer system that makes the patients' long distance follow-up possible. Our diagnostic expert system shell, which is appropriate for using the symptoms and criteria scheme of the internationally accepted diagnostic systems such as DSM and ICD, helps to archive homogeneous, up-to-date psychiatric nosology; this is essential for the correct diagnostic, statistical, and scientific work. Let us introduce our expert system. It consists of four parts: administration, diagnostic decision support system, activities concerning treatment, and statistics. The part called "Administration" contains all data about actual and emitted in-patients and out-patients, including their particulars and data necessary for health insurance (duration of treatment, diagnosis); here we find and edit medical documents. The most important part of the "Auctoritas" system is the "Diagnostic decision support system." In practice, expert systems use decision trees with yes-no logic, fuzzy logic, and pattern matching on the basis of the method of deduction; and backward chaining or forward chaining on the basis of the direction of deduction. Our system uses the methods of fuzzy logic and backward chaining. In other medical disciplines, good results are achieved by applying the pattern matching method; to make validity and verification researches, however, these systems are inappropriate. The diagnoses relying on the up-to-date psychiatric diagnostic systems--DSM-IV and ICD-X--are based on classical logic and can be correctly validated and verified. Hence we have chosen the fuzzy logic, which is the up-to-date extension of classical logic and influences the validity and verification researches, for the construction of our system. The diagnostic part is a shell that can be filled up optionally with knowledge bases of the DSM-IV, ICD-X, or other diagnostic systems and has the following structure. The diagnostic course is biphased as we can differ symptoms and criteria (duration of the illness, aethyological factors). We managed to extend the traditional applications using yes-no logic with three factors that make the system more sensitive and flexible. These are the scaling, sorting by importance of symptoms, and reliability-validity results. The "scaling" means that the physician scales the input symptoms by severity; this influences the statistical probability of the possible diagnoses. "Sorting by importance" is gauging certain symptoms by importance in a syndrome. Finally the third point, "reliability-validity results," means taking account of the latest validity values of a certain disorder of the used diagnostic system--according to the latest validity researches--and the diagnostical reliability of our expert system. The "Activity concerning treatment" is a practical part of our program that contents the examination and therapy scheduling and monitoring results. Under the point of "Statistics," we can prepare all data of the patients in various ways. In summary, the "Auctoritas" computer system is a global database managing the newly-developed advisory system; it is appropriate for managing a complete hospital network system for the continuing individual long-distance observation of patients. It collects all the necessary information of one patient in one file. The long-term benefit is that it can compile and process large amounts of information about the patients and help physicians come to scientific conclusions for research and publications.

Decision Making, Computer-Assisted↗

PubMed2000

[Expert system for aiding diagnosis in hearing tests].

For expert systems intended to aid diagnosis, a structure with five levels is proposed. These levels are the original area, the parameter and a reduced parameter layer, the classification and the final-decision layer. On the basis of this structures, an expert system was developed specifically for neonatal hearing screening with transitory evoked otoacoustic emissions (TEOAE). In a second step, this system was investigated for its suitability to classify emissions, regardless of patient age. For the comparison measurements in 252 mainly adult patients, some with an acquired hearing impairment, were used. To adapt the pass/fail decision to the extended evaluation criteria, the false classifications from a first run with the new data were used for training. Thereafter, the expert system, working with a wider data basis, classified the new data with a sensitivity that was increased by 4.8% to 97.2%, and a 2.0% improvement in specificity to 95.5% when classifying new data, These results, together with those of 97.3% and 94.3% achieved with exclusively neonatal TEOAE classification, clearly show the advantage of the expert system structures chosen, and document evidence of the practical applicability of the method.

Adult↗

PubMed1986

[Testing an expert system for hypertension].

An Expert System (ES) has been connected to a database management system for the management and follow-up of hypertensive patients. The patient data base, called Artemis, contains approximately 18,000 medical records. About 90% of the initial informations used by the ES is contained in the medical records of the Artemis data base. The knowledge base consists of 870 rules. A first group of rules allows the description of knowledge structures (hierachies, graphs and mutual exclusions). The second group consists of production rules which describe the dynamic reasoning of the expert. The inference engine uses a combination of forward and backward chaining. The ES produce diagnostic hypotheses (possible causes of hypertension) and therapeutic suggestions before and after requiring additional information (patient supplementary interrogation, biological or radiological investigations). The evaluation of the diagnostic performance of the ES was made on 40 confirmed cases of secondary hypertension (SH) and 40 cases of essential hypertension (EH). The initial initial diagnosis, just after the forward chaining step, was correct in 17 cases of SH and 32 cases of EH. The final diagnosis proposed after several steps of forward and backward chaining was correct in 37 cases (92%) of SH and 36 (90%) of EH. Averages of 5 (EH) and 8 (SH) questions were formulated by the ES to reach the final diagnosis. The integration of the ES to the database is expected to facilitate the validation of the knowledge base and to enhance its overall acceptability. Whether or not such an integration will be useful and accepted as a complementary tool by physicians remains however an open question.

Decision Making, Computer-Assisted↗

PubMedDate not supplied

Machine learning for an expert system to predict preterm birth risk.

OBJECTIVE: Develop a prototype expert system for preterm birth risk assessment of pregnant women. Normal gestation involves a term of 40 weeks, but because 8-12% of the newborns in the United States are delivered prior to 37 weeks' gestation, problems associated with prematurity continue to plague individuals, families, and the health care system. DESIGN: A knowledge-base development methodology used machine learning, statistical analysis, and validation techniques to analyze three large datasets (18,890 subjects and 214 variables). The dependent (i.e., decision) variable studied was weeks of gestation at delivery, with dichotomous coding of preterm delivery (prior to 37 weeks) and full-term delivery (37+ weeks). RESULTS: Machine learning with a program named Learning from Examples using Rough Sets (LERS) induced 520 usable rules that were entered into a prototype expert system. The prototype expert system was 53-88% accurate in predicting preterm delivery for 9,419 patients. CONCLUSION: The prototype expert system was more accurate than traditional manual techniques in predicting preterm birth.

Adult↗

PubMed1987

Expert systems: frames, rules or logic for species identification?

The role of expert systems in species identification, with particular reference to the problems posed by damaged specimens and inexperienced taxonomists, is discussed. Of the three main types of expert systems available, the frame-based system is shown to provide the most appropriate model for a taxonomic expert system rather than a logic- or rule-based system. The advantages of an expert system over other computer-aided methods of identification are considered. A rule-based system requires the original knowledge (species descriptions) to be structured into rules, whereas a frame-based system can store the generic and specific descriptions in a series of frames. The frames fall into a hierarchy which closely resembles the taxonomic hierarchy, and down which information can be inherited. Two aspects of frame-based systems considered are the use of probabilities in identification, and the optimum structure of the knowledge base. The conventional use of probabilities is to provide an indication of the correctness of the result. However, in some studies involving the identification of many specimens, the speed of identification may be increased (with a reduction in accuracy) if identifications are made to a predetermined probability level. Although frames allow accurate representation of the taxonomic hierarchy, a semantic net, incorporating structures of the organism and/or details of the habitat may result in a more efficient expert system.

Animals↗

PubMed2001

Experiences of otoneurological expert system for vertigo.

We have developed an OtoNeurological Expert system (ONE) to aid the diagnostics of vertigo, to assist teaching and to implement the database for research. The database contains detailed information on the patient history, signs and test results necessary for the diagnostic work with vertiginous patients. The pattern recognition method was used in the reasoning process. Questions regarding symptoms, signs and test results are weighted and scored for each disease, and the most likely disease is recognized from the defined disease profiles. Uncertainties in reasoning, caused by missing information, were solved with a method resembling fuzzy logic. We have also applied adaptive computer applications, such as genetic algorithms and decision trees, in the reasoning process. In the validation the expert system ONE proved to be a sound decision maker, by solving 65% of the cases correctly, while the physicians' mean was 69%. To improve the expert system ONE further, a follow-up should be implemented for the patients, to ease the diagnostic work of some difficult diseases. The six diseases were detected with high accuracy also with adaptive learning methods and discriminant analysis. An expert system is a practical tool in otoneurology. We aim to construct a hybrid program for the reasoning, where the best reasoning method for each disease is used.

Decision Making↗

PubMed1986

[The heuristics of expert systems in radiodiagnosis].

In the practice of creating expert systems, the radiologist and his team are considered as "the expert" who leads the job of the "cognitian or cognitician". Different kinds of expert systems can be imagined. The author describes the main characteristics of heuristics in redefining semiology, semantics and rules of picture reading. Finally it is the experience of the couple "expert and cognitician" which will in the future grant for the success of expert systems in radiology.

Expert Systems↗

PubMed1992

Clinical assessment of the knowledge base of an expert system for data analysis in laboratory medicine.

Despite the apparent demand for a consultation system, only a few expert systems have been developed for laboratory medicine. Some studies on the diagnostic precision of such systems have been reported, but the efficiency of their knowledge bases has not yet been investigated. An expert system, named BLOOD, for data analysis in a hematology laboratory, which is written in C-Prolog and runs on VAX-station, has already been reported to have excellent diagnostic reliability and ability to cope with the fuzziness involved in clinical diagnostic procedures. A quantitative examination of the knowledge base of BLOOD using real laboratory data from 58 patients diagnosed as having iron deficiency anemia clearly revealed the verbosity of the knowledge base, and proved that it was effective for obtaining a group of essential diagnostic rules.

Anemia, Hypochromic↗

PubMed1989

Expert systems and the pancreatic cancer problem: decision support in the pre-operative diagnosis.

In this paper, after reviewing the main issue in artificial intelligence, decision support systems, medical decision-making, expert systems and some of their applications in medicine, we focus on the diagnostic aspect of pancreatic cancer. We briefly examine the most significant applications both from the oncological and from the diagnostic point of view. We discuss the medical problems mentioning incidence and mortality, aetiological factors and diagnosis, considering the roles of surgery and adjuvant therapies. Finally we justify the decision to develop an expert system in such a medical domain and discuss the SPES (Surgical Pancreatic Expert System) project, its parts dealing with the different medical phases of pancreatic cancer diagnosis and therapy: pre-operative, intra-operative and adjuvant therapies. In particular we discuss diagnostic aspects of pancreatic cancer disease, pointing out the aims of the project, methodologies, tools used and future developments.

Decision Support Techniques↗

PubMed2001

Counselor and stimulus control enhancements of a stage-matched expert system intervention for smokers in a managed care setting.

BACKGROUND: Previous research has demonstrated the efficacy of an interactive expert system intervention for smoking cessation for a general population. The intervention provides individualized feedback that guides participants through the stages of change for cessation. Enhancing the expert system by adding proactive telephone counseling or a stimulus control computer designed to produce nicotine fading could produce preventive programs with greater population impacts. METHODS: Four interventions were compared: (a) the interactive expert system intervention; (b) the expert system intervention plus counselor calls; (c) the expert system intervention plus the stimulus control computer; and (d) an assessment only condition. A 4 (intervention) x 4 (occasions) (0,6,12, and 18 months) design was used. Smokers were contacted at home via telephone or mail. The initial subject pool was the 24,178 members of a managed care company. Screening was completed for 19,236 members (79.6%), of whom 4,653 were smokers; 85.3% of the smokers were enrolled. RESULTS: Thirty-eight percent were in the precontemplation stage, 45% in the contemplation stage, and only 17% in the preparation stage. At 18 months, the expert system resulted in 23.2% point prevalence abstinence, which was 33% greater than that of assessment only. The counselor enhancement produced increased cessation at 12 months but not at 18 months. The stimulus control computer produced no improvement, resulting in 20% worse cessation rates than the assessment only condition. CONCLUSIONS: The enhanced conditions failed to outperform the expert system alone. The study also demonstrated the ability of the interactive expert system to produce significantly greater cessation in a population of smokers than assessment alone.

Adult↗

PubMed1990

MYCIN and NEOMYCIN: two approaches to generating explanations in rule-based expert systems.

The prototypical rule-based expert system is MYCIN, a computer program developed in the 1970's to diagnose and recommend therapy for serious infections. MYCIN is able to explain its reasoning at any point in a consultation by listing the rules it has under consideration at that moment. However, when MYCIN's rules were used as the subject matter for a computerized infectious disease tutoring system, it became apparent that these rules contained implicit knowledge about how to perform diagnostic tasks and that this knowledge was inaccessible to the explanation system and, therefore, to students. This paper briefly describes NEOMYCIN, an expert system that makes this implicit knowledge explicit, and shows the effect that this reconfiguration of knowledge has on generating explanations.

Diagnosis, Computer-Assisted↗

PubMed1992

A cost effective expert system to assist physicians: epileptologists' assistant.

While medical expert systems helped demonstrate that artificial intelligence was possible, few medical systems have been heralded as practical successes. We believe that expert systems will be practical successes if they cost effectively handle most of a physician's workload (i.e., routine care). To accomplish this goal, technology must appear invisible to the user; the system must be intuitive and anticipate users' needs. "Epileptologists' Assistant" is an example of our approach of combining a graphical user interface with an expert system and data base in a system to help in a routine specialty clinic. The goal is for two nurses and a physician to handle the workload of three physicians while increasing the quality of care. The current system reduces physician time by 66%. Our ultimate goal is to create a unified family of systems for medical specialties.

Costs and Cost Analysis↗

PubMed1993

Graphical knowledge acquisition for medical diagnostic expert systems.

Like many textbook authors use text systems for writing their books, expert system authors should have easy to use knowledge acquisition systems for entering and testing their knowledge bases by themselves without much help from 'knowledge engineers'. In this paper, we report on a graphical knowledge acquisition tool (CLASSIKA) based on an expert system shell for heuristic classification (MED2) and designed for direct use by domain experts. We demonstrate how the system has been used for building a rather large expert system for diagnosing rheumatology diseases which is now being tested in clinical use.

Artificial Intelligence↗