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At least 793 records · Page 44Linked to original sources
PubMed1997

Standardizing data collection and decision making with an expert system.

The software for the pilot system has been completed. The appropriateness of the risk factor weights needs to be evaluated by clinical testing. However, this does not prevent the system from being used to teach the philosophy of risk group identification and selection of different management strategies according to disease activity. The current system does demonstrate a dynamic relationship between caries risk assessment/activity and different management strategies. A formal scientific evaluation of the effectiveness of the system as a teaching tool is being developed.

Data Collection↗
PubMed1988

An expert system designed as a tutoring tool in cervical cytology: TTCC-1 system.

A knowledge-based intelligent system being developed as a computer-assisted tutoring tool that can be used by teachers and students of cervical cytology is described. This system uses a combination of frames and rules as knowledge representation. Combination data structures called "prototypes" characterize the typical cytoplasmic and nuclear features of classes of cervical cells by using a specific prototype for each hypothesized cell type. A hypothesis-directed approach is used for problem solving. Rules are associated with each prototype or hypothesis. There are two major kinds of rules: deducing rules, used for deducing certainty factors (expressed as degrees of certainty of the hypotheses), and strategy rules, used for guiding the user in reaching a conclusion about the test cell. With an easy-to-use interactive menu, the user inputs a series of sets of qualitative cell features, which the system utilizes at various inference stages and finally arrives at a decision about the cell type.

Cell Biology↗
PubMed1995

Machine learning for development of an expert system to predict premature birth.

Normal pregnancy involves a term of 40 weeks gestation. Problems associated with low birthweight and prematurity continue to plague childbearing families and the healthcare system because 8-12% of all newborns in the United States deliver prior to 37 weeks gestation. The high cost of caring for premature babies increasingly treats all pregnant women as if they are 'high risk' for preterm birth. Artificial intelligence techniques used a machine learning program named LERS1 with large datasets (n = 18,890; 214 variables), statistical analysis, expert verification techniques, and a prototype expert system2 that yielded improved accuracy (53-90%) over existing manual techniques (17-38%) for predicting preterm birth.

Adult↗
PubMed2002

Use of a novel rule-based expert system in the detection of changes in the ST segment and the T wave in long duration ECGs.

The development of a new fast and robust computerised system is examined in detecting electrocardiogram (ECG) changes in long duration ECG recordings. The system distinguishes these changes between ST-segment deviation and T-wave alterations and can support the produced diagnosis by providing explanations for the decisions made. The European Society of Cardiology ST-T Database was used for evaluating the performance of the system. Sensitivity and positive predictive accuracy were the performance measures used and the proposed system scored 92.02% and 93.77%, respectively, in detecting ST-segment episodes and 91.09% and 80.09% in detecting T-wave episodes. By using the chi-square test we also compared the performance of the system between ECG recordings with minimal and substantial amount of noise. The sensitivity of the proposed system is higher than of other algorithms reported in the literature and the positive predictive accuracy is comparable to, or better than, most of them.

Algorithms↗
PubMed1995

Protocol care for hypertension supported by an expert system.

1. PURPOSE. Hypertension if one of the major prevalent diseases that influences the prognosis of chronic diseases. Primary care should attract much attention in the management of hypertension. The management of hypertension includes not only the use of antihypertensiove drugs, but also the modification of unhealthy lifestyles. Multi-dimensional approaches are required for the management of hypertensive patients. This system supports the standard protocol care for hypertensive patients and the database for clinical epidemiology. 2. DESIGN CONSIDERATIONS. This system has several functions that support the appropriate management of hypertensive patients. The first one is clinical database management. The second is the evaluation of the clinical conditions in hypertension. The third is the decision support system for the selection of treatments for hypertension. 3. SYSTEM DESCRIPTION. This system administers the clinical database, which includes symptoms and signs, laboratory data, and prescriptions. The database deals with the temporal course of the patient's status. The system that evaluates the patient's condition and the decision support system have some knowledge bases. The knowledge bases consist of the evaluation of the patient's condition, the appropriate selection of laboratory examinations, and suggestions for treatments, which involve a life-style modification and the proper prescription of medication. 4. STATUS REPORTS. The relational database was developed for handling the patient's records. These records were displayed on the terminal according to the temporal sequence. The graphical representations of the medical data were displayed in order to understand the patient's status. 5. LESSON LEARNED. This kind of protocol care system is expected to support the proper medical care of patients. Excess medications and laboratory examinations will be excluded under the protocol care, thus reducing unnecessary medical expense. The system will enhance the randomized clinical trials that verify the effects of the treatments. Violation of the protocol care is treated according to the several levels. 6. FUTURE PLANS. This system will be introduced to the occupational health care fields. The efficacy of improving the clinical quantity, and the effect of reducing the medical budget concerning hypertension will be verified.

Clinical Protocols↗
PubMed1991

The design and automated testing of an expert system for the differential diagnosis of acute stroke.

Stroke is the third leading cause of death in the United States and a major source of morbidity. [1] Recent studies have shown a potential use for thrombolytic agents in the treatment of ischemic stroke (IS) but these agents are contraindicated in intracerebral hemorrhage (ICH). A computed tomographic scan is used to distinguish between these two stroke types prior to the use of thrombolytic agents, but may not be readily obtainable. Decision making aids such as algorithms developed at Guy's Hospital and Strong Memorial Hospital have been designed in an attempt to make this distinction on clinical grounds. We have constructed computerized medical decision-making (CMD) systems based upon these algorithms and compared their performance to a system we developed with the use of National Stroke Data Bank data. Relevant medical data for each of 337 patient cases in the Mount Sinai Hospital Stroke Data Bank were presented to each of the CMD systems. In consideration of the clinical task of using thrombolytic agents, we attempted to maximize the positive predictive value (PPV) for ischemic stroke. The CMD systems based upon the Guy's Hospital and Mount Sinai algorithms produced PPV's of 95% and 94% with sensitivities of 77% and 78% respectively compared to a PPV of 93% and sensitivity of 56% with the Strong Memorial CMD system. The Mount Sinai CMD system was judged more efficacious than the Guy's Hospital system in that it required less clinical information that could be more easily obtained to arrive at similar results.

Acute Disease↗
PubMed1987

PHOENIX. An expert system for selecting diagnostic imaging procedures.

The authors developed a computer system that assists physicians in planning the use of diagnostic imaging procedures. This system, the PHOENIX Radiology Consultant, guides the user through a rational, sequential work-up for a variety of common clinical problems. PHOENIX provides explanations of its reasoning, descriptions of the imaging procedures, and citations of relevant literature.

Diagnostic Imaging↗
PubMed1993

[Expert system for the design of removable partial denture].

After analyzing the knowledge of the design of R.P.D. and the prosthodontist's general thinking modules, we may divide the knowledge concerning partial denture design into four levels: Domain, Inference, Task, Tactics and use the most mature method of production rules to express various design theories, principles and experiences. By the method of inference, use relevant prosthodontic rules in accordance with the positions and number of lost tooth of the actual cases and getting the relevant result of the design, it is differ from the general date base system of R.P.D. This system is realized on PC-computer and take up about 35 KB, it might accommodate all possible type of deficiency combinations, that is 65,534 in the upper and lower partial edentulate arches and relevant schemes of R.P.D.

Computer-Aided Design↗