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At least 1,243 records · Page 69Linked to original sources
PubMed2003

Multiobjective water resources systems analysis using genetic algorithms--application to Chou-Shui River Basin, Taiwan.

Multipurpose operation is adopted by most reservoirs in Taiwan in order to maximize the benefits of power generation, water supply, irrigation and recreational purposes. A multiobjective approach can be used to obtain trade-off curves among these multipurpose targets. The weighting method, in which different weighting factors are used for different purposes, was used in this research work. In Taiwan, most major reservoirs are operated by rule curves. Genetic algorithms with characteristics of artificial intelligence were applied to obtain the optimal rule curves of the multireservoir system under multipurpose operation in Chou-Shui River Basin in central Taiwan. The model results reveal that different shapes of rule curves under different weighting factors on targets can be efficiently obtained by genetic algorithms. Pareto optimal solutions for a trade-off between water supply and hydropower were obtained and analyzed.

Algorithms↗
PubMed2004

Medical expert systems developed in j.MD, a Java based expert system shell: application in clinical laboratories.

Growing complexity of diagnostic tests, combined with increased workload, stringent laboratory accreditation demands, continuous shortening of turn-around-time and budget restrictions have forced laboratories to automate most of their iterative tasks. Introduction of artificial intelligence by means of expert systems has gained an important place in this automation process. Different parts of clinical laboratory activity can benefit from their implementation and the present project deals with one aspect, namely the clinical interpretation of diagnostic tests. This paper describes how j.MD, a new Java based expert system shell, was used to reprogram the expert system for interpretation of amylase isoenzyme patterns that has been in use for many years in our laboratory, and that was originally programmed in Pro.MD, a Prolog based expert system shell. One of the most important advantages of the j.MD system is its bidirectional link with the laboratory information system. This project shows how expert systems for the interpretation of complex diagnostic tests that demand specific expertise can become an integrated part of the automated clinical chemistry lab.

Alkaline Phosphatase↗
PubMed2002

Psychological questionnaires application and processing for detection of psychosomatic states of an operator.

This paper describes the method of detection of psychosomatic states of an operator. Understanding of these states can be used in closing of external human feedback. Detection uses psychological self-diagnostic questionnaires and is supported by experimentally gained data. The next approach of processing is designed by statistical methods and by methods of artificial intelligence.

Emotions↗
PubMed2002

Assessing association rules and decision trees on analysis of diabetes data from the DiabCare program in France.

Recent advances in information technology have made it possible to solve increasingly complex problems, and also to collect and store huge amounts of information. These vast quantities of data further have to be transformed into relevant value-added and "decision-quality" knowledge. It is against this background that the KDD (Knowledge Discovery in Databases), a multidisciplinary field using computer learning, artificial intelligence, statistics, database technology, expert systems, and data visualization, appeared in the early 90's. In order to assess these technologies in the medical field, we have tested some of these techniques on a large database at our disposal, named DiabCare stemming from the WHO - DiabCare program for the application of the Saint-Vincent Declaration. It contains evaluation data on the health care of patients with diabetes, and in particular, its complications. So far, data analysis has been done using classical statistical methods, and we now intend to make use of such data-mining tools as Associations Rules and Decision and Classification Trees for further exploration of this database. The results presented here show that data mining techniques can be used successfully to extract knowledge from medical databases. The results obtained using Association Rules and especially Decision Trees are very promising.

Algorithms↗
PubMed2004

The GLARE approach to clinical guidelines: main features.

In this paper, we present GLARE, a domain-independent prototypical system for acquiring, representing and executing clinical guidelines. GLARE has been built within a 7-year project with Azienda Ospedaliera San Giovanni Battista in Turin (one of the largest hospitals in Italy) and has been successfully tested on clinical guidelines in different domains, including bladder cancer, reflux esophagitis, and heart failure. GLARE is characterized by the adoption of advanced Artificial Intelligence (AI) techniques, to support medical decision making and to manage temporal knowledge.

Decision Making, Computer-Assisted↗
PubMed2005

[The application of biomed-informatics in cardiovascular research--data and knowledge].

With the development of biotechnology, especially the projects which lead to high throughout experimental results, lots of data have been jammed in every related fields, and broke the balance between data and knowledge in these fields. It is urgent to refine these data, and let them be more productive. To avoid these data being decaying into rubbish, technology and theory such as database, statistics, data mining, knowledge management, and artificial intelligence had been applied into biologic and medical study fields. So, a new standalone research subject--biomed-informatics has been formed. This paper reviewed the application of biomed-informatics in cardiovascular research, and gave the view for the future development of this subject.

Animals↗
PubMed1992

Treatment decisions in axillary node-negative breast cancer patients.

Treatment decisions must be made on 9000 axillary node-negative breast cancer patients each month in the United States. Which of these patients will benefit from adjuvant therapy is a major question. Valid methods are needed to distinguish those patients who are "cured" from those who will suffer a cancer recurrence. A complex network of prognostic variables enters into the treatment decision, together with a risk-versus-benefit assessment. We are using a neural-network-based form of artificial intelligence that, once "trained" with data representing an event and its outcome, can identify subsets of patients with low recurrence risks. Larger data sets are being evaluated with the hope of introducing the neural-network technique to routine clinical practice.

Breast Neoplasms↗
PubMedDate not supplied

[The electrophysiologycal and information aspects of stress development].

In the first part of this paper the modern concepts of stress and possibility of diagnostics of early stress manifestations are considered. Though significant advances of researches of this problem many methodological and methodical aspects are continued to be discussed. The categorical proff are stage development of adaptation processes and there classification [H. Selye]. A long-term studies of adaptation processes of students in semesters and exams have been conducted by authors with specially development and to accumulate new data. The second part of this paper is the survey of these results published in different time. The polyparametric technology including some electrophysiological methods with the system analysis using artificial intelligence algorithms and methods of pattern recognition permitted to show some regular dependences in relationships of electrophysiological parameters and there variations under stress development. It was shown for diagnostics of stress stages in necessary to assess not only absolute values of parameters but also there relationships, which are new diagnostics signs considering as information connections between physiological systems. The main characteristic of functional state of an organism is balance of parameter's relationships in totality, representing invariant for healthy persons. In condition of disharmony of parameter relationships there are aroused the strain, overstrain functional state or failure of adaptation in depends on a degree of misbalance. The represented new data don't contradict to known reasons and mechanisms of stress and accumulated data of its variety manifestations. As the work-hypothesis the information-wave model of appearance of psychosomatic reactions of stress is introduced.

Electrophysiology↗
PubMed2006

Reification of abstract concepts to improve comprehension using interactive virtual environments and a knowledge-based design: a renal physiology model.

Several abstract concepts in medical education are difficult to teach and comprehend. In order to address this challenge, we have been applying the approach of reification of abstract concepts using interactive virtual environments and a knowledge-based design. Reification is the process of making abstract concepts and events, beyond the realm of direct human experience, concrete and accessible to teachers and learners. Entering virtual worlds and simulations not otherwise easily accessible provides an opportunity to create, study, and evaluate the emergence of knowledge and comprehension from the direct interaction of learners with otherwise complex abstract ideas and principles by bringing them to life. Using a knowledge-based design process and appropriate subject matter experts, knowledge structure methods are applied in order to prioritize, characterize important relationships, and create a concept map that can be integrated into the reified models that are subsequently developed. Applying these principles, our interdisciplinary team has been developing a reified model of the nephron into which important physiologic functions can be integrated and rendered into a three dimensional virtual environment called Flatland, a virtual environments development software tool, within which a learners can interact using off-the-shelf hardware. The nephron model can be driven dynamically by a rules-based artificial intelligence engine, applying the rules and concepts developed in conjunction with the subject matter experts. In the future, the nephron model can be used to interactively demonstrate a number of physiologic principles or a variety of pathological processes that may be difficult to teach and understand. In addition, this approach to reification can be applied to a host of other physiologic and pathological concepts in other systems. These methods will require further evaluation to determine their impact and role in learning.

Comprehension↗
PubMed2006

[Assessment of giant panda habitat based on integration of expert system and neural network].

To conserve giant panda effectively, it is important to understand the spatial pattern and temporal change of its habitat. Mapping is an effective approach for wildlife habitat evaluation and monitoring. The application of recently developed artificial intelligence tools, including expert systems and neural networks, could integrate qualitative and quantitative information for modeling complex systems, and built the information into a GIS, which could be helpful for giant panda habitat mapping. This study built a mapping approach for giant panda habitat mapping, which integrated expert system and neural network classifiers (ESNNC), and used multi-type data within GIS. The giant panda habitat types and their suitability were mapped by ESNNC. The results showed that the habitat types and their suitability in Foping Nature Reserve were assessed with a higher accuracy (> 80 %) by ESNNC, compared with non-integrated classifiers, i. e., expert system, neural network, and maximum likelihood. Z-statistic test showed that ESNNC was significantly better than the other three non-integrated classifiers. It was recommended that the integrated approach could be widely applied into wildlife habitat assessment.

Animals↗
PubMed2005

Neural network-longitudinal assessment of the Electronic Anti-Retroviral THerapy (EARTH) cohort to follow response to HIV-treatment.

HIV infection is for the most part a chronic and asymptomatic disease. To properly monitor the health status of infected individuals it is important to use host and viral surrogate markers as well as pharmacokinetic parameters. Disease progression, assessment of the antiviral potency of the drugs and response to therapy can only be monitored by repetitive measures of viral and host parameters. To prevent the emergence of antiviral drug-resistance, long term side effects and to decide on the appropriate treatment choices, a comprehensive assessment of all contributing factors, medical and non-medical, is necessary. However, the relationship between treatment outcomes with disease markers and other contributing factors is not simple. To date, a model that accurately predicts the likelihood of disease progression or treatment failure in HIV infected patients does not exist. Extending our previous work in this area, we developed temporal Artificial Intelligence models based on Jordan-Elman networks to longitudinally follow viral surrogate markers together with demographics, biochemical and laboratory data to describe the drug-virus-host interactions in over 4000 HIV adult patients. In an international (multi-continent) study of HIV clinical and laboratory data, the profiles of drug-naïve as well as treated patients were evaluated during a 20 year follow-up. Validation of models on a subset of this cohort (n=595) estimated the sensitivity and specificity of treatment success/failure, under different management modalities for individual patients. ROC-curves predicted: virologic success from baseline (ROC=0.871) in drug-naïve previously non-treated patients, switch from virologic success/ failure to failure/success if ever and when (ROC=0.625), switch to virologic success/failure from failure/success within 6 months (ROC=0.722) following a previous switch. This tool may be helpful in the design of longitudinal clinical trials.

Adult↗
PubMed2006

Human hormone function emulator.

This paper describes the addition of simulated hormone action to the author's modular, open-systems, computerized human nervous system function emulator. For this project he revived his 32-year old design for a pulse-integrating artificial neuron with controlled voltage droop and reset, to serve as the principal linear computational element. This neuron sums bursts of quantized presynaptic impulses to produce a simulated altered cell membrane voltage. When coupled with a variable-threshold Schmitt trigger, voltage changes can initiate a mock action potential signal. These altered neural membrane voltages and action potential signals are utilized in the nervous system emulator to qualitatively emulate the level of simulated hormone activity, to react to changes in the electrochemical environment, and to signal initiation of high level behavioral responses in the artificial intelligence system.

Action Potentials↗
PubMed2006

[Preliminary study of serum proteome biomarkers of hypopharyngeal squamous cell carcinoma].

OBJECTIVE: To screen the serum proteome biomarkers of hypopharyngeal squamous cell carcinoma (HSCC) and to establish a predictive model for early detection of HSCC. METHODS: Serum samples were collected from 48 HSCC patients before surgery and 52 age and sex-matched individuals without cancer used as controls. The samples were divided into 2 sets: training set (including 36 HSCC patients and 36 controls) and blind testing set (including 12 HSCC patients and 16 controls). With WCX2 and IMAC3 protein chips, surface-enhanced laser desorption/ionization (SELDI) was used to analyze the serum protein profiling. 72 samples of the training set were analyzed by a decision tree algorithm to be able to differentiate HSCC patients from controls. Double-blind test was used to determine the sensitivity and specificity of the classification model. RESULTS: Ranging from 2000 - 50000 (M/Z), 11 potential biomarkers on WCX2 and 19 biomarkers on IMAC3 protein chip could differentiate HSCC patients from the control set (P < 10(-5)). Among them 4 candidate protein peaks with the m/z values of 7796, 4216, 5927, and 5361 were selected to be used to establish a predictive model by Biomarker Pattern Software. The model separated effectively the HSCC samples from the control samples, achieving a sensitivity of 94.44%, and a specificity of 88.89%. An accuracy of 85.71% (24/28), sensitivity of 91.67% (11/12), specificity of 81.25% (13/16), positive predictive value of 78.57%% (11/14), and negative predictive value of 92.85% (13/14) were validated in the double-blind testing set. CONCLUSION: The SELDI-TOF-MS Protein Chip combined with artificial intelligence classification algorithm helps find serum proteome biomarkers and establish predictive model for early diagnosis of HSCC. This technique has potential for the development of a screening test for the detection of HSCC.

Adult↗
PubMedDate not supplied

[A new information technology for system diagnosis of functional activity of human organs].

The goal of this work was to consider a new diagnostic technology based on analysis of objective information parameters of functional activity and interaction of normal and pathologically changed human organs. The technology is based on the use of very low power millimeter (EHF) radiation emitted by human body and other biological objects in the process of vital activity. The importance of consideration of the information aspect of vital activity from the standpoint of the theory of functional systems suggested by P. K. Anokhin is emphasized. The suggested information technology is theoretically substantiated. The capabilities of the suggested technology for diagnosis, as well as the difficulties of its practical implementation caused by very low power of electromagnetic fields generated by human body, are discussed. It is noted that only use of modern radiophysical equipment together with new software based on specially developed algorithms made it possible to construct a medical EHF diagnostic system for effective implementation of the suggested technology. The system structure, functions of its components, the examination procedure, and the form of representation of diagnostic information are described together with the specific features of applied software based on the principle of maximal objectivity of analysis and interpretation of the results of diagnosis on the basis of artificial intelligence algorithms. The diagnostic capabilities of the system are illustrated by several examples.

Biophysics↗
PubMedDate not supplied

[Experience in the use of equipment for ECG system analysis in municipal polyclinics].

Two electrocardiographs, an analog-digital electrocardiograph with preliminary analog filtering of signal and a smart cardiograph implemented as a PC-compatible device without preliminary analog filtering, are considered. Advantages and disadvantages of ECG systems based on artificial intelligence are discussed. ECG interpretation modes provided by the two electrocardiographs are considered. The reliability of automatic ECG interpretation is assessed. Problems of rational use of automated ECG processing systems are discussed.

Artifacts↗
PubMed1991

Applications of expert computer systems.

An expert system is a computer program which uses artificial intelligence to make logical decisions on the basis of input data. The rules the system uses to make its decisions are called heuristics which can be provided in the form of IF . . . THEN statements, or they can be learned by the system from examples. The term "expert" is used because the rules or examples come from human experts and the program is considered to have captured their expertise. Currently there are many expert systems in business and medical use but few, if any, are used in optometry. The rule-oriented nature of many ophthalmic procedures suggests a future role for these systems, but their cost-effectiveness may not yet be favorable enough to justify development of expert systems for optometric practice.

Diagnosis, Computer-Assisted↗
PubMed1991

Prospects of expert systems in psychiatric diagnostics.

The paper deals in particular with the theoretical foundations of introducing computer technology and techniques into psychiatric diagnostics. It is done from the aspect of present expert systems of the type FEL-EXPERT, PCE expert, CODEX, etc., as well as from the aspect of a prospect for the future in connection with the development of systems of the so called artificial intelligence.

Expert Systems↗
PubMed1991

Digital chest radiology.

With an increasing number of storage phosphor computed radiography units being installed worldwide, the evaluation of digital chest radiology is no longer being confined to experimental determinations of adequate imaging variables. There has been a substantial increase in the number of clinically based studies that have compared computed radiography with conventional film radiography. The applications of computed radiography are widening and further developments in dual-energy imaging and scanning equalization radiography have become possible through the use of phosphor storage computed radiography. Computer-assisted diagnosis applied to chest radiography may shortly be revolutionized by artificial intelligence, which is capable of assimilating both radiographic and clinical data.

Humans↗