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PubMed2005

Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence.

In many manufacturing environments, automated guided vehicles are used to move the processed materials between various pickup and delivery points. The assignment of vehicles to unit loads is a complex problem that is often solved in real-time with simple dispatching rules. This paper proposes an automated guided vehicles dispatching approach based on computational intelligence. We adopt a fuzzy multicriteria decision strategy to simultaneously take into account multiple aspects in every dispatching decision. Since the typical short-term view of dispatching rules is one of the main limitations of such real-time assignment heuristics, we also incorporate in the multicriteria algorithm a specific heuristic rule that takes into account the empty-vehicle travel on a longer time-horizon. Moreover, we also adopt a genetic algorithm to tune the weights associated to each decision criteria in the global decision algorithm. The proposed approach is validated by means of a comparison with other dispatching rules, and with other recently proposed multicriteria dispatching strategies also based on computational Intelligence. The analysis of the results obtained by the proposed dispatching approach in both nominal and perturbed operating conditions (congestions, faults) confirms its effectiveness.

Algorithms↗
PubMed2003

Which clinical decisions benefit from automation? A task complexity approach.

OBJECTIVE: To describe a model for analysing complex medical decision making tasks and for evaluating their suitability for automation. METHOD: Assessment of a decision task's complexity in terms of the number of elementary information processes (EIPs) and the potential for cognitive effort reduction through EIP minimisation using an automated decision aid. RESULTS: The model consists of five steps: (1) selection of the domain and relevant tasks; (2) evaluation of the knowledge complexity for tasks selected; (3) identification of cognitively demanding tasks; (4) assessment of unaided and aided effort requirements for this task accomplishment; and (5) selection of computational tools to achieve this complexity reduction. The model is applied to the task of antibiotic prescribing in critical care and the most complex components of the task identified. Decision aids to support these components can provide a significant reduction of cognitive effort suggesting this is a decision task worth automating. CONCLUSION: We view the role of decision support for complex decision to be one of task complexity reduction, and the model described allows for task automation without lowering decision quality and can assist decision support systems developers.

Artificial Intelligence↗
PubMed1999

A pilot study for identifying at risk thyroid lesions by means of a decision tree run on clinicocytological variables.

Fine-needle aspiration biopsy (FNAB) is safe, inexpensive, minimally invasive, and highly accurate in the diagnosis of nodular diseases of the thyroid. However, FNAB does not provide a reliable benign versus malignant diagnosis for 100% of the cases analysed. It is possible to increase the accuracy of the cytological diagnosis by means of information contributed by different clinical variables. In the present study we evaluate the diagnostic value of 10 variables in addition to FNAB on a series of 218 specimens for which we obtained histological diagnoses including 37 cancers (17%). The diagnostic information contributed by these variables was analyzed by means of the Decision Tree technique, an artificial intelligence-related method which forms part of the Supervised Learning algorithms. The results show that Decision Trees enable some subpopulations of patients with uncertain FNAB results to be characterized.

Adolescent↗
PubMed1995

Computer modeling of prostate cancer treatment. A paradigm for oncologic management?

This article discusses the relevance of computer modeling to the management of prostate cancer. Several computer modeling techniques are reviewed and the advantages and disadvantages of each are discussed. An example that uses a computer model to compare alternative strategies for clinically localized prostate cancer is examined in detail. The quality of the data used in computer models is critical, and these models play an important role in medical decision making.

Adenocarcinoma↗
PubMed1998

The decision support system for telemedicine based on multiple expertise.

This paper discusses the application of artificial intelligence in telemedicine and some of our research results in this area. The main goal of our research is to develop methods and systems to collect, analyse, distribute and use medical diagnostics knowledge from multiple knowledge sources and areas of expertise. Use of modern communication tools enable a physician to collect and analyse information obtained from experts worldwide with the help of a decision support medical system. In this paper we discuss a multilevel representation and processing of medical data using a system which evaluates and exploits knowledge about the behaviour of statistical diagnostics methods. The presented technique is able to acquire semantically-essential information from the complex dynamics of quasi-periodical medical signals by applying recursively-ordinary statistical tools. A method and an algorithm are elaborated to select automatically the most appropriate diagnostics method for each case under consideration. We suggest the use of a voting-type technique to search for consensus among the different opinions of medical experts. Research results can be applied in the development of a telediagnostics expert medical system and medical teleconsulting support system.

Algorithms↗
PubMed1993

An aggregation of pro and con evidence for medical decision support systems.

One promising way to increase the classification accuracy of medical decision support systems is to implement heuristic combinations of pattern recognition and artificial intelligence tools. A parallel between "cognition" model and differential diagnostic task is sketched accentuating the aggregation of activating and restraining inputs and corresponding PRO and CON evidence in medicine. On the basis of this paradigm a trainable model of a fuzzy neuron is proposed which resembles some elements from the physician's decision process. An example from aviation medicine is presented which demonstrates the enhanced performance.

Aerospace Medicine↗
PubMed1998

Improving morphology-based malignancy grading schemes in astrocytic tumors by means of computer-assisted techniques.

We propose an original methodology which improves the accuracy of the prognostic values associated with conventional morphologically-based classifications in supratentorial astrocytic tumors in the adult. This methodology may well help neuropathologists, who must determine the aggressiveness of astrocytic tumors on the basis of morphological criteria. The proposed methodology comprises two distinct steps, i.e. i) the production of descriptive quantitative variables (related to DNA ploidy level and morphonuclear aspects) by means of computer-assisted microscopy and ii) data analysis based on an artificial intelligence-related method, i.e. the decision tree approach. Three prognostic problems were considered on a series of 250 astrocytic tumors including 39 astrocytomas (AST), 47 anaplastic astrocytomas (ANA) and 164 glioblastomas (GBM) identified in accordance with the WHO classification. These three problems concern i) variations in the aggressiveness level of the high-grade tumors (ANA and GBM), ii) the detection of the aggressive as opposed to the less aggressive low-grade astrocytomas (AST), and iii) the detection of the aggressive as opposed to the less aggressive anaplastic astrocytomas (ANA). Our results show that the proposed computer-aided methodology improves conventional prognosis based on conventional morphologically-based classifications. In particular, this methodology enables some reference points to be established on the biological continuum according to the sequence AST-->ANA-->GBM.

Adult↗
PubMed2007

A systems model of leadership: WICS.

This article reviews a systems model of leadership. According to the model, effective leadership is a synthesis of wisdom, creativity, and intelligence (WICS). It is in large part a decision about how to marshal and deploy these resources. One needs creativity to generate ideas, academic (analytical) intelligence to evaluate whether the ideas are good, practical intelligence to implement the ideas and persuade others of their worth, and wisdom to balance the interests of all stakeholders and to ensure that the actions of the leader seek a common good. The article relates the current model to other extant models of leadership.

Character↗
PubMed2000

Clinical workstations supporting evidence-based medicine.

The application of the principles of evidence-based medicine is based on a rigorous analysis of clinical research tailored to the individual characteristics of a specific patient. Thus the physician must have information about the patient and about the latest results of clinical research simultaneously. We present a computer-based clinical workstation offering sources for both kinds of information: the current patient data is delivered by access to the electronic patient record and the results of research may be searched at the Internet, Intranet, or other online databases. Performing an evaluation study we are testing this configuration in the setting of a university hospital. The physicians are interviewed about their use of the single information sources and their consequences for clinical research and evidence-based medicine. We hope to show that in spite of some technical and methodological problems the clinical workstation is a promising tool for decision-making at the clinical workplace.

Artificial Intelligence↗
PubMed2001

The use of consequential reasoning in cancer chemotherapy.

Knowledge-based decision support systems traditionally rely on condition-action rule structures, an adequate representation for simple decisions. In complex domains an important part of decision-making includes analysis of the consequences of a decision. Consequential reasoning is particularly important in medicine as potential risk and/or benefit can be included. In this paper, a knowledge structure and inference engine is described that permits the representation and analysis of consequential reasoning in a computer-assisted decision support system. The use of consequential reasoning is then illustrated in an application designed to assist in cancer chemotherapy decisions. The result is a method that is sensitive to individual patient reactions to chemotherapy agents, permitting an individualized approach to therapy. Individualized drug therapy is becoming increasingly feasible due to advances made in the field of genomics. The system is structured so that new information can be incorporated easily. Although the application shown here is to chemotherapy, the general methodology can be used in any area in which the consequences should significantly influence the decision.

Artificial Intelligence↗
PubMed1996

Decision-support systems in dentistry.

Decision-support systems hold a specialized body of knowledge in computerized form such that the non- specialist can obtain expert-level information. The goal of these systems in clinical sciences is usually to assist patient care by providing the clinician with improved diagnosis or treatment planning. Decision-support systems consist of three components: the user interface through which the clinician or patient enters signs or symptoms, the set of data describing clinical knowledge in the domain of the program, and an inference engine to manipulate the data set in light of a patient's specific signs or symptoms to arrive at a diagnosis or treatment plan. Such systems usually use one of three mechanisms of analysis alone or in combination: classification trees, Bayesian conditional probabilities, or rule-based (heuristic) systems. Numerous problems must be solved before decision-support systems will become commonplace in clinical practice. Data entry of patients' signs and symptoms is often tedious. The quality of the clinician's initial observations is of great importance in determining the quality of the output. It is also often difficult to convey to a program the subtlety of clinical information observed. Knowledge required in clinical data bases is often unavailable or imprecise. As these and other challenges are addressed we can anticipate increased utility of decision support programs in the future.

Algorithms↗
PubMed2003

Towards knowledge-based systems in clinical practice: development of an integrated clinical information and knowledge management support system.

Given that clinicians presented with identical clinical information will act in different ways, there is a need to introduce into routine clinical practice methods and tools to support the scientific homogeneity and accountability of healthcare decisions and actions. The benefits expected from such action include an overall reduction in cost, improved quality of care, patient and public opinion satisfaction. Computer-based medical data processing has yielded methods and tools for managing the task away from the hospital management level and closer to the desired disease and patient management level. To this end, advanced applications of information and disease process modelling technologies have already demonstrated an ability to significantly augment clinical decision making as a by-product. The wide-spread acceptance of evidence-based medicine as the basis of cost-conscious and concurrently quality-wise accountable clinical practice suffices as evidence supporting this claim. Electronic libraries are one-step towards an online status of this key health-care delivery quality control environment. Nonetheless, to date, the underlying information and knowledge management technologies have failed to be integrated into any form of pragmatic or marketable online and real-time clinical decision making tool. One of the main obstacles that needs to be overcome is the development of systems that treat both information and knowledge as clinical objects with same modelling requirements. This paper describes the development of such a system in the form of an intelligent clinical information management system: a system which at the most fundamental level of clinical decision support facilitates both the organised acquisition of clinical information and knowledge and provides a test-bed for the development and evaluation of knowledge-based decision support functions.

Artificial Intelligence↗
PubMed1994

Case-based reasoning and imaging procedure selection.

RATIONALE AND OBJECTIVES: Case-based reasoning, an artificial intelligence technique for learning and reasoning from experience, has shown great potential for use in decision support systems. The authors developed and tested a prototype case-based decision support system to explore the applicability of this technique to the selection of diagnostic imaging procedures. METHODS: A case-based system, ProtoISIS, was developed based on the Protos learning apprentice. ProtoISIS learned the domain of ultrasonography and body computed tomography by reviewing 200 consecutive cases of actual requests for imaging procedures. ProtoISIS was tested by using it to classify four sets of 25 cases of actual imaging procedure requests. RESULTS: ProtoISIS correctly classified 72% of the imaging-procedure requests. Its performance improved as it gained experience: in the last two test series, it correctly classified 84% of the cases presented. CONCLUSIONS: Case-based reasoning can be applied successfully to the selection of diagnostic imaging procedures and holds potential for use in clinical decision support aids. Further work is necessary to realize a clinically useful system.

Artificial Intelligence↗
PubMed2001

An intelligent data acquisition system for simultaneous screening of microsomal stability and metabolite profiling by liquid chromatography/mass spectrometry.

This paper describes the development of a mass spectrometer-based, intelligent, programmable, sample-selection data acquisition system with two unique features. One is that the system allows automatic determination of the mass to charge ratio (m/z) of an unknown compound and the utilization of the molecular ion information to perform selective ion monitoring (SIM) experiments for quantitation. The other is its decision-making capability to select intelligently different samples and perform different experiments during data acquisition. These features were demonstrated by the application of the system to simultaneous screening for the microsomal stability and metabolite profiling of adatanserin. In this application, the data acquisition system continuously calculated the peak areas of adatanserin from SIM analyses of a batch of microsomal incubates stopped at various time points. Once the peak area of adatanserin had dropped to an arbitrarily predefined 60% of the initial value, the system made a decision to perform metabolite profiling of the sample. This decision initiated a series of automated operations, such as selecting a sample for re-analysis, changing the data acquisition time and liquid chromatographic gradient and switching the SIM mode to the data-dependent product ion scanning mode. The completed analysis of the batch of samples provided information both on the microsomal stability and on the metabolic profile of adatanserin. This simultaneous approach to investigating microsomal stability and metabolite profiling significantly increases the throughput for drug discovery support.

Algorithms↗
PubMed2002

Automatic generation of a metamodel from an existing knowledge base to assist the development of a new analogous knowledge base.

Knowledge acquisition is a key step in the development of knowledge-based systems and methods have been proposed to help elicitating a domain-specific task model from a generic task model. We explored how an existing validated knowledge base (KB) represented by a decision tree could be automatically processed to infer a higher level domain-specific task model. On-codoc is a guideline-based decision support system applied to breast cancer therapy. Assuming task identity and ontological proximity between breast and lung cancer domains, the generalization of the breast can-cer KB should allow to build a metamodel to serve as a guide for the elaboration of a new specific KB on lung cancer. Two types of parametrized generalization methods based on tree structure simplification and ontological abstraction were used. We defined a similarity distance and a generalization coefficient to select the best metamodel identified as the closest to the original decision tree of the most generalized metamodels.

Artificial Intelligence↗
PubMed1993

Medical decision making based on inductive learning method.

Medical decision making based on inductive learning has been studied in order to collect experience necessary for practical use of such methods in clinical and epidemiological work. The decision trees have been constructed by using the modified Quinlan's approach based on choosing relevant attributes according to their informativity. An inductive learning software tool, ASSISTANT Professional, has been used for experimenting. The variability in results has been studied under varying learning conditions. Two sets of data have been chosen for learning experiments: from a study on rheumatoid factors in patients with rheumatoid arthritis, and from an epidemiological investigation of aging. The results of this study indicate the necessity to determine inductive learning parameters for each particular problem. The pruning procedure is always recommended as it eliminates redundant elements in the tree. In problems with greater number of attributes, however, pruning itself is not guaranteeing satisfactory solutions. Interventions like the change of the minimal weight threshold might improve the situation. If these precautions are met, the method of inductive learning seems to be a useful guide in practical clinical and epidemiological decisions.

Aging↗
PubMed1996

The contribution of image cytometry and artificial intelligence-related methods of numerical data analysis for adipose tumor histopathologic classification.

Thirty-five lipomatous tumors were quantitatively described using 47 variables generated by means of computer-assisted microscope analysis. Of these 47 quantitative variables, 27 were computed on Feulgen-stained specimens (25 on cytologic and 2 on histologic samples) and, of the remaining 20, 8 related to vimentin and S-100 protein immunostaining patterns and the other 12 to the glycohistochemical staining patterns of peanut agglutinin, succinylated wheat germ agglutinin, and concavalin A agglutinin. The 35 lipomatous tumors included 6 atypical lipomas and 8 well differentiated, 5 dedifferentiated, 6 myxoid, and 10 pleomorphic liposarcomas. The actual diagnostic value contributed by each of the 47 variables with respect to the 5 lipomatous tumor groups was determined by means of the decision tree technique, an artificial intelligence-related algorithm that forms part of the supervised learning algorithms. Of the 47 quantitative variables, the decision tree technique retained 8: i.e., 2 tissue architecture-, 2 DNA ploidy level-, 2 morphonuclear-, 1 lectin histochemical-, and 1 vimentin immunostain-related variables. The decision tree technique made use of these 8 variables to set up logical rules that make it possible to identify atypical lipomas from well differentiated liposarcomas, on the one hand, and dedifferentiated liposarcomas from those that are well differentiated and pleomorphic, on the other. Thus, the combination of an artificial intelligence algorithm analyzing quantitative variables generated by means of the computer-assisted microscope analysis of cytologic and histologic samples from lipomatous tumors can be considered an expert system contributing significant diagnostic information to conventional diagnosis.

Algorithms↗
PubMed2002

Complexity of environment and parsimony of decision rules in insect societies.

This paper shows how colonies of social insects process information and solve problems in a complex environment, while keeping some parsimony at the level of the individuals' decision rules. Two studies on ant foraging reveal the diversity of adaptive colony-level patterns that can be generated through self-organization, based on the same individual-level recruitment rules. Regarding prey scavenging, the "ability to retrieve the prey" rule accounts for changes in foraging patterns, with increasing prey size, that show all stages intermediate between an individual and a mass exploitation of food resources. Regarding liquid food foraging, the "ability to ingest a desired volume" rule enables a colony to adjust the number of tending ants to the honeydew production of aphids. In both cases, decision rules are based on intelligent criteria that intrinsically integrate information on multiple variables that are relevant to the ants. Furthermore, the environment can contribute directly to the emergence of collective patterns, independently of any individual behavioral changes. Each environmental factor, including abiotic ones, that alters the dynamics of information transfer in group-living animals should be reconsidered not simply as a constraint but also as a part of the decision-making process and as a agent that shapes the collective pattern.

Animal Communication↗