Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “decision intelligence”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 559 records · Page 31Linked to original sources
PubMed2003

Artificial neural networks for decision-making in urologic oncology.

The authors are presenting a thorough introduction in Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. The article covers a description of Artificial Neural Network methodology and points out the differences of Artificial Intelligence to traditional statistic models in terms of serving patients and clinicians, in a different way than current statistical analysis. Since Artificial Intelligence is not yet fully understood by many practicing clinicians, the authors have reviewed a careful selection of articles in order to explore the clinical benefit of Artificial Intelligence applications in modern Urology questions and decision-making. The data are from real patients and reflect attempts to achieve more accurate diagnosis and prognosis, especially in prostate cancer that stands as a good example of difficult decision-making in everyday practice. Experience from current use of Artificial Intelligence is also being discussed, and the authors address future developments as well as potential problems such as medical record quality, precautions in using ANNs or resistance to system use, in an attempt to point out future demands and the need for common standards. The authors conclude that both methods should continue to be used in a complementary manner. ANNs still do not prove always better as to replace standard statistical analysis as the method of choice in interpreting medical data.

Humans↗
PubMed1994

Integrated decision support system/image archive for histological typing of breast cancer using a relation oriented inference system.

Histological typing of invasive breast cancer according to the World Health Organisation criteria is prognostically relevant, because some histological subtypes have a markedly better prognosis. However, reproducibility of histological typing is not high because of the absence of strict typing criteria, variations in the application of the typing criteria and the usually limited illustration of the relevant criteria. The aim of this study was to develop an expert system based on highly structured histological typing criteria, integrated with high-quality microscope images to illustrate the typing criteria. This system should be useful as a decision support system in the diagnosis of breast cancers and should increase the reproducibility of histological typing. Criteria for typing were extracted from textbooks and, based on experience, these criteria were structured and implemented in the Relation Oriented Inference System (ROIS), in which information can be structured by defining relations. Illustrative black and white images were digitized and integrated into the shell. The performance of the resulting decision support system was evaluated by a group of six pathologists using a set of slides covering the spectrum of the most frequently occurring histological types of invasive breast cancer. The pathologists first assessed histological type according to standard morphological procedures. The cases were then reassessed with the decision support system available for consultation. The use of the decision support system appeared to influence the previously assessed histological type in about half of the cases. Using the decision support system, histological typing was more uniform and more in accord with a 'gold standard' set by two experts.(ABSTRACT TRUNCATED AT 250 WORDS)

Artificial Intelligence↗
PubMed1991

Computer based decision support in dentistry.

Work analyses in dental practices have revealed a need for improvements especially in regard to patient dental record, decision support for diagnosis and therapy and patient recall. An adequate decision support for diagnosis, therapy and prevention requires the use of the most advanced methods of informatics and computer bases interactive multimedia technology as well as of advanced human-computer interface techniques.

Appointments and Schedules↗
PubMed1995

[Organizational decision making in health: the case of dengue].

Intelligent organizations (IO) represent a valuable tool to organize and guide dengue fever surveillance, prevention and control interventions. IO entail state of the art technology in managerial science to generate behavioral frameworks of organizational structures and policies. They present a systematic description of problems and construct computerized models to develop systemic thinking; they produce a shared vision and build progressive mental learning and advancement models. Also, IO promote team building and personal control skills. Scientific-technological advances have produced a wealth of information in medicine, with the corresponding growth of organizations and difficulty of responses because of sudden and incessant change. This new environment calls for the application of IO know-how. This article is oriented to prove the usefulness of the IO technology in the ordering and systematization of the reports about the medical sciences facts. Dengue was chosen to exemplify the use of IO technology as it represents an increasing health problem in America, as well as in Mexico; it is so complex that it can evolve to a more serious problem, besides it can be analyzed within a systemic method.

Aedes↗
PubMed2004

An intelligent case-adjustment algorithm for the automated design of population-based quality auditing protocols.

We develop a method and algorithm for deciding the optimal approach to creating quality-auditing protocols for guideline-based clinical performance measures. An important element of the audit protocol design problem is deciding which guide-line elements to audit. Specifically, the problem is how and when to aggregate individual patient case-specific guideline elements into population-based quality measures. The key statistical issue involved is the trade-off between increased reliability with more general population-based quality measures versus increased validity from individually case-adjusted but more restricted measures done at a greater audit cost. Our intelligent algorithm for auditing protocol design is based on hierarchically modeling incrementally case-adjusted quality constraints. We select quality constraints to measure using an optimization criterion based on statistical generalizability coefficients. We present results of the approach from a deployed decision support system for a hypertension guideline.

Algorithms↗
PubMedDate not supplied

A UMLS-based knowledge acquisition tool for rule-based clinical decision support system development.

Decision support systems in the medical field have to be easily modified by medical experts themselves. The authors have designed a knowledge acquisition tool to facilitate the creation and maintenance of a knowledge base by the domain expert and its sharing and reuse by other institutions. The Unified Medical Language System (UMLS) contains the domain entities and constitutes the relations repository from which the expert builds, through a specific browser, the explicit domain ontology. The expert is then guided in creating the knowledge base according to the pre-established domain ontology and condition-action rule templates that are well adapted to several clinical decision-making processes. Corresponding medical logic modules are eventually generated. The application of this knowledge acquisition tool to the construction of a decision support system in blood transfusion demonstrates the value of such a pragmatic methodology for the design of rule-based clinical systems that rely on the highly progressive knowledge embedded in hospital information systems.

Artificial Intelligence↗
PubMed1992

Knowledge-based decision support for patient monitoring in cardioanesthesia.

An approach to generating 'intelligent alarms' is presented that aggregates many information items, i.e. measured vital signs, recent medications, etc., into state variables that more directly reflect the patient's physiological state. Based on these state variables the described decision support system AES-2 also provides therapy recommendations. The assessment of the state variables and the generation of therapeutic advice follow a knowledge-based approach. Aspects of uncertainty, e.g. a gradual transition between 'normal' and 'below normal', are considered applying a fuzzy set approach. Special emphasis is laid on the ergonomic design of the user interface, which is based on color graphics and finger touch input on the screen. Certain simulation techniques considerably support the design process of AES-2 as is demonstrated with a typical example from cardioanesthesia.

Anesthesia↗
PubMed1991

Concurrent use of the Wechsler Memory Scale-Revised and the WAIS-R.

The Wechsler Memory Scale-Revised (WMS-R) is often used in combination with the Wechsler Adult Intelligence Scale-Revised (WAIS-R). The clinician must determine whether variation between Indexes and IQs is due to measurement error, or whether it reflects real discrepancies in underlying abilities. This article presents a table of statistically significant differences between WMS-R Indexes and WAIS-R IQs. Although psychometric difficulties and procedural limitations render the tabled values somewhat inaccurate, the values can nevertheless be used to augment clinical decision.

Humans↗
PubMed1996

Don't care values in induction.

Inductive learning algorithms are powerful tools for the extraction of knowledge from data. Their success in medical domains is well-known. In medical diagnosis domains and generally in real-world applications among other problems, inductive learning algorithms have to deal with unknown values. In most cases unknown values are treated as missing ones, i.e. unknown values which are related to the class of training examples, but are missing due to lack of measurements. In this paper we address the problem of don't care values, which are unknown, because they are irrelevant to the class of the examples. The distinction of don't care values and missing ones is important in medical domains. With this distinction the experts are able to relate each diagnosis to the appropriate subset of attributes. We present techniques for dealing efficiently with don't care values in the induction of decision trees. Furthermore, we examine the importance of the distinction between missing and don't care values and we investigate the existence of don't care values instead of missing ones, in medical and non-medical real-world datasets.

Algorithms↗
PubMed2003

Exploring the neurological substrate of emotional and social intelligence.

The somatic marker hypothesis posits that deficits in emotional signalling (somatic states) lead to poor judgment in decision-making, especially in the personal and social realms. Similar to this hypothesis is the concept of emotional intelligence, which has been defined as an array of emotional and social abilities, competencies and skills that enable individuals to cope with daily demands and be more effective in their personal and social life. Patients with lesions to the ventromedial (VM) prefrontal cortex have defective somatic markers and tend to exercise poor judgment in decision-making, which is especially manifested in the disadvantageous choices they typically make in their personal lives and in the ways in which they relate with others. Furthermore, lesions to the amygdala or insular cortices, especially on the right side, also compromise somatic state activation and decision-making. This suggests that the VM, amygdala and insular regions are part of a neural system involved in somatic state activation and decision-making. We hypothesized that the severe impairment of these patients in real-life decision-making and an inability to cope effectively with environmental and social demands would be reflected in an abnormal level of emotional and social intelligence. Twelve patients with focal, stable bilateral lesions of the VM cortex or with right unilateral lesions of the amygdala or the right insular cortices, were tested on the Emotional Quotient Inventory (EQ-i), a standardized psychometric measure of various aspects of emotional and social intelligence. We also examined these patients with various other procedures designed to measure decision-making (the Gambling Task), social functioning, as well as personality changes and psychopathology; standardized neuropsychological tests were applied to assess their cognitive intelligence, executive functioning, perception and memory as well. Their results were compared with those of 11 patients with focal, stable lesions in structures outside the neural circuitry thought to mediate somatic state activation and decision-making. Only patients with lesions in the somatic marker circuitry revealed significantly low emotional intelligence and poor judgment in decision-making as well as disturbances in social functioning, in spite of normal levels of cognitive intelligence (IQ) and the absence of psychopathology based on DSM-IV criteria. The findings provide preliminary evidence suggesting that emotional and social intelligence is different from cognitive intelligence. We suggest, moreover, that the neural systems supporting somatic state activation and personal judgment in decision-making may overlap with critical components of a neural circuitry subserving emotional and social intelligence, independent of the neural system supporting cognitive intelligence.

Adult↗
PubMedDate not supplied

GASTON: an architecture for the acquisition and execution of clinical guideline-application tasks.

Recently, studies have shown the benefits of using clinical guidelines in the practice of medicine. There have been numerous efforts to develop clinical decision support systems that support guideline-based care in an automated fashion, covering a wide range of clinical settings and tasks. Despite these efforts, only a few systems progressed beyond the prototype stage and the research laboratory. For guideline-based clinical decision support systems to be successful, a balance must be made between intuitive but imprecise representations usually encountered by most of today's systems and representations that support a strong underlying clinical performance model. The project described in this paper tries to achieve such a balance. It presents the GASTON architecture that contains a set of reusable software components for the application of guidelines, including design-time components to facilitate the guideline authoring process based on guideline representation models along with execution-time components for building decision support systems that incorporate these guidelines. This architecture was used to develop several guideline representation models such as a rule-based representation to model rule-based guidelines and guideline representation models that address more complex tasks. Also, decision support systems that incorporate these models were developed with the architecture. For the representation and application of various classes of guidelines, rules were also viewed as instances of more complex tasks. By identifying similar characteristics of sets of rules, we developed several tasks such as a drug intera ction and drug contraindication task. Based on these models, we have developed and validated guidelines and decision support systems for use in several application domains such as intensive care, family physicians and psychiatry. In order to be able to represent more complex time-oriented plans, new guideline representation models are being developed.

Algorithms↗
PubMed2001

Knowledge-based and data-driven models in arrhythmia fuzzy classification.

OBJECTIVES: Fuzzy rules automatically derived from a set of training examples quite often produce better classification results than fuzzy rules translated from medical knowledge. This study aims to investigate the difference in domain representation between a knowledge-based and a data-driven fuzzy system applied to an electrocardiography classification problem. METHODS: For a three-class electrocardiographic arrhythmia classification task a set of fifteen fuzzy rules is derived from medical expertise on the basis of twelve electrocardiographic measures. A second set of fuzzy rules is automatically constructed on thirty-nine MIT-BIH database's records. The performances of the two classifiers on thirteen different records are comparable and up to a certain extent complementary. The two fuzzy models are then analyzed, by using the concept of information gain to estimate the impact of each ECG measure on each fuzzy decision process. RESULTS: Both systems rely on the beat prematurity degree and the QRS complex width and neglect the P wave existence and the ST segment features. The PR interval is not well characterized across the fuzzy medical rules while it plays an important role in the data-driven fuzzy system. The T wave area shows a higher information gain in the knowledge based decision process, and is not very much exploited by the data-driven system. CONCLUSIONS: The main difference between a human designed and a data driven ECG arrhythmia classifier is found about the PR interval and the T wave.

Arrhythmias, Cardiac↗
PubMed2002

Evolution in medical decision making.

The classical approach to medical decision making can be limited by the underlying theories. The evolutionary computation is a different concept, which can find many different solutions of the problem. In medicine, this is useful because of different expectations the decision system must face. We implemented a tool for genetic induction of vector decision trees, which are a good choice for a medical decision model because of their simplicity and transparency. The vector decision tree gives multiple classifications in one single pass. Evolutionary development of such trees achieved good results when the results were statistically compared to those of other classical methods. For medical interpretation however a cooperation with doctors is needed to verify the model build.

Algorithms↗
PubMed2001

On prognostic models, artificial intelligence and censored observations.

The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

Algorithms↗
PubMed1997

Artificial intelligence elements in multimedia system for surgery.

The paper presents a concept of a computer system designed to assist a surgeon's work both before and during a surgical operation. The aim of multimedia in the system is to ensure comfort in communication between an operating surgeon and his assistants. The elements of artificial intelligence, on the other hand, are to assist the surgeon in taking optimal decisions in difficult and untypical situations.

Artificial Intelligence↗
PubMed1999

A programmable rules engine to provide clinical decision support using HTML forms.

The authors have developed a simple method for specifying rules to be applied to information on HTML forms. This approach allows clinical experts, who lack the programming expertise needed to write CGI scripts, to construct and maintain domain-specific knowledge and ordering capabilities within WizOrder, the order-entry and decision support system used at Vanderbilt Hospital. The clinical knowledge base maintainers use HTML editors to create forms and spreadsheet programs for rule entry. A test environment has been developed which uses Netscape to display forms; the production environment displays forms using an embedded browser.

Artificial Intelligence↗
PubMed2003

Modelling the world in real time: how robots engineer information.

Programming robots and other autonomous systems to interact with the world in real time is bringing into sharp focus general questions about representation, inference and understanding. These artificial agents use digital computation to interpret the data gleaned from sensors and produce decisions and actions to guide their future behaviour. In a physical system, however, finite computational resources unavoidably impose the need to approximate and make selective use of the information available to reach prompt deductions. Recent research has led to widespread adoption of the methodology of Bayesian inference, which provides the absolute framework to understand this process fully via modelling as informed, fully acknowledged approximation. The performance of modern systems has improved greatly on the heuristic methods of the early days of artificial intelligence. We discuss the general problem of real-time inference and computation, and draw on examples from recent research in computer vision and robotics: specifically visual tracking and simultaneous localization and mapping.

Artificial Intelligence↗
PubMed2004

The role of networks and artificial intelligence in nanotechnology design and analysis.

Techniques with their origins in artificial intelligence have had a great impact on many areas of biomedicine. Expert-based systems have been used to develop computer-assisted decision aids. Neural networks have been used extensively in disease classification and more recently in many bioinformatics applications including genomics and drug design. Network theory in general has proved useful in modeling all aspects of biomedicine from healthcare organizational structure to biochemical pathways. These methods show promise in applications involving nanotechnology both in the design phase and in interpretation of system functioning.

Artificial Intelligence↗