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At least 847 records · Page 47Linked to original sources
PubMed1997

Fuzzy set theory in medicine.

Indeed, the complexity of biological systems may force us to alter in radical ways our traditional approaches to the analysis of such systems. Thus, we may have to accept as unavoidable a substantial degree of fuzziness in the description of the behavior of biological systems as well as in their characterization. This fuzziness, distasteful though it may be, is the price we have to pay for the ineffectiveness of precise mathematical techniques in dealing with systems comprising a very large number of interacting elements or involving a large number of variables in their decision trees.

Artificial Intelligence↗
PubMedDate not supplied

Using new reasoning technology in chemical information systems.

Unreliability of numerical data causes difficulties in computer systems for decision-making, risk assessment, and similar activities. Much human judgment is non-numerical and able to make useful evaluations of alternatives under uncertainty. The Logic of Argumentation (LA) offers a basis for computerized support of decision-making in the absence of numerical data, and it is being used in a project on carcinogenic risk assessment, StAR. There are potential applications of LA in other artificial intelligence systems in chemistry, such as for synthesis planning.

Artificial Intelligence↗
PubMed2004

Organic and psychogenic factors leading to executive dysfunctions in a patient suffering from surgery of a colloid cyst of the Foramen of Monro.

The authors report a 41-year-old female patient who had suffered from colloid cyst of the Foramen of Monro. After surgical intervention in which the cyst was completely removed, her hydrocephalus decreased to normal ventricle size measured by MRI. However, the patient became depressive and reported vast difficulties in everyday life decision-making. An examination twenty months after surgery indicated psychiatric symptoms and abnormalities in personality. Neuropsychological investigation revealed average to above average performance in anterograde memory, attention, information processing, and intelligence. In contrast, the patient was severely impaired in decision-making, complex executive functions, and social cognition. In 18F-2-fluoro-2-deoxy-D-glucose (18FDG-PET) hypometabolism in bilateral dorsolateral prefrontal cortex, cingulate cortex and left fusiform gyrus was observed. The authors conclude that in this case decision-making deficits and executive dysfunctions are influenced by both organic and psychogenic factors.

Adult↗
PubMed2006

Active and dynamic information fusion for multisensor systems with dynamic Bayesian networks.

Many information fusion applications are often characterized by a high degree of complexity because: (1) data are often acquired from sensors of different modalities and with different degrees of uncertainty; (2) decisions must be made efficiently; and (3) the world situation evolves over time. To address these issues, we propose an information fusion framework based on dynamic Bayesian networks to provide active, dynamic, purposive and sufficing information fusion in order to arrive at a reliable conclusion with reasonable time and limited resources. The proposed framework is suited to applications where the decision must be made efficiently from dynamically available information of diverse and disparate sources.

Algorithms↗
PubMedDate not supplied

Decision support for patient management in oncology.

In this paper a novel approach to the development of the architecture of a knowledge-based decision support system for the management of patients with cancer of the breast is described. Its initial design and subsequent realization in a prototype version was facilitated by examining closely the overall clinical task and identifying its associated activities and related knowledge. Implementation in KEE highlights the value of rigorous conceptual modelling that leads to a design able to assess treatment response and disease progression as well as providing specific therapy advice. The approach is general and may be applied to the development of decision support systems for other areas of cancer and medicine.

Artificial Intelligence↗
PubMedDate not supplied

Integration of knowledge-based system and database for identification of disturbances in fluid and electrolyte balance.

We describe a knowledge-based system which automatically identifies fluid and electrolyte disorders in intensive care patients. The knowledge-based system was built and interfaced to an existing patient data management system (PDMS) in Kuopio University Central Hospital to evaluate the potential of knowledge-based techniques in information management and decision support in the high dependency environment. Because of the integration, the system does not require any manual data input, and it provides a natural extension and increased performance to a current patient data management system used in clinical practise. The paper discusses design considerations and gives the system description. The evaluation of the experimental system in clinical use showed that it performed almost as well as junior clinicians of the intensive care unit.

Artificial Intelligence↗
PubMed2006

Analysis of reproductive performance of lactating cows on large dairy farms using machine learning algorithms.

The fertility of lactating dairy cows is economically important, but the mean reproductive performance of Holstein cows has declined during the past 3 decades. Traits such as first-service conception rate and pregnancy status at 150 d in milk (DIM) are influenced by numerous explanatory factors common to specific farms or individual cows on these farms. Machine learning algorithms offer great flexibility with regard to problems of multicollinearity, missing values, or complex interactions among variables. The objective of this study was to use machine learning algorithms to identify factors affecting the reproductive performance of lactating Holstein cows on large dairy farms. This study used data from farms in the Alta Genetics Advantage progeny-testing program. Production and reproductive records from 153 farms were obtained from on-farm DHI-Plus, Dairy Comp 305, or PCDART herd management software. A survey regarding management, facilities, labor, nutrition, reproduction, genetic selection, climate, and milk production was completed by managers of 103 farms; body condition scores were measured by a single evaluator on 63 farms; and temperature data were obtained from nearby weather stations. The edited data consisted of 31,076 lactation records, 14,804 cows, and 317 explanatory variables for first-service conception rate and 17,587 lactation records, 9,516 cows, and 341 explanatory variables for pregnancy status at 150 DIM. An alternating decision tree algorithm for first-service conception rate classified 75.6% of records correctly and identified the frequency of hoof trimming maintenance, type of bedding in the dry cow pen, type of cow restraint system, and duration of the voluntary waiting period as key explanatory variables. An alternating decision tree algorithm for pregnancy status at 150 DIM classified 71.4% of records correctly and identified bunk space per cow, temperature for thawing semen, percentage of cows with low body condition scores, number of cows in the maternity pen, strategy for using a clean-up bull, and milk yield at first service as key factors.

Algorithms↗
PubMed2001

RASTA: a distributed temporal abstraction system to facilitate knowledge-driven monitoring of clinical databases.

The time dimension is very important for applications that reason with clinical data. Unfortunately, this task is inherently computationally expensive. As clinical decision support systems tackle increasingly varied problems, they will increase the demands on the temporal reasoning component, which may lead to slow response times. This paper addresses this problem. It describes a temporal reasoning system called RASTA that uses a distributed algorithm that enables it to deal with large data sets. The algorithm also supports a variety of configuration options, enabling RASTA to deal with a range of application requirements.

Algorithms↗
PubMed1999

The development and evaluation of CADMIUM: a prototype system to assist in the interpretation of mammograms.

We have developed CADMIUM, a novel approach for the design of systems to assist in the interpretation of medical images. CADMIUM uses symbolic reasoning to relate information obtained from image processing to the decisions radiologists take. The approach is based on a symbolic decision procedure which has already been used successfully in a variety of nonimaging clinical decision systems. In CADMIUM this decision procedure is extended with models of three generic image interpretation tasks: detection, measurement and classification of image features. The extended procedure is used to construct the lines of reasoning needed in each task and to control the acquisition of information by image processing. CADMIUM has been evaluated as an aid to the differential diagnosis of microcalcifications on mammographic images. Radiographers who had been trained to interpret images performed better when using the advice provided by the system.

Artificial Intelligence↗
PubMed2001

ONCODOC: a successful experiment of computer-supported guideline development and implementation in the treatment of breast cancer.

Originally published as textual documents, clinical practice guidelines have poorly penetrated medical practice because their editorial properties do not allow the reader to easily solve, at the point of care, a given medical problem. However, despite the proliferation of implemented clinical practice guidelines as decision support systems providing an easy access to patient-centered information, there is still little evidence of high physician compliance to guidelines recommendations. Apart from physicians' psychological reluctance, the incompleteness of guideline knowledge and the impreciseness of the terms used, another reason may be that, although suited to average patients, clinical practice guideline recommendations are not a substitute for the physician-controlled clinical judgement that should be applied to each actual individual patient. Therefore, computer-based approaches based on the automation of context-free operationalization of guideline knowledge, although providing uniform optimal strategies to problem-focused care delivery, may generate inappropriate inferences for a specific patient that the physician does not follow in practice. Rather than providing automated decision support, ONCODOC allows the clinician to control the operationalization of guideline knowledge through his hypertextual reading of a knowledge base encoded as a decision tree. In this way, he has the opportunity to interpret the information provided in the context of his patient, therefore, controlling his categorization to the closest matching formal patient. Experimented in life-size ONCODOC demonstrated good appropriation of the system by physicians with significantly high scores of compliance. We successfully tested the implemented strategy and the knowledge base in a second medical institution, giving then a noticeable example of reuse and sharing of encoded guideline knowledge across institutions.

Artificial Intelligence↗
PubMed2005

Effects of information and machine learning algorithms on word sense disambiguation with small datasets.

Current approaches to word sense disambiguation use (and often combine) various machine learning techniques. Most refer to characteristics of the ambiguity and its surrounding words and are based on thousands of examples. Unfortunately, developing large training sets is burdensome, and in response to this challenge, we investigate the use of symbolic knowledge for small datasets. A naïve Bayes classifier was trained for 15 words with 100 examples for each. Unified Medical Language System (UMLS) semantic types assigned to concepts found in the sentence and relationships between these semantic types form the knowledge base. The most frequent sense of a word served as the baseline. The effect of increasingly accurate symbolic knowledge was evaluated in nine experimental conditions. Performance was measured by accuracy based on 10-fold cross-validation. The best condition used only the semantic types of the words in the sentence. Accuracy was then on average 10% higher than the baseline; however, it varied from 8% deterioration to 29% improvement. To investigate this large variance, we performed several follow-up evaluations, testing additional algorithms (decision tree and neural network), and gold standards (per expert), but the results did not significantly differ. However, we noted a trend that the best disambiguation was found for words that were the least troublesome to the human evaluators. We conclude that neither algorithm nor individual human behavior cause these large differences, but that the structure of the UMLS Metathesaurus (used to represent senses of ambiguous words) contributes to inaccuracies in the gold standard, leading to varied performance of word sense disambiguation techniques.

Algorithms↗
PubMed1989

"Desktop knowledge": a new focus for medical education and decision support.

Physicians today are faced with "data overload" and, paradoxically, "information underload"--the inability to locate pertinent, needed knowledge in a sea of data with which they are inundated. Increasingly, the professional functions of the physician are becoming focused on the desktop workstation, in terms of its ability to provide "windows" into local databases and knowledge resources, and to serve as an access port to other networked resources. A challenge we now face is to develop means for structuring the vast potentially available knowledge resources in such a manner that access to pertinent knowledge can be facilitated, and to develop acceptable interfaces to the knowledge resources so that a user can effectively navigate through them. The complexity of this task is due to the nature of the knowledge resources--knowledge can be in a variety of forms, ranging from textual and pictorial material, to structured representations, to more dynamic embodiments in the form of procedures. In the Decision Systems Group we have focused on the development of a prototype desktop knowledge management environment known as Explorer-2, with the objective of providing a consistent interface for access to a wide variety of knowledge. Our accomplishments to date encompass the incorporation into the Explorer-2 environment of adaptations of textbook chapters and books, image data bases, simulations, and expert systems. Navigational aids are provided by a semantic net browser using both MeSH and augmented taxonomies and by a graphical overview map.(ABSTRACT TRUNCATED AT 250 WORDS)

Artificial Intelligence↗
PubMedDate not supplied

Fuzzy logic for decision support in chronic care.

Computerized clinical guidelines can provide significant benefits in terms of health outcomes and costs, however, their effective computer implementation presents significant problems. Vagueness and ambiguity inherent in natural language (textual) clinical guidelines makes them problematic for formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. In care plan on-line (CPOL), an intranet-based chronic disease care planning system for general practitioners (GPs) in use in South Australia, we formally treat fuzziness in interpretation of quantitative data, formulation of recommendations and unequal importance of clinical indicators. We use expert judgment on cases, as well as direct estimates by experts, to optimize aggregation operators and treat heterogeneous combinations of conjunction and disjunction that are present in the natural language decision rules formulated by specialist teams.

Artificial Intelligence↗
PubMedDate not supplied

Giving the older driver enough perception-reaction time.

Many types of behavior slow with age, and older drivers require more time to process information and make decisions. How greater response time requirements relate to the design of the highway environment and to features of new in-vehicle technologies is discussed. The design requirements for many different aspects of roadway geometrics and traffic control devices are based on design driver perception-reaction times. However, without knowing how much slower older drivers are than the design driver, it is difficult to determine whether they are adequately protected by current design practice and, if not, what the desired change in design should be. Attempts to address this have been hampered by the lack of directly relevant data, the situation-specific nature of certain findings, and the older driver's ability to compensate for declines in basic skills. Also discussed are the promise and problems of new in-vehicle technologies for providing the older driver with adequate time to act. These intelligent vehicle highway systems (IVHS) may speed navigational decision making and recognition of hazards and maneuver requirements. However, the possibility for interfering with quick responding also arises, and IVHS systems might disadvantage the older driver in other ways.

Aged↗
PubMed2001

Machine learning for medical diagnosis: history, state of the art and perspective.

The paper provides an overview of the development of intelligent data analysis in medicine from a machine learning perspective: a historical view, a state-of-the-art view, and a view on some future trends in this subfield of applied artificial intelligence. The paper is not intended to provide a comprehensive overview but rather describes some subareas and directions which from my personal point of view seem to be important for applying machine learning in medical diagnosis. In the historical overview, I emphasize the naive Bayesian classifier, neural networks and decision trees. I present a comparison of some state-of-the-art systems, representatives from each branch of machine learning, when applied to several medical diagnostic tasks. The future trends are illustrated by two case studies. The first describes a recently developed method for dealing with reliability of decisions of classifiers, which seems to be promising for intelligent data analysis in medicine. The second describes an approach to using machine learning in order to verify some unexplained phenomena from complementary medicine, which is not (yet) approved by the orthodox medical community but could in the future play an important role in overall medical diagnosis and treatment.

Artificial Intelligence↗
PubMed2001

Knowledge discovering from clinical data based on classification tasks solving.

After the abundant literature on normative decision analysis centered on Bayes' rule, the artificial intelligence approach left aside the proposed formalism in favor of symbolic approach. Thus, diagnosis was identified as a dynamic cognitive process, characterized by the search for evidence to test a given hypothesis where heuristic thinking plays a significant role. Approach presented in this study is knowledge discovering system to test a given (manually or automatically) hypothesis, based on three mathematical models of classification: model of classes stability, model of linear envelope, model of multilayer neural network. All the models characterizes as quantitative methods, and works as cognitive tools for new laws searching.

Classification↗
PubMed1996

Database and knowledge base integration in decision support systems.

Since decision support systems (DSS) in medicine often are linked to clinical databases it is important to find methods that facilitate the work for DSS developers to implement database queries in the knowledge base (KB). This paper presents a method for linking clinical databases to a KB with Arden Syntax modules. The method is based on a query meta database including templates for SQL queries. During knowledge module authoring the medical expert only refers to a code in the query meta database. Our method uses standard tools so it can be implemented on different platforms and linked to different clinical databases.

Artificial Intelligence↗
PubMed2003

Mathematical modeling of decision making: a soft and fuzzy approach to capturing hard decisions.

This research focuses on a modeling approach and set of mathematical tools that were derived from research on intelligence systems, namely fuzzy system modeling. This study systematically evaluates these tools as an approach for modeling human decision making, contrasting the approach with more traditional methods based on regression. The research was conducted using experts and a simulated task environment related to allocating rewards in the form of merit pay. The results indicate that fuzzy system models generally perform as well as or better than both linear and nonlinear regression methods in terms of model fit. These results are discussed in terms of issues regarding modeling precision versus parsimony, the value of adaptive modeling techniques, empirical versus subjective approaches to model building, and individual differences in judgment strategies. Potential applications of this research include using the modeling approach studied to build higher-fidelity models that yield new insights and a better understanding of decision-making strategies and environments.

Decision Making↗