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PubMed1993

Development of an expert system for pediatric auditory brainstem response interpretation.

Expert systems are computer programs which incorporate artificial intelligence technology and are created to emulate the decision-making abilities of human experts. The advantage of such systems lies in their ability to capture and model expert problem solving knowledge in a domain and make it available to an unlimited number of consumers in an economic and efficient way. The purpose of this project was to develop an expert system to interpret infant auditory brainstem response data as entered by the user. The resulting system provides diagnostic conclusions regarding hearing status, type of hearing loss, and brainstem function at an accuracy level equal to that of a human expert.

Artificial Intelligence↗
PubMed2002

A review of evidence of health benefit from artificial neural networks in medical intervention.

The purpose of this review is to assess the evidence of healthcare benefits involving the application of artificial neural networks to the clinical functions of diagnosis, prognosis and survival analysis, in the medical domains of oncology, critical care and cardiovascular medicine. The primary source of publications is PUBMED listings under Randomised Controlled Trials and Clinical Trials. The rĵle of neural networks is introduced within the context of advances in medical decision support arising from parallel developments in statistics and artificial intelligence. This is followed by a survey of published Randomised Controlled Trials and Clinical Trials, leading to recommendations for good practice in the design and evaluation of neural networks for use in medical intervention.

Decision Support Techniques↗
PubMedDate not supplied

Decision support computer program for cancer pain management.

The purpose of the study was to develop an initial version of computer software that could assist nurses' decision making about cancer pain reported by women from diverse cultural groups. This cross-sectional study included two phases: (1) data collection and (2) development of computer software. Data were collected using an Internet survey and e-mail group discussions of 19 faculty members from 10 countries who were self-identified experts in oncology nursing. The data were analyzed using descriptive statistics and content analysis. The findings indicated ethnic, gender, geographic, and age differences in cancer pain descriptions. Based on the collected data, a decision support computer program for cancer pain management, including (1) a knowledge base generation module, (2) a decision-making module, and (3) a self-adaptation module, was developed. Based on the study findings, suggestions for future research and practice related to cancer pain and expert systems were proposed.

Artificial Intelligence↗
PubMed2003

The perception of breast cancers--a spatial frequency analysis of what differentiates missed from reported cancers.

The primary detector of breast cancer is the human eye. Radiologists read mammograms by mapping exogenous and endogenous factors, which are based on the image and observer, respectively, into observer-based decisions. These decisions rely on an internal schema that contains a representation of possible malignant and benign findings. Thus, to understand the hits and misses made by the radiologists, it is important to model the interactions between the measurable image-based elements contained in the mammogram and the decisions made. The image-based elements can be of two types, i.e., areas that attracted the visual attention of the radiologist, but did not yield a report, and areas where the radiologist indicated the presence of an abnormal finding. In this way, overt and covert decisions are made when reading a mammogram. In order to model this decision-making process, we use a system that is based upon the processing done by the human visual system, which decomposes the areas under scrutiny in elements of different sizes and orientations. In our system, this decomposition is done using wavelet packets (WPs). Nonlinear features are then extracted from the WP coefficients, and an artificial neural network is trained to recognize the patterns of decisions made by each radiologist. Afterwards, the system is used to predict how the radiologist will respond to visually selected areas in new mammogram cases.

Artificial Intelligence↗
PubMed2005

Two-year follow-up of intelligence after pediatric epilepsy surgery.

Research findings concerning cognitive effects of pediatric epilepsy surgery form an important basis for decisions about surgery. However, most follow-up studies have been of limited duration. In this study, a 2-year follow-up of intelligence was undertaken. Risk factors were analyzed. Included were 38 patients aged 3 to 17 years. Surgery was left in 19 patients and right in 19 patients. Types of surgery included temporal lobe resection (n = 23), extratemporal or multilobar resection (n = 8), and hemispherectomy (n = 7). The Wechsler Scales of Intelligence were administered presurgically, 6 months postsurgically, and 2 years postsurgically. No significant change in verbal or performance intelligence quotient (IQ) was demonstrated on a group level. Lateralization, type of surgery, age at surgery, sex, and presurgical IQ did not affect outcome. Across assessments, IQ scores of left-hemisphere patients were lower than those of right-hemisphere patients. Scores of patients in the hemispherectomy group were lower than those of the extratemporal or multilobar resection group, which were lower than the temporal lobe resection group. Scores improved significantly in six patients and deteriorated in seven. In conclusion, epilepsy surgery in children and adolescents does not, in general, have a significant impact on cognitive development in a 2-year perspective. In individual patients, poor seizure control and extensive surgery for Rasmussen's encephalitis were related to a deterioration of IQ.

Adolescent↗
PubMed2004

A similarity function to evaluate the orthodontic condition in patients with cleft lip and palate.

The objective of this work is the modeling of a similarity function adapted to the medical environment using the logical-combinatorial approach of pattern recognition theory, and its application to compare the orthodontic conditions of patients with cleft-primary palate and/or cleft-secondary palate congenital malformations. The variables in domains with no a priori algebraic or topological structure are objects whose similarity or difference is evaluated by comparison criteria functions. The range of these functions is an ordered set normalized into the unit interval, and they are designed to allow differentiation and non-uniform treatment of the object-variables. The analogy between objects is formalized as a similarity function that stresses the relations among the comparison criteria and evaluates the partial descriptions (partial similarity/difference) or total descriptions (total similarity/difference) of the objects. For the orthodontic problem we defined a set of 12 variables featuring the unilateral/bilateral fissures, the conditions of maxilla, premaxilla, mandible and patient's bite. The comparison criteria (logical for malocclusion, fuzzy for maxillary collapse unilateral/anteroposterior and for overbite, and Boolean for protrusive/retrusive premaxilla conditions) were assigned a relevance factor based on the orthodontist accumulated knowledge and experience. The modeling of the similarity function and its effectiveness in comparing orthodontic conditions in patients are illustrated by the study of four clinical cases with different clefts. The results through similarity are close to the expected ones. Moreover evaluated at different moments it allows to assess the effect of treatment in a single patient, hence providing valuable auxiliary criteria for medical decision making as to the patient's rehabilitation. We include the potential extension of the methodology to other medical disciplines such as speech therapy and reconstructive surgery.

Algorithms↗
PubMed1995

Information, intelligence, and interface: the pillars of a successful medical information system.

This paper addresses three key issues facing developers of clinical and/or research medical information systems. 1. INFORMATION. The basic function of every database is to store information about the phenomenon under investigation. There are many ways to organize information in a computer; however only a few will prove optimal for any real life situation. Computer Science theory has developed several approaches to database structure, with relational theory leading in popularity among end users [8]. Strict conformance to the rules of relational database design rewards the user with consistent data and flexible access to that data. A properly defined database structure minimizes redundancy i.e.,multiple storage of the same information. Redundancy introduces problems when updating a database, since the repeated value has to be updated in all locations--missing even a single value corrupts the whole database, and incorrect reports are produced [8]. To avoid such problems, relational theory offers a formal mechanism for determining the number and content of data files. These files not only preserve the conceptual schema of the application domain, but allow a virtually unlimited number of reports to be efficiently generated. 2. INTELLIGENCE. Flexible access enables the user to harvest additional value from collected data. This value is usually gained via reports defined at the time of database design. Although these reports are indispensable, with proper tools more information can be extracted from the database. For example, machine learning, a sub-discipline of artificial intelligence, has been successfully used to extract knowledge from databases of varying size by uncovering a correlation among fields and records[1-6, 9]. This knowledge, represented in the form of decision trees, production rules, and probabilistic networks, clearly adds a flavor of intelligence to the data collection and manipulation system. 3. INTERFACE. Despite the obvious importance of collecting data and extracting knowledge, current systems often impede these processes. Problems stem from the lack of user friendliness and functionality. To overcome these problems, several features of a successful human-computer interface have been identified [7], including the following "golden" rules of dialog design [7]: consistency, use of shortcuts for frequent users, informative feedback, organized sequence of actions, simple error handling, easy reversal of actions, user-oriented focus of control, and reduced short-term memory load. To this list of rules, we added visual representation of both data and query results, since our experience has demonstrated that users react much more positively to visual rather than textual information. In our design of the Orthopaedic Trauma Registry--under development at the Carolinas Medical Center--we have made every effort to follow the above rules. The results were rewarding--the end users actually not only want to use the product, but also to participate in its development.

Artificial Intelligence↗
PubMed1997

A confirmatory factor analysis of the structure of tacit knowledge in nursing.

The purpose of this study was to examine the structure of tacit knowledge in the nursing profession. Using research in implicit learning and practical intelligence as a theoretical framework, a paper-and-pencil measure of tacit knowledge for nurses was developed and refined. Five models, each representing an established theory of traditional intelligence, were compared in terms of fit to the data. The results of the confirmatory factor analysis suggest that Model 3, a hierarchical model of intelligence, was the most plausible representation of tacit knowledge in nursing. The results indicate that the portion of managerial decision making learned implicitly on the job is mainly accounted for by managing tasks and others. Suggestions for nursing education include teaching effective strategies for managing tasks, such as handling increased workloads, establishing priorities, and delegating responsibility.

Decision Making↗
PubMed1996

Decision support techniques for the interpretation of quantitative amino acid data.

Quantitative amino acid profiles are traditionally reported as a list of individual concentrations and reference intervals. By the use of selected examples it is shown that alternative methods of presenting and assessing these results can enhance both the quantity and quality of information. A candidate decision support tool is offered as an aid to the interpretation of amino acid profiles.

Algorithms↗
PubMed2001

Individual differences in performance on elementary cognitive tasks (ECTs): lawful vs. problematic parameters.

Over the past 2 decades, the cognitive-correlates approach has dominated investigations into the nature of intelligence. This research program relies on a number of processing speed parameters (apart from "average performance"). These measures include the slope, intercept, and intraindividual variability of both decision time and movement time. By correlating these measures with established markers of intelligence, researchers postulate theoretical models underlying these information-processing constructs. However, there is a lack of substantive evidence that these phenomena are as robust within the individual as has been proposed. The authors tested the properties of intraindividual parameters by asking participants (N = 179) to perform 10 elementary cognitive tasks (ECTs). Detailed analyses revealed that average performance parameters, extracted from these ECTs, behaved lawfully. However, up to 40% of participants failed to provide acceptable indices of intraindividual model fit. Similarly, intraindividual variability measures appeared less valid than previously suggested. The implications of these findings for cognitive and biological models of intelligence are discussed.

Adolescent↗
PubMed1994

A support for decision-making: cost-sensitive learning system.

This paper investigates a machine learning (ML) algorithm for supporting a decision-making system that is able to handle diagnostic problems. The input data are expressed by solved cases of patients' diagnoses, and the output is formed by a set of decision rules which may be directly exploited for a decision support. We have chosen the methodology of covering ML algorithms, namely the CN2 algorithm, as a starting point, and designed and implemented a certain extension of CN2 that comprises: advanced discretizing numerical attributes and incorporating attribute cost to economize the classification.

Algorithms↗
PubMedDate not supplied

Using the ID3 algorithm to find discrepant diagnoses from laboratory databases of thyroid patients.

Rare cases are a central problem when an expert system is constructed from example cases with machine learning techniques. It is difficult to make a decision support system (DSS) to cover all possible clinical cases. An inductive learning program can be used to construct an expert system for detecting cases that differ from routine cases. The ID3 algorithm and the pessimistic pruning algorithm were tested in this study: a DSS was built directly from the data of patient records. A decision tree was generated, and the cases misclassified by the decision tree as compared with the classifications of a clinician were listed on a checklist, which formed the feedback to the clinician. In clinical situations about 5-10% of functional thyroid disorders may be misclassified. At this error level, the method found over 90% of the errors with a specificity of 95%. In simple medical classification tasks this dynamic self-learning system can be used to create a DSS that can assist in the quality control of clinical decision making.

Adult↗
PubMed1993

Guaranteeing real-time response with limited resources.

Unanticipated problems detected by patient-monitoring systems may sometimes require real-time response in order to provide high-quality care and avoid catastrophic outcomes. In this paper, we present an approach for guaranteeing a response to such events by a monitoring agent even in situations where we have limited problem-solving resources. We show that an action-based hierarchy can accomplish this goal. We also analyze the performance of this hierarchy under varying resource availability and discuss decision-theoretic approaches to enable us to best structure such a hierarchy. We also describe an implementation of these ideas, called ReAct, in the BB1 architecture. All the ideas are illustrated with examples from the surgical intensive care unit (SICU).

Algorithms↗
PubMed1983

Computer realization of some decision process in neurological diagnostics.

A digital computer-assisted system is described which enables acquisition of data on an objective neurologic finding and results of the cerebellum examinations. On the basis of the registered finding the system provides full automated topographic or syndromological diagnosis. It avails of artificial intelligence and operates the data base of concrete medical knowledge using logical decision making rules equivalent with diagnostic views of a neurologist.

Computers↗
PubMed1987

Computer modeling of clinical judgment.

Four main developments leading to computer modeling of clinical judgment are described in this paper. These include information processing psychology, clinical vs. statistical prediction studies, behavioral decision theory, and Bayesian decision analysis approaches. One clear catalyst in these developments has been the computer, which has been used as an information management tool rather than a data-processing device. Future directions of these efforts are delineated, and problems as well as prospects of computerizing clinical judgment are described.

Artificial Intelligence↗
PubMed2000

Discovery of predictive models in an injury surveillance database: an application of data mining in clinical research.

A new, evolutionary computation-based approach to discovering prediction models in surveillance data was developed and evaluated. This approach was operationalized in EpiCS, a type of learning classifier system specially adapted to model clinical data. In applying EpiCS to a large, prospective injury surveillance database, EpiCS was found to create accurate predictive models quickly that were highly robust, being able to classify > 99% of cases early during training. After training, EpiCS classified novel data more accurately (p < 0.001) than either logistic regression or decision tree induction (C4.5), two traditional methods for discovering or building predictive models.

Artificial Intelligence↗
PubMed1993

Formalized decision support for cardiovascular intensive care.

The massive volume of hemodynamic data routinely available within the Cardiovascular Intensive Care Unit (CVICU) can adversely affect the quality, relevance, and timing of hemodynamic management decisions on patients after cardiac surgery. Yet, at the same time, the lack of appropriate treatment-outcome data and access to prior CV case histories deprives the clinician of any opportunity to improve personal decision-making skill and assess the effectiveness of various treatment methods. This paper presents a formalized decision-support model for CVICU that incorporates expert and quantitative knowledge, as well as prior outcome and case experience to augment the clinician's decision-making capability. This includes the use of optimal hemodynamic patterns derived from outcome analysis as therapy goals, expert rules and trend analysis to interpret incoming data, standardized protocols based on predefined hemodynamic patterns from clinical cases, and access to the database for similar case comparison. Most importantly, the model suggests an integrated approach where the clinical database not only is a documentation source for the patient, but also can serve as an outcome research database where clinical experience can be formalized and combined with expert knowledge to influence future therapy decisions. At present, a prototype is being developed at the CVICU of the University of Alberta Hospitals on a Unix platform using ART-IM, C and Ingres. Once implemented, the prototype will be evaluated on a small group of CV patients for its effectiveness and acceptability to clinicians.

Artificial Intelligence↗
PubMed1993

Reaction times and intelligence: a comparison of Chinese-American and Anglo-American children.

Chinese-American and Anglo-American school children were compared on a nonverbal test of intelligence (Raven's Progressive Matrices) and on twelve chronometric variables which measure the speed with which basic information processes (e.g. stimulus apprehension, decision, and discrimination) can be carried out. All of these tasks are correlated with psychometric intelligence. The two groups differed significantly on most of the variables, but the differences appear to be multidimensional and are not simply due to a group difference in psychometric intelligence, equivalent to about 5 IQ points in favour of the Chinese-Americans. The results are compared with those of Lynn and his colleagues on Bristish, Japanese, and Hong Kong children, and both consistencies and inconsistencies are found.

Asian↗