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At least 1,153 records · Page 64Linked to original sources
PubMed1990

Extracting knowledge from a large primary health care database using a knowledge-based statistical approach.

Clinical databases from automated medical records represent a growing resource for deriving new medical knowledge. In this study a large primary health care database was explored with respect to the association between hypertension and diabetes. Data collection was made with a query language, and data analysis performed with an interactive knowledge-based statistical tool, MAXITAB, employing a multivariate tabular analysis technique. In the study population of 6660 patients the prevalence of diabetes was almost three times higher for hypertensive patients than for those with no hypertension. Conversely, the prevalence of hypertension was 2.6 times higher for diabetic patients than for those with no diabetes. The results support the assumption of a relationship between hypertension and diabetes, although the question of causality between the two diagnoses remains unsolved. Knowledge-based statistical tools of this kind may be feasible for exploring large clinical databases and may result in new medical hypotheses, worthy of further investigation.

Aged↗
PubMed2006

Hybrid specification, storage, retrieval and runtime application of clinical guidelines.

Clinical guidelines are a major tool in improving the quality of medical care. However, most guidelines are in free text, are not machine-comprehensible and are not easily accessible to clinicians at the point of care. We have designed and implemented a web-based, modular, distributed architecture, the Digital Electronic Guideline Library (DeGeL), which facilitates gradual conversion of clinical guidelines from text to a formal representation in the chosen target guideline ontology. The architecture supports guideline classification, semantic markup, context-sensitive search, browsing, run-time application and retrospective quality assessment. The DeGeL hybrid meta-ontology includes elements common to all guideline ontologies, such as semantic classification and domain knowledge; it also includes four content-representation formats: free text, semi-structured text, semi-formal representation and a formal representation. These formats support increasingly sophisticated computational tasks. Guidelines can thus be in a hybrid representation in which guidelines, and even parts of the same guideline, might exist at different formalisation levels. We have also developed and rigorously evaluated a methodology and an associated web-based tool, Uruz, for gradually structuring and semi-formalising free-text clinical guidelines. Finally, we have designed, implemented and evaluated a new approach, the hybrid runtime application model, for supporting runtime application of clinical guidelines that are not necessarily in a machine-comprehensible format; in particular, when the guideline is in a semi-formal representation and the patient's data are either in an electronic medical record or in a paper format. The tool implementing this new approach, the Spock module, is customised at this point to the Asbru guideline specification language and exploits the hybrid structure of guidelines in DeGeL. The Spock module also exploits our temporal-abstraction mediator to the patient record, IDAN, and our interactive intelligent-visualisation tool, KNAVE-II.

Decision Support Systems, Clinical↗
PubMed1991

An empirical analysis of likelihood-weighting simulation on a large, multiply connected medical belief network.

We are developing a probabilistic reformulation of the Quick Medical Reference (QMR) system. Our current probabilistic model of the QMR knowledge base of internal medicine consists of a two-level, multiply connected, belief network. Because of the size and connectivity of this belief network, most exact algorithms for calculating the posterior marginal probabilities of diseases are not applicable. In this paper, we analyze the convergence properties of an approximation algorithm, called likelihood-weighting simulation, on the QMR-DT belief network. Specifically, on two difficult diagnostic cases, we examine the effects of Markov blanket scoring, importance sampling, and self-importance sampling, demonstrating that the Markov blanket scoring and self-importance sampling significantly improve the convergence of the simulation on our model.

Algorithms↗
PubMed1991

RECPAM: a computer program for recursive partition and amalgamation for survival data and other situations frequently occurring in biostatistics. III. Classification according to a multivariate construct. Application to data on Haemophilus influenzae type b meningitis.

The methodology of RECursive Partition and AMalgamation (RECPAM) previously presented in Parts I and II (A. Ciampi et al., Computer. Methods Progr. Biomed. 26 (1988) 239-256 and 30 (1989) 283-296) pursues its development with an application to predict long-term effects of a disease given a set of clinical information measured at the time of illness. This paper illustrates how RECPAM deals with a situation typical in Medical Informatics applied to data on Haemophilus influenzae type b meningitis.

Adolescent↗
PubMed1990

Decision-making abilities of mothers with mental retardation.

The decision-making abilities of mothers with mental retardation and contrast mothers were examined. Both groups received two high- and two low-risk vignettes in full and partial formats. Although there were no significant differences between the two groups on the appropriateness of their decisions, as expected, the mothers with mental retardation scored lower both on the recall and elicited measures. Both groups scored lower on the partial formats than on the full formats, and on the high-risk vignettes than on the low-risk ones. These results suggest that familiarity with the parenting situation as well as the degree of risk inherent in that situation is of importance when one is examining decision making for either group. The results also suggest that although mothers with mental retardation can make in vitro decisions that are similar to those of nonhandicapped mothers of the same socioeconomic background, additional research must examine in vivo decision making for both groups.

Adult↗
PubMed1995

Control theory as a conceptual framework for intensive care monitoring.

Monitoring patients hospitalized in hemato-oncology departments to undergo clinical protocols of therapy is a complex task. The main difficulty arises in the management of the oncology protocol as well as in the management of critical episodes of acute illness which frequently occur due to high toxicity of the used antimitotics. This problem of controlling a patient's condition can be conceptualized within the control theory paradigm as the task of controlling a process whose state changes over time and can deviate unacceptably from a normal range. Upon request of a French clinical department of hemato-oncology, we developed an intelligent patient monitor named SEPIA to assist clinicians in this task. Following the control theory analogy at the level of knowledge bases design, we have modeled the medical knowledge as control information to represent the medical actions, and state information is used as feedback control to characterize the patient's state. After a general presentation of SEPIA, the analogies between control theory actors and SEPIA's components are specified. The article then focuses on state variables representation. The data reduction process, activated as new data are given to the system, is described. A simulated running session is finally presented to illustrate the whole reasoning process.

Algorithms↗
PubMedDate not supplied

High-cost, high-tech medicine: are we getting our money's worth?

Increasingly stringent fiscal restraints on the spending for health care, driven by the scarcity of health resources, has introduced a need to apply cost containment measures to nearly all aspects of medical care. Intelligent cost containment measures must include application of disciplined logic to the decision making process of when to use high-tech, high-cost interventions. Such decision making depends upon knowledge of the basic concepts of economic and cost-benefit analyses, outcome (benefit) studies, and some principles of decision-threshold analysis. These basic principles are reviewed, and the potential impact of application of strategies for cost containment in the operating room and the intensive care unit is discussed.

Cost-Benefit Analysis↗
PubMed2005

Constraint reasoning in deep biomedical models.

OBJECTIVE: Deep biomedical models are often expressed by means of differential equations. Despite their expressive power, they are difficult to reason about and make decisions, given their non-linearity and the important effects that the uncertainty on data may cause. The objective of this work is to propose a constraint reasoning framework to support safe decisions based on deep biomedical models. METHOD: The methods used in our approach include the generic constraint propagation techniques for reducing the bounds of uncertainty of the numerical variables complemented with new constraint reasoning techniques that we developed to handle differential equations. RESULTS: The results of our approach are illustrated in biomedical models for the diagnosis of diabetes, tuning of drug design and epidemiology where it was a valuable decision-supporting tool notwithstanding the uncertainty on data. CONCLUSION: The main conclusion that follows from the results is that, in biomedical decision support, constraint reasoning may be a worthwhile alternative to traditional simulation methods, especially when safe decisions are required.

Artificial Intelligence↗
PubMed2005

Knowledge-discovery incorporated evolutionary search for microcalcification detection in breast cancer diagnosis.

OBJECTIVES: The presence of microcalcifications (MCs), clusters of tiny calcium deposits that appear as small bright spots in a mammogram, has been considered as a very important indicator for breast cancer diagnosis. Much research has been performed for developing computer-aided systems for the accurate identification of MCs, however, the computer-based automatic detection of MCs has been shown difficult because of the complicated nature of surrounding of breast tissue, the variation of MCs in shape, orientation, brightness and size. METHODS AND MATERIALS: This paper presents a new approach for the effective detection of MCs by incorporating a knowledge-discovery mechanism in the genetic algorithm (GA). In the proposed approach, called knowledge-discovery incorporated genetic algorithm (KD-GA), the genetic algorithm is used to search for the bright spots in mammogram and a knowledge-discovery mechanism is integrated to improve the performance of the GA. The function of the knowledge-discovery mechanism includes evaluating the possibility of a bright spot being a true MC, and adaptively adjusting the associated fitness values. The adjustment of fitness is to indirectly guide the GA to extract the true MCs and eliminate the false MCs (FMCs) accordingly. RESULTS AND CONCLUSIONS: The experimental results demonstrate that the incorporation of knowledge-discovery mechanism into the genetic algorithm is able to eliminate the FMCs and produce improved performance comparing with the conventional GA methods. Furthermore, the experimental results show that the proposed KD-GA method provides a promising and generic approach for the development of computer-aided diagnosis for breast cancer.

Algorithms↗
PubMed2006

The computational neurobiology of learning and reward.

Following the suggestion that midbrain dopaminergic neurons encode a signal, known as a 'reward prediction error', used by artificial intelligence algorithms for learning to choose advantageous actions, the study of the neural substrates for reward-based learning has been strongly influenced by computational theories. In recent work, such theories have been increasingly integrated into experimental design and analysis. Such hybrid approaches have offered detailed new insights into the function of a number of brain areas, especially the cortex and basal ganglia. In part this is because these approaches enable the study of neural correlates of subjective factors (such as a participant's beliefs about the reward to be received for performing some action) that the computational theories purport to quantify.

Animals↗
PubMed2005

Nursing constraint models for electronic health records: a vision for domain knowledge governance.

Various forms of electronic health records (EHRs) are currently being introduced in several countries. Nurses are primary stakeholders and need to ensure that their information and knowledge needs are being met by such systems information sharing between health care providers to enable them to improve the quality and efficiency of health care service delivery for all subjects of care. The latest international EHR standards have adopted the openEHR approach of two-level modelling. The first level is a stable information model determining structure, while the second level consists of constraint models or 'archetypes' that reflect the specifications or clinician rules for how clinical information needs to be represented to enable unambiguous data sharing. The current state of play in terms of international health informatics standards development activities is providing the nursing profession with a unique opportunity and challenge. Much work has been undertaken internationally in the area of nursing terminologies and evidence-based practice. This paper argues that to make the most of these emerging technologies and EHRs we must now concentrate on developing a process to identify, document, implement, manage and govern our nursing domain knowledge as well as contribute to the development of relevant international standards. It is argued that one comprehensive nursing terminology, such as the ICNP or SNOMED CT is simply too complex and too difficult to maintain. As the openEHR archetype approach does not rely heavily on big standardised terminologies, it offers more flexibility during standardisation of clinical concepts and it ensures open, future-proof electronic health records. We conclude that it is highly desirable for the nursing profession to adopt this openEHR approach as a means of documenting and governing the nursing profession's domain knowledge. It is essential for the nursing profession to develop its domain knowledge constraint models (archetypes) collaboratively in an international context.

Artificial Intelligence↗
PubMed2002

How well do antidepressants work in older people? A systematic review of Number Needed to Treat.

OBJECTIVES: To make direct comparisons between studies of antidepressant treatment of older people using Number Needed to Treat (NNT) analysis. METHODS: Medline and Embase Search 1966-1999 and contact with manufacturers of antidepressant drugs asking for any relevant data on file; NNT analyses of outcome in terms of efficacy and adverse effects. RESULTS: Most antidepressant trials show efficacy; this is less clear for moclobemide and fluoxetine. Head-to-head comparisons between antidepressants showed significant superiority for paroxetine over fluoxetine and a trend in favour of SSRIs and venlafaxine over tricyclics. CONCLUSIONS: NNT analysis is one way of providing intelligible information on antidepressant efficacy and adverse effects which can inform clinical decisions. LIMITATIONS: Many studies do not present data in a form amenable to NNT analysis. Most head-to-head comparisons between antidepressants are underpowered.

Aged↗
PubMed2001

Case-based reasoning for antibiotics therapy advice: an investigation of retrieval algorithms and prototypes.

We have developed an antibiotics therapy advice system called ICONS for patients in an intensive care unit (ICU) who have caught an infection as additional complication. Since advice for such critically ill patients is needed very quickly and as the actual pathogen still has to be identified by the laboratory, we use an expected pathogen spectrum based on medical background knowledge and known resistances. The expected pathogen spectra and the resistance information are periodically updated from laboratory results. To speed up the process of finding suitable therapy recommendations, we have applied case-based reasoning (CBR) techniques. As all required information should always be up to date in medical expert systems, new cases should be incrementally incorporated into the case base and outdated ones should be updated or erased. For reasons of space limitations and of retrieval time an indefinite growth of the case base should be avoided. To fulfill these requirements we propose that specific single cases should be generalised to more general prototypical ones and that subsequent redundant cases should be erased. In this paper, we present evaluation results of different generation strategies for generalised cases (prototypes). Additionally, we compare measured retrieval times for two indexing retrieval algorithms: simple indexing, which is appropriate for small and medium case bases, and tree-hash retrieval, which is advantageous for large case bases.

Algorithms↗
PubMedDate not supplied

Scenario recognition for temporal reasoning in medical domains.

The recognition of high level clinical scenes is fundamental in patient monitoring. In this paper, we propose a technique for recognizing a session, i.e. the clinical process evolution, by comparison against a predetermined set of scenarios, i.e. the possible behaviors for this process. We use temporal constraint networks to represent both scenario and session. Specific operations on networks are then applied to perform the recognition task. An index of temporal proximity is introduced to quantify the degree of matching between two temporal networks in order to select the best scenario fitting a session. We explore the application of our technique, implemented in the Déjà Vu system, to the recognition of typical medical scenarios with both precise and imprecise temporal information.

Airway Obstruction↗
PubMed2000

The determinants of sexual intercourse before age 16.

PURPOSE: To identify risk and protective factors for initiation of sexual intercourse before age 16 years at the level of the individual, family, and school. METHODS: A longitudinal study based on a cohort of 1020 people born in Dunedin, New Zealand in 1972/73 and followed up to age 21 years. Demographic characteristics of the sample were similar to the New Zealand population of that age, except that a smaller proportion (3%) were Maori or Pacific Island Polynesian. Information on individual, family and school factors was collected by interview with parents at ages 3, 5, 7 and 9 years and then by postal questionnaire two-yearly up to 15 years. Subjects were assessed two-yearly from age 3 years and interviewed about their behaviours and ambitions at ages 11, 13, and 15 years. Questions about age at first intercourse were asked by computer at age 21 years. Multivariate logistic regression was used to model associations with age of first intercourse less than 16 years. RESULTS: Data on age at first intercourse were available for 926/1020 (91%) of surviving members of the cohort assembled at age 3 years. Overall 27.5% of males and 31.7% of females reported sexual intercourse before age 16 years. In multivariate analyses the independent predictors for early sexual initiation for males were: not having outside home interests at age 13 years, no religious activity at age 11 years, not being attached to school at age 15 years, a low reading score, and a diagnosis of conduct disorder in early adolescence. For females, independent predictors were: socioeconomic status in the middle range, mother having her first child before age 20 years, IQ in the middle range, not being attached to school, being in trouble at school, planning to leave school early, cigarette smoking and higher self-esteem score. CONCLUSIONS: Individual and school factors appear to be more important than family composition or socioeconomic status in the decision to have sexual intercourse before age 16 years. The lowering of age at first intercourse may be partly a cohort effect related to high rates of teenage childbearing in the mothers' generation, and to changes in social acceptability of early sexual behaviour.

Adolescent↗
PubMed2004

Effects of perinatal exposure to PCBs on neuropsychological functions in the Rotterdam cohort at 9 years of age.

PCBs are known for their neurotoxic properties, especially on the developing brain. To increase insight into the neurotoxic effects of PCB exposure, the authors studied the effects of perinatal exposure to environmental levels of these compounds on different neuropsychological domains. In 9-year-old children of the Rotterdam PCB--dioxin cohort, higher prenatal PCB levels were associated with longer response times (RTs), more variation in RTs, and lower scores on the Tower of London (TOL; Shallice, 1982). A longer breast-feeding duration was associated with lower TOL scores and with better spatial organizational skills. There was some evidence of negative effects of lactational exposure to PCBs on scores on the TOL.

Breast Feeding↗