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At least 1,099 records · Page 61Linked to original sources
PubMed2006

Synergy between Competitive Intelligence (CI), Knowledge Management (KM) and Technological Foresight (TF) as a strategic model of prospecting--the use of biotechnology in the development of drugs against breast cancer.

The aim of this paper is to demonstrate the synergy between Competitive Intelligence, Knowledge Management and Technological Foresight, and to emphasize the proposal of a strategic model of data prospecting as a mechanism to support decision-making in regard to three approaches for sustainable development and innovation: technological, social and economic. The use of biotechnology in the development of drugs against breast cancer is the case study. The article shows the results of data and text mining in specialized medical and patent databases, identifying the most frequently cited drugs, as well as the authors of research, and the inventors of new technology at the beginning of the 21st century. In addition, the study includes reference to Brazilian competence in breast cancer area, the international trends in drugs for treatment of this cancer, leading international institutions and Brazilian competencies. A framework is presented, which could serve as a guide and support for the decision-making process.

Biotechnology↗
PubMedDate not supplied

Using intelligent search for finding medical sites.

In this paper we present an intelligent search tool, which we developed in order to automate search and evaluation of websites. We used TFIDF heuristics to determine term frequency and decision trees to evaluate the quality of sites. Training set for the decision tree contained manually evaluated websites. Each website was described by the combination of various attributes, complexity metrics and the evaluation. The intelligent search tool is equipped with a user-friendly interface, which enables people to exploit the tool to its limits with minimum effort, in their quest for information. For testing purposes, we looked for sites with telemedical content. The set of sites, which was the result of using the intelligent search tool, has been evaluated by a group of students.

Decision Trees↗
PubMed1991

Methods for knowledge extraction from a clinical database on liver diseases.

We performed exploratory data analysis (EDA) to examine the hidden structure in liver disease data. The purpose was to demonstrate the potential of statistical techniques for extracting knowledge from an active HIS (hospital information system) database with decision support. The goal is to give strong support to the creation of new rules or "tuning" of old rules in the knowledge base. This would facilitate utilization of large patient databases, now commonly available, to help build/update decision support systems for improved patient care. Several statistical techniques were investigated. Stepwise discriminant analysis was found to be a good method in discriminating among different disease classes. Results showed that classification strength of a few (3) variables was similar to all the available (19) variables. Other important issues in the work are treatment of missing values as well as atypical values in medical databases. In estimating missing values we utilized both statistical methods and artificial intelligence approaches. Both these approaches were promising in the estimation of missing values. The study showed that several statistical approaches are possible for knowledge extraction from clinical data collected retrospectively.

Artificial Intelligence↗
PubMedDate not supplied

Multicriteria fuzzy assignment method: a useful tool to assist medical diagnosis.

The aim of this paper is to provide a concise portrayal of medical applications of a new fuzzy classification method called PROAFTN, which uses a multicriteria decision aid approach. The review summarises and discusses medical applications of the proposed method in acute leukemia, astrocytic and bladder tumours. Although still an investigative method, the preliminary results are very encouraging and demonstrate the potential performances of this procedure for solving medical classification problems.

Artificial Intelligence↗
PubMed2026

Artificial intelligence in treatment prediction for skeletal Class III malocclusion: A systematic review.

In skeletal Class III patients, treatment options range from orthodontics to orthognathic surgery. Choosing the optimal approach requires a comprehensive clinical evaluation, which may be supported by AI tools. The aim of this study was to assess the performance of AI models in predicting the need for orthognathic surgery and in identifying predictors influencing treatment decisions. A PRISMA-guided electronic database search (PubMed, Web of Science; 2009-2024; English/French) was performed to identify studies using machine learning (ML) or deep learning (DL) on cephalometric and clinical data. After screening and assessment for eligibility, 15 studies were critically appraised. Model performance was summarized using accuracy, sensitivity, specificity, and the area under the curve (AUC). ML algorithms (particularly Random Forest and XGBoost) and DL models (ResNet-based convolutional neural networks (CNNs)) achieved high accuracy for predicting surgical need. Frequently selected predictors included Wits appraisal, ANB angle, the maxillomandibular ratio (Mx/Md), overjet, and the divergence of the lower gonial angle. AI methods show promise for assisting treatment decisions in Class III malocclusion, with Random Forest and XGBoost performing well on tabular cephalometric data and CNNs on imaging. Larger, multicentre datasets and external validation are needed to improve reliability, address bias, and support clinical implementation.

Humans↗
PubMed2006

Predicting carcinoid heart disease with the noisy-threshold classifier.

OBJECTIVE: To predict the development of carcinoid heart disease (CHD), which is a life-threatening complication of certain neuroendocrine tumors. To this end, a novel type of Bayesian classifier, known as the noisy-threshold classifier, is applied. MATERIALS AND METHODS: Fifty-four cases of patients that suffered from a low-grade midgut carcinoid tumor, of which 22 patients developed CHD, were obtained from the Netherlands Cancer Institute (NKI). Eleven attributes that are known at admission have been used to classify whether the patient develops CHD. Classification accuracy and area under the receiver operating characteristics (ROC) curve of the noisy-threshold classifier are compared with those of the naive-Bayes classifier, logistic regression, the decision-tree learning algorithm C4.5, and a decision rule, as formulated by an expert physician. RESULTS: The noisy-threshold classifier showed the best classification accuracy of 72% correctly classified cases, although differences were significant only for logistic regression and C4.5. An area under the ROC curve of 0.66 was attained for the noisy-threshold classifier, and equaled that of the physician's decision-rule. CONCLUSIONS: The noisy-threshold classifier performed favorably to other state-of-the-art classification algorithms, and equally well as a decision-rule that was formulated by the physician. Furthermore, the semantics of the noisy-threshold classifier make it a useful machine learning technique in domains where multiple causes influence a common effect.

Algorithms↗
PubMed1996

A prototype computer decision support system for the management of asthma.

Asthma is a chronic disease estimated to affect 6-7% of the total UK population. In addition, a number of studies have shown that asthma has become commoner since the 1970s, especially in children. The diagnosis of asthma can be difficult and its management requires the involvement of patients in a long-term treatment plan, something which general practitioners may be unable to achieve easily in the average 10-min consultation. As a consequence, asthma is underdiagnosed and undertreated. Deaths from the disease are often avoidable with timely and sufficient use of the available medication. In order to support this, the British Thoracic Society (BTS) has published guidelines for asthma management based upon a stepwise approach, in which a patient is categorized as being on one of five steps according to the severity of his or her asthma. The guidelines give "rules of thumb" for deciding when the patient should move up or down the steps. The most recent version of the guidelines also included special rules for children. Within a recent European Community project on Advanced Informatics in Medicine (AIM), we developed a prototype decision support system for asthma management targeted at the primary care setting and based on the British Thoracic Society guidelines. This paper reports this development, and describes the further work needed on the prototype. Plans for evaluation of the knowledge bases and for future full application production are also described.

Artificial Intelligence↗
PubMedDate not supplied

New tools for decision making in the 1990s.

The article is the final installment in a three-part series describing the methods by which hospital trustees and CEO's may enable their facilities to address the health care demands of the 1990s. The first two articles appeared in CHA's Hospital Trustee in 1991. In this article, the authors identify and discuss the emerging intelligence tools which will help hospital trustees and CEO's make more effective management decisions.

Canada↗
PubMed2004

New paradigms in telemedicine: ambient intelligence, wearable, pervasive and personalized.

After decades of development of information systems dedicated to health professionals, there is an increasing demand for personalized and non-hospital based care. An especially critical domain is cardiology: almost two third of cardiac deaths occur out of hospital, and victims do not survive long enough to benefit from in-hospital treatments. We need to reduce the time before treatment. But symptoms are often interpreted wrongly. The only immediate diagnostic tool to assess the possibility of a cardiac event is the electrocardiogram (ECG). Event and transtelephonic ECG recorders are used to improve decision making but require setting up new infrastructures. The European EPI-MEDICS project has developed an intelligent Personal ECG Monitor (PEM) for the early detection of cardiac events. The PEM embeds advanced decision making techniques, generates different alarm levels and forwards alarm messages to the relevant care providers by means of new generation wireless communication. It is cost saving, involving care provider only if necessary and requiring no specific infrastructure. This solution is a typical example of pervasive computing and ambient intelligence that demonstrates how personalized, wearable, ubiquitous devices could improve healthcare.

Artificial Intelligence↗
PubMed1995

Cost-effectiveness league tables: think of the fans.

In a recent issue of Health Policy, Birch and Gafni argued against the use of cost-effectiveness league tables in health care decision making. They argued that league tables should be returned to where they are best used and understood--the sports pages. Recently the debate about the presentation and interpretation of cost-effectiveness data has been given an additional impetus in the UK through the publication, by the Department of Health, of the Register of Cost-Effectiveness Studies (RCES). During the production of the RCES, it became apparent that there were similarities between the decision makers' thirst for economic data and the sports fans' thirst for information about their team. In this paper we review the pros and cons of using published cost-effectiveness data in decision making, compared with the local team approach suggested by Birch and Gafni. We conclude that there are advantages from using published data, providing these are produced according to standardized methods and interpreted intelligently. Most importantly, cost-effectiveness data, whether published or generated locally, are unlikely to give decision makers a technical solution to the resource allocation problem. Rather, they should be viewed as a stimulus for local discussion and debate.

Cost-Benefit Analysis↗
PubMedDate not supplied

Medical informatics and clinical decision making: the science and the pragmatics.

There are important scientific and pragmatic synergies between the medical decision making field and the emerging discipline of medical informatics. In the 1970s, the field of medicine forced clinically oriented artificial intelligence (AI) researchers to develop ways to manage explicit statements of uncertainty in expert systems. Classic probability theory was considered and discussed, but it tended to be abandoned because of complexities that limited its use. In medical AI systems, uncertainty was handled by a variety of ad hoc models that simulated probabilistic considerations. To illustrate the scientific interactions between the fields, the author describes recent work in his laboratory that has attempted to show that formal normative models based on probability and decision theory can be practically melded with AI methods to deliver effective advisory tools. In addition, the practical needs of decision makers and health policy planners are increasingly necessitating collaborative efforts to develop a computing and communications infrastructure for the decision making and informatics communities. This point is illustrated with an example drawn from outcomes management research.

Artificial Intelligence↗
PubMed2006

Automatic generation of spoken dialogue from medical plans and ontologies.

This paper presents some research undertaken as part of the EU-funded HOMEY project, into the application of intelligent dialogue systems to healthcare systems. The work presented here concentrates on the ways in which knowledge of underlying task structure (e.g., a medical guideline) can be combined with ontological knowledge (e.g., medical semantic dictionaries) to provide a basis for the automatic generation of flexible and re-configurable dialogue. This approach is next evaluated via a specific application that provides decision support to general practitioners to help determine whether or not a patient should be referred to a cancer specialist. The competence of the resulting dialogue application, its speech recognition performance, and dialogue performance are all evaluated to determine the applicability of this approach.

Artificial Intelligence↗
PubMed1994

Effects of speech intelligibility level on concurrent visual task performance.

Four experiments were performed to determine if changes in the level of speech intelligibility in an auditory task have an impact on performance in concurrent visual tasks. The auditory task used in each experiment was a memory search task in which subjects memorized a set of words and then decided whether auditorily presented probe items were members of the memorized set. The visual tasks used were an unstable tracking task, a spatial decision-making task, a mathematical reasoning task, and a probability monitoring task. Results showed that performance on the unstable tracking and probability monitoring tasks was unaffected by the level of speech intelligibility on the auditory task, whereas accuracy in the spatial decision-making and mathematical processing tasks was significantly worse at low speech intelligibility levels. The findings are interpreted within the framework of multiple resource theory.

Adult↗
PubMed2001

Procreative beneficence: why we should select the best children.

Eugenic selection of embryos is now possible by employing in vitro fertilization (IVF) and preimplantation genetic diagnosis (PGD). While PGD is currently being employed for the purposes of detecting chromosomal abnormalities or inherited genetic abnormalities, it could in principle be used to test any genetic trait such as hair colour or eye colour. Genetic research is rapidly progressing into the genetic basis of complex traits like intelligence and a gene has been identified for criminal behaviour in one family. Once the decision to have IVF is made, PGD has few 'costs' to couples, and people would be more inclined to use it to select less serious medical traits, such as a lower risk of developing Alzheimer Disease, or even for non-medical traits. PGD has already been used to select embryos of a desired gender in the absence of any history of sex-linked genetic disease. I will argue that: (1) some non-disease genes affect the likelihood of us leading the best life; (2) we have a reason to use information which is available about such genes in our reproductive decision-making; (3) couples should select embryos or fetuses which are most likely to have the best life, based on available genetic information, including information about non-disease genes. I will also argue that we should allow selection for non-disease genes even if this maintains or increases social inequality. I will focus on genes for intelligence and sex selection. I will defend a principle which I call Procreative Beneficence: couples (or single reproducers) should select the child, of the possible children they could have, who is expected to have the best life, or at least as good a life as the others, based on the relevant, available information.

Beneficence↗
PubMed1995

Development of a connectionist expert system to identify foot problems based on under-foot pressure patterns.

The first phase of developing a connectionist expert system is described in this study. Dynamic pressure patterns below the foot obtained by the emed f system of 18 subjects were classified into one of the three categories: normals, pes cavus and hallux valgus. As opposed to the traditional approach for the analysis of the pressure patterns by quantifying isolated segments, a neural network based expert system was trained to perform objective classification of the patterns while keeping their integrity. The results of the backpropagation paradigm were unexpectedly good considering the limiting factors which hindered the performance of the neural networks. The successes indicate the potential of artificial intelligence in automated analysis of foot pathologies based on theemed f system. RELEVANCE:--This paper describes the first steps towards a novel automated screening system, which classifies the patient with foot abnormalities into the pathological category he or she belongs to, based on the pressure pattern below the foot. The decision making is objective, and shows a practical example of the use of artificial intelligence.

Journal Article↗
PubMed1997

Temporal reasoning and temporal data maintenance in medicine: issues and challenges.

We present a brief, nonexhaustive overview of research efforts in designing and developing time-oriented systems in medicine. The growing volume of research on time-oriented systems in medicine can be viewed from either an application point of view, focusing on different generic tasks (e.g. diagnosis) and clinical areas (e.g. cardiology), or from a methodological point of view, distinguishing between different theoretical approaches. In this overview, we focus on highlighting methodological and theoretical choices, and conclude with suggestions for new research directions. Two main research directions can be noted: temporal reasoning, which supports various temporal inference tasks (e.g. temporal abstraction, time-oriented decision support, forecasting, data validation), and temporal data maintenance, which deals with storage and retrieval of data that have heterogeneous temporal dimensions. Efforts common to both research areas include the modeling of time, of temporal entities, and of temporal queries. We suggest that tasks such as abstraction of time-oriented data and the handling of different temporal-granularity levels should provide common ground for collaboration between the two research directions and fruitful areas for future research.

Artificial Intelligence↗
PubMed1999

WAX ActiveLibrary: a tool to manage information overload.

WAX Active-Library (Cambridge Centre for Clinical Informatics) is a knowledge management system that seeks to support doctors' decision making through the provision of electronic books containing a wide range of clinical knowledge and locally based information. WAX has been piloted in several regions in the United Kingdom and formally evaluated in 17 GP surgeries based in Cambridgeshire. The evaluation has provided evidence that WAX Active-Library significantly improves GPs' access to relevant information sources and by increasing appropriate patient management and referrals this might also lead to an improvement in clinical outcomes.

Artificial Intelligence↗