Words and plans: early language and the development of intelligent action.
Explore the source record for details and available documents.
Age Factors↗
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.
How to conduct competitive intelligence in your biotech startup.
Explore the source record for details and available documents.
Biotechnology↗
ATIM, accompanying measure on the assessment of information technology in medicine.
ATIM is an Accompanying Measure under the AIM programme which aims at developing consensus on methods and criteria for assessment of Information Technologies applied in health care and at the application of these methods in two AIM project lines: (a) Knowledge Based and Decision Support Systems and (b) Medical Multimedia Workstations and Images. ATIM builds on, and coordinates assessments and evaluations which are planned in current AIM projects. It consists of workshops, meetings and the coordination of assessments in the two project lines. Networking is an essential element. The main goal of ATIM is to demonstrate the value of assessment to the various actors in this field. One of the end-products of ATIM will be a handbook on how to assess information technologies in medicine, with practical examples from AIM projects.
Artificial Intelligence↗
Integrating a modern knowledge-based system architecture with a legacy VA database: the ATHENA and EON projects at Stanford.
We present a methodology and database mediator tool for integrating modern knowledge-based systems, such as the Stanford EON architecture for automated guideline-based decision-support, with legacy databases, such as the Veterans Health Information Systems & Technology Architecture (VISTA) systems, which are used nation-wide. Specifically, we discuss designs for database integration in ATHENA, a system for hypertension care based on EON, at the VA Palo Alto Health Care System. We describe a new database mediator that affords the EON system both physical and logical data independence from the legacy VA database. We found that to achieve our design goals, the mediator requires two separate mapping levels and must itself involve a knowledge-based component.
Artificial Intelligence↗
Interpersonal attractiveness and clinical decision in alcoholism treatment.
The authors investigated the interactive contribution of patient characteristics and staff perceptions of the patient to decisions concerning the provision of continued alcoholism treatment following an initial 2-week evaluation period. Staff perceptions contributed significantly more to an individual's acceptance or rejection than did patient characteristics. A high interpersonal attractiveness rating also led to a significantly more positive evaluation of the patients' level of intelligence, knowledge of current events, morality, and adjustment. The present results and research findings with general psychiatric populations are compared.
Adult↗
Smoking and Raven IQ.
Nicotine has recently been shown to enhance measures of information processing speed including the decision time (DT) component of simple and choice reaction time and the string length measure of evoked potential waveform complexity. Both (DT and string length) have been previously demonstrated to correlate with performance on standard intelligence tests (IQ). We therefore hypothesised that nicotine is acting to improve intellectual performance on the elementary information processing correlates of IQ. In the current experiment we tested this hypothesis using the Raven Advanced Progressive Matrices (APM) test. APM scores were significantly higher in the smoking session compared to the non-smoking session, suggesting that nicotine acts to enhance physiological processes underlying performance on intellectual tasks.
Adolescent↗
Highly scalable and robust rule learner: performance evaluation and comparison.
Business intelligence and bioinformatics applications increasingly require the mining of datasets consisting of millions of data points, or crafting real-time enterprise-level decision support systems for large corporations and drug companies. In all cases, there needs to be an underlying data mining system, and this mining system must be highly scalable. To this end, we describe a new rule learner called DataSqueezer. The learner belongs to the family of inductive supervised rule extraction algorithms. DataSqueezer is a simple, greedy, rule builder that generates a set of production rules from labeled input data. In spite of its relative simplicity, DataSqueezer is a very effective learner. The rules generated by the algorithm are compact, comprehensible, and have accuracy comparable to rules generated by other state-of-the-art rule extraction algorithms. The main advantages of DataSqueezer are very high efficiency, and missing data resistance. DataSqueezer exhibits log-linear asymptotic complexity with the number of training examples, and it is faster than other state-of-the-art rule learners. The learner is also robust to large quantities of missing data, as verified by extensive experimental comparison with the other learners. DataSqueezer is thus well suited to modern data mining and business intelligence tasks, which commonly involve huge datasets with a large fraction of missing data.
Algorithms↗
Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.
Cancer remains one of the leading global health burdens, with increasing complexity in genomic, imaging, and clinical datasets presenting significant challenges for effective management. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges by enabling pattern recognition, knowledge integration, and data-driven decision-making. This review highlights recent advances in the application of AI across cancer research, diagnosis, and therapy. In research, AI accelerates drug discovery and repurposing, enhances genomic data interpretation, and facilitates biomarker identification through multi-omics integration. In diagnosis, AI has demonstrated high technical performance in radiology for lesion detection and image segmentation, in pathology for tumour grading and molecular prediction, and in liquid biopsy for non-invasive biomarker analysis. In therapy, AI supports precision medicine by predicting treatment responses, monitoring disease progression, and optimizing clinical trial design. Despite these advances, barriers such as data heterogeneity, algorithmic bias, interpretability, and regulatory challenges remain. Future directions, including explainable AI, federated learning, multimodal modelling, and digital twins, hold promise for translating AI-driven innovations into routine oncology practice. Significance Statement This review provides a timely synthesis of recent (2020-2025) advances in artificial intelligence across cancer research, diagnosis, and therapy, highlighting applications in drug discovery, genomics, multi-omics biomarker identification, and clinical decision-making. By integrating technological progress with translational and clinical relevance, this work serves as a valuable resource for bridging AI innovation with precision oncology practice. As a narrative review, the literature was identified through targeted PubMed, Scopus, and Google Scholar searches, combining terms for artificial intelligence, machine learning, and deep learning with cancer-related keywords, with priority given to peer-reviewed studies published between 2020 and 2025, seminal earlier works, and official regulatory or guideline documents. Within each domain, representative studies were selected to illustrate methodological diversity, clinical context, and current translational readiness rather than to provide exhaustive coverage of an extremely rapidly evolving field.
Artificial intelligence↗
Application of Omics Technologies for Cowpea Improvement.
Cowpea (Vigna unguiculata) is a vital crop for food security, nutrition, and climate resilience in sub-Saharan African and other semi-arid regions. However, its improvement is constrained by the complexity of polygenic traits such as drought tolerance, pest resistance, and seed quality. Conventional breeding, while foundational, remains insufficient to address these challenges at the required pace. Recent advances in multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, provide new opportunities to dissect complex traits, identify candidate genes, and accelerate the development of resilient, high-yielding cultivars. This review presents a critical synthesis of current applications of omics technologies in cowpea improvement, highlighting their contributions to stress adaptation, nutritional enhancement, and precision breeding. The review also examines key technical and institutional constraints limiting the adoption of omics-assisted breeding in cowpea, including inadequate research infrastructure, challenges in multi-omics data integration, and limited technical capacity across breeding programs in sub-Saharan Africa. It discusses strategies to address these barriers through regional collaboration, investment in bioinformatics capacity, and the integration of computational approaches into breeding pipelines. Overall, the review concludes that combining multi-omics technologies with artificial intelligence and machine learning has strong potential to improve genotype-phenotype prediction, accelerate breeding decisions, and support the development of climate-resilient and nutritionally enhanced cowpea cultivars.
cowpea↗
Conceptual representation of patient data bases.
Design of a computer-based assistant for patient data bases is discussed. A conceptual model for such an assistant, which can represent signs, symptoms, lab data, and radiological and morphological findings is described. The query language, interpretive reports, summary findings, and related issues in providing intelligent consultation are discussed. The role of such assistants in the context of a decision-making environment--human or automated--is emphasized.
Cholestasis↗
Training a learning vector quantization network using the pattern electroretinography signals.
In this study, the pattern electroretinography (PERG) signals derived from evoked potential across retinal cells of subjects after visual stimulation were analyzed using artificial neural network (ANN) with 172 healthy and 148 diseased subjects. ANN was employed to PERG signals to distinguish between healthy eye and diseased eye. Supervised network examined was a competitive learning vector quantization network. The designed classification structure has about 94% sensitivity, 90.32% specifity, 5.94% false negative, 9.67% false positive and correct classification is calculated to be 92%. Testing results were found to be compliant with the expected results that are derived from the physician's direct diagnosis. The end benefit would be to assist the physician to make the final decision without hesitation.
Adult↗
PROforma: a general technology for clinical decision support systems.
The need for flexible and well understood knowledge representations which are capable of capturing clinical guidelines and protocols for decision support systems is widely recognised. The PROforma method for specifying clinical guidelines and protocols comprises a graphical notation for their design, and a formal knowledge representation language to enable them to be executed by a computer to support the management of medical procedures and clinical decision making. PROforma technology consists of a graphical knowledge editor for the creation of guidelines, and an enactment engine for testing and executing them. This paper provides an overview of the motivation and structure of PROforma, and illustrates its use in the development of clinical applications.
Adult↗
Enhancement of cognitive functioning and self-regulation of learning in adolescents.
This study assessed the effects of the administration of a package of activities, known as Portfolio, on adolescents' cognitive functioning and self-regulation of learning. The study was carried out with a group of 109 students (mean age 15 years old) from the first level of Vocational Training. The students had learning difficulties, were unmotivated to study, and had behavior problems. A quasi-experimental pretest-posttest design was employed. The intervention involved group sessions in which certain, specially selected tasks from the psychopedagogic Instrumental Enrichment Program, the Philosophy for Children Program, and Project Intelligence were carried out. The intervention tasks were distributed over the entire school year. Statistically significant differences were observed between the experimental and the control groups on measures of general intelligence, cognitive flexibility, and metacognitive strategies (all p < .01). Statistically significant gains were observed for the experimental group on measures of decision making, problem solving, and self-regulation of learning (all p < .01).
Adolescent↗
Somatic versus nonsomatic shoulder and back pain experience in middle age in relation to body build, physical fitness, bone mineral content, gamma-glutamyltransferase, occupational workload, and psychosocial factors.
This study selected 205 subjects aged 55 years who had shoulder and/or back pain complaints to determine the relationship of somatic and nonsomatic pain experience to body build, physical fitness, bone mineral content, serum levels of gamma-glutamyltransferase, occupational workload and intelligence test, educational level, life success, social support, stress at work, monotonous work, and job decision latitude. Men with a somatic back pain drawing experienced more stress at work and had higher serum levels of gamma-glutamyltransferase, indicating a higher intake of alcohol and/or painkillers, but were more satisfied with their jobs compared with men who had a nonsomatic pain drawing. Women with obvious shoulder signs and symptoms and/or a somatic back pain drawing tended heavier than women without symptoms, had higher serum levels of gamma-glutamyltransferase, and found their jobs more mentally demanding than did women with shoulder and/or back pain experience but without obvious shoulder signs and symptoms and/or a nonsomatic back pain drawing.
Back Pain↗
Novel round-robin tabu search algorithm for prostate cancer classification and diagnosis using multispectral imagery.
Quantitative cell imagery in cancer pathology has progressed greatly in the last 25 years. The application areas are mainly those in which the diagnosis is still critically reliant upon the analysis of biopsy samples, which remains the only conclusive method for making an accurate diagnosis of the disease. Biopsies are usually analyzed by a trained pathologist who, by analyzing the biopsies under a microscope, assesses the normality or malignancy of the samples submitted. Different grades of malignancy correspond to different structural patterns as well as to apparent textures. In the case of prostate cancer, four major groups have to be recognized: stroma, benign prostatic hyperplasia, prostatic intraepithelial neoplasia, and prostatic carcinoma. Recently, multispectral imagery has been used to solve this multiclass problem. Unlike conventional RGB color space, multispectral images allow the acquisition of a large number of spectral bands within the visible spectrum, resulting in a large feature vector size. For such a high dimensionality, pattern recognition techniques suffer from the well-known "curse-of-dimensionality" problem. This paper proposes a novel round-robin tabu search (RR-TS) algorithm to address the curse-of-dimensionality for this multiclass problem. The experiments have been carried out on a number of prostate cancer textured multispectral images, and the results obtained have been assessed and compared with previously reported works. The system achieved 98%-100% classification accuracy when testing on two datasets. It outperformed principal component/linear discriminant classifier (PCA-LDA), tabu search/nearest neighbor classifier (TS-1NN), and bagging/boosting with decision tree (C4.5) classifier.
Artificial Intelligence↗
Risk within reason.
Advances in low-level risk detection threaten to engulf us with information. Regulators typically respond to each newly highlighted risk, whether painstakingly uncovered through scientific investigation or divulged with fanfare by the media, on an ad hoc basis. This response makes it hard to relate disparate risks to the overall risk level and impedes intelligent risk reduction, which must consider the costs and benefits involved. Efficient risk management requires decisions not only about what to regulate and how stringently, but also about the appropriate division of labor among the agents influencing risks. These agents include individuals, whose potential contributions too often are overlooked, corporations, and government.
Decision Making↗
Reasoning in expert system ONE for vertigo work-up.
An otoneurological expert system (ONE) was developed to help collect data and diagnose the work-up of vertigo of both central and peripheral diseases causing vertigo. Patient history and otoneurological and other examination results are used in the reasoning process. The history is interactively collected and is complemented with clinical examination results. The case history data can be either mandatory or supportive. Mandatory questions are used to confirm a diagnosis, and conflicting answers are used to reject an unlikely disease. Supportive questions support or suppress a diagnosis, but their presence is not obligatory. The reasoning procedure of ONE scores every question independently for different diagnoses, depending on how well they agree with the symptom entity of a disease. Diagnostic criteria are set for each disease, in Meniere's disease, for example, the full triad is required. Graphic displays illustrate the linear and nonlinear correlation between the symptoms and diseases. For instance, both second-long Tumarkin-type attacks and attacks lasting hours give a high score while intermediately long attacks score much lower in Meniere's disease. To be able to take even rare diseases into consideration we try to diminish the possibility of a wrong decision rather than maximize the likelihood of reaching only one right decision.
Artificial Intelligence↗
Considering a microcomputer? Beware the problems and risks.
The use of microcomputers, although increasingly common in the clinical laboratory, is not problem- or risk-free. Some of these problems and risks are discussed in this article and suggestions for minimizing them are offered. Forewarned is forearmed. The author hopes that pointing out the risks and deficiencies of microcomputers will promote their intelligent application in the clinical laboratory.
Computers↗