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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,639 records · Page 91Linked to original sources

PubMed1989

Computers in the medical workplace.

Whether your greatest need is to have a neatly typed letter or an accurately aged accounts receivable report, or it's critical that you create an electronic medical record for decision support, the computer in the medical workplace should: 1. Save time for staff and patients. 2. Be convenient to use. 3. Respond quickly to the user. 4. Be flexible. 5. Free staff for intelligent work. 6. Minimize or eliminate redundancy. 7. Allow retrospective retrieval. If your computer and software produces the above results, you've chosen appropriate software and right equipment for your specific needs and your work place.

Computers↗

PubMed1984

Local anesthetics in dental practice.

Local anesthetics are the most widely used drugs in dentistry today. Knowledge of the pharmacology and toxicology of these agents will result in their intelligent and judicious use. The choice of local anesthetic should be individualized for each patient. The duration of the dental procedure should be weighed against the duration of action of the local anesthetic; a decision should be made as to whether a vasoconstrictor is needed to prolong its action. However, if the duration of numbness is too long, the possibility of self-mutilation must be considered in certain patients (for example, children and the mentally retarded). In other patients with whom postoperative pain is expected, it may be beneficial to administer a long-acting local anesthetic such as bupivacaine for control of postoperative pain. The total dose of local anesthetic and vasoconstrictor must be determined for each patient based upon body weight; the maximal dosages for each agent should be known. Small children or frail individuals will require below average dosages. The use of a vasoconstrictor may constitute the limiting factor to the total number of local anesthetic cartridges that can be administered safely over a given period of time. Certain medical problems, such as cardiovascular system impairments or hyperthyroidism, may influence the choice of anesthetic and the quantity of vasoconstrictor. An understanding of the physicochemical properties of local anesthetics is also important to a rational process of selection. There are several causes for failure to achieve profound regional anesthesia. These include inflammation and/or infection, anatomic variation, intravascular injection, accessory innervation, and deflection of the needle. Inflammation and infection reduce the efficacy of a local anesthetic by reducing its bioavailability. Local anesthetics with low pKa values (for instance, mepivacaine) are the most effective in this clinical situation. Other causes of inadequate regional anesthesia are primarily related to technique of administration and can be circumvented by the use of the periodontal ligament injection. A comprehensive review of this technique and its application has been presented by the Council on Dental Materials and Equipment of the American Dental Association. Occasionally, a clinician may be unsuccessful at achieving regional anesthesia despite these additional measures. Highly anxious dental patients or patients with a genuine tolerance to local anesthetics normally pose the most problems. Transitional block or threshold block phenomena should also be suspected in these situations.(ABSTRACT TRUNCATED AT 400 WORDS)

Anesthesia, Dental↗

PubMed2006

Hypoplastic left heart syndrome: knowledge discovery with a data mining approach.

Hypoplastic left heart syndrome (HLHS) affects infants and is uniformly fatal without surgical palliation. Post-surgery mortality rates are highly variable and dependent on postoperative management. A data acquisition system was developed for collection of 73 physiologic, laboratory, and nurse-assessed parameters. The acquisition system was designed for the collection on numerous patients. Data records were created at 30s intervals. An expert-validated wellness score was computed for each data record. To efficiently analyze the data, a new metric for assessment of data utility, the combined classification quality measure, was developed. This measure assesses the impact of a feature on classification accuracy without performing computationally expensive cross-validation. The proposed measure can be also used to derive new features that enhance classification accuracy. The knowledge discovery approach allows for instantaneous prediction of interventions for the patient in an intensive care unit. The discovered knowledge can improve care of complex to manage infants by the development of an intelligent bedside advisory system.

Algorithms↗

PubMed2005

An intelligent remote monitoring system for artificial heart.

A web-based database system for intelligent remote monitoring of an artificial heart has been developed. It is important for patients with an artificial heart implant to be discharged from the hospital after an appropriate stabilization period for better recovery and quality of life. Reliable continuous remote monitoring systems for these patients with life support devices are gaining practical meaning. The authors have developed a remote monitoring system for this purpose that consists of a portable/desktop monitoring terminal, a database for continuous recording of patient and device status, a web-based data access system with which clinicians can access real-time patient and device status data and past history data, and an intelligent diagnosis algorithm module that noninvasively estimates blood pump output and makes automatic classification of the device status. The system has been tested with data generation emulators installed on remote sites for simulation study, and in two cases of animal experiments conducted at remote facilities. The system showed acceptable functionality and reliability. The intelligence algorithm also showed acceptable practicality in an application to animal experiment data.

Decision Support Systems, Clinical↗

PubMed2006

Machine learning techniques in disease forecasting: a case study on rice blast prediction.

BACKGROUND: Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown data points and longer training times, there is need for exploiting new prediction softwares for better understanding of plant-pathogen-environment relationships. Further, there is no online tool available which can help the plant researchers or farmers in timely application of control measures. This paper introduces a new prediction approach based on support vector machines for developing weather-based prediction models of plant diseases. RESULTS: Six significant weather variables were selected as predictor variables. Two series of models (cross-location and cross-year) were developed and validated using a five-fold cross validation procedure. For cross-year models, the conventional multiple regression (REG) approach achieved an average correlation coefficient (r) of 0.50, which increased to 0.60 and percent mean absolute error (%MAE) decreased from 65.42 to 52.24 when back-propagation neural network (BPNN) was used. With generalized regression neural network (GRNN), the r increased to 0.70 and %MAE also improved to 46.30, which further increased to r = 0.77 and %MAE = 36.66 when support vector machine (SVM) based method was used. Similarly, cross-location validation achieved r = 0.48, 0.56 and 0.66 using REG, BPNN and GRNN respectively, with their corresponding %MAE as 77.54, 66.11 and 58.26. The SVM-based method outperformed all the three approaches by further increasing r to 0.74 with improvement in %MAE to 44.12. Overall, this SVM-based prediction approach will open new vistas in the area of forecasting plant diseases of various crops. CONCLUSION: Our case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases. In this direction, we have also developed a SVM-based web server for rice blast prediction, a first of its kind worldwide, which can help the plant science community and farmers in their decision making process. The server is freely available at http://www.imtech.res.in/raghava/rbpred/.

Agriculture↗

PubMed1987

Artificial intelligence--its use in nephrology.

Artificial intelligence techniques and expert systems are graduating from research laboratories to enter many human domains of activities. However, initial expectations of the seventies have been transformed with time to a more sober reality. Starting from examples in nephrology, this paper tries to give a balanced view of this new technology. Justification, indication and limitation of present expert systems are discussed as regards their possible goals. The respective roles of the experts and knowledge engineers are described. The need to integrate the artificial intelligence approach into present patient database management systems in order to build up expert database systems is evaluated.

Database Management Systems↗

PubMed1994

Parameters for a knowledge base for acute appendicitis.

Acute appendicitis is the most common cause of acute abdomen requiring surgical intervention. The clinical diagnosis of acute appendicitis is not always easy due to variable symptomatology, particularly at the onset. The contributions of history-taking and physical examination in the diagnosis of acute appendicitis was studied in connection with the Research Committee of the World Organization of Gastroenterology (OMGE) survey of acute abdominal pain. Especially the suitability of diagnostic parameters in the construction of an expert system for automatic decision making was studied. The results clearly show that it is possible to construct an expert system for automatic decision making in the diagnosis of acute appendicitis.

Abdomen, Acute↗

PubMed2004

Bioterrorism and smallpox planning: information and voluntary vaccination.

Although smallpox was declared eradicated in 1980, there are fears that stocks of the virus manufactured for military purposes by the Soviet Union may have fallen into the hands of "rogue nations" or terrorists. Worries about bioterrorism have thus sparked debate about whether or not the smallpox vaccine, which can be dangerous, should be offered to the general public. Meaningful public debate on this issue requires expert information about the likelihood that the virus will in fact be used as a weapon. Informed voluntary individual decision making, about whether to get vaccinated if vaccine is made available to the public, would similarly require appreciation of the likelihood of attack. Public deliberation and private deliberation thus both require briefing by the intelligence community.

Bioterrorism↗

PubMed1988

Patterns and contrasts in ophthalmic investigation.

The Snellen test has been the most popular clinical measurement of spatial vision for over a century, but it does not fully express the visual ability of an individual. For more analytical purposes the information capacity of the visual system may be assessed by tests of contrast sensitivity and peripheral vision. The visual system selectively reduces the spatial information content of the visual field to avoid overloading the limited capacity for perception and decision making in the brain. The ways in which this reduction occurs and the processing of spatial information is of interest in many disciplines, and theoretical knowledge has been accelerated by the study of artificial intelligence. These processes may be investigated in human subjects by psychophysical and electrophysiological techniques. This provides additional information for diagnostic purposes and will form the basis of new systems of clinical investigation.

Evoked Potentials, Visual↗

PubMed1998

TACHY: an expert system for the management of supraventricular tachycardia in the elderly.

PURPOSE: Many physicians find the management of supraventricular tachyarrhythmia (SVT) in the elderly complex and challenging. With the use of artificial intelligence theory, we developed an interactive computer expert system, TACHY, to recommend therapies and warn physicians of potential therapeutic side effects. METHODS: We developed a knowledge base that stores guidelines for the management of SVT in the elderly. After the diagnosis of current SVT was input into the computer, TACHY generated a list of therapeutic options as hypotheses. TACHY then prompted the user to provide current patient-specific clinical information, and the optimal therapeutic option was then selected. Potential therapeutic side effects were also displayed. TACHY was tested in a retrospective and a prospective study. The retrospective study, comprising 96 patients with 126 episodes of SVT, was performed to determine the concordance of therapy between TACHY and cardiologists. A prospective study in 18 patients with 26 episodes of SVT was also performed to validate TACHY's recommendations in restoring sinus rhythm. RESULTS: In the retrospective study the concordance between TACHY and the cardiologist's first therapeutic option was 95.9% (121 of 126 episodes). In the prospective study sinus rhythm was restored in 18 (69%) of 26 episodes of SVT by the first therapeutic option recommended by TACHY. In the remaining eight episodes use of the second or third suggestion of TACHY was successful in controlling the ventricular response to SVT. No adverse reaction to the therapeutic options suggested by TACHY occurred. CONCLUSION: TACHY, a knowledge-based computer expert system that simulates human decision making, produced promising results in the management of SVT in the elderly.

Aged↗

PubMed2004

Education-stratified base-rate information on discrepancy scores within and between the Wechsler Adult Intelligence Scale--Third Edition and the Wechsler Memory Scale--Third Edition.

The Wechsler Adult Intelligence Scale--Third Edition (WAIS-III; D. Wechsler, 1997a) and the Wechsler Memory Scale--Third Edition (WMS-III; D. Wechsler, 1997b) are 2 of the most frequently used measures in psychology and neuropsychology. To facilitate the diagnostic use of these measures in the clinical decision-making process, this article provides information on education-stratified, directional prevalence rates (i.e., base rates) of discrepancy scores between the major index scores for the WAIS-III, the WMS-III, and between the WAIS-III and WMS-III. To illustrate how such base-rate data can be clinically used, this article reviews the relative risk (i.e., odds ratio) of empirically defined "rare" cognitive deficits in 2 of the clinical samples presented in the WAIS-III--WMS-III Technical Manual (The Psychological Corporation, 1997).

Educational Status↗

PubMed1992

Prognosis of prenatally diagnosed children with sex chromosome aneuploidy.

Sex chromosome aneuploidy (SCA) occurs in about 1/250 amniocenteses, and the significance of the long-term prognosis of fetuses with SCA is of concern to prospective parents and health care providers. Longitudinal studies in an unselected group of newborn infants with SCA diagnosed postnatally have refuted allegations of mental retardation but have documented an increased risk for developmental problems. Of the 530 phone consultations with parents faced with a prenatal diagnosis of SCA, 68% continued the pregnancy. Twenty of the oldest subsequently born children (now 7-14 years old) were available for follow-up. In this small sample and age group, the propositi are progressing developmentally at a rate comparable to their sibs and are doing better at school and in peer relations than the SCA group diagnosed postnatally. Only 2 have documented IQs as low as 90. The documented IQs of the remainder, none of whom are sex chromosome mosaics, are all over 110. The parent population in this prenatally diagnosed group is unique and different from that of the postnatally diagnosed group in that over 85% of them are college graduates, often professionals, and upper socioeconomic individuals. The developmental competence of this SCA sample may be attributable to the supportive environment provided by these families, all of whom made a conscious decision to continue the pregnancy.

Adolescent↗

PubMed2026

Oncotype DX: Clinical Utility, Evidence, and Future Trends in Personalized Breast Cancer Management.

The Oncotype DX assay has revolutionized the management of early-stage, hormone receptor-positive, HER2-negative breast cancer. Developed in 2004, it quantifies 21 genes to generate a recurrence score that predicts distant recurrence risk and guides adjuvant chemotherapy. Multiple studies have validated its reliability and clinical utility in enabling more precise risk stratification and individualized treatment planning, thereby minimizing unnecessary chemotherapy exposure and improving patient outcomes. Leading oncology organizations such as the American Society of Clinical Oncology and National Comprehensive Cancer Network have incorporated it into their clinical guidelines. Beyond its well-established role in adjuvant chemotherapy decision-making, Oncotype DX is increasingly being investigated in broader clinical contexts, including lymph node-positive breast cancer, neoadjuvant therapy, radiotherapy, and ductal carcinoma in situ. Ongoing research and technological advancements, such as artificial intelligence-based predictive models and novel biomarker identification, hold significant promise for further enhancing its predictive accuracy and expanding its applications. This review synthesizes current evidence supporting the clinical utility of Oncotype DX, discusses evolving applications, and highlights future directions for integrating this genomic tool into precision oncology practice.

Humans↗

PubMed1973

The normal.

Explore the source record for details and available documents.

Adaptation, Physiological↗

PubMed1988

Perspectives on clinical decision-making: the application of pattern analysis.

It is the thesis of this presentation that more precise diagnoses and prognostications result when clinical information is analyzed as data-sets or patterns rather than collections of discrete data. Two examples are given in support of the thesis: the classification by computer of QRS complexes in the ambulatory electrocardiogram and the prediction of risk for recurrent ventricular tachycardia. The advantages to be gained from pattern analysis are: (1) significant variables are not preselected and the data are, therefore, unbiased, and (2) nuances in clinical patterns become evident when patients are presented as data-sets. It is proposed that competent physicians undoubtedly use pattern analysis in their decision-making and that expert systems designed to simulate physician behavior might be more accurate if based in pattern analysis.

Artificial Intelligence↗

PubMed1998

Properties of support vector machines.

Support vector machines (SVMs) perform pattern recognition between two point classes by finding a decision surface determined by certain points of the training set, termed support vectors (SV). This surface, which in some feature space of possibly infinite dimension can be regarded as a hyperplane, is obtained from the solution of a problem of quadratic programming that depends on a regularization parameter. In this article, we study some mathematical properties of support vectors and show that the decision surface can be written as the sum of two orthogonal terms, the first depending on only the margin vectors (which are SVs lying on the margin), the second proportional to the regularization parameter. For almost all values of the parameter, this enables us to predict how the decision surface varies for small parameter changes. In the special but important case of feature space of finite dimension m, we also show that m + 1 SVs are usually sufficient to determine the decision surface fully. For relatively small m, this latter result leads to a consistent reduction of the SV number.

Artificial Intelligence↗

PubMedDate not supplied

Precaution: belief, regulatory system, and overarching principle.

The precautionary principle has been incorporated as a belief statement in international agreements for more than a decade. The 1998 Wingspread definition of the principle was the first to bring together four components that, in the past two years, have formed the elements of a broader, overarching approach to precaution that is a robust basis for its specific implementation: prompt action even in the face of scientific uncertainty, burden of proof and persuasion on proponents of potentially hazardous technologies, assessment of alternatives, and transparency. This broad approach to precaution is in direct conflict with the simplistic, easily manipulated principles and methods of risk-assessment-based risk management being exported by U.S. officialdom. In contrast to risk assessment, precaution, broadly defined, incorporates the full range of human intelligence in the task of protecting human health and the environment: flexibility, foresight, fairness, thoughtful consideration, and honesty.

Decision Making, Organizational↗

PubMed2026

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep learning techniques. OBJECTIVE: This study aimed to investigate the application of deep learning technology, specifically the U-Net model, in classifying and grading lymphoma subtypes to enhance diagnostic precision and efficiency. METHODS: In this study, the U-Net model was used as the primary tool for image segmentation integrated with attention mechanisms and residual networks for feature extraction and classification. A total of 620 high-quality histopathological images representing 3 major lymphoma subtypes were collected from The Cancer Genome Atlas and the Cancer Imaging Archive. All images underwent standardized preprocessing, including Gaussian filtering for noise reduction, histogram equalization, and normalization. Data augmentation techniques such as rotation, flipping, and scaling were applied to improve the model's generalization capability. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. Five-fold cross-validation was used to assess model robustness. Performance was benchmarked against mainstream convolutional neural network architectures, including fully convolutional network, SegNet, and DeepLabv3+. RESULTS: The U-Net model achieved high segmentation accuracy, effectively delineating lesion regions and improving the quality of input for classification and grading. The incorporation of attention mechanisms further improved the model's ability to extract key features, whereas the residual structure of the residual network enhanced classification accuracy for complex images. In the test set (N=1250), the proposed fusion model achieved an accuracy of 92% (1150/1250), a sensitivity of 91.04% (1138/1250), a specificity of 89.04% (1113/1250), and an F1-score of 90% (1125/1250) for the classification of the 3 lymphoma subtypes, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97). The high sensitivity and specificity of the model indicate strong clinical applicability, particularly as an assistive diagnostic tool. CONCLUSIONS: Deep learning techniques based on the U-Net architecture offer considerable advantages in the automated classification and grading of lymphoma subtypes. The proposed model significantly improved diagnostic accuracy and accelerated pathological evaluation, providing efficient and precise support for clinical decision-making. Future work may focus on enhancing model robustness through integration with advanced algorithms and validating performance across multicenter clinical datasets. The model also holds promise for deployment in digital pathology platforms and artificial intelligence-assisted diagnostic workflows, improving screening efficiency and promoting consistency in pathological classification.

Humans↗