Search PubMed⌕ Search

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.

At least 433 records · Page 24Linked to original sources
PubMedDate not supplied

Ten years of medical informatics. Introduction.

The discussions of the Tenth Anniversary of the Symposium on Computer Applications in Medical Care (SCAMC) are summarized. Eight different subject areas are addressed: Medical informatics and medical education; Decision making, medical artificial intelligence, modelling and simulations; Image processing, 3-D graphics, and computer networks; Reimbursement policy, legal and regulatory issues; Encoding and representation of medical meaning; Ambulatory medical records systems; Hospital information systems; and Software environments for developing medical information systems. The activities of the 10th SCAMC consisted of Tutorials, Panel Discussions, a Plenary Session, Scientific Demonstrations, and an International Student Paper Competition in Medical Informatics.

Computer Communication Networks↗
PubMedDate not supplied

The Section on Medical Expert and Knowledge-Based Systems at the Department of Medical Computer Sciences of the University of Vienna Medical School.

The Section on Medical Expert and Knowledge-Based Systems at the Department of Medical Computer Sciences pursues methodological research in and practical development of knowledge-based computer systems to assist in the decision-making processes for all areas of medical application. Vagueness of medical terms, uncertainty in the co-occurrence of medical entities, and incompleteness in medical theories are well-known characteristics of medical knowledge and ought to be considered in practically-used medical knowledge-based systems. We found that fuzzy set theory and fuzzy logic are powerful theories that model the above-mentioned characteristics. Fuzzy set theory and fuzzy logic were applied in the following systems: CADIAG-II and MedFrame/CADIAG-IV, FuzzyARDS, and FuzzyKBWean. CADIAG-II and MedFrame/CADIAG-IV are framework programs for consultation systems to aid in the differential diagnostic process in internal medicine. FuzzyARDS is an intelligent on-line monitoring program of data from patients with acute respiratory distress syndrome (ARDS) at an intensive care unit (ICU). It employs fuzzy trend detection and fuzzy automata. FuzzyKBWean is an open-loop fuzzy control program for optimization and quality control of the ventilation and weaning process of patients after cardiac surgery at the ICU. The above-mentioned computer systems have reached the state of extensive clinical integration and testing at the Vienna General Hospital. The obtained results show the applicability and usefulness of these systems.

Artificial Intelligence↗
PubMed2002

Expected value prioritization of prompts and reminders.

Computer-based prompting and reminder systems have been shown to be highly effective in increasing rates of preventive services delivery. However, there are many more recommended preventive services than can be practically included in a typical clinic visit. Therefore prioritization of preventive services prompts is necessary. We describe two approaches to prioritizing preventive services prompts based on expected value decision making. One method involves a static, global prioritization across all preventive services and has been used in a production system for almost 7 years. The second method uses influence diagrams to prioritize prompts dynamically, based on individual patient data. The latter approach is still under development. Both methods are labor intensive and require a combination of epidemiologic data and expert judgment. Compromises in strictly normative process were necessary to achieve user satisfaction.

Artificial Intelligence↗
PubMed2003

A knowledge-based approach to the deflocculation problem: integrating on-line, off-line, and heuristic information.

A knowledge-based approach for the supervision of the deflocculation problem in activated sludge processes was considered and successfully applied to a full-scale plant. To do that, a methodology that integrates on-line, off-line and heuristic information has been proposed. This methodology consists of three steps: (i). development of a decision tree (which involves knowledge acquisition and representation); (ii). implementation into a rule-based system; and (iii). validation. The set of symptoms most useful in diagnosing the deflocculation problem has been identified, the different branches to diagnose pin-point floc and dispersed growth have been built (using generic and specific knowledge), and all this knowledge has been codified into an object-oriented shell. The results obtained in the application of this knowledge-based approach to the Granollers WWTP (which treats about 130000 inhabitants-equivalents) showed that the system was able to identify correctly the problem with reasonable accuracy. Our positive experience building this system suggests that this approach is a practical and valuable element to include in an intelligent supervisory system combining numerical and reasoning techniques.

Artificial Intelligence↗
PubMed1996

Deep assessment of machine learning techniques using patient treatment in acute abdominal pain in children.

Learning from patient records may aid knowledge acquisition and decision making. Existing inductive machine learning (ML) systems such us NewId, CN2, C4.5 and AQ15 learn from past case histories using symbolic and/or numeric values. These systems learn symbolic rules (IF... THEN like) which link an antecedent set of clinical factors to a consequent class or decision. This paper compares the learning performance of alternative ML systems with each other and with respect to a novel approach using logic minimization, called LML, to learn from data. Patient cases were taken from the archives of the Paediatric Surgery Clinic of the University Hospital of Crete, Heraklion, Greece. Comparison of ML system performance is based both on classification accuracy and on informal expert assessment of learned knowledge.

Abdomen, Acute↗
PubMed2005

Tissue engineering scheming by artificial intelligence.

Tissue engineers are often confused when seeking the most effective, economical and secure scheme for tissue engineering. The aim of this study is to generate tissue engineering schemes with artificial intelligence instead of human intelligence. The experimental data of tissue engineered cartilage were integrated and standardized with a centralized database, and a scheme engine was developed using artificial intelligent methods (artificial neural networks and decision trees). The scheme engine was trained with existing cases in the database, and then was used to generate tissue engineering schemes for new experimental animals. Following the schemes generated by the artificial intelligent system, we cured 18 of the 20 experimental animals. In conclusion, artificial intelligence is a powerful method for decision making in the tissue engineering realm.

Algorithms↗
PubMed2026

Circulating Tumor DNA in Breast Cancer: A Liquid Biopsy Revolution for Non-Invasive Genomic Profiling and Clinical Decision-Making.

Breast cancer remains the most frequently diagnosed cancer and a leading cause of cancer-related mortality among women worldwide, underscoring the need for accurate, minimally invasive biomarkers to support precision oncology. Conventional tissue biopsy remains the standard for molecular characterization but is limited by its invasiveness, inability to capture spatial and temporal tumor heterogeneity, and challenges in serial monitoring. Circulating tumor DNA (ctDNA), a tumor-derived fraction of cell-free DNA, has emerged as a promising liquid biopsy biomarker capable of providing real-time genomic information throughout disease progression. This narrative review examines recent advances in ctDNA biology, analytical technologies, clinical applications, current limitations, and future directions in breast cancer management. A structured literature search of PubMed/MEDLINE, Scopus, Embase, Web of Science, and Google Scholar identified relevant English-language publications from 2015 to 2026. Current evidence indicates that highly sensitive platforms, including digital PCR, BEAMing, and next-generation sequencing, can detect clinically actionable alterations in genes such as PIK3CA, ESR1, TP53, ERBB2, AKT1, and BRCA1/2. ctDNA has demonstrated particular utility in identifying minimal residual disease, monitoring therapeutic response, detecting emerging resistance mechanisms, and guiding targeted treatment selection in advanced breast cancer. However, applications in early cancer detection, population screening, and artificial intelligence-assisted clinical decision-making remain investigational. Widespread clinical implementation is constrained by low ctDNA abundance in early-stage disease, analytical variability, limited assay standardization, and cost considerations. Continued technological innovation, prospective multicenter validation, standardized testing protocols, and evidence-based clinical guidelines are essential to fully integrate ctDNA into routine precision breast cancer care.

breast cancer↗
PubMed1995

Structured knowledge representation: an improved methodology for communication of hospital policy.

The optimal operational integrity of a health care facility depends upon the correct interpretation of an adherence to well designed and written policies. Memos describing policy and procedures can be ambiguous, hindering their comprehension rather than helping it. Two alternative methods have been developed for communicating policy: the algorithmic flow chart and a computer decision support program. To determine the best means of communicating policy, the written memo, flow chart, and computer program were compared in scenario presentation interviews. The average time required to complete the test scenarios was five minutes for the traditional memo, two minutes for the algorithm, and two minutes for the computer program. Accuracy improves markedly from the traditional memo to the computer program. ANOVA reveals that the flow chart and computer program are significantly superior to memos as methods for communicating hospital policy.

Algorithms↗
PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗
PubMed1994

The use of the Arden Syntax for MLMs in HIOS+, a decision support system for general practitioners in The Netherlands.

This paper covers query creation and evaluation in HIOS+, a General Practitioner Decision Support System. The International Classification of Primary Care (ICPC), free-text International Classification of Health Problems in Primary Care (ICHPPC)-II-Defined criteria, and the Arden Syntax for medical logic modules (MLMs) are building blocks in query creation with the free-text formalizer (FF) module. The backward-reasoning formal interpreter (FI) module evaluates queries by checking them against medical patient data. It can advise or warn users on the suitability of a registered diagnostic ICPC code in a patient's case. In all, 101 queries were created in MLM form on a wide range of diseases. The MLMs and FI were evaluated in a field and a laboratory test.

Artificial Intelligence↗
PubMed2004

Knowledge management in pediatric pain: mapping on-line expert discussions to medical literature.

Clinical decision-making can be vastly improved with the availability of the right medical knowledge at the right time. This concept paper presents a knowledge management re-search program to (a) identify, capture and organize the tacit knowledge inherent within on-line problem-solving discussions between pediatric pain practitioners; (b) establish linkages between topic-specific pediatric pain discussions and corresponding published medical literature on children's pain available at PubMed--i.e. linking tacit expert knowledge to explicit medical literature; and (c) make these knowledge re-sources available to pediatric pain practitioners via the WWW for timely access to various modalities of clinical knowledge.

Artificial Intelligence↗
PubMedDate not supplied

Knowledge-based patient screening for rare and emerging infectious/parasitic diseases: a case study of brucellosis and murine typhus.

Many infectious and parasitic diseases, especially those newly emerging or reemerging, present a difficult diagnostic challenge because of their obscurity and low incidence. Important clues that could lead to an initial diagnosis are often overlooked, misinterpreted, not linked to a disease, or disregarded. We constructed a computer-based decision support system containing 223 infectious and parasitic diseases and used it to conduct a historical intervention study based on field investigation records of 200 cases of human brucellosis and 96 cases of murine typhus that occurred in Texas from 1980 through 1989. Knowledge-based screening showed that the average number of days from the initial patient visit to the time of correct diagnosis was significantly reduced (brucellosis-from 17.9 to 4.5 days, p = 0.0001, murine typhus-from 11.5 to 8.6 days, p = 0.001). This study demonstrates the potential value of knowledge-based patient screening for rare infectious and parasitic diseases.

Artificial Intelligence↗
PubMed1991

[Medical diagnosis].

The diagnostic process holds a firm position in medical practice, but is often claimed to be part of the "art of medicine", partially beyond reach of rational and logical analysis. Research in clinical cognition, decision analysis and artificial intelligence have, however, elucidated essential parts of medical diagnosis. A characteristic feature of diagnosis is the manner in which uncertainties are handled. Early generation of hypotheses about the nature of the condition present seems to be useful method. Similarly, probabilistic, causal and deterministic reasoning can be illustrated by diagnostic models which have found favor during recent years. A certain type of cognitive process (heuristic) is employed when assessing information of probabilistic nature. The diagnostic models are partial and concern the parts of the process, which may be represented verbally and consciously. This raises the question of how the clinician actually draws upon experience (background knowledge), which preconditions shape the generation of applicable diagnostic hypotheses and how the diagnostic capability of the individual physician can be facilitated.

Clinical Competence↗
PubMed2000

Design, implementation and evaluation of a clinical decision support system to prevent adverse drug events.

Adverse drug events are known to be a major health problem worldwide. It is estimated that the annual costs related to these events in the United States are greater than the total costs with cardiovascular disease care. Decision support systems that assist drug ordering have demonstrated to be a powerful tool to prevent prescription errors and adverse drug events. On the other hand, some issues related to the development, implementation, configuration, and evaluation of these decision support systems still need further research. This paper presents the development and evaluation of a decision support system prototype that helps with the prevention of adverse drug events by detecting drug-drug interactions in drug orders. The structure of the system tries to solve some of the problems described by the literature, such as integration with hospital information systems, adaptability to local needs, and knowledge base maintenance. The proposed model has shown to be an effective method for representing drug-drug interactions. The prototype was evaluated by a retrospective study using a dataset with 37.237 prescriptions. The system was able to detect 10.044 (27.0%) orders containing one or more drug-drug interactions. Among these interactions, 6.4% had high severity. In a future study, it is intended to apply the developed system in a real-time on-line environment, evaluating the benefits achieved in terms of improvement in medical practice and patient outcomes.

Adverse Drug Reaction Reporting Systems↗
PubMed2003

Step-by-step mark-up of medical guideline documents.

Approaches to formalization of medical guidelines can be divided into model-centric and document-centric. While model-centric approaches dominate in the development of clinical decision support applications, document-centric, mark-up-based formalization is suitable for application tasks requiring the 'literal' content of the document to be transferred into the formal model. Examples of such tasks are logical verification of the document or compliance analysis of health records. The quality and efficiency of document-centric formalization can be improved using a decomposition of the whole process into several explicit steps. We present a methodology and software tool supporting the step-by-step formalization process. The knowledge elements can be marked up in the source text, refined to a tree structure with increasing level of detail, rearranged into an XML knowledge base, and, finally, exported into the operational representation. User-definable transformation rules enable to automate a large part of the process. The approach is being tested in the domain of cardiology. For parts of the WHO/ISH Guidelines for Hypertension, the process has been carried out through all the stages, to the form of executable application, generated automatically from the XML knowledge base.

Artificial Intelligence↗
PubMed1995

Prudent expert systems with credentials: managing the expertise of decision support systems.

'Black box' expert systems (ES) are mistrusted by clinicians. Errors generated by medical ES are also a significant cause for concern. We report new ES properties--prudence and credentials--that improve error management and underpin a new approach for improving the credibility of ES for clinical users. Prudent ES modify their output according to past experience. For a knowledge base built from 1610 cases, feature exception prudence (FEP) detected all interpretation errors (100% sensitivity for error detection). Although the false positive rate for FEP was high (47%), the 100% sensitivity meant that the 53% of cases that did not produce flags could be exempted from human validation. As more cases are processed, fewer cases should need human validation. Feature recognition prudence (FRP), a property of ripple down rules (RDR), proposed the correct alternative conclusion in 14% of incorrectly interpreted cases. Human expert validation of the flagged cases enabled context-sensitive credentials (accuracy, incidence and specificity of a given conclusion) to accumulate. Credentials should enable the user to judge the credibility of the ES output. An error management strategy based on credentialed, prudent ES should reduce the impact of error in the clinical environment. The empowerment of clinicians to critically evaluate ES credibility may facilitate greater confidence in, and acceptance of, ES by clinicians.

Artificial Intelligence↗
PubMed2003

A meta-data model for knowledge in decision support systems.

Clinical decision support such as alerts, reminders and guidance are driven by rules often distributed among a variety of applications in a healthcare information system. Due to the increasing size of rule bases, there is a growing need to manage this dispersed knowledge in an integrated environment. A system for management of executable clinical knowledge such as rules should (1) assist in the development and maintenance of rules throughout the rules' life-cycles, (2) support search and retrieval of rules in the knowledge base (e.g., rules for diabetes, rules created by a particular individual), and (3) facilitate the analyses of rules in the knowledge base (e.g., identify rules not updated in the last year). In order to create such a clinical knowledge management system it is necessary to model the meta-data of rules. There have been efforts to document meta-data about rules within the Arden Syntax Medical Logical Modules' project. However, the maintenance and library categories in that project allow mainly free-text information about a rule. We have created a comprehensive meta-data structure and taxonomy for describing clinical rules that supports the features of a knowledge management system. We also tested this model using a representative set of rules.

Artificial Intelligence↗
PubMed1998

Using classification tree and logistic regression methods to diagnose myocardial infarction.

Early and accurate diagnosis of myocardial infarction (MI) in patients who present to the Emergency Room (ER) complaining of chest pain is an important problem in emergency medicine. A number of decision aids have been developed to assist with this problem but have not achieved general use. Machine learning techniques, including classification tree and logistic regression (LR) methods, have the potential to create simple but accurate decision aids. Both a classification tree (FT Tree) and an LR model (FT LR) have been developed to predict the probability that a patient with chest pain is having an MI based solely upon data available at time of presentation to the ER. Training data came from a data set collected in Edinburgh, Scotland. Each model was then tested on a separate Edinburgh data set, as well as on a data set from a different hospital in Sheffield, England. Previously published models, the Goldman classification tree[1] and Kennedy LR equation[2], were evaluated on the same test data sets. On the Edinburgh test set, results showed that the FT Tree, FT LR, and Kennedy LR performed equally well, with ROC curve areas of 94.04%, 94.28%, and 94.30%, respectively, while the Goldman Tree's performance was significantly poorer, with an area of 84.03%. The difference in ROC areas between the first three models and the Goldman model is significant beyond the 0.0001 level. On the Sheffield test set, results showed that the FT Tree, FT LR, and Kennedy LR ROC areas were not significantly different (p > = 0.17), while the FT Tree again outperformed the Goldman Tree (p = 0.006). Unlike previous work[3], this study indicates that classification trees, which have certain advantages over LR models, may perform as well as LR models in the diagnosis of patients with MI.

Algorithms↗