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At least 1,405 records · Page 78Linked to original sources
PubMed2000

Neuropsychological deficit profiles in systemic lupus erythematosus.

Although neuropsychological deficits have been reported in several cognitive domains in patients with systemic lupus erythematosus (SLE), there is considerable variability in the literature about which neuropsychological domains are most affected. Similar to studies that demonstrated that specific profiles of neuropsychological deficits exist for those with traumatic brain injury (TBI; Johnstone, Hexum, & Ashkanazi, 1995), this study examined whether a specific pattern of deficits is present in SLE. By comparing reading scores (as estimates of premorbid ability) to tests of concurrent cognitive abilities (i.e., memory, attention, etc.), it was determined that SLE presents a profile distinct from TBI, with the most significant impairments noted in expressive language (Zdiff = -1.39), attention (Zdiff = -0.41), and speed of processing (Zdiff = -0.40). In contrast to TBI, no impairment was noted in intelligence, memory, or cognitive flexibility. Results suggest that memory problems reported by individuals with SLE may be related to inattention. Clinical implications are discussed.

Adult↗
PubMed1988

Evaluating antiarrhythmic strategies: a knowledge-based system for exploring clinical data.

Medical therapy for cardiac arrhythmias is still to a large extent based on empirical methods. Assessing and evaluating different therapeutical strategies constitutes the starting point for inducing decision methods to select the appropriate regimen for an individual patient. We designed a computer-based system that establishes a set of heuristic rules linking attributes in a data base of patients with rhythm disturbances. A feasibility analysis conducted on a small set of 23 patients indicated that constraints on the number of attributes and their clinical relevancy together with a representation scheme for temporal changes have to be incorporated to provide for a useful and efficient algorithm.

Algorithms↗
PubMed1994

A corpus-based study of repair cues in spontaneous speech.

The occurrence of disfluencies in fully natural speech poses difficult challenges for spoken language understanding systems. For example, although self-repairs occur in about 10% of spontaneous utterances, they are often unmodeled in speech recognition systems. This is partly due to the fact that little is known about the extent to which cues in the speech signal may facilitate automatic repair processing. In this paper, acoustic and prosodic cues to self-repairs are identified, based on an analysis of a corpus taken from the ARPA Air Travel Information System database, and methods are proposed for exploiting these cues for repair detection, especially the task of modeling word fragments, and repair correction. The relative contributions of these speech-based cues, as well as other text-based repair cues, are examined in a statistical model of repair site detection that achieves a precision rate of 91% and recall of 86% on a prosodically labeled corpus of repair utterances.

Artificial Intelligence↗
PubMed1994

Connectionism and psychoanalysis.

A currently interesting set of models of perception, learning, and cognition--known as connectionist or neural net systems--have contributed to changes in the way cognitive scientists view brain function. A fruitful interaction between brain models and computer models leads us to think that the brain may be less dependent on a central processor, that there may be much brain work that is self-organizing, and that mind-brain dualism may be unnecessary. This article explores the implications for psychoanalytic theory that emerge from these new models.

Artificial Intelligence↗
PubMed2001

Efficient DNA database laboratory strategy for high through-put STR typing of reference samples.

DNA intelligence databases were installed successfully in various countries during the past few years. It is a general trend that laboratories performing STR analysis for DNA databases have to adjust to increased sample through-put, especially when dealing with a high number of reference samples. In contrast to routine forensic casework analysis, where samples of suspects and unknown samples are interpreted with regard to the specific circumstances of the case and are kept distinctly apart from other cases, DNA databases consist of single, primarily unlinked DNA profiles. Problems areas associated with the high number of anonymous DNA profiles are the risk of logistic errors, such as sample mix-up during the laboratory procedure, and the risk of typing errors during manual transcription of data and/or results. Thus, DNA databases clearly require new laboratory strategies to rise to the challenge. This paper presents an efficient automated laboratory strategy on the platform of a laboratory management information system (LIMS) with the Austrian DNA Intelligence Database as example. Two goals were tackled in particular: first, data safety by avoiding both manual interaction during critical laboratory steps (i.e. when DNA is transferred form one tube into another), and errors due to manual transcription of sample information and results. Secondly, efficient sample processing by automizing the laboratory procedure with the help of robotic instruments, thus, giving the DNA staff more time to analyze data.

Austria↗
PubMed1997

NéoGanesh: a working system for the automated control of assisted ventilation in ICUs.

Automating the control of therapy administered to a patient requires systems which integrate the knowledge of experienced physicians. This paper describes NéoGanesh, a knowledge-based system which controls, in closed-loop, the mechanical assistance provided to patients hospitalized in intensive care units. We report on how new advances in knowledge representation techniques have been used to model medical expertise. The clinical evaluation shows that such a system relieves the medical staff of routine tasks, improves patient care, and efficiently supports medical decisions regarding weaning. To be able to work in closed-loop and to be tested in real medical situations, NéoGanesh deals with a voluntarily limited problem. However, embedded in a powerful distributed environment, it is intended to support future extensions and refinements and to support reuse of knowledge bases.

Artificial Intelligence↗
PubMed1995

Third generation electronic medical record knowledge based perspectives.

There is a need to develop better electronic medical records. One possible solution is to put more and more 'routine' medical knowledge into systems handling medical records. In this paper, we analyze the current state-of-the-art of knowledge-based medical record handling; we mainly consider the work of Rector et al. [1]. We offer a more detailed 'four level' knowledge level model compared to the 'two level' model of Rector. The EMR of the future might be approached with a top-down method, using the above mentioned 'four level' model.

Artificial Intelligence↗
PubMed2026

An AI-assisted Clinical Decision Support System for Green Classification of Cystocele on Dynamic Transperineal Ultrasound.

Green classification of cystocele on dynamic transperineal ultrasound (TPUS) remains operator-dependent because it requires manual frame selection and landmark-based assessment of the Valsalva maneuver. We developed a workflow-oriented AI-assisted clinical decision support system for automated urethrovesical junction localization and dynamic Green classification and prospectively evaluated its standalone and reader-support performance. This diagnostic accuracy and reader study included 881 patients from a tertiary referral hospital, comprising a retrospective development cohort (n = 688) and an independent prospective test cohort (n = 193). A nested subset of 67 prospective patients was used for a reader study involving two junior and two intermediate radiologists under unaided and AI-assisted conditions. In the complete prospective test cohort, Green-AttGRU achieved a macro-averaged AUC of 0.939 (95% CI, 0.897-0.971) and an overall accuracy of 0.902 (95% CI, 0.860-0.943). In the reader study, overall accuracy increased from 0.761 to 0.821 without AI to 0.851-0.881 with AI, while macro-F1 increased from 0.660 to 0.777 to 0.820-0.860. Overall inter-reader agreement increased from a Fleiss' κ of 0.453 to 0.786, and pooled median interpretation time decreased from 26.7 s to 9.9 s. These findings support the preliminary feasibility of the system as a workflow-oriented decision-support tool for dynamic TPUS interpretation.

Humans↗
PubMed1993

The representation of medical reasoning models in resolution-based theorem provers.

First-order predicate logic essentially is a language to express knowledge concerning objects and relationships between objects in a domain. Many medical problems can be cast naturally in such terms. In this paper the suitability of logic as a knowledge-representation formalism for building medical expert systems is investigated. In particular, we investigate the logical representation of three typical reasoning models in medicine: diagnostic, anatomical and causal reasoning. It turns out that each of these models has its own characteristic logical structure. Furthermore, the pragmatics of using theorem-proving techniques in consulting such logic-based medical expert systems is discussed. In particular, attention is paid to the use of a meta-level architecture to improve the applicability of theorem-proving techniques in building expert systems.

Anatomy↗
PubMed1994

Effective retrieval in Hospital Information Systems: the use of context in answering queries to Patient Discharge Summaries.

The move towards the electronic storage of medical records in Hospital Information Systems (HISs) presents significant challenges for AI retrieval techniques. In this paper, we argue that adequate information retrieval in such systems will have to rely on the exploitation of the conceptual knowledge in those records rather than superficial string searches. However, this course of action is dependent on the developments of natural language processing techniques and on retrieval systems that can exploit semantic/conceptual knowledge. We present a retrieval system, which attempts to realise the second of these developments. This system, called CONIR [developed in the context of the European Community project MENELAS (AIM 2023)] operates in the domain of Patient Discharge Summaries on coronary illness. CONIR uses flexible retrieval techniques, that exploit conceptual context information, over a database of elaborated semantic records. In the course of the paper we outline the sorts of knowledge structures that are required to do this type of retrieval and indicate how they are constructed.

Abstracting and Indexing↗
PubMed2025

Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives.

Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI's role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemedicine, and wearable health technologies. Leveraging machine learning and deep learning, AI can analyze complex data sets, including electronic health records, medical imaging, and genomic profiles, to identify patterns, predict disease progression, and recommend optimized treatment strategies. AI also has the potential to promote equity by enabling cost-effective, resource-efficient solutions in low-resource and remote settings, such as mobile diagnostics, wearable biosensors, and lightweight algorithms. Successful deployment requires addressing critical challenges, including data privacy, algorithmic bias, model interpretability, regulatory oversight, and maintaining human clinical oversight. Emphasizing scalable, ethical, and evidence-driven implementation, key strategies include clinician training in AI literacy, adoption of resource efficient tools, global collaboration, and robust regulatory frameworks to ensure transparency, safety, and accountability. By complementing rather than replacing healthcare professionals, AI can reduce errors, optimize resources, improve patient outcomes, and expand access to quality care. This review emphasizes the responsible integration of AI as a powerful catalyst for innovation, sustainability, and equity in healthcare delivery worldwide.

Humans↗
PubMed1991

INFORM: integrated support for decisions and activities in intensive care.

Many medical decision support systems that have been developed in the past have failed to enter routine clinical practice. Often this is because the developers have failed to analyse in sufficient detail the precise user requirements, because they have produced a system which takes too narrow a view of the patient, or because the decision support facilities have not been sufficiently well integrated into the routine clinical data handling activities. In this paper we discuss how the AIM-INFORM project is setting out to deal with these issues, in the context of the provision of decision support in the intensive care unit.

Artificial Intelligence↗
PubMed2006

The usability axiom of medical information systems.

INTRODUCTION: In this article we begin by connecting the concept of simplicity of user interfaces of information systems with that of usability, and the concept of complexity of the problem-solving in information systems with the concept of usefulness. We continue by stating "the usability axiom" of medical information technology: information systems must be, at the same time, usable and useful. We then try to show why, given existing technology, the axiom is a paradox and we continue with analysing and reformulating it several times, from more fundamental information processing perspectives. DISCUSSION: We underline the importance of the concept of representation and demonstrate the need for context-dependent representations. By means of thought experiments and examples, we advocate the need for context-dependent information processing and argue for the relevance of algorithmic information theory and case-based reasoning in this context. Further, we introduce the notion of concept spaces and offer a pragmatic perspective on context-dependent representations. We conclude that the efficient management of concept spaces may help with the solution to the medical information technology paradox. Finally, we propose a view of informatics centred on the concepts of context-dependent information processing and management of concept spaces that aligns well with existing knowledge centric definitions of informatics in general and medical informatics in particular. In effect, our view extends M. Musen's proposal and proposes a definition of Medical Informatics as context-dependent medical information processing. SUMMARY: The axiom that medical information systems must be, at the same time, useful and usable, is a paradox and its investigation by means of examples and thought experiments leads to the recognition of the crucial importance of context-dependent information processing. On the premise that context-dependent information processing equates to knowledge processing, this view defines Medical Informatics as a context-dependent medical information processing which aligns well with existing knowledge centric definitions of our field.

Algorithms↗
PubMed2000

A knowledge-based patient assessment system: conceptual and technical design.

This paper describes the design of an inpatient patient assessment application that captures nursing assessment data using a wireless laptop computer. The primary aim of this system is to capture structured information for facilitating decision support and quality monitoring. The system also aims to improve efficiency of recording patient assessments, reduce costs, and improve discharge planning and early identification of patient learning needs. Object-oriented methods were used to elicit functional requirements and to model the proposed system. A tools-based development approach is being used to facilitate rapid development and easy modification of assessment items and rules for decision support. Criteria for evaluation include perceived utility by clinician users, validity of decision support rules, time spent recording assessments, and perceived utility of aggregate reports for quality monitoring.

Artificial Intelligence↗
PubMed2005

Evaluation of two dependency parsers on biomedical corpus targeted at protein-protein interactions.

We present an evaluation of Link Grammar and Connexor Machinese Syntax, two major broad-coverage dependency parsers, on a custom hand-annotated corpus consisting of sentences regarding protein-protein interactions. In the evaluation, we apply the notion of an interaction subgraph, which is the subgraph of a dependency graph expressing a protein-protein interaction. We measure the performance of the parsers for recovery of individual dependencies, fully correct parses, and interaction subgraphs. For Link Grammar, an open system that can be inspected in detail, we further perform a comprehensive failure analysis, report specific causes of error, and suggest potential modifications to the grammar. We find that both parsers perform worse on biomedical English than previously reported on general English. While Connexor Machinese Syntax significantly outperforms Link Grammar, the failure analysis suggests specific ways in which the latter could be modified for better performance in the domain.

Abstracting and Indexing↗
PubMed2005

Literature mining and database annotation of protein phosphorylation using a rule-based system.

MOTIVATION: A large volume of experimental data on protein phosphorylation is buried in the fast-growing PubMed literature. While of great value, such information is limited in databases owing to the laborious process of literature-based curation. Computational literature mining holds promise to facilitate database curation. RESULTS: A rule-based system, RLIMS-P (Rule-based LIterature Mining System for Protein Phosphorylation), was used to extract protein phosphorylation information from MEDLINE abstracts. An annotation-tagged literature corpus developed at PIR was used to evaluate the system for finding phosphorylation papers and extracting phosphorylation objects (kinases, substrates and sites) from abstracts. RLIMS-P achieved a precision and recall of 91.4 and 96.4% for paper retrieval, and of 97.9 and 88.0% for extraction of substrates and sites. Coupling the high recall for paper retrieval and high precision for information extraction, RLIMS-P facilitates literature mining and database annotation of protein phosphorylation.

Abstracting and Indexing↗
PubMed2006

Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction.

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have been evolved. In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for epileptic seizure detection. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Some conclusions concerning the impacts of features on the detection of epileptic seizures were obtained through analysis of the ANFIS. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANN model in terms of training performances and classification accuracies. The results confirmed that the proposed ANFIS model has some potential in epileptic seizure detection. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.

Electroencephalography↗
PubMed2001

Representing and querying conceptual graphs with relational database management systems is possible.

This is an experimental study on the feasibility of maintaining medical concept dictionaries in production grade relational database management systems (RDBMS.) In the past, RDBMS did not support transitive relational structures and had therefore been unsuitable for managing knowledge bases. The revised SQL-99 standard, however, may change this. In this paper we show that modern RDBMS that support recursive queries are capable of querying transitive relationships in a generic data model. We show a simple but efficient indexed representation of transitive closure. We could confirm that even challenging combined transitive relationships can be queried in SQL.

Artificial Intelligence↗