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PubMed1993

Aid to discovery in medicine using formal induction techniques.

This article presents a particular approach to the subfield of artificial intelligence referred to as "aid to scientific discovery." The progress made in formal induction techniques suggests that the acquisition and discovery of knowledge may be considered as a refinement cycle in which the computer can play a central role. Some of the perspectives and the main difficulties are illustrated with a study done on the discovery of the causes of scurvy, and some links are made to the concept of scientific dogmas.

Artificial Intelligence↗

PubMed2026

AI-Based 3D Heterogeneous Network Model for Functional Prediction of Epigenetics.

Human biology and diseases are the result of constantly evolving processes within an intricately complex molecular network of interactions, such as epigenetic regulation. Epigenetics refers to heritable changes in gene expression that occur without alterations to the underlying DNA sequence. These changes, driven by mechanisms such as DNA methylation, histone modifications, and noncoding RNAs, play critical roles in regulating chromatin structure and gene activity. Epigenetic regulation offers valuable insights into biological systems, and when integrated with sophisticated analyses, it enables us to gain insights into gene regulation and cellular behavior. Here, we describe an artificial intelligence (AI)-based model that is capable of generating 3-dimensional (3D) heterogeneous network by integrating multimodal data for the functional prediction of epigenetic mechanisms, emphasizing its applications in medicine, developmental biology, and personalized therapeutics. Heterogeneous networks in biology are powerful tools for understanding the complex interactions and interdependencies within biological systems. Key advancements in AI and multiomics data integration have propelled this field, offering new insights into disease mechanisms, biomarker discovery, and therapeutic interventions.

Epigenesis, Genetic↗

PubMedDate not supplied

Beyond robotics: a new proactive research initiative from the EU IST program.

A research initiative on embodied artificial intelligence has been launched in 2004 by the Future and Emerging Technologies (FET) arm of the Information Society Technologies (IST) programme of the European Union. The initiative is called "Beyond Robotics" to emphasize that the research projects funded are required to set ambitious objectives and to aim at breakthroughs going well beyond the state of the art. Four projects worth 20 M E of EC funding were selected for 2004-2008 to address the objectives of the initiative.

Artificial Intelligence↗

PubMedDate not supplied

A computational approach to medical imaging.

Notwithstanding the progress in medical imaging by means of computer-based techniques, several problems still remain unsolved in this field. In particular, a unified approach for the treatment of biological complexity and variability is lacking. Moreover, perceptive and cognitive aspects of medical vision play an important role in a computational approach to medical imaging and must be carefully considered. The recent developments of Computer Vision and Artificial Intelligence suggest that such a computational approach is feasible. As a consequence, symbolic representations of the clinical information contained in the images as well as adequate processing techniques are necessary. In this way the treatment of uncertainty and the qualitative analysis are made possible. Moreover, due to the intrinsic homogeneity of symbolic representations, the comparison of different image sources, signals and clinical data is attainable. In the paper, the basic principles of Computer Vision are summarized and the need of a specific computational theory for medical vision is emphasized. Afterwards, the main characteristics of integrated systems for computational imaging in medicine, are described. Some examples relative to the imaging of the cardiovascular system are also given. Although the development of artificial vision systems in biomedicine is still an area of research, very promising perspectives are opened by a computational approach.

Artificial Intelligence↗

PubMedDate not supplied

Data mining as a tool for research and knowledge development in nursing.

The ability to collect and store data has grown at a dramatic rate in all disciplines over the past two decades. Healthcare has been no exception. The shift toward evidence-based practice and outcomes research presents significant opportunities and challenges to extract meaningful information from massive amounts of clinical data to transform it into the best available knowledge to guide nursing practice. Data mining, a step in the process of Knowledge Discovery in Databases, is a method of unearthing information from large data sets. Built upon statistical analysis, artificial intelligence, and machine learning technologies, data mining can analyze massive amounts of data and provide useful and interesting information about patterns and relationships that exist within the data that might otherwise be missed. As domain experts, nurse researchers are in ideal positions to use this proven technology to transform the information that is available in existing data repositories into useful and understandable knowledge to guide nursing practice and for active interdisciplinary collaboration and research.

Algorithms↗

PubMed1990

[Computer and acid-base disorders].

The diagnosis of acid-base disorders has been subject to computerised analysis for many years. Expert systems, an application of artificial intelligence, have been validated. They are of help to clinical decision making, enabling the different diagnostic possibilities of given acid-base and hydro-electrolytic situations to be evaluated and appropriate therapy to be instituted. Computer networks improve the transmission of laboratory results to the intensive care units and the integration of data facilitates the monitoring of biochemical tendencies. Computers can also be used for teaching acid-base physiology and pathology.

Acid-Base Imbalance↗

PubMed1994

Automated Speech-recognition Anatomic Pathology (ASAP) reporting.

Artificial intelligence speech-recognizers integrated with Laboratory information and Telefaxcommunication Systems have allowed for totally automated surgical pathology reporting. Automated Speech-Recognition Anatomic Pathology (ASAP) reporting improves the speed, text accuracy, comprehensiveness, and workflow management of diagnostic reports while eliminating support personnel. Healthcare reform goals of increased productivity and economy are furthered. Reports are rendered "as soon as possible" (ASAP) expediting appropriate clinical management and decreased length of stay and hospital costs.

Artificial Intelligence↗

PubMed2002

Recent innovations in intensive care unit risk-prediction models.

During the past 20 years, ICU risk-prediction models have undergone significant development, validation, and refinement. Among the general ICU severity of illness scoring systems, the Acute Physiology and Chronic Health Evaluation (APACHE), Mortality Prediction Model (MPM), and the Simplified Acute Physiology Score (SAPS) have become the most accepted and used. To risk-adjust patients with longer, more severe illnesses like sepsis and acute respiratory distress syndrome, several models of organ dysfunction or failure have become available, including the Multiple Organ Dysfunction Score (MODS), the Sequential Organ Failure Assessment (SOFA), and the Logistic Organ Dysfunction Score (LODS). Recent innovations in risk adjustment include automatic physiology and diagnostic variable retrieval and the use of artificial intelligence. These innovations have the potential of extending the uses of case-mix and severity-of-illness adjustment in the areas of clinical research, patient care, and administration. The challenges facing intensivists in the next few years are to further develop these models so that they can be used throughout the IUC stay to assess quality of care and to extend them to more specific patient groups such as the elderly and patients with chronic ICU courses.

Artificial Intelligence↗

PubMed1995

[The use of the microcomputer in selecting the basic cause of death].

The article begins with a discussion of some mortality statistics issues, problems encountered in the manual selection of underlying cause of death, and also the increasing need for information on associated causes. These circumstances led the National Center for Health Statistics to develop the computerized ACME System, which has been in use in São Paulo State since 1983. The ACME System's requirement of a mainframe computer, as well as other operational limitations, has prevented its installation throughout the country. In order to standardize and improve the quality of mortality data in Brazil, the Informatics Department of the Ministry of Health's National Health Foundation and the World Health Organization (WHO) Collaborating Center for the Classification of Diseases in Portuguese developed the microcomputer-based Underlying Cause Selection System (SCB) in 1993. This is an expert system that employs artificial intelligence techniques to reproduce the reasoning of a coder in selecting the underlying cause of death, according to the rules and provisions of the Ninth Revision of the International Classification of Diseases. The SCB has a very user-friendly interface, occupies 2.6 megabytes of hard disk space, and can run on any 386 or higher XT or AT computer. In addition to selecting the underlying cause of death, the system stores data on associated conditions.

Artificial Intelligence↗

PubMed1993

Using the UMLS Semantic Network as a basis for constructing a terminological knowledge base: a preliminary report.

Sharing and reuse of knowledge bases is recognized in Artificial Intelligence and Medical Informatics as beneficial, but difficult. Reusing an existing knowledge base can save considerable time and effort during the knowledge engineering phase, and facilitates integration of systems. However, the degree to which knowledge can be shared among different applications is still mainly an empirical question. In this paper, we describe the preliminary results of our attempt to reuse the UMLS Semantic Network as an ontology for the knowledge base of a patient education system.

Algorithms↗

PubMed2005

[Algorithms of artificial neural networks--practical application in medical science].

Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer applications of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. This paper presents practical aspects of scientific application of ANN in medicine using widely available algorithms. Several main steps of analysis with ANN were discussed starting from material selection and dividing it into groups, to the quality assessment of obtained results at the end. The most frequent, typical reasons for errors as well as the comparison of ANN method to the modeling by regression analysis were also described.

Algorithms↗

PubMed1988

CASE, the computer-automated structure evaluation system, as an alternative to extensive animal testing.

CASE, an artificial intelligence system with demonstrated ability to predict biological activity based on structural considerations, correctly predicts animal carcinogenicity. It can, therefore, play a pivotal role in classifying chemicals as carcinogens and prioritizing them for further testing. Additionally, CASE shows promise in the design of pharmacologically active agents by reducing the number of drugs that need to be synthesized and tested. For both of these applications, CASE provides a mechanism to conserve animal and other testing resources.

Animal Testing Alternatives↗

PubMed1988

Automatic classification of two-dimensional gel electrophoresis pictures by heuristic clustering analysis: a step toward machine learning.

The interpretation of two-dimensional gel electrophoresis (2-DGE) profiles can be facilitated by artificial intelligence and machine learning programs. We have incorporated into our 2-DGE computer analysis system (termed MELANIE-Medical Electrophoresis Analysis Interactive Expert system) a program which automatically classifies 2-DGE patterns using heuristic clustering analysis. This program is a step toward machine learning. In this publication, we describe the classification method and the preliminary results obtained with liver biopsy electrophoretograms. Heuristic clustering is also compared to other classification techniques.

Algorithms↗

PubMed2001

Knowledge-based system for structured examination, diagnosis and therapy in treatment of traumatised teeth.

Dental trauma in children and adolescents is a common problem, and the prevalence of these injuries has increased in the last 10-20 years. A dental injury should always be considered an emergency and, thus, be treated immediately to relieve pain, facilitate reduction of displaced teeth, reconstruct lost hard tissue, and improve prognosis. Rational therapy depends upon a correct diagnosis, which can be achieved with the aid of various examination techniques. It must be understood that an incomplete examination can lead to inaccurate diagnosis and less successful treatment. Good knowledge of traumatology and models of treatments can also reduce stress and anxiety for both the patient and the dental team. Knowledge-based Systems (KBS) are a practical implementation of Artificial Intelligence. In complex domains which humans find difficult to understand, KBS can assist in making decisions and can also add knowledge. The aim of this paper is to describe the structure of a knowledge-based system for structured examination, diagnosis and therapy for traumatised primary and permanent teeth. A commercially available program was used as developmental tool for the programming (XpertRule, Attar, London, UK). The paper presents a model for a computerised decision support system for traumatology.

Adolescent↗

PubMed1998

Development of a health promotion system for the elderly: Committee of Health Evaluation for Elderly Persons Council of Japan AMHTS Institutions.

A new health promotion approach for elderly persons is required which maintains not only their physical health but also their quality of life. We are developing a health promotion system which makes use of questionnaires dealing with physical conditions and quality of life, and makes health reports. Health evaluation is carried out in three steps. First, detailed information about the physical health of each client is collected. Second, quality of life is evaluated according to five health indicators. Last, health recommendations are generated. An artificial intelligence (AI) program produces detailed questions to collect necessary information for the evaluation of a client's health. The information related to quality of life is converted into five health indicators and presented as a radar-chart in documents and displays. The knowledge-based AI program automatically generates the health recommendation documents. This information is available for physicians and nurses for health counseling.

Aged↗

PubMed1998

A pragmatic implementation of medical temporal reasoning for clinical medicine.

In most development tools for knowledge-based systems object-property-value-triples are the basic ontological representation. We have extended this approach for temporal reasoning by replacing the values with time-objects. A time-object is a combination of a value with a time-point or a time-interval. By preserving the object-property-value-triples basic artificial intelligence techniques can be applied without modifications in the temporal case. The greater complexity of temporal inferences is compensated by a blackboard control architecture enabling small knowledge-sources. The control of the strategic reasoning is in accordance with the select and test model. A prototype of a knowledge-based advisor for the acute radiation syndrome has been implemented and partially evaluated. Due to the affected proliferative tissues it is a strongly time-oriented medical domain.

Acute Disease↗

PubMed2026

Big data in multiple sclerosis.

PURPOSE OF REVIEW: This review summarizes recent key advancements in multiple sclerosis (MS) achieved through the utilization of big data from diverse sources and advanced analytical techniques. RECENT FINDINGS: Real-world evidence (RWE) derived from MS big data has significantly enhanced treatment strategies, redefined the concept of disease progression, refined prognostic models, and facilitated personalized medicine. RWE has highlighted the long-term benefits of early intensive treatment compared to escalation strategies, the unfavorable risk profile associated with treatment de-escalation and the importance of managing treatments during pregnancy. Additionally, it has revealed similarities and differences in the effectiveness and safety of specific high-efficacy therapies, as well as key predictors for switching treatments. RWE has also emphasized the central role of progression independent of relapse activity as a significant driver of disability and predictor of unfavorable long-term outcomes in both adult and pediatric onset MS. A data-driven approach utilizing artificial intelligence and big data has established a comprehensive framework for understanding the disease's evolution. Multimodal big data frameworks - encompassing clinical data, MRI, genomics, biomarkers, and app-based metrics - have demonstrated their ability to enhance diagnostic performance and risk stratification in MS. SUMMARY: Big data approaches are transforming MS research and clinical practice by providing stronger RWE to guide therapeutic decision-making, refining models of disease progression, and developing more precise prognostic tools.

Humans↗

PubMed1995

Steering through the murky waters of a scientific conflict: situated and symbolic models of clinical cognition.

The situated action perspective, which embraces a diversity of views, challenges several of the fundamental assumptions of the symbolic information-processing framework underlying cognitive science and artificial intelligence. In this paper, we consider the following issues; symbolic representations, plans and actions, distributed cognition, and the transfer of learning. We evaluate each of these issues in terms of research and theories in clinical cognition and examine the implications for education and training, and for the integration of intelligent systems in medical practice. We argue for a reconceptualization of the symbolic framework in terms of the way the role of internal representations and cognitive activities are perceived. However, symbolic representations are integral to medical cognition and should continue to be central in any theoretical framework. A re-examination of cognitive science in medicine in terms of the relationship among physicians, technology, and the workplace could prove to be constructive in bridging the gap between theory and practice.

Artificial Intelligence↗