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PubMed1986

Future programs at the National Library of Medicine.

The future of the National Library of Medicine will be shaped by a number of scientific, technical, and social influences. Among these are the continuing rapid development of computer technology and storage systems. Artificial intelligence techniques, factual databases, the emergence of medical informatics as a formal discipline, and the development of Integrated Academic Information Management Systems (IAIMS) are also important influences on the direction of the library. Public policy issues will influence the future of NLM--among them, sharing of scientific information between nations and the role of federal agencies in dissemination of information domestically. The formal, long-range plan now being prepared for the library by panels of expert advisers will be a guide for future programs and goals.

Artificial Intelligence↗
PubMed2003

Automated spoken dialog system for home care and data acquisition from chronic patients.

Recent advances in automatic speech recognition and related technologies allow computers to carry on conversations by telephone. We developed an intelligent dialog system that interacts with hypertensive patients to collect data about their health status. Patients thus avoid the inconvenience of going for frequent visits that monitor some clinical variables they can easily measure at home; the physician is facilitated in acquiring and reviewing patient information and related risk indicators, which are evaluated from the data according to noted guidelines. The system described here is a prototype of future configurable and component-based dialog systems, which may allow a new modality for users and physicians to access electronic health records.

Access to Information↗
PubMed2000

Cognitive function in patients with Cushing syndrome: a longitudinal perspective.

The purposes of this study were to examine the level of improvement of cognitive function 12 months posttreatment in adult patients with Cushing syndrome (CS), the relationships of cognitive function to duration of CS or recovery of the hypothalamic pituitary adrenal (HPA) axis, and depression and improved cognitive functioning. Thirty-three patients with CS and a matched comparison group were enrolled. IQ, depression, and endocrine factors were measured during the active phase of CS and at 12 months posttreatment for CS. Results show no group differences in cognitive function across time but a trend for CS patients to have lower IQ scores at baseline. Individual differences in performance were striking. For some subscales of IQ there was a positive relationship with recovery of the HPA axis and a negative relationship with duration of CS as well as an improvement if depression had decreased. Limitations of the study are cited along with clinical implications and directions for future research.

Adult↗
PubMed1988

Intelligent computer-based assessment and psychotherapy. An expert system for sexual dysfunction.

New and converging developments in the areas of artificial intelligence, intelligent tutoring systems, and cognitive therapy have made possible a new approach to computer-assisted assessment and psychotherapy. This new approach combines the capacity for intelligent therapeutic dialogue with the presentation of individualized therapeutic interventions. Previous attempts at computerized psychotherapy are reviewed to highlight a newly developed rule-based expert system, Sexpert, which assesses and treats sexual dysfunction. Preliminary observations concerning couples' reaction to and acceptance of Sexpert are presented.

Artificial Intelligence↗
PubMed2002

[Telemedicine: chances and risks].

PROBLEM: Due to the rapid progress in the fields of information technology and data networks, telemedicine applications are growing in number. Besides curative telemedicine, the electronic exchange of medical data and the integration of health information systems between health care providers is gaining importance. Through the improved accessibility of electronic patient record information, considerable risks arise. METHODS: A project for the interconnection of medical picture archiving and communication systems (PACS) between several hospitals is presented and the possibilities for further developing such networking systems utilizing new software technologies for transparent data access between different locations (GRID) and for decision support (software agents) are considered. RESULTS: The availability of the electronic patient record via the data network and the perspective of semi intelligent software systems automatically preparing the data bears great potential for a boost in treatment quality and efficiency. Systems for unique electronic patient identification and for secure digital signature are a prerequisite, but per se not enough to ensure the protection of data against illegitimate access. CONCLUSION: Despite quality and efficiency benefits, challenges in the protection of sensible data and in the change of the physicians role result.

Artificial Intelligence↗
PubMed1994

Expert systems in the control of animal cell culture processes: potentials, functions, and perspectives.

In recent years, the development of advanced systems for bioprocess monitoring and control has become an area of intensive research. Along with traditional techniques, there are several new approaches which are increasingly being applied to bioprocess operations. Among these, of special note is expert system technology, which provides possibilities for the design of efficient bioprocess control systems with new functional capabilities. This technology has been successfully applied to variety of microbial processes at laboratory and industrial scale. The present paper analyzes the possibility for application of expert systems to animal cell cultures processes whose high complexity is well suited to expert control. The discussion focuses on the organization and the functionality of the intelligent control systems, and covers some practical aspects of their design.

Animals↗
PubMed1996

The impact of clinical information systems research on the future of advanced practice nursing.

Expert systems are "intelligent" computer programs designed to mimic the decision making of a human expert. This article describes two formal evaluations of one of the first nursing expert systems. Initial results were positive, however, subsequent analysis identified significant limitations in the ability of the expert system to mimic the consultation process of advanced practice nurses (APNs). Because few newly developed systems are being subjected to a clinical trial or to the scrutiny reported here, developing systems may prove to be cost inefficient over time. APNs must be actively involved in the design, development, and evaluation of all nursing expert systems.

Expert Systems↗
PubMed1997

DIABETOR computer aided tutoring in diabetes management.

Computer Aided Instruction (CAI) and Computer Aided Learning (CAL) Systems, are software systems that can tutor people in a given domain. Medicine is a field that is particularly amenable to computer aided instruction because one is allowed to experiment with a large number of hypothetical simulated patient/disease cases, without the ill consequences of the wrong decision in real life. This paper presents an Intelligent Tutoring system for the Management of the diabetes disease, and specifically for instruction in insulin administration. The system will be used for the education of medical personnel (general practitioners, nurses), as well as students of medicine in diabetes management. It is based on an existing expert system for diabetes management called DIABETES as well as on the knowledge of expert diabetologists.

Artificial Intelligence↗
PubMed2005

Using key performance indicators as knowledge-management tools at a regional health-care authority level.

The advantages of the introduction of information and communication technologies in the complex health-care sector are already well-known and well-stated in the past. It is, nevertheless, paradoxical that although the medical community has embraced with satisfaction most of the technological discoveries allowing the improvement in patient care, this has not happened when talking about health-care informatics. Taking the above issue of concern, our work proposes an information model for knowledge management (KM) based upon the use of key performance indicators (KPIs) in health-care systems. Based upon the use of the balanced scorecard (BSC) framework (Kaplan/Norton) and quality assurance techniques in health care (Donabedian), this paper is proposing a patient journey centered approach that drives information flow at all levels of the day-to-day process of delivering effective and managed care, toward information assessment and knowledge discovery. In order to persuade health-care decision-makers to assess the added value of KM tools, those should be used to propose new performance measurement and performance management techniques at all levels of a health-care system. The proposed KPIs are forming a complete set of metrics that enable the performance management of a regional health-care system. In addition, the performance framework established is technically applied by the use of state-of-the-art KM tools such as data warehouses and business intelligence information systems. In that sense, the proposed infrastructure is, technologically speaking, an important KM tool that enables knowledge sharing amongst various health-care stakeholders and between different health-care groups. The use of BSC is an enabling framework toward a KM strategy in health care.

Greece↗
PubMed1999

[Expert systems in pulmonology].

Expert systems are software systems that can successfully compare to human experts. Their purpose is mostly advisory. Besides, they give explanation and advices to human experts when performing certain tasks. They are intelligent information systems, and are capable to explain and justify their conclusions. Knowledge systems are smaller software systems, and are usually less successful than human experts. Main reasons for expert systems development in medicine are: need for justification of decisions, need for enhancing performances in many uncertain relations; need for explaining of decision making process++ etc. One of the reasons of developing knowledge-based systems was that conventional statistic formalisms have not provided satisfactory solutions in medical decision making (MDM). Also, today, the relations between cases and conclusions are not universally valid. So, few causes can provide the same conclusion. Besides, data are not necessarily absolutely accurate. The area of applying expert systems is very wide: diagnosis, prognosis, education, managing etc. Basic structure of expert system consists of: knowledge, data base, inferring mechanism, explaining mechanism and user-interface. In this paper we presented several expert systems which are actually used in practice, especially in internal disciplines: Internist, Mycin, Onkocyn, DXplain.

Expert Systems↗
PubMed1995

[Medical expert systems].

Expert systems are software systems that can successfully compare to human experts. Their purpose is mostly advisory. Besides, they give explanation and advice to human experts when performing certain tasks. They are intelligent information systems, and are capable to explain and justify their conclusions. Knowledge systems are smaller software systems, and are usually less successful than human experts. Main reasons for expert systems development in medicine are: need for justification of decisions, need for enhancing performances in many uncertain relations; need for explaining of decision making process etc. One of the reasons of developing knowledge-based systems was that conventional statistic formalisms have not provided satisfactory solutions in medical decision making (MDM). Also, today, the relations between cases and conclusions are not universally valid. So, few causes can provide the same conclusion. Besides, data are not necessarily absolutely accurate. The area of applying expert systems is very wide: diagnosis, prognosis, self-education, directing etc. Basic structure of expert system consists of: knowledge, data base, inferring mechanism, explaining mechanism and user-interface. Though, expert systems also have certain bad features: primarily, they are not physicians i.e. they can not examine a patient. Furthermore, expert system that is good for one certain area is often not good for another one. There are some cases, when these systems can confuse a physician and make him to make a wrong decision. This occurs very often in two specific cases: when the clinical situation is urgent; and when accuracy of clinical information is not definite.

Expert Systems↗
PubMed1988

Technical intuition in system diagnosis, or accessing the libraries of the mind.

Expert diagnosticians draw upon rich, integrated structures of knowledge and associated action rules to achieve their prodigious performances. Studies in both medical diagnosis and electronic troubleshooting have revealed that it is particularized knowledge that plays a vital role for experts in both domains, not inexplicable powers of intuition. Advanced knowledge engineering methods have been developed and applied in a number of Air Force technical domains so that experts' mental databases and procedural libraries can be made explicit enough to serve as targets of instruction. Instructional principles have been derived from this knowledge engineering work and are guiding the development of a new generation of Air Force technical training systems. The training dictum is to teach from realistic cases so that theory of system operation and specialized solution methods are taught in tandem. The learning environments will further foster the welding of factual knowledge to action rules by providing "assisted laboratory" experiences via intelligent tutoring systems. In these environments, trainees can practice carrying out diagnoses on increasingly complex problems with the help of an articulate expert coach.

Expert Systems↗
PubMed2001

Online pattern recognition in intensive care medicine.

In intensive care physiological variables of the critical-ly ill are measured and recorded in short time intervals. The existing alarm systems based on fixed thresholds produce a large number of false alarms. Usually the change of a variable over time is more informative than one pathological value at a particular time point. Intelligent alarm systems which detect important changes within a physiological time series are needed for suitable bedside decision support. There are various approaches to modeling time-dependent data and also several methodologies for pattern detection in time series. We compare several methodologies de-signed for online detection of measurement artifacts, level changes, and trends for a proper classification of the patient s state by means of a comparative case-study.

Aged↗
PubMed2026

CAUSAL artificial intelligence and data-driven decision intelligence in personalized medicine: a review of healthcare informatics systems.

This review examines the integration of causal artificial intelligence (AI) and data-driven decision intelligence within healthcare informatics systems to advance personalized medicine and clinical decision-making. A narrative review methodology was employed, synthesizing interdisciplinary literature from major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Studies focusing on causal inference, decision intelligence, and healthcare informatics applications in personalized medicine were included. Data were extracted on methodological approaches, healthcare settings, analytical techniques, and clinical applications, followed by thematic synthesis. Findings indicate that causal AI enhances clinical decision support by enabling estimation of treatment effects and simulation of intervention outcomes at the individual patient level. Integration of multimodal health data such as electronic health records, genomic data, and real-time monitoring improves prediction accuracy and supports tailored treatment strategies. Additionally, causal models improve interpretability, fostering clinician trust and facilitating transparent decision-making. Robust healthcare informatics infrastructures, including interoperable systems and data warehouses, were identified as critical enablers of causal analytics. Overall, causal AI represents a transformative advancement in healthcare analytics, supporting more informed, individualized, and evidence-based clinical decisions. Its integration within healthcare informatics systems has significant potential to improve patient outcomes and guide the future of intelligent, personalized healthcare delivery.

Precision Medicine↗
PubMed2003

Hypothalamic-pituitary-adrenal reactivity in boys with attention deficit hyperactivity disorder.

The hypothesis 'whether subjects with attention-deficit/ hyperactivity disorder (ADHD), who showed under-reactivity of the hypothalamic-pituitary-adrenal (HPA) axis to stress, would make more commission errors in attention tasks', was examined. Forty-three boys, with ADHD, who visited the psychiatric outpatient clinic, at Kangbuk Samsung Hospital, were the subjects of this study. Both pre- and post-test morning saliva samples were collected from the patients at the Korean Educational Development Institute-Wechsler Intelligence Scale for Children (KEDI-WISC), and Tests of Variables of Attention (T.O.V.A.) performed. The Standard scores of the T.O.V.A were compared between the patients with decreases, or increases, in the salivary cortisol levels after the test. Decreases, or increases in the salivary cortisol levels after the test were shown in 28 and 15 patients, respectively. The patients with decreased cortisol levels after the test tended to make more commission errors in compared with those with increased cortisol levels. The patients with the decreased cortisol levels after test had more omission errors in the first quarter of the test, and more commission errors in the second half of the test compared to those with the increased cotisol levels. Subjects who show decreased salivary cortisol levels after stress make more commission errors in attention tests. This suggests that the blunted HPA axis response to stress is related to the impulsivity in patients with ADHD.

Attention↗
PubMed1997

Poor prognosis for existing monitors in the intensive care unit.

OBJECTIVE: To identify areas requiring the most urgent improvement in the intensive care unit (ICU); and to accurately determine the positive predictive value of routine critical care patient monitoring alarms, as well as the common causes for false-positive alarms. DESIGN: Prospective, observational study. SETTING: A multidisciplinary ICU in a university-affiliated children's hospital (excluding children with primary heart disease). INTERVENTIONS: The occurrence rate, cause, and appropriateness of all alarms from tracked monitors were recorded by a trained observer and validated by the bedside nurse over a 10-wk period for a single bedspace at a time. MEASUREMENTS AND MAIN RESULTS: After 298 monitored hrs, 86% of a total 2,942 alarms were found to be false-positive alarms, while an additional 6% were classified as clinically irrelevant true alarms. Only 8% of all alarms tracked during the study period were determined to be true alarms with clinical significance. Alarms were also classified according to whether they were clearly associated with a "patient intervention" (18%), were clearly not associated with a patient intervention (74%), or had unclear association to interventions (8%). While 11% of "nonpatient intervention" alarms were clinically significant true alarms, only 2% of "patient intervention" alarms were so. Positive predictive values for the various devices ranged from < 1% for the pulse oximeter's heart rate signal to 74% for the arterial catheter's mean systemic blood pressure signal during periods free from patient interventions. The pulse oximeter caused false-positive alarms most frequently, with common reasons being bad data format/bad connection and poor contact. CONCLUSION: Efforts to develop intelligent monitoring systems have more potential to deliver significantly improved patient care by initially targeting especially weak areas in ICU monitoring, such as pulse oximetry reliability.

Critical Care↗
PubMed1983

A system for the analysis of long-term electrocardiographic studies in clinical research and training.

A computer system has been developed for the analysis of data from long-term electrocardiographic studies in the context of an institution committed to clinical research and training. The major characteristics required of such a system are intelligence, flexibility, friendliness, and maintainability. These attributes are achieved by a user interface which consists of menus and interactive graphics, and by the use of highly modular software developed in a high-level programming language. The system has been used in studies of the effects of drugs on cardiac arrhythmias and has been easy to learn and convenient to use.

Academies and Institutes↗
PubMed2006

Design aspects of a distributed clinical trials information system.

BACKGROUND: Clinical trials informatics has evolved through the development of multiple applications addressing distinct parts of the clinical trials cycle. This setting creates difficulties in the transport and sharing of data among applications that serve a common functionality. PURPOSE: We present an alternative approach for the design of clinical trials information systems consisting of loosely coupled components standing on a comprehensive model of the global clinical trial process. METHODS: We describe how such a structure is able to support the development and implementation of complex knowledge-driven modules, such as automated database query systems, reporting systems and intelligent data-analysis tools, and how currently available internet technologies may be used to support the independent development of applications and collaboration between researchers. RESULTS: These principles were applied to the development of a fully functional system that has been in production for eight years in a diversity of pharmaceutical and academic drug trials. Marked time savings in database set-up and statistical reporting have been documented, as well as good reliability in the selection of appropriate statistical methods to clinical trial data analysis. CONCLUSIONS: In order to meet the expected functionality and to avoid the proliferation of databases and software applications, clinical trials information systems need to be based on a generic model of clinical trials and on a distributed architecture.

Clinical Trials as Topic↗