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PubMed2004

Artificial intelligence in medicine.

INTRODUCTION: Artificial intelligence is a branch of computer science capable of analysing complex medical data. Their potential to exploit meaningful relationship with in a data set can be used in the diagnosis, treatment and predicting outcome in many clinical scenarios. METHODS: Medline and internet searches were carried out using the keywords 'artificial intelligence' and 'neural networks (computer)'. Further references were obtained by cross-referencing from key articles. An overview of different artificial intelligent techniques is presented in this paper along with the review of important clinical applications. RESULTS: The proficiency of artificial intelligent techniques has been explored in almost every field of medicine. Artificial neural network was the most commonly used analytical tool whilst other artificial intelligent techniques such as fuzzy expert systems, evolutionary computation and hybrid intelligent systems have all been used in different clinical settings. DISCUSSION: Artificial intelligence techniques have the potential to be applied in almost every field of medicine. There is need for further clinical trials which are appropriately designed before these emergent techniques find application in the real clinical setting.

Artificial Intelligence↗
PubMed2005

Detection of heart murmurs using wavelet analysis and artificial neural networks.

This paper presents the algorithm and technical aspects of an intelligent diagnostic system for the detection of heart murmurs. The purpose of this research is to address the lack of effectively accurate cardiac auscultation present at the primary care physician office by development of an algorithm capable of operating within the hectic environment of the primary care office. The proposed algorithm consists of three main stages. First; denoising of input data (digital recordings of heart sounds), via Wavelet Packet Analysis. Second; input vector preparation through the use of Principal Component Analysis and block processing. Third; classification of the heart sound using an Artificial Neural Network. Initial testing revealed the intelligent diagnostic system can differentiate between normal healthy heart sounds and abnormal heart sounds (e.g., murmurs), with a specificity of 70.5% and a sensitivity of 64.7%.

Algorithms↗
PubMed2003

An advanced artificial intelligence tool for menu design.

The computer-assisted menu design still remains a difficult task. Usually knowledge that aids in menu design by a computer is hard-coded and because of that a computerised menu planner cannot handle the menu design problem for an unanticipated client. To address this problem we developed a menu design tool, MIKAS (menu construction using incremental knowledge acquisition system), an artificial intelligence system that allows the incremental development of a knowledge-base for menu design. We allow an incremental knowledge acquisition process in which the expert is only required to provide hints to the system in the context of actual problem instances during menu design using menus stored in a so-called Case Base. Our system incorporates Case-Based Reasoning (CBR), an Artificial Intelligence (AI) technique developed to mimic human problem solving behaviour. Ripple Down Rules (RDR) are a proven technique for the acquisition of classification knowledge from expert directly while they are using the system, which complement CBR in a very fruitful way. This combination allows the incremental improvement of the menu design system while it is already in routine use. We believe MIKAS allows better dietary practice by leveraging a dietitian's skills and expertise. As such MIKAS has the potential to be helpful for any institution where dietary advice is practised.

Artificial Intelligence↗
PubMed1997

Design and validation of an intelligent patient monitoring and alarm system based on a fuzzy logic process model.

The process of patient care performed by an anaesthesiologist during high invasive surgery requires fundamental knowledge of the physiologic processes and a long standing experience in patient management to cope with the inter-individual variability of the patients. Biomedical engineering research improves the patient monitoring task by providing technical devices to measure a large number of a patient's vital parameters. These measurements improve the safety of the patient during the surgical procedure, because pathological states can be recognised earlier, but may also lead to an increased cognitive load of the physician. In order to reduce cognitive strain and to support intra-operative monitoring for the anaesthesiologist an intelligent patient monitoring and alarm system has been proposed and implemented which evaluates a patient's haemodynamic state on the basis of a current vital parameter constellation with a knowledge-based approach. In this paper general design aspects and evaluation of the intelligent patient monitoring and alarm system in the operating theatre are described. The validation of the inference engine of the intelligent patient monitoring and alarm system was performed in two steps. Firstly, the knowledge base was validated with real patient data which was acquired online in the operating theatre. Secondly, a research prototype of the whole system was implemented in the operating theatre. In the first step, the anaesthetists were asked to enter a state variable evaluation before a drug application or any other intervention on the patient into a recording system. These state variable evaluations were compared to those generated by the intelligent alarm system on the same vital parameter constellations. Altogether 641 state variable evaluations were entered by six different physicians. In total, the sensitivity of alarm recognition is 99.3%, the specificity is 66% and the predictability is 45%. The second step was performed using a research prototype of the system in anaesthesiological routine. The evaluation of 684 events yielded a sensitivity, specificity and predictability of the alarm recognition of more than 99%.

Anesthesiology↗
PubMed2005

Integrated microelectronics for smart textiles.

The combination of textile fabrics with microelectronics will lead to completely new applications, thus achieving elements of ambient intelligence. The integration of sensor or actuator networks, using fabrics with conductive fibres as a textile motherboard enable the fabrication of large active areas. In this paper we describe an integration technology for the fabrication of a "smart textile" based on a wired peer-to-peer network of microcontrollers with integrated sensors or actuators. A self-organizing and fault-tolerant architecture is accomplished which detects the physical shape of the network. Routing paths are formed for data transmission, automatically circumventing defective or missing areas. The network architecture allows the smart textiles to be produced by reel-to-reel processes, cut into arbitrary shapes subsequently and implemented in systems at low installation costs. The possible applications are manifold, ranging from alarm systems to intelligent guidance systems, passenger recognition in car seats, air conditioning control in interior lining and smart wallpaper with software-defined light switches.

Algorithms↗
PubMed1994

A knowledge-based alarm system for monitoring cardiac operated patients--assessment of clinical performance.

An intelligent alarm system for the postoperative monitoring of cardiac surgery patients, which did not require any manual data entries, was tested in two phases. A clinician monitored at bedside the patients' recovery and verified clinically abnormal physiological states. After the first test with ten patients, the system's rulebase was upgraded and then tested with an additional 15 patients. The alarm system employed two PC/ATs and was programmed to give notice of four pathological states (hyperdynamic state, hypovolemic state, hypoventilation and left ventricular failure) at two levels of urgency (alarm and alert levels). The monitoring lasted 5.4 +/- 1.7 hours per patient (mean +/- S.D.), totalling 134.7 hours. The system alarmed 27 times during the first and 73 times during the second phase of the testing. The sensitivity of the alarms was 100% in both phases, and the specificities increased from 20.0% to 73.9% and from 59.1% to 70.0% for the alarms and the alerts, respectively. This computerized decision support system based exclusively on data available in the automatically collected data base had a low false positive rate and gave early warnings about pathological states in the homogeneous group of adult postoperative cardiac patients.

Adult↗
PubMed1995

Computational intelligence for laboratory information systems.

Non-linear models, such as given by neural networks and fuzzy logic, have established a good reputation for medical data analysis as computational and logical counterparts to statistical methods. Whereas multilayer perceptrons perform well with large data sets, a combination of neural learning together with fuzzy logical network interpretations provides a network reduction well suited for smaller data sets. The aim of this paper is to present an approach to neural fuzzy systems data analysis and knowledge acquisition in laboratory information systems. We also describe a software system, DiagaiD, which provides an analysis and development workbench involving laboratory data.

Clinical Laboratory Information Systems↗
PubMedDate not supplied

Sleep Expert--an intelligent medical decision support system for sleep disorders.

A new type of associative knowledge-based decision support system (Sleep Expert) for the diagnosis and classification of sleep disorders is described. Sleep Expert is based on the International Classification of Sleep Disorders (1990). The programming system used was KnowledgePro (Windows), a high-level language that integrates object-oriented programming, hypertext and expert system technologies. Sleep Expert is an interactive program composed of 288 separate integrated submodules and 264 text files. The program includes eight reasoning questions about symptoms setting the limits for the diagnosis subset. The user obtains a list of possible diagnoses on the screen where he/she can examine their criteria. The program has been written in such a form that the user can freely associate and can move forwards and backwards. Detailed information is included in hypertext.

Diagnosis, Computer-Assisted↗
PubMed2001

Development and assessment of an intelligent shelf life decision system for quality optimization of the food chill chain.

The principles of application of a Shelf Life Decision System (SLDS) for the optimization of the distribution of chilled fresh and minimally processed food products are developed. The SLDS integrates predictive kinetic models of food spoilage, data on initial quality from rapid techniques, and the capacity to continuously monitor temperature history of the food product with Time Temperature Integrators (TTIs) into an effective chill chain management tool that leads to an improved narrow distribution of quality at consumption time, effectively reducing the probability of products consumed past shelf life end. The applicability and effectiveness of the SLDS is demonstrated and evaluated based on actual food spoilage and TTI kinetics and chill chain data employing the Monte Carlo simulation method.

Animals↗
PubMed2005

Future of computer implant technology and intelligent human-machine systems.

Linking the human nervous system and brain directly to a computer opens up innumerable possibilities, not only in the future world of medicine, but also as a potential way of technically evolving all humans. This, however, presents something of an ethical problem. Nevertheless, the only way to actually find out what is realistically possible and what is not is to carry out practical experimentation using implant technology and to witness the results. This chapter describes the most recent self-experimentation trials carried out by the author and his team.

Biomedical Technology↗
PubMed2006

Developing and evaluating a wireless speech-and-touch-based interface for intelligent comprehensive triage support systems.

Continuous speech recognition (CSR) technology appears promising in mobile nursing but is not yet well studied. We developed and evaluated bimodal CSR and touchscreen triage support systems in the Emergency department (ED) of a medical center with 2700 beds in 2004-5. Evaluation results show that the average accuracy rates of systems ranged from 94 to 98%. Results suggest that the ED nurses were significantly more willing to use CSR combined with touchscreen systems than the others, such as PDA and CSR alone. A more flexible interface, not efficiency, might be the main reason for this finding.

Attitude to Computers↗
PubMed1994

An intelligent Computer-Assisted Instruction system for clinical case teaching.

The use of computers in the field of medical education is common. Our purpose is to present a Computer-Assisted Instruction system which has been developed over ten years at the University of Compiègne and the University of Rennes Medical School. This system can be used to help the student to solve clinical cases by analyzing and critiquing their answers and by using a knowledge base which has been previously structured in a rule network. It is an intelligent Computer-Assisted Instruction system comprising an author module, a pedagogical module and a student module. The CAI system can be used as a simulation model for any type of diagnostic or therapeutic problem. In this paper we present the author and pedagogical module which have been built using our previous work on intelligent computer-assisted instruction systems.

Artificial Intelligence↗
PubMed1982

Origins of anthropoid intelligence IV. Role of prefrontal system in delayed alternation and spatial reversal learning in a conservative eutherian (Paraechinus hypomelas).

A conservative eutherian mammal (the hedgehog, Paraechinus hypomelas) was tested on delayed alternation performance and spatial reversal learning before and after ablations of the prefrontal cortex. The anatomical results show that the cortical focus of the projections of the medial dorsal nucleus, the prefrontal cortex, does not include the neocortex on the dorsal convexity of the hedgehog's frontal lobe but, instead, the perirhinal and pregenual neocortex immediately surrounding the frontal convexity. The behavioral results show that normal performance of hedgehogs on these two behavioral tests depends upon the integrity of their prefrontal cortex, but not on the integrity of their frontal convexity or olfactory bulbs. The similarity in the results obtained from prefrontal hedgehogs and a divergent variety of other species with prefrontal ablations indicates that the role of the prefrontal system in the abilities measured by these two tests is at least as old as Eutheria and, thus, probably imposed persistent constraints on subsequent evolutionary modifications of the prefrontal system.

Animals↗
PubMed2003

Human intelligence: the brain, an electromagnetic system synchronised by the Schumann Resonance signal.

The human brain is a biological organ. On one hand it is soft, flexible and adaptive, but on the other hand is relatively stable and coherent with well developed intelligence. In order to retain intelligent thinking in a soft and adaptive organ there needs to be a constant, globally available, synchronization system that continuously stabilizes the brain. Rapid intelligence and reactions requires and electromagnetic signalling system, supported by a biochemical system. The Schumann Resonance signal provides a brain frequency range matching electromagnetic signal, providing the synchronization needed for intelligence.

Action Potentials↗
PubMed2001

Entropy generation method to quantify thermal comfort.

The present paper presents a thermodynamic approach to assess the quality of human-thermal environment interaction and quantify thermal comfort. The approach involves development of entropy generation term by applying second law of thermodynamics to the combined human-environment system. The entropy generation term combines both human thermal physiological responses and thermal environmental variables to provide an objective measure of thermal comfort. The original concepts and definitions form the basis for establishing the mathematical relationship between thermal comfort and entropy generation term. As a result of logic and deterministic approach, an Objective Thermal Comfort Index (OTCI) is defined and established as a function of entropy generation. In order to verify the entropy-based thermal comfort model, human thermal physiological responses due to changes in ambient conditions are simulated using a well established and validated human thermal model developed at the Institute of Environmental Research of Kansas State University (KSU). The finite element based KSU human thermal computer model is being utilized as a "Computational Environmental Chamber" to conduct series of simulations to examine the human thermal responses to different environmental conditions. The output from the simulation, which include human thermal responses and input data consisting of environmental conditions are fed into the thermal comfort model. Continuous monitoring of thermal comfort in comfortable and extreme environmental conditions is demonstrated. The Objective Thermal Comfort values obtained from the entropy-based model are validated against regression based Predicted Mean Vote (PMV) values. Using the corresponding air temperatures and vapor pressures that were used in the computer simulation in the regression equation generates the PMV values. The preliminary results indicate that the OTCI and PMV values correlate well under ideal conditions. However, an experimental study is needed in the future to fully establish the validity of the OTCI formula and the model. One of the practical applications of this index is that could it be integrated in thermal control systems to develop human-centered environmental control systems for potential use in aircraft, mass transit vehicles, intelligent building systems, and space vehicles.

Algorithms↗
PubMed2005

The correlation between striatal dopamine D2/D3 receptor availability and verbal intelligence quotient in healthy volunteers.

BACKGROUND: Although a correlation between the central dopaminergic system and intelligence may exist, the results from imaging studies remain inconclusive. The aim of this study was to explore the relationship between striatal dopamine D2/D3 receptor availability and verbal intelligence quotient (VIQ) using single photon emission computed tomography (SPECT). METHOD: Striatal D2/D3 receptor availability of 64 healthy subjects was determined with the [123I]iodobenzamide ([123I]IBZM) ligand. Intelligence quotients (IQs) of the subjects were measured by the Wechsler Adult Intelligence Scale--Revised (WAIS-R). RESULTS: In addition to age, left striatal D2/D3 receptor availability correlated positively with VIQ. In females, left striatal D2/D3 receptor availability was the only variable that correlated significantly with the similarities subtest of VIQ. CONCLUSIONS: There is a relationship between left striatal D2/D3 receptor availability and verbal intelligence, which varies, predominantly in males.

Adult↗