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PubMed2001

Using questions and interests to guide data mining for medical quality management.

The healthcare sector is currently facing both the economic necessity and the technical opportunity of a data based approach to quality management. Against this background, a process model for such a data based medical quality management is proposed and intelligent data mining methods are applied to patient data. Intelligent data mining incorporates advantages of both knowledge acquisition from data and from experts. A controlled language for business questions is presented which abstracts from database and data mining terminology to allow high-level interaction. Objective and subjective interestingness of results is measured and used to filter and sort.

Artificial Intelligence↗
PubMed2005

Intelligent robust control for uncertain nonlinear time-varying systems and its application to robotic systems.

This paper addresses the problem of designing adaptive fuzzy-based (or neural network-based) robust controls for a large class of uncertain nonlinear time-varying systems. This class of systems can be perturbed by plant uncertainties, unmodeled perturbations, and external disturbances. Nonlinear H(infinity) control technique incorporated with adaptive control technique and VSC technique is employed to construct the intelligent robust stabilization controller such that an H(infinity) control is achieved. The problem of the robust tracking control design for uncertain robotic systems is employed to demonstrate the effectiveness of the developed robust stabilization control scheme. Therefore, an intelligent robust tracking controller for uncertain robotic systems in the presence of high-degree uncertainties can easily be implemented. Its solution requires only to solve a linear algebraic matrix inequality and a satisfactorily transient and asymptotical tracking performance is guaranteed. A simulation example is made to confirm the performance of the developed control algorithms.

Algorithms↗
PubMed1991

Intelligent management of epidemiologic data.

In the lifecycle of epidemiologic data three steps can be identified: production, interpretation and exploitation for decision. Computerized support can be precious, if not indispensable, at any of the three levels, therefore several epidemiologic data management systems were developed. In this paper we focus on intelligent management of epidemiologic data, where intelligence is needed in order to analyze trends or to compare observed with reference value and possibly detect abnormalities. After having outlined the problems involved in such a task, we show the features of ADAMS, a system realized to manage aggregated data and implemented in a personal computer environment.

Artificial Intelligence↗
PubMed2004

An intelligent case-adjustment algorithm for the automated design of population-based quality auditing protocols.

We develop a method and algorithm for deciding the optimal approach to creating quality-auditing protocols for guideline-based clinical performance measures. An important element of the audit protocol design problem is deciding which guide-line elements to audit. Specifically, the problem is how and when to aggregate individual patient case-specific guideline elements into population-based quality measures. The key statistical issue involved is the trade-off between increased reliability with more general population-based quality measures versus increased validity from individually case-adjusted but more restricted measures done at a greater audit cost. Our intelligent algorithm for auditing protocol design is based on hierarchically modeling incrementally case-adjusted quality constraints. We select quality constraints to measure using an optimization criterion based on statistical generalizability coefficients. We present results of the approach from a deployed decision support system for a hypertension guideline.

Algorithms↗
PubMed2005

[An intelligent optometry system based on Client/Server Mode].

The optometry in physical examinations is conducted manually at present and this method is neither precise nor efficient. After studying the standard logarithmic visual acuity charts which is popular in our country, we have designed an optometry system based on Client/Server Computing Mode. The system's architecture and its working principle are also presented in the article.

Algorithms↗
PubMed2000

Agent-oriented captology for medical informatics.

Considering that neither captology nor agent-orientation, are applied in medical informatics, as they could be, the paper presents a broad-spectrum generic architectural framework to support developing adaptive medical applications, based on synergistic correlation between persuasive interfaces and intelligent agents. Their main features are adapted for medical informatics. Lying on this groundwork, the design space for agent-oriented persuasive applications is defined and several guidelines for its main dimensions are given. The approach is instantiated through an agent-based test-bench application, having the purpose to persuade to quit smoking.

Artificial Intelligence↗
PubMed2001

AIM: a personal view of where I have been and where we might be going.

My own career in medical informatics and AI in medicine has oscillated between concerns with medical records and concerns with knowledge representation with decision support as a pivotal integrating issue. It has focused on using AI to organise information and reduce 'muddle' and improve the user interfaces to produce 'useful and usable systems' to help doctors with a 'humanly impossible task'. Increasingly knowledge representation and ontologies have become the fulcrum for orchestrating re-use of information and integration of systems. Encouragingly, the dilemma between computational tractability and expressiveness is lessening, and ontologies and description logics are joining the mainstream both in AI in Medicine and in Intelligent Information Management generally. It has been shown possible to scale up ontologies to meet medical needs, and increasingly ontologies are playing a key role in meeting the requirements to scale up the complexity of clinical systems to meet the ever increasing demands brought about by new emphasis on reduction of errors, clinical accountability, and the explosion of knowledge on the Web.

Artificial Intelligence↗
PubMed1997

Clinical-HINTS: integrated intelligent ICU patient monitoring and information management system.

Clinical-HINTS (Health Intelligence System) is a horizontally integrated decision support system (DSS) designed to meet the requirements for intelligent real-time clinical information management in critical care medical environments and to lay the foundation for the development of the next generation of intelligent medical instrumentation. The system presented was developed to refine and complement the information yielded by clinical laboratory investigations, thereby benefiting the management of the intensive care unit (ICU) patient. More specifically, Clinical-HINTS was developed to provide computer-based assistance with the acquisition, organisation and display, storage and retrieval, communication and generation of real-time patient-specific clinical information in an ICU. Clinical-HINTS is an object-oriented system developed in C+2 to run under Microsoft Windows as an embryo intelligent agent. Current generic reasoning skills include perception and reactive cognition of patient status but exclude therapeutic action. The system monitors the patient by communicating with the available sources of data and uses generic reasoning skills to generate intelligent alarms, or HINTS, on various levels of interpretation of an observed dysfunction, even in the presence of complex disorders. The system's communication and information management capabilities are used to acquire physiological data, and to store them along with their interpretations and any interventions for the dynamic recognition of interrelated pathophysiological states or clinical events.

Artificial Intelligence↗
PubMed2003

A multi-agent intelligent environment for medical knowledge.

AMPLIA is a multi-agent intelligent learning environment designed to support training of diagnostic reasoning and modelling of domains with complex and uncertain knowledge. AMPLIA focuses on the medical area. It is a system that deals with uncertainty under the Bayesian network approach, where learner-modelling tasks will consist of creating a Bayesian network for a problem the system will present. The construction of a network involves qualitative and quantitative aspects. The qualitative part concerns the network topology, that is, causal relations among the domain variables. After it is ready, the quantitative part is specified. It is composed of the distribution of conditional probability of the variables represented. A negotiation process (managed by an intelligent MediatorAgent) will treat the differences of topology and probability distribution between the model the learner built and the one built-in in the system. That negotiation process occurs between the agents that represent the expert knowledge domain (DomainAgent) and the agent that represents the learner knowledge (LearnerAgent).

Artificial Intelligence↗
PubMed1997

Evaluation of automatically learned intelligent alarm systems.

In this contribution it is investigated whether a combination of mathematical simulation and inductive machine learning can replace the usual knowledge elicitation techniques. To test this a domain was selected for which knowledge based systems had a high performance: intelligent alarm systems. A mathematical model of a breathing circuit and ventilated patient was implemented in PSpice. Airway pressure, gas flows and CO2 concentration were simulated with this model, during normal functioning of the breathing circuit and during several mishaps, for a wide range of simulated patients. With an inductive machine learning program, classification trees were created from the simulated patient data. The classification trees described each breathing circuit mishap in terms of changes in signal feature values with respect to the normal situation and were implemented as alarm system knowledge bases. The alarm systems were tested with data measured at 17 mechanically ventilated animals. During ventilation of the animals several mishaps were introduced. For each animal, 93-100% of all mishaps could be detected correctly by the alarm systems. The false alarm rate ranged on average from one false alarm per h to one false alarm every 2.5 h. It was concluded that the suggested approach to knowledge elicitation was successful.

Animals↗
PubMed2006

[The design and applications of a non-invasive intelligent detector for cardiovascular functions].

An apparatus based on a high sensitive sensor which detects cardiovascular functions is introduced in this paper. Some intelligent detecting technologies, such as syntactic pattern recognition and a medical expert system are used in this detector. Its embedded single-chip microcomputer processes and analyzes pulse signals for gaining automatically the parameters about heart, blood vessel and blood etc., so as to get the health evaluation, correct medical diagnosis and prediction of cardiovascular diseases.

Algorithms↗
PubMed2005

An intelligent ventilation and oxygenation management system in neonatal intensive care using fuzzy trend template fitting.

This paper describes an open loop feedback intelligent system for neonatal intensive care management. The system provides a tool enabling the user to make the final decision to accept or reject the advice given. The system collects 18 parameters from the bedside monitor and ventilator using a Medical Information Bus (MIB) system. Comparison between the system's recommendations and seven clinical users (three doctors and four nurses) actions was made during monitoring of seven neonates with gestation age of 27-31 weeks for 124.13 h (mu=17.7329, sigma=5.3843 h, range=10.40-23.85 h). The validation process compared the recommendations triggered by the system with the user feedback (agree, disagree, wait). The system made 191 recommendations in total, 33 of which (17%) were for ventilation and 158 (83%) for oxygenation. The clinician agreed with the system ventilation decisions in 30 occasions (91%) and in 148 occasions for the system oxygenation decisions (94%). The overall percentage of the agreement between the system and the clinician was 93%.

Artificial Intelligence↗
PubMed1992

Do biomolecules process information differently than synthetic organic molecules?

This paper compares information/signal processing in synthetic and biological molecules. The role of conformation-based (shape-based) mechanisms and electrostatic interactions in molecular recognition is discussed. In biological electron transfer, the 'electron shuttle'-mediated mechanism is contrasted with the mechanism based on pre-formed 'electron wires'. While biological information processing is thought to be more distributed (less discrete), an example of molecular switch is presented: visual transduction. We further speculate that visual transduction may be implemented in the form of a switch based on electrostatic interactions. The concept of intelligent materials is discussed with the well-known Bohr effect of hemoglobin oxygenation. Based on these examples, we argue that there are no fundamental differences between synthetic and biological molecules in their mode of information processing. In the pursuit of novel paradigms of molecular information processing, we also perceive no conflicts in developing molecular devices that emulate the switching function of conventional microelectronic devices.

Artificial Intelligence↗
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

GenIE: an intelligent system for writing genetic counseling patient letters.

We are developing GenIE, a prototype intelligent system to create first drafts of genetic counseling patient letters. GenIE will apply natural language generation techniques to construct the first draft of a letter for subsequent review and editing, if needed, by the genetic counselor. For purposes of knowledge acquisition, we have been analyzing a corpus of patient letters. Based on the corpus analysis we are developing a knowledge base and text generation strategies.

Artificial Intelligence↗
PubMed2004

SIVA: a hybrid knowledge-and-model-based advisory system for intensive care ventilators.

The Sheffield Intelligent Ventilator Advisor is a hybrid knowledge-and-model-based advisory system designed for intensive care ventilator management. It consists of a top-level fuzzy rule-based module to give the qualitative component of the advice, and a lower-level model-based module to give the quantitative component of the advice. It is structured to offer adaptive patient-specific decision support. It can be operated in either invasive or noninvasive modes depending on the availability of data from invasive clinical measurements. The user can choose between the full-advisory mode and the clinician-directed mode. The advice given by the top-level module has been validated against retrospective real patient data and compared with intensivists expertise and performance under simulation conditions. Closed-loop simulations were performed assuming various clinical scenarios including sudden changes in the patient parameters such as the shunt or deadspace with noise and disturbances. They have shown that the advice given was appropriate and the blood gases resulting from the closed-loop decision support were acceptable. The system was also shown to be tolerant to noise and disturbances. It is implemented in MATLAB/SIMULINK and LabVIEW.

Artificial Intelligence↗
PubMed2005

Software and knowledge engineering aspects of smart homes applied to health.

Smart Home technology offers a viable solution to the increasing needs of the elderly, special needs and home based-healthcare populations. The research to date has largely focused on the development of communication technologies, sensor technologies and intelligent user interfaces. We claim that this technological evolution has not been matched with a step of a similar size on the software counterpart. We particularly focus on the software that emphasizes the intelligent aspects of a Smart Home and the difficulties that arise from the computational analysis of the information collected from a Smart Home. The process of translating information into accurate diagnosis when using non-invasive technology is full of challenges, some of which have been considered in the literature to some extent but as yet without clear landmarks.

Aged↗
PubMed2000

Decision-support and intelligent tutoring systems in medical education.

One of the challenges in medical education is to teach the decision-making process. This learning process varies according to the experience of the student and can be supported by various tools. In this paper we present several approaches that can strengthen this mechanism, from decision-support tools, such as scoring systems, Bayesian models, neural networks, to cognitive models that can reproduce how the students progressively build their knowledge into memory and foster pedagogic methods.

Artificial Intelligence↗