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PubMed2001

A formal framework of knowledge to support rational psychoactive drug selection.

Rational psychoactive drug selection is a data and knowledge intensive task which requires true expertise from clinical, pathophysiological and pharmacotherapeutic knowledge. This paper presents a framework of knowledge, which relates concepts from several disciplines required for psychoactive drug selection in a formal way. A framework, when based on formal semantics, avoids ambiguity and gives conceptual clarity and supports precise use of terminology which is required when many domain experts (clinicians, pharmacologists and basic science researchers) are involved. A formal framework permits linking of existing classification systems. It furthermore can serve as a knowledge base for drug selection decision support systems.

Artificial Intelligence↗
PubMedDate not supplied

Comprehensible evaluation of prognostic factors and prediction of wound healing.

We analyzed the data of a controlled clinical study of the chronic wound healing acceleration as a result of electrical stimulation. The study involved a conventional conservative treatment, sham treatment, biphasic pulsed current, and direct current electrical stimulation. Data was collected over 10 years and suffices for an analysis with machine learning methods. So far, only a limited number of studies have investigated the wound and patient attributes which affect the chronic wound healing. There is none to our knowledge to include treatment attributes. The aims of our study are to determine effects of the wound, patient and treatment attributes on the wound healing process and to propose a system for prediction of the wound healing rate. First we analyzed which wound and patient attributes play a predominant role in the wound healing process and investigated a possibility to predict the wound healing rate at the beginning of the treatment based on the initial wound, patient and treatment attributes. Later we tried to enhance the wound healing rate prediction accuracy by predicting it after a few weeks of the wound healing follow-up. Using the attribute estimation algorithms ReliefF and RReliefF we obtained a ranking of the prognostic factors which was comprehensible to experts. We used regression and classification trees to build models for prediction of the wound healing rate. The obtained results are encouraging and may form a basis for an expert system for the chronic wound healing rate prediction. If the wound healing rate is known, then the provided information can help to formulate the appropriate treatment decisions and orient resources towards individuals with poor prognosis.

Algorithms↗
PubMed1990

Computer-assisted techniques for evaluation and treatment of hypertensive patients.

An integrated approach, progressively implemented in the ARTEMIS system since 1975, is described for the computerized management of hypertensive patients. From a medical point of view, computerized programs can be used to memorize patients' individual records and profiles, to facilitate patient management and follow-up, to store medical knowledge about hypertension and to provide facilities for decision making at the level either of the individual patient or of the population followed up. From a technical point of view, the methodology used integrates data and knowledge management facilities into the same software. Five hypertension clinics are presently using the system in France and more than 22,000 records have been registered. Answer rates to 12 mandatory questions regarding past history and examination at first visit were superior to 95% in 19,601 records created between January 1976 and December 1987. Patient database interrogation can be used to evaluate the sensitivity and specificity of various signs and symptoms for the diagnosis of secondary hypertension, and to predict, for each patient, his/her cardiovascular risk, the risk of drop-out, the risk of insufficient blood pressure control and the probable blood pressure level. It also serves to test the content and validity of the associated expert system which is progressively built up. A prospective evaluation of the performance of the expert system on 80 cases of hypertension showed overall agreement between the specialists and the expert system ranging from 58 to 91% depending on the decision.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed2001

Supporting medical decisions with vector decision trees.

The article presents the extension of a common decision tree concept to a multidimensional - vector - decision tree constructed with the help of evolutionary techniques. In contrary to the common decision tree the vector decision tree can make more than just one suggestion per input sample. It has the functionality of many separate decision trees acting on a same set of training data and answering different questions. Vector decision tree is therefore simple in its form, is easy to use and analyse and can express some relationships between decisions not visible before. To explore and test the possibilities of this concept we developed a software tool--DecRain--for building vector decision trees using the ideas of evolutionary computing. Generated vector decision trees showed good results in comparison to classical decision trees. The concept of vector decision trees can be safely and effectively used in any decision making process.

Algorithms↗
PubMed2001

The value of intelligent multimedia simulation for teaching clinical decision-making skills.

This paper examines the value of using intelligent multimedia simulation for the teaching of nursing clinical decision-making skills. The possibilities of multimedia-based educational resources are examined and the rapid growth and questionable effectiveness of current multimedia computer-based learning applications for nursing students are discussed. The advantages and disadvantages of this technology and the problems developing intelligent agent-based systems are examined. A case study is presented which uses a modular design with an integrated intelligent agent and knowledge base. It is argued that by using this type of approach, the real value of intelligent CBL to provide individual formative advice to students in a simulated experience can be realized.

Computer-Assisted Instruction↗
PubMed2003

Hot spot detection based on feature space representation of visual search.

This paper presents a new framework for capturing intrinsic visual search behavior of different observers in image understanding by analysing saccadic eye movements in feature space. The method is based on the information theory for identifying salient image features based on which visual search is performed. We demonstrate how to obtain feature space fixation density functions that are normalized to the image content along the scan paths. This allows a reliable identification of salient image features that can be mapped back to spatial space for highlighting regions of interest and attention selection. A two-color conjunction search experiment has been implemented to illustrate the theoretical framework of the proposed method including feature selection, hot spot detection, and back-projection. The practical value of the method is demonstrated with computed tomography image of centrilobular emphysema, and we discuss how the proposed framework can be used as a basis for decision support in medical image understanding.

Adult↗
PubMed2000

Finding similar cases within a hospital information system.

Most of the theoretical medical knowledge comes from literature. The knowledge obtained from the vast majority of patients is then lost. The vast majority of patients do not participate in the elaboration of medical knowledge, apart from the lucky few entering a clinical trial or a published case study. Moreover, locally treated patients do not always correspond to the same time, space or age context as literature patients. How can the knowledge of one patient be used for treating other patients? How can we save the knowledge of our own patients? Hospital information systems contain a lot of detailed and precise information about many patients over several years. Databases containing detailed information can provide solutions based on case analysis (Case-based reasoning or "similar case approach"). An example of a Geneva's decision system called Archimed is shown here.

Artificial Intelligence↗
PubMed2000

Evaluation of DIABNET, a decision support system for therapy planning in gestational diabetes.

DIABNET is a knowledge-based system designed to aid doctors with therapy planning in gestational diabetes. The system core is a qualitative model, implemented by a Causal Probabilistic Network, that is able to detect the insulin effectiveness on a daily basis. DIABNET analyses monitoring data and proposes quantitative changes in insulin therapy and qualitative diet modifications. This paper proposes an evaluation methodology to assess the system performance when working in a real scenario. The methodology manages the absence of a gold standard and includes: a subjective analysis based on questionnaires and an objective analysis based on a quantitative comparison of the system's and experts' proposals. The paper also shows the results of two experiments in which expert diabetologists evaluated the therapeutical advice provided by DIABNET during the follow up of 9 patients with gestational diabetes. DIABNET detected the need of a therapy modification in 92% of the cases showing its appropriateness for automatic alarm generation. Around 80% of the proposals were accepted by experts. The evaluation results are encouraging and allow characterisation of the system's performance when proposing therapy modifications. Evaluation in its turn helps to refine the knowledge managed by DIABNET and enables us to look towards the further clinical use of DIABNET as a decision tool in gestational diabetes integrated in a telemedicine service.

Artificial Intelligence↗
PubMedDate not supplied

An architecture for knowledge-based construction of decision models.

Clinical application of decision analysis has been limited by unfamiliarity of clinicians with the technique, large data requirements, and the length of time needed to construct models. In order to make decision modeling more accessible to clinicians, the authors developed a computer program to construct decision models automatically. The system contains two separate knowledge bases. One contains frames encoding knowledge of the medical domain, the evaluation of pulmonary disease in patients infected with the human immunodeficiency virus (HIV). The other contains rules of correct decision model construction that guide the selection of items from the domain knowledge base and their insertion into the decision model. The system can create either a tree or an influence diagram that satisfies previously published critiquing rules. The system has the potential to enable novices to construct useful decision models and to provide individualized decision-analytic advice to clinicians in real time.

AIDS-Related Opportunistic Infections↗
PubMed1999

Take care: guidelines for patients with chronic hepatitis C.

Alcohol consumption has significant impact on the condition of the liver, by itself, and even more in conjunction with other liver diseases such as chronic hepatitis C. Drinking habits might be delicate issues to address and could harm otherwise satisfying communication. Therefore, we intended to outline guidelines for advising hepatitis C patients concerning alcohol consumption. Analysis of a relatively limited knowledge base revealed the complexity of the disease rather than statistically significant findings regarding consumption. Thus, we instead chose to suggest a set of patient educational guidelines, which could be implemented on the Internet, hypothesizing that a better informed patient will be more able to comply with restrictions concerning alcohol consumption. A brief ad hoc evaluation pointed out Internet as a favourable media to present the information. We also suggest a tentative algorithm for further development of clinical decision support systems addressing monitoring of chronic hepatitis C patients.

Adolescent↗
PubMed2005

Ontology-based knowledge repository support for healthgrids.

Healthgrids unite a large amount of independent and distributed organisations to provide for various healthcare services. Often the involved organisations can belong to different areas of healthcare and even different countries. However to achieve efficient operation they have to act in a well coordinated manner. As a result, an efficient knowledge sharing between multiple participating parties of the healthgrid is required. The paper describes application of an earlier developed ontology-driven KSNet (Knowledge Source Network) - approach to knowledge repository support for healthgrids. This approach is based on representation of knowledge via ontologies using formalism of object-oriented constraint networks. Such representation makes it possible to define and solve various tasks from the areas of management, planning, configuration, etc., by using constraint solving engines such as, for instance, ILOG or CLP. The major discussed aspects cover the formalism of knowledge representation via ontologies and implementation of the approach as a decision support system for a case study from the area of health service logistics.

Artificial Intelligence↗
PubMed2005

Temporal reasoning for decision support in medicine.

OBJECTIVE: Handling time-related concepts is essential in medicine. During diagnosis it can make a substantial difference to know the temporal order in which some symptoms occurred or for how long they lasted. During prognosis the potential evolutions of a disease are conceived as a description of events unfolding in time. In therapy planning the different steps of treatment must be applied in a precise order, with a given frequency and for a certain span of time in order to be effective. This article offers a survey on the use of temporal reasoning for decision support-related tasks in medicine. MATERIAL AND METHODS: Key publications of the area, mainly circumscribed to the latest two decades, are reviewed and classified according to three important stages of patient treatment requiring decision support: diagnosis, prognosis and therapy planning/management. Other complementary publications, like those on time-centered information storage and retrieval, are also considered as they provide valuable support to the above mentioned three stages. RESULTS: Key areas are highlighted and used to organize the latest contributions. The survey of previous research is followed by an analysis of what can still be improved and what is needed to make the next generation of decision support systems for medicine more effective. CONCLUSIONS: It can be observed that although the area has been considerably developed, there are still areas where more research is needed to make time-based systems of widespread use in decision support-related areas of medicine. Several suggestions for further exploration are proposed as a result of the survey.

Artificial Intelligence↗
PubMed2000

Transforming XML-based electronic patient records for use in medical case based reasoning systems.

Electronic patient records (EPR) can be regarded as an implicit source of clinical behaviour and problem-solving knowledge, systematically compiled by clinicians. We present an approach, together with its computational implementation, to pro-actively transform XML-based EPR into specialised Clinical Cases (CC) in the realm of Medical Case Base Systems. The 'correct' transformation of EPR to CC involves structural, terminological and conceptual standardisation, which is achieved by a confluence of techniques and resources, such as XML, UMLS (meta-thesaurus) and medical knowledge ontologies. We present below the functional architecture of a Medical Case-Base Reasoning Info-Structure (MCRIS) that features two distinct, yet related, functionalities: (1) a generic medical case-based reasoning system for decision-support activities; and (2) an EPR-CC transformation system to transform typical EPR's to CC.

Artificial Intelligence↗
PubMed1999

Applying data mining in healthcare: an info-structure for delivering 'data-driven' strategic services.

Presently, there is a growing demand from the healthcare community to leverage upon and transform the vast quantities of healthcare data into value-added, 'decision-quality' knowledge, vis-à-vis, strategic knowledge services oriented towards healthcare management and planning. To meet this end, we present a Strategic Knowledge Services Info-structure that leverages on existing healthcare knowledge/data bases to derive decision-quality knowledge-knowledge that is extracted from healthcare data through services akin to knowledge discovery in databases and data mining.

Artificial Intelligence↗
PubMed1987

Computer programs to support clinical decision making.

Computer programs to assist with medical decision making have long been anticipated by physicians with both curiosity and concern. This article summarizes the current status of computer-based medical decision support, the goals of system developers, the reasons for slow progress since the field began almost 30 years ago, and the logistical and scientific challenges that lie ahead. It emphasizes in particular that decision-support programs are intended to serve as tools for trained practitioners who retain ultimate responsibility for determining diagnostic and therapeutic strategies.

Artificial Intelligence↗
PubMed2005

Improving outcomes in radiology: bringing computer-based decision support and education to the point of care.

Many computer applications have been developed in radiology and other medical disciplines to help physicians make decisions. Artificial intelligence (AI)--an approach to computer-based manipulation of symbols to simulate human reasoning--forms the basis of many of these systems. This article's goals are to: acquaint the reader with the motivations and opportunities for computer-based medical decision support systems; identify AI techniques and applications in radiology decision making; assess the impact of these technologies; and consider new directions and opportunities for AI in radiology. Among the exciting new directions is the use of AI to integrate radiology reporting, online decision support, and just-in-time learning to provide useful information and continuing education that is embedded within a radiologist's daily workflow.

Decision Making, Computer-Assisted↗
PubMed2000

A model for optimal sequential decisions applied to liver transplantation.

The present investigation aims at the construction of a sequential decision rule derived from a decision-theoretic model. An optimal clinical management strategy based on patient risk assessment is to be found. A novel decision theoretic cost model has been selected for this clinical classification task: costs were determined by specifying a minimum acceptable sensitivity and specificity of the overall procedure. Non-linear optimisation combined with a robust partial classification method is used to find the earliest possible decision step where a final decision can be made subject to these quality restrictions. The probabilities needed in the model are estimated from data provided by a clinical study on liver transplantation patients. Decision steps for the decision-theoretic model were chosen before and after donor organ assessment, and postoperatively in the intensive care unit. Clinical parameters acquired in between decision steps were combined to scores obtained from artificial neural networks (ANNs). The encouraging results show the applicability of the model in the clinical setting.

Artificial Intelligence↗