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PubMed1990

Illness-episode approach: costs and benefits of medigap insurance.

Over two-thirds of Medicare beneficiaries have private supplementary coverage, but few know enough about Medicare, their own supplements, or available alternatives to make intelligent comparisons and informed purchasing decisions. The illness-episode approach, a new way to provide insurance information to Medicare beneficiaries, calculates out-of-pocket costs likely to be faced by beneficiaries experiencing 13 illnesses, under Medicare alone and under different medigap policies. Applying the approach to six policies marketed in Los Angeles in 1986 revealed that plans varied widely in their ability to reduce financial vulnerability; many still leave the elderly with substantial out-of-pocket costs.

Community Participation↗
PubMed1998

Physiology of aging.

Aging could be due to reduced ability to adapt to stress and/or aging of immune system. It is suggested that body cells (except germ cells and transformed cells) bear specific 'death' genes responsible for aging process. However, current concepts of free radicals (chemical intermediate) and aging, postulate that intermolecular linkages between degradation products of lipid oxidation (when free radical is a polysaturated fatty acid) results in inactive polymers interfering with cell activity which could be inactivated by natural or artificial antioxidants (e.g. vit.E, vit.C) With aging body composition and conformation changes resulting in diminished activity of vital organs and structural changes in the body involving skin, muscle, bones etc. with altered look, diminished movements, hearting, vision etc. However, changes in higher function cause decline in intelligence, memory, ability to take decision, slow reaction and decrease ability to learn new skills resulting in more rigid attitude in elderly. Balanced diet plays an important role in delaying aging process and sexual activity promotes active life span.

Aged↗
PubMed1995

A computer-assisted case report and diagnosis system: sharing the knowledge database ADM and using hypermedia techniques.

The practice of medicine is characterized by its great variability and by many rare diseases. When the medical students work in hospital units, they must learn the general medical practice in the care of the patient. The purpose of this work was to present a French multifunction decision aid system using artificial intelligence techniques and Hypercard tools for different modules. Through an ergonomic interface, the system assists the user in the construction of medical observations, suggests diagnostic hypothesis, provides documentation and helps the user perform retrieval tasks. The knowledge comes from senior experts and from the pre-existent and large knowledge database, ADM.

Computer Peripherals↗
PubMed1996

Informed consent: the nurse's dilemma.

The author builds upon the concept of informed consent whereby the patient agrees to undergo experimental medical procedures. Ideally, the doctor will inform the patient fully on the proposed treatment so as to assure the patient's right to participate intelligently and freely in the decisions regarding his treatment. The nurse is drawn into the doctor-patient relationship in cases where the patient seeks her counsel because he feels insufficiently informed by the doctor, or because the nurse becomes aware of inadequacies in the information-giving process. She is then faced by the nurse's dilemma: a conflict between the loyalties she owes to her patient and to her physician team mate. A work sheet is presented which can help the nurse decide upon the proper course of action in solving this dilemma, guided by her personal and professional beliefs and by specific ethical concepts in the Code for Nurses. Solution of the dilemma and attainment of informed consent requires willing cooperation between doctor and nurse. Both can develop the skills for imparting information to the patient under difficult conditions and for verifying its comprehension by the patient. Both must learn to respect the patient's decision and to temper their professional skills with sensitivity, a strong moral sense and a deep respect for their fellow human beings.

Decision Making↗
PubMed2005

A criticality-based framework for task composition in multi-agent bioinformatics integration systems.

MOTIVATION: During task composition, such as can be found in distributed query processing, workflow systems and AI planning, decisions have to be made by the system and possibly by users with respect to how a given problem should be solved. Although there is often more than one correct way of solving a given problem, these multiple solutions do not necessarily lead to the same result. Some researchers are addressing this problem by providing data provenance information. Others use expert advice encoded in a supporting knowledge-base. In this paper, we propose an approach that assesses the importance of such decisions with respect to the overall result. We present a way of measuring decision criticality and describe its potential use. RESULTS: A multi-agent bioinformatics integration system is used as the basis of a framework that facilitates such functionality. We propose an agent architecture, and a concrete bioinformatics example (prototype) is used to show how certain decisions may not be critical in the context of more complex tasks.

Algorithms↗
PubMed2006

A new computer-based decision-support system for the interpretation of bone scans.

OBJECTIVE: To develop a completely automated method, based on image processing techniques and artificial neural networks, for the interpretation of bone scans regarding the presence or absence of metastases. METHODS: A total of 200 patients, all of whom had the diagnosis of breast or prostate cancer and had undergone bone scintigraphy, were studied retrospectively. Whole-body images, anterior and posterior, were obtained after injection of 99mTc-methylene diphosphonate. The study material was randomly divided into a training group and a test group, with 100 patients in each group. The training group was used in the process of developing the image analysis techniques and to train the artificial neural networks. The test group was used to evaluate the automated method. The image processing techniques included algorithms for segmentation of the head, chest, spine, pelvis and bladder, automatic thresholding and detection of hot spots. Fourteen features from each examination were used as input to artificial neural networks trained to classify the images. The interpretations by an experienced physician were used as the 'gold standard'. RESULTS: The automated method correctly identified 28 of the 31 patients with metastases in the test group, i.e., a sensitivity of 90%. A false positive classification of metastases was made in 18 of the 69 patients not classified as having metastases by the experienced physician, resulting in a specificity of 74%. CONCLUSION: A completely automated method can be used to detect metastases in bone scans. Future developments in this field may lead to clinically valuable decision-support tools.

Adult↗
PubMed1993

Constructing a minimal diagnostic decision tree.

Classification trees and discriminant function analysis were employed in order to ascertain whether a small number of diagnostic decision rules could be extracted from a large inventory of items. Several models, involving up to 17 symptoms, that led to a broad psychiatric diagnosis were then tested on a small validation sample of 53 patients. All methods, with the exception of CART used without any pruning, generated identical trees involving four items. Almost 90% of the validation sample was able to be correctly classified by all methods although poor classification performance was noted in the case of one particular diagnosis, Schizoaffective Psychosis. In contrast, stepwise linear discriminant analysis originally selected 17 items, although three out of the first four items selected were identical to those chosen by the tree-building methods. Although more research is required, there are indications that the latter methods may be usefully employed in constructing parsimonious decision trees.

Algorithms↗
PubMed2002

MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring.

Development and evaluation of Intensive Care Unit (ICU) decision-support systems would be greatly facilitated by the availability of a large-scale ICU patient database. Following our previous efforts with the MIMIC (Multi-parameter Intelligent Monitoring for Intensive Care) Database, we have leveraged advances in networking and storage technologies to develop a far more massive temporal database, MIMIC II. MIMIC II is an ongoing effort: data is continuously and prospectively archived from all ICU patients in our hospital. MIMIC II now consists of over 800 ICU patient records including over 120 gigabytes of data and is growing. A customized archiving system was used to store continuously up to four waveforms and 30 different parameters from ICU patient monitors. An integrated user-friendly relational database was developed for browsing of patients' clinical information (lab results, fluid balance, medications, nurses' progress notes). Based upon its unprecedented size and scope, MIMIC II will prove to be an important resource for intelligent patient monitoring research, and will support efforts in medical data mining and knowledge-discovery.

Artificial Intelligence↗
PubMed2004

Universal electronic health record MUDR.

One of the important research tasks of the European Centre for Medical Informatics, Statistics and Epidemiology - Cardio (EuroMISE Centre - Cardio) is the applied research in the field of electronic health record design including electronic medical guidelines and intelligent systems for data mining and decision support. The research in the field of data storage and data acquisition was inspired by several European projects and standards, mostly by the I4C and TripleC projects. Based on experience gathered during cooperation in the TripleC project we have proposed a description of a flexible information storage model. The motivation for this effort was the large variability of the set of collected features in different departments - including temporal variability. Therefore, a dynamically extensible and modifiable structure of items is needed. In our model we use two basic structures called the knowledge base and data files. The main function of the knowledge base is to express the hierarchy of collectable features - medical concepts, their characteristics and relations among them. The data files structure is used to store the patient's data itself. These two structures can be described using graph theory expressions. Based on this model, a three-layer system architecture named "Multimedia Distributed Record" (MUDR) has been proposed and implemented. During the implementation, modern technologies such as Web Services, SOAP and XML were used. For the practical usage of EHR MUDR, an intelligent application called MUDRc (MUDR Client) was created. It enables physicians to use EHR MUDR in a flexible way. During the development process, maximum emphasis was placed on user-friendliness and comfortable usage of this application. Several methods of data entry can be used: pre-defined forms, direct entry into the tree data structure of the EHR MUDR, or automatic unstructured free-text report parsing and data retrieval. The system enables fast and simple importing and exporting of data as well. The system integrates modern multimedia formats (X-ray photos, sonography and other pictures, video-sequences, audio records) as well as progressive methods of decision support systems realized by medical guidelines and other modules.

Artificial Intelligence↗
PubMed1994

Fundamentals of clinical methodology: 1. Differential indication.

Progress in the theory and practice of artificial intelligence in medicine requires awareness of basic issues in medical problem solving. To stimulate discussion and research on this subject, in a series of articles some logical, methodological and meta-theoretical problems of clinical practice will be studied. The present paper reconstructs clinical decision-making as a computable process of action planning.

Artificial Intelligence↗
PubMed1998

An integrated approach for a knowledge-based clinical workstation: architecture and experience.

Today, the demand for medical decision support to improve the quality of patient care and to reduce costs in health services is generally recognized. Nevertheless, decision support is not yet established in daily routine within hospital information systems which often show a heterogeneous architecture but offer possibilities of interoperability. Currently, the integration of decision support functions into clinical workstations is the most promising way. Therefore, we first discuss aspects of integrating decision support into clinical workstations including clinical needs, integration of database and knowledge base, knowledge sharing and reuse and the role of standardized terminology. In addition, we draw up functional requirements to support the physician dealing with patient care, medical research and administrative tasks. As a consequence, we propose a general architecture of an integrated knowledge-based clinical workstation. Based on an example application we discuss our experiences concerning clinical applicability and relevance. We show that, although our approach promotes the integration of decision support into hospital information systems, the success of decision support depends above all on an adequate transformation of clinical needs.

Artificial Intelligence↗
PubMed2006

Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma.

The degree of malignancy in brain glioma is assessed based on magnetic resonance imaging (MRI) findings and clinical data before operation. These data contain irrelevant features, while uncertainties and missing values also exist. Rough set theory can deal with vagueness and uncertainty in data analysis, and can efficiently remove redundant information. In this paper, a rough set method is applied to predict the degree of malignancy. As feature selection can improve the classification accuracy effectively, rough set feature selection algorithms are employed to select features. The selected feature subsets are used to generate decision rules for the classification task. A rough set attribute reduction algorithm that employs a search method based on particle swarm optimization (PSO) is proposed in this paper and compared with other rough set reduction algorithms. Experimental results show that reducts found by the proposed algorithm are more efficient and can generate decision rules with better classification performance. The rough set rule-based method can achieve higher classification accuracy than other intelligent analysis methods such as neural networks, decision trees and a fuzzy rule extraction algorithm based on Fuzzy Min-Max Neural Networks (FRE-FMMNN). Moreover, the decision rules induced by rough set rule induction algorithm can reveal regular and interpretable patterns of the relations between glioma MRI features and the degree of malignancy, which are helpful for medical experts.

Adolescent↗
PubMed1998

Discrimination between chronic pancreatitis and pancreatic adenocarcinoma using artificial intelligence-related algorithms based on image cytometry-generated variables.

The incidence of pancreatic adenocarcinomas (PA) is increased in the setting of chronic pancreatitis. Distinguishing chronic pancreatitis from pancreatic adenocarcinomas is often difficult, and is based on routine brush cytological specimens provided during endoscopic retrograde cholangiopancreatography (ERCP). Reactive epithelial changes in chronic pancreatitis may appear similar to those of a well-differentiated cancer. Brush cytology specimens were obtained during ERCP from 49 patients with diseases for which the differential diagnosis included chronic pancreatitis and/or pancreatic adenocarcinoma Image cytometry was performed involving the assessment of between 200-400 Feulgen-stained nuclei per case; for each case, 40 quantitative cytometric variables were generated. Data analysis was performed using artificial intelligence methods of data classification that produced decision trees and production rule systems. Different classification models were produced for a subset of 34 patients. The best models were identified by the use of a sampling technique (leave-one-out), and were tested on the remaining 15 patients. These models were based on 5 of the 40 variables associated with a significant discriminatory function. Pancreatic adenocarcinoma was diagnosed in the training data set of 34 patients during a leave-one-out process with an estimated sensitivity of 91% and specificity of 87%. Both sensitivity and specificity were 80% in the independent test set of 15 patients. We conclude that inflammatory and malignant pancreatic epithelia exhibit distinct morphological features that can be distinguished by decision tree-based classifiers employing image-cytometric numerical data.

Adenocarcinoma↗
PubMed2004

A hybrid fuzzy logic/constraint satisfaction problem approach to automatic decision making in simulation game models.

Possible techniques for representing automatic decision-making behavior approximating human experts in complex simulation model experiments are of interest. Here, fuzzy logic (FL) and constraint satisfaction problem (CSP) methods are applied in a hybrid design of automatic decision making in simulation game models. The decision processes of a military headquarters are used as a model for the FL/CSP decision agents choice of variables and rulebases. The hybrid decision agent design is applied in two different types of simulation games to test the general applicability of the design. The first application is a two-sided zero-sum sequential resource allocation game with imperfect information interpreted as an air campaign game. The second example is a network flow stochastic board game designed to capture important aspects of land manoeuvre operations. The proposed design is shown to perform well also in this complex game with a very large (billionsize) action set. Training of the automatic FL/CSP decision agents against selected performance measures is also shown and results are presented together with directions for future research.

Algorithms↗
PubMed1999

Information and redundancy: key concepts in understanding the genetic control of health and intelligence.

A model is proposed in which information from the environment is analysed by complex biological decision-making systems which are highly redundant. A correct response is intelligent behaviour which preserves health; incorrect responses lead to disease. Mutations in genes which code for the redundant systems will accumulate in the genome and impair decision-making. The number of mutant genes will depend upon a balance between the new mutation rate per generation and systems of elimination based on synergistic interaction in redundant systems. This leads to a polygenic pattern of inheritance for intelligence and the common diseases. The model also gives a simple explanation for some of the hitherto puzzling aspects of work on the genetic basis of intelligence including the recorded rise in IQ this century. There is a prediction that health, intelligence and socio-economic position will be correlated generating a health differential in the social hierarchy. Furthermore, highly competitive societies will place those least able to cope in the harshest environment and this will impair health overall. The model points to a need for population monitoring of somatic mutation in order to preserve the health and intelligence of future generations.

Genetic Predisposition to Disease↗
PubMed1991

A problem decomposition method for efficient diagnosis and interpretation of multiple disorders.

Diagnosis of multiple disorders can be made more efficient by reasoning explicitly about problem decompositions. A diagnostic problem can be decomposed by hypothesizing about common and disjoint cause relationships among the given symptoms. The resulting structure exploits computational principles of causal intersection, subproblem independence, and minimal factorability to increase efficiency. By assigning structure to a problem, the symptom decomposition approach offers a new type of decision-support task called symptom interpretation. Experimental results indicate that symptom decomposition yields substantial increases in performance compared to existing methods for multidisorder diagnosis.

Algorithms↗
PubMed1995

Ontology-based configuration of problem-solving methods and generation of knowledge-acquisition tools: application of PROTEGE-II to protocol-based decision support.

PROTEGE-II is a suite of tools and a methodology for building knowledge-based systems and domain-specific knowledge-acquisition tools. In this paper, we show how PROTEGE-II can be applied to the task of providing protocol-based decision support in the domain of treating HIV-infected patients. To apply PROTEGE-II, (1) we construct a decomposable problem-solving method called episodic skeletal-plan refinement, (2) we build an application ontology that consists of the terms and relations in the domain, and of method-specific distinctions not already captured in the domain terms, and (3) we specify mapping relations that link terms from the application ontology to the domain-independent terms used in the problem-solving method. From the application ontology, we automatically generate a domain-specific knowledge-acquisition tool that is custom-tailored for the application. The knowledge-acquisition tool is used for the creation and maintenance of domain knowledge used by the problem-solving method. The general goal of the PROTEGE-II approach is to produce systems and components that are reusable and easily maintained. This is the rationale for constructing ontologies and problem-solving methods that can be composed from a set of smaller-grained methods and mechanisms. This is also why we tightly couple the knowledge-acquisition tools to the application ontology that specifies the domain terms used in the problem-solving systems. Although our evaluation is still preliminary, for the application task of providing protocol-based decision support, we show that these goals of reusability and easy maintenance can be achieved. We discuss design decisions and the tradeoffs that have to be made in the development of the system.

Artificial Intelligence↗
PubMed2003

Measuring the impact of diagnostic decision support on the quality of clinical decision making: development of a reliable and valid composite score.

OBJECTIVE: Few previous studies evaluating the benefits of diagnostic decision support systems have simultaneously measured changes in diagnostic quality and clinical management prompted by use of the system. This report describes a reliable and valid scoring technique to measure the quality of clinical decision plans in an acute medical setting, where diagnostic decision support tools might prove most useful. DESIGN: Sets of differential diagnoses and clinical management plans generated by 71 clinicians for six simulated cases, before and after decision support from a Web-based pediatric differential diagnostic tool (ISABEL), were used. MEASUREMENTS: A composite quality score was calculated separately for each diagnostic and management plan by considering the appropriateness value of each component diagnostic or management suggestion, a weighted sum of individual suggestion ratings, relevance of the entire plan, and its comprehensiveness. The reliability and validity (face, concurrent, construct, and content) of these two final scores were examined. RESULTS: Two hundred fifty-two diagnostic and 350 management suggestions were included in the interrater reliability analysis. There was good agreement between raters (intraclass correlation coefficient, 0.79 for diagnoses, and 0.72 for management). No counterintuitive scores were demonstrated on visual inspection of the sets. Content validity was verified by a consultation process with pediatricians. Both scores discriminated adequately between the plans of consultants and medical students and correlated well with clinicians' subjective opinions of overall plan quality (Spearman rho 0.65, p < 0.01). The diagnostic and management scores for each episode showed moderate correlation (r = 0.51). CONCLUSION: The scores described can be used as key outcome measures in a larger study to fully assess the value of diagnostic decision aids, such as the ISABEL system.

Artificial Intelligence↗