[Studies on learning choice reactions. II. On the theory of learning behavior directed toward relevant performance].
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Attitude↗
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Results for “decision intelligence”
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A method for measuring subjective probability.
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Artificial Intelligence↗
Machine learning for development of an expert system to predict premature birth.
Normal pregnancy involves a term of 40 weeks gestation. Problems associated with low birthweight and prematurity continue to plague childbearing families and the healthcare system because 8-12% of all newborns in the United States deliver prior to 37 weeks gestation. The high cost of caring for premature babies increasingly treats all pregnant women as if they are 'high risk' for preterm birth. Artificial intelligence techniques used a machine learning program named LERS1 with large datasets (n = 18,890; 214 variables), statistical analysis, expert verification techniques, and a prototype expert system2 that yielded improved accuracy (53-90%) over existing manual techniques (17-38%) for predicting preterm birth.
Adult↗
The relative accuracy of a variety of medical diagnostic programs.
Acute abdominal pain is one of the most widely studied applications of computer-aided diagnosis. The usual approach is to apply Bayes' theorem with the assumption of conditional independence ("independence Bayes"). We compared various approaches to designing diagnostic programs for abdominal pain of suspected gynaecological origin. The methods range from statistical to knowledge-based. All programs were evaluated using a database of 1,270 cases collected retrospectively. Our results suggest that in this application no significant improvement in accuracy can be made by taking interactions into account, either by statistical or by knowledge-based means; independence Bayes is near-optimal. As far as accuracy is concerned, there appears to be little point in pursuing knowledge-based approaches. However, the "nearest neighbours" method using a new metric appears to be at least as accurate as independence Bayes. We argue that the nearest neighbours method is more suitable than independence Bayes for clinical use because of greater accountability.
Abdominal Pain↗
Representing hospital events as complex conditionals.
We have developed an approach to medical knowledge representation whereby simple medical concepts are combined to yield complex statements of testable medical logic. The logic is created from a small number of generic medical concepts that are instantiated and combined to create the rules. Rule writing is done through a rule editor and requires knowledge of the system's data dictionaries, though no programming is required. We have used the approach to create a large knowledge base including panic lab alerting rules, drug-laboratory interaction alerting rules, an adverse drug event monitor, and a drug-age interaction detection program. The rules have been used as part of an alerting system and for data collection to determine the frequency of events of interest. The scheme is extensible and yields a readable form of the created knowledge. The scheme holds great promise as a durable form of medical knowledge representation.
Artificial Intelligence↗
Real time monitoring in the control theory paradigm.
Monitoring patients hospitalized in hemato-oncology departments to undergo clinical protocols of therapy is a complex task. The main difficulty arises in the follow-up of the oncology protocol and in the management of critical episodes of acute illness which frequently occur due to the high toxicity of the antimitotics used. This problem can be conceptualized within the control theory paradigm as the task of controlling a process whose state can deviate unacceptably from a normal range. Following the control theory analogy at the level of knowledge bases design, we have modeled the medical knowledge as control information to represent the medical actions, and state information is used as a feedback control to readjust the command.
Aged↗
CLECOS_P: clinical evolution control system on Parkinsonian patients undergoing neural transplantation.
The CLECOS_P system was conceived for registering and automating the processing of clinical evaluations performed on patients with Parkinson's disease who undergo functional neurosurgery and/or neural transplant. CLECOS_P represents the first time a computerized system is able to offer--with high precision and considerable time-savings--an integral analysis of the evolutive behavior of the universe in integrated variables at the core assessment program for intracerebral transplantations (CAPIT). CAPIT is used internationally for the evaluation and follow-up of patients with this pathology who have undergone neural transplant. We used the so-called MEDSAC methodology for the preparation of this system. The methodology that was used for the design of an intelligent system aimed at medical decision-making was based on the quantitative analysis of the clinical evolution. At the present moment, there are 20 patients controlled by this system: 11 bilaterally transplanted, 9 unilaterally (registered in ranks of 3 months before operation up to 1, 2, 3, 6, 9, 12, 18, and 24 months after operation). The application of CLECOS_P to these patients permitted the evaluation of 400 clinical variables, where a better evolutive characterization of the patients was obtained, thus getting most favorable results with personalized therapeutic methods aimed at raising their quality of life. CLECOS_P is used in a multi-user environment on a local area network running Novell Netware version 3.11.
Brain Tissue Transplantation↗
Do cognitive abilities decline with age?
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Aged↗
Multi-objective optimization in radiotherapy: applications to stereotactic radiosurgery and prostate brachytherapy.
Treatment planning for radiation therapy is a multi-objective optimization process. Here we present a machine intelligent scheme for treatment planning based on multi-objective decision analysis (MODA) and genetic algorithm (GA) optimization. Multi-objective ranking strategies are represented in the L(p) metric under the displaced ideal model. Goal setting, protocol satisficing and fuzzy ranking of objective importance can be incorporated into the decision scheme to assimilate clinical decision making. For distance measures in the L(p) metric, a dynamic gauge function is defined based on the state energy of the decision system, which is assumed to undergo thermodynamic cooling with iteration time. The MODA scheme interacts with a robust GA engine, which adaptively evolves in the multi-modal landscape that defines the treatment plan quality. A conventionally challenging case of stereotactic radiosurgery of a brain lesion was selected for GA optimization. The resulting dose distributions are compared to human-developed plans, which are commonly regarded as clinically relevant and empirically optimal. The GA-optimized plans achieve substantially better sparing of critical normal neuroanatomy surrounding the brain lesion while respecting the preset constraints on tumor dose uniformity. In addition, machine optimization tends to produce novel treatment strategies which complements expert knowledge. The run time for producing an optimal plan is considerably shorter than the typical planning time for human experts, thus GA can also be used to aid the human treatment planning process. In prostate brachytherapy, MODA-GA was specifically applied to non-ideal conditions in which typical surgical uncertainties in seed implant positioning occur, where noisy objectives were introduced into the optimization scheme. The noisy system is found to be manageable by MODA-GA at uncertainty levels corresponding to reasonably proficient surgery teams. In contrast, noisy objectives would be very difficult to explore by human expert planners. Potential use of noisy optimization with time series analysis is being explored for error-corrective computer guidance in the operating room for prostate seed implantation. In conclusion, the combination of MODA and GA optimization offers both a solution to practical treatment planning tasks and the potential for real time applications in radiotherapy.
Algorithms↗
Monitoring quality of health care with relational patterns.
This paper describes the extraction of relational patterns from hospital discharge data for the purpose of monitoring the quality of health care. We discuss what relational patterns are and how they support the extraction of meaningful patterns from data that are expressive, comprehensive, and easy to interpret. We demonstrate how relational patterns can be applied to identify poor practices embedded in hospitalization processes, for instance, help trigger subsequent inquiries and support decision-making processes.
Algorithms↗
[An expert system of aiding decision making in breast pathology connected to a clinical data base].
The René Huguenin Cancer Center holds a medical file for each patient which is intended to store and process medical data. Since 1970, we introduced computerization: a development plan was elaborated and simultaneously a statistical software (Clotilde--GSI/CFRO) was selected. Thus, we now have access to a large database, structured according to medical rationale, and utilizable with methods of artificial intelligence towards three objectives: improved data acquisition, decision making and exploitation. The first application was to breast pathology, which represents one of the Center's primary activities. The structure of the data concerning patients is by all criteria part of the medical knowledge. This information needs to be presented as well as processed with a suitable language. To this end, we chose a language-oriented object, Mering II, usable with Apple and IBM 4 micro-computers. This project has already allowed to work out an operational model.
Breast Neoplasms↗
The future of knowledge-based components in the electronic health record.
Traditional monolithic healthcare information systems (HIS) no longer meet the requirements of today's distributed enterprises and the rapidly changing healthcare environment. The ability of applications to communicate, interpret, and act intelligently upon complex healthcare information has assumed paramount importance. The future lies in the development of flexible component-based architectures that can operate seamlessly within the workflow of a healthcare environment. A key design goal is "graceful degradation," i.e., providing the best decision support possible within the context of available patient data. The First DataBank Drug Toolkit is used as a case study. Several technical challenges associated with building truly plug and play components are discussed.
Artificial Intelligence↗
May we support your decision?
Expert systems to support medical decision-making have so far achieved few successes. Current technical developments, however, may overcome some of the limitations. Although there are several theoretical currents in medical artificial intelligence, there are signs of them converging. Meanwhile, decision support systems, which set themselves more modest goals than replicating or improving on clinicians' expertise, have come into routine use in places where an adequate electronic patient record exists. They may also be finding a wider role, assisting in the implementation of clinical practice guidelines. There is, however, still much uncertainty about the kinds of decision support that doctors and other health care professionals are likely to want or accept.
Bayes Theorem↗
Accuracy of intelligent medical systems.
Intelligent medical systems are a special kind of medical software in general, and just as any medical software system they should make accurate presumptions. However, accuracy of intelligent medical systems is highly dependent on various factors such as: choosing an appropriate basic method (i.e. decision trees, neural networks), induction method (i.e. purity measures) and appropriate support methods (i.e. discretization, pruning, boosting). In this paper we present the results of extensive research of the above alternatives on 54 UCI databases and their influence on the accuracy of decision trees, which constitute one of the most desirable forms of intelligent medical systems. We also introduce new hybrid purity measures that on some databases outperform other purity measures. The results presented here show that the selection of the right purity measure with the proper discretization method and application of the boosting method can really make a difference in terms of higher accuracy of induced decision trees. Thereafter choosing the appropriate factors that can increase the accuracy of the induced decision tree is a very demanding and time-consuming task.
Algorithms↗
Test-retest stability of the Hiskey-Nebraska Test of Learning Aptitude in a sample of hearing-impaired children and adolescents.
The test-retest stability of the Hiskey-Nebraska Test of Learning Aptitude (H-NTLA) was examined in a group of hearing-impaired children and adolescents. Test-retest correlations for subjects retested after approximately 1 year, 3 years, and 5 years were .79, .85, and .62 respectively. These findings are similar to those reported for normal subjects in studies using verbal intelligence measures. In spite of reasonably high test-retest correlations, more than one half of the sample showed a 10-point or greater difference in Learning Quotient between the two evaluations, and more than one third of the sample showed a 15-point or greater difference. These findings demonstrate the necessity of basing important decisions on more than one measure of intelligence.
Adolescent↗
[Organization of medical knowledge and the support of decision making with modern algorithmic methods].
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Algorithms↗
Computer-based clinical decision aids. A review of methods and assessment of systems.
During the last three decades a great deal of research has been devoted to the development of integrated clinical decision support systems. This report aims to give a basic understanding of what is required for such a system. By means of a large literature study a survey is given of the major components of computer-based clinical aid systems. The main approaches and several aspects of evaluation of such programs are described. The computer has several inherent capabilities which are suitable for medical problem solving and can help in the formalization of medical knowledge. The components of such systems include the computer database, the reasoning engine and the user interface. The different approaches on which the reasoning engine is built are based on manipulation of information and advocate the use of knowledge to construct a solution to a problem. The information in the mode vary from data-intensive to knowledge-intensive. Assessment of decision support systems is a very important phase in the development of such systems. Evaluation should be made on the accuracy of the program, the nature of the system, the use of the data and the acceptance by the target users. Whatever the model is, its effectiveness will depend on the data with which the program has to work. Acceptance by physicians depends among other things on ease of use of the user interface. Profound changes in the delivery of health care will be induced through the rapid growth of on-line computer communication together with the development of integrated clinical decision support systems and electronic medical records. Notwithstanding the rapid growth of computer technology, computer-aided decision making is in its infancy and real support in daily practice is not yet achieved.
Algorithms↗
Design of a computer-assisted programme supporting the selection and clinical management of patients referred for liver transplantation.
The paper describes the knowledge-base of the expert system OLT 1, developed to support medical decision-making in patients referred for liver transplantation. The paper goes through the real clinical problems, and describes both structural (organization of the domain knowledge) and functional aspects (reasoning algorithm and strategies for clinical decision). According to the programme, patients referred to Liver Transplant Centres may be enrolled and ranked in the waiting list, included in a stand-by list for treatment and re-evaluation, or discharged. All decisions are made on the basis of well-assessed and objective criteria.
Aged↗