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PubMed2000

Knowledge representation and tool support for critiquing clinical trial protocols.

The increasing complexities of clinical trials have led to increasing costs for investigators and organizations that author and administer those trials. The process of authoring a clinical trial protocol, the document that specifies the details of the study, is usually a manual task, and thus authors may introduce subtle errors in medical and procedural content. We have created a protocol inspection and critiquing tool (PICASSO) that evaluates the procedural aspects of a clinical trial protocol. To implement this tool, we developed a knowledge base for clinical trials that contains knowledge of the medical domain (diseases, drugs, lab tests, etc.) and of specific requirements for clinical trial protocols (eligibility criteria, patient treatments, and monitoring activities). We also developed a set of constraints, expressed in a formal language, that describe appropriate practices for authoring clinical trials. If a clinical trial designed with PICASSO violates any of these constraints, PICASSO generates a message to the user and a list of inconsistencies for each violated constraint. To test our methodology, we encoded portions of a hypothetical protocol and implemented designs consistent and inconsistent with known clinical trial practice. Our hope is that this methodology will be useful for standardizing new protocols and improving their quality.

Artificial Intelligence↗

PubMed2002

A situational approach to the design of a patient-oriented disease-specific knowledge base.

We have developed a situational approach to the organization of disease-specific information that seeks to provide patients with targeted access to content in a knowledge base. Our approach focuses on dividing a defined knowledge base into sections corresponding to discrete clinical events associated with the evaluation and treatment of a specific disorder. Common reasons for subspecialty referral are used to generate situational statements that serve as entry points into the knowledge base. Each section includes defining questions generated using keywords associated with specific topics. Defining questions are linked to patient-focused answers. Evaluation of a thyroid cancer web site designed using this approach has identified high ratings for usability, relevance, and comprehension of retrieved information. This approach may be particularly useful in the development of resources for newly diagnosed patients.

Artificial Intelligence↗

PubMed1995

Using intermediate states to improve the ability of the Arden Syntax to implement care plans and reuse knowledge.

The Arden Syntax is one of a few knowledge representation languages currently in use for clinical decision support. While some of these languages are being used in active patient care settings, none have gained widespread acceptance as a clinical tool. Prior attempts to represent temporally complex care plans in the Arden Syntax have revealed difficulties in representing and tracking series of consecutive time-oriented events and recommendations, in sharing and reusing knowledge and in dealing with unobtainable data. In an attempt to improve Arden's ability to deal with these problems and demonstrate the importance of these factors, the clinical event monitor has been adapted to store coded data representing Intermediate States in the Columbia Presbyterian Medical Center (CPMC) central data repository. The Intermediate States define the current state of the patient as laid out in the care plan. Four care plans were constructed. The findings include an improved ability to track complex series of events and recommendations over long periods of time. The knowledge generated by the electronic care plans was able to be reused by the care plan that generated it, by other elements of the knowledge base and by non-decision support applications. Modular development, facilitated by the changes, simplified dealing with data not available to the central data repository by aiding the implementation of those parts of the care plan for which sufficient data is available.

Artificial Intelligence↗

PubMed2006

Rule generation for protein secondary structure prediction with support vector machines and decision tree.

Support vector machines (SVMs) have shown strong generalization ability in a number of application areas, including protein structure prediction. However, the poor comprehensibility hinders the success of the SVM for protein structure prediction. The explanation of how a decision made is important for accepting the machine learning technology, especially for applications such as bioinformatics. The reasonable interpretation is not only useful to guide the "wet experiments," but also the extracted rules are helpful to integrate computational intelligence with symbolic AI systems for advanced deduction. On the other hand, a decision tree has good comprehensibility. In this paper, a novel approach to rule generation for protein secondary structure prediction by integrating merits of both the SVM and decision tree is presented. This approach combines the SVM with decision tree into a new algorithm called SVM_ DT, which proceeds in three steps. This algorithm first trains an SVM. Then, a new training set is generated through careful selection from the output of the SVM. Finally, the obtained training set is used to train a decision tree learning system and to extract the corresponding rule sets. The results of the experiments of protein secondary structure prediction on RS126 data set show that the comprehensibility of SVM_DT is much better than that of the SVM. Moreover, the generalization ability of SVM_DT is better than that of C4.5 decision trees and is similar to that of the SVM. Hence, SVM_DT can be used not only for prediction, but also for guiding biological experiments.

Algorithms↗

PubMed2005

An efficient comprehensive search algorithm for tagSNP selection using linkage disequilibrium criteria.

MOTIVATION: Selecting SNP markers for genome-wide association studies is an important and challenging task. The goal is to minimize the number of markers selected for genotyping in a particular platform and therefore reduce genotyping cost while simultaneously maximizing the information content provided by selected markers. RESULTS: We devised an improved algorithm for tagSNP selection using the pairwise r(2) criterion. We first break down large marker sets into disjoint pieces, where more exhaustive searches can replace the greedy algorithm for tagSNP selection. These exhaustive searches lead to smaller tagSNP sets being generated. In addition, our method evaluates multiple solutions that are equivalent according to the linkage disequilibrium criteria to accommodate additional constraints. Its performance was assessed using HapMap data. AVAILABILITY: A computer program named FESTA has been developed based on this algorithm. The program is freely available and can be downloaded at http://www.sph.umich.edu/csg/qin/FESTA/

Algorithms↗

PubMed2002

Preintegration lateral inhibition enhances unsupervised learning.

A large and influential class of neural network architectures uses postintegration lateral inhibition as a mechanism for competition. We argue that these algorithms are computationally deficient in that they fail to generate, or learn, appropriate perceptual representations under certain circumstances. An alternative neural network architecture is presented here in which nodes compete for the right to receive inputs rather than for the right to generate outputs. This form of competition, implemented through preintegration lateral inhibition, does provide appropriate coding properties and can be used to learn such representations efficiently. Furthermore, this architecture is consistent with both neuroanatomical and neurophysiological data. We thus argue that preintegration lateral inhibition has computational advantages over conventional neural network architectures while remaining equally biologically plausible.

Algorithms↗

PubMedDate not supplied

Multiparametric time course prognoses by means of case-based reasoning and abstractions of data and time.

In this paper we describe an approach to utilize Case-Based Reasoning methods for trend prognoses for medical problems. Since using conventional methods for reasoning over time does not fit for course predictions without medical knowledge of typical course pattern, we have developed abstraction methods suitable for integration into our Case-Based Reasoning system ICONS. These methods combine medical experience with prognoses of multiparametric courses. We have chosen the monitoring of the kidney function in an Intensive Care Unit (ICU) setting as an example for diagnostic problems. On the ICU, the monitoring system NIMON provides a daily report based on current measured and calculated kidney function parameters. We abstract these parameters to a daily kidney function state. Subsequently, we use these states to generate course-characteristic trend descriptions of the renal function over the course of time. Using Case-Based Reasoning retrieval methods, we search in the case base for courses similar to the current trend descriptions. Finally, we present the current course together with similar courses as comparisons and as possible prognoses to the user.

Artificial Intelligence↗

PubMed2003

Intelligent optimal control with dynamic neural networks.

The application of neural networks technology to dynamic system control has been constrained by the non-dynamic nature of popular network architectures. Many of difficulties are-large network sizes (i.e. curse of dimensionality), long training times, etc. These problems can be overcome with dynamic neural networks (DNN). In this study, intelligent optimal control problem is considered as a nonlinear optimization with dynamic equality constraints, and DNN as a control trajectory priming system. The resulting algorithm operates as an auto-trainer for DNN (a self-learning structure) and generates optimal feed-forward control trajectories in a significantly smaller number of iterations. In this way, optimal control trajectories are encapsulated and generalized by DNN. The time varying optimal feedback gains are also generated along the trajectory as byproducts. Speeding up trajectory calculations opens up avenues for real-time intelligent optimal control with virtual global feedback. We used direct-descent-curvature algorithm with some modifications (we called modified-descend-controller-MDC algorithm) for the optimal control computations. The algorithm has generated numerically very robust solutions with respect to conjugate points. The adjoint theory has been used in the training of DNN which is considered as a quasi-linear dynamic system. The updating of weights (identification of parameters) are based on Broyden-Fletcher-Goldfarb-Shanno BFGS method. Simulation results are given for an intelligent optimal control system controlling a difficult nonlinear second-order system using fully connected three-neuron DNN.

Artificial Intelligence↗

PubMed1988

Neural-space generalization of a topological transformation.

An investigation is performed to assess the generalization capability found in neural network paradigms to approximate a 2-dimensional coordinate (topological) transformation. A developed strategy uses the example to give a physical meaning to what is meant by generalization. The example shows how to use a neural network paradigm to generalize a two-degree of freedom topological transformation from cartesian end-point coordinates to corresponding joint angle coordinates based only on examples of the mapping. The importance of this example is that it provides a clear understanding of how and what a neural network is actually communications and brings a theoretical idea to a useful understanding. When examples characterize the topology, a collective generalization property begins to emerge and the network learns the topology. If the network is presented with additional examples of the transformation, the network can generate the corresponding joint angles to any unseen position, that is, by generalization. It is also significant that the network's generalization property emerges from the network based on very few training examples! Further, the networks power exists with very few neurons. Results suggest the use of the paradigm's generalization capability to provide solutions to unknown or intractable algorithms for applications.

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↗

PubMed2001

Knowledge discovery approach to automated cardiac SPECT diagnosis.

The paper describes a computerized process of myocardial perfusion diagnosis from cardiac single proton emission computed tomography (SPECT) images using data mining and knowledge discovery approach. We use a six-step knowledge discovery process. A database consisting of 267 cleaned patient SPECT images (about 3000 2D images), accompanied by clinical information and physician interpretation was created first. Then, a new user-friendly algorithm for computerizing the diagnostic process was designed and implemented. SPECT images were processed to extract a set of features, and then explicit rules were generated, using inductive machine learning and heuristic approaches to mimic cardiologist's diagnosis. The system is able to provide a set of computer diagnoses for cardiac SPECT studies, and can be used as a diagnostic tool by a cardiologist. The achieved results are encouraging because of the high correctness of diagnoses.

Artificial Intelligence↗

PubMedDate not supplied

Extracting the principal behavior of a probabilistic supervisor through neural networks ensemble.

In this paper, we propose a model of a neural network ensemble that can be trained with a supervisor having two kinds of input-output functions where the occurrence probability of each function is not even. This condition can be likened to a learning condition, in which the learning data are hampered by noise. In this case, the neural network has the impression that the learning supervisor (object) has a probabilistic behavior in which the supervisor generates correct learning data most of the time but occasionally generates erroneous ones. The objective is to train the neural network to approximate the greatest distributed input-output relation, which can be considered to be the principal nature of the supervisor, so that we can obtain a neural network that is able, to some extent, to suppress the ill effect of erroneous data encountered during the learning process.

Algorithms↗

PubMed1995

A rational reconstruction of INTERNIST-I using PROTEGE-II.

PROTEGE-II is a methodology and a suite of tools that allow developers to build and maintain knowledge-based systems in a principled manner. We used PROTEGE-II to reconstruct the well-known INTERNIST-I system, demonstrating the role of a domain ontology (a framework for specification of a model of an application area), a reusable problem-solving method, and declarative mapping relations in creating a new, working program. PROTEGE-II generates automatically a domain-specific knowledge-acquisition tool, which, in the case of the INTERNIST-I reconstruction, has much of the functionality of the QMR-KAT knowledge-acquisition tool. This study provides a means to understand better both the PROTEGE-II methodology and the models that underlie INTERNIST-I.

Artificial Intelligence↗

PubMed1998

Knowledge discovery and data mining to assist natural language understanding.

As natural language processing systems become more frequent in clinical use, methods for interpreting the output of these programs become increasingly important. These methods require the effort of a domain expert, who must build specific queries and rules for interpreting the processor output. Knowledge discovery and data mining tools can be used instead of a domain expert to automatically generate these queries and rules. C5.0, a decision tree generator, was used to create a rule base for a natural language understanding system. A general-purpose natural language processor using this rule base was tested on a set of 200 chest radiograph reports. When a small set of reports, classified by physicians, was used as the training set, the generated rule base performed as well as lay persons, but worse than physicians. When a larger set of reports, using ICD9 coding to classify the set, was used for training the system, the rule base performed worse than the physicians and lay persons. It appears that a larger, more accurate training set is needed to increase performance of the method.

Artificial Intelligence↗

PubMed2006

Time dependent neural network models for detecting changes of state in complex processes: applications in earth sciences and astronomy.

A computational intelligence approach is used to explore the problem of detecting internal state changes in time dependent processes; described by heterogeneous, multivariate time series with imprecise data and missing values. Such processes are approximated by collections of time dependent non-linear autoregressive models represented by a special kind of neuro-fuzzy neural network. Grid and high throughput computing model mining procedures based on neuro-fuzzy networks and genetic algorithms, generate: (i) collections of models composed of sets of time lag terms from the time series, and (ii) prediction functions represented by neuro-fuzzy networks. The composition of the models and their prediction capabilities, allows the identification of changes in the internal structure of the process. These changes are associated with the alternation of steady and transient states, zones with abnormal behavior, instability, and other situations. This approach is general, and its sensitivity for detecting subtle changes of state is revealed by simulation experiments. Its potential in the study of complex processes in earth sciences and astrophysics is illustrated with applications using paleoclimate and solar data.

Artificial Intelligence↗

PubMed2005

Prediction of caspase cleavage sites using Bayesian bio-basis function neural networks.

MOTIVATION: Apoptosis has drawn the attention of researchers because of its importance in treating some diseases through finding a proper way to block or slow down the apoptosis process. Having understood that caspase cleavage is the key to apoptosis, we find novel methods or algorithms are essential for studying the specificity of caspase cleavage activity and this helps the effective drug design. As bio-basis function neural networks have proven to outperform some conventional neural learning algorithms, there is a motivation, in this study, to investigate the application of bio-basis function neural networks for the prediction of caspase cleavage sites. RESULTS: Thirteen protein sequences with experimentally determined caspase cleavage sites were downloaded from NCBI. Bayesian bio-basis function neural networks are investigated and the comparisons with single-layer perceptrons, multilayer perceptrons, the original bio-basis function neural networks and support vector machines are given. The impact of the sliding window size used to generate sub-sequences for modelling on prediction accuracy is studied. The results show that the Bayesian bio-basis function neural network with two Gaussian distributions for model parameters (weights) performed the best and the highest prediction accuracy is 97.15 +/- 1.13%. AVAILABILITY: The package of Bayesian bio-basis function neural network can be obtained by request to the author.

Algorithms↗

PubMedDate not supplied

A knowledge-based information system for advice in the crisis management of the patient with burns.

A knowledge-based information system that has been designed to be used as an electronic advisor to guide in fluid resuscitation and in the management of the most frequently occurring complications during the first 48 hours after burn injury is described. The system was also developed for training physicians and nurses and may eventually be used for peer review of the management of patients in the burn unit. Ten data screens are used for entry of the administrative data, the clinical background, and the monitored data. The latter include tables for recording fluid therapy and laboratory results. The knowledge base consists of a series of heuristic decision rules that were formulated by a burn care expert and that express the Uppsala fluid resuscitation program to prevent burn shock. The data recorded for a patient are compared with the data in the knowledge base, and the appropriate conclusions are generated. The system's conclusions, the fluid and ventilation prescription, and other required patient management measures are then displayed as a report. The underlying reasoning for each case may be explored by means of the system's explanation facility. The system has been successfully validated by 125 hypothetic cases that represent typical situations of patients with severe burns.

Adult↗

PubMed2004

Prediction of ultrasound-mediated disruption of cell membranes using machine learning techniques and statistical analysis of acoustic spectra.

Although biological effects of ultrasound must be avoided for safe diagnostic applications, ultrasound's ability to disrupt cell membranes has attracted interest as a method to facilitate drug and gene delivery. This paper seeks to develop "prediction rules" for predicting the degree of cell membrane disruption based on specified ultrasound parameters and measured acoustic signals. Three techniques for generating prediction rules (regression analysis, classification trees and discriminant analysis) are applied to data obtained from a sequence of experiments on bovine red blood cells. For each experiment, the data consist of four ultrasound parameters, acoustic measurements at 400 frequencies, and a measure of cell membrane disruption. To avoid over-training, various combinations of the 404 predictor variables are used when applying the rule generation methods. The results indicate that the variable combination consisting of ultrasound exposure time and acoustic signals measured at the driving frequency and its higher harmonics yields the best rule for all three rule generation methods. The methods used for deriving the prediction rules are broadly applicable, and could be used to develop prediciton rules in other scenarios involving different cell types or tissues. These rules and the methods used to derive them could be used for real-time feedback about ultrasound's biological effects.

Animals↗