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PubMed2004

Cognitive map formation through sequence encoding by theta phase precession.

The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. The associative connections in the hippocampus imply that a neural entity represents the map as a geometrical network of hippocampal cells in terms of a chart. According to recent experimental observations, the cells fire successively relative to the theta oscillation of the local field potential, called theta phase precession, when the animal is running. This observation suggests the learning of temporal sequences with asymmetric connections in the hippocampus, but it also gives rather inconsistent implications on the formation of the chart that should consist of symmetric connections for space coding. In this study, we hypothesize that the chart is generated with theta phase coding through the integration of asymmetric connections. Our computer experiments use a hippocampal network model to demonstrate that a geometrical network is formed through running experiences in a few minutes. Asymmetric connections are found to remain and distribute heterogeneously in the network. The obtained network exhibits the spatial localization of activities at each instance as the chart does and their propagation that represents behavioral motions with multidirectional properties. We conclude that theta phase precession and the Hebbian rule with a time delay can provide the neural principles for learning the cognitive map.

Algorithms↗

PubMed2006

Evolving physically simulated flying creatures for efficient cruising.

The body-brain coevolution of aerial life forms has not been developed as far as aquatic or terrestrial locomotion in the field of artificial life. We are studying physically simulated 3D flying creatures by evolving both wing shapes and their controllers. A creature's wing is modeled as a number of articulated cylinders, connected by triangular films (patagia). The wing structure and its motor controllers for cruising flight are generated by an evolutionary algorithm within a simulated aerodynamic environment. The most energy-efficient cruising speed and the lift and drag coefficients of each flier are calculated from its morphological characteristics and used in the fitness evaluation. To observe a wide range of creature size, the evolution is run separately for creatures categorized into three species by body weight. The resulting creatures vary in size from pigeons to pterosaurs, with various wing configurations. We discuss the characteristics of shape and motion of the evolved creatures, including flight stability and Strouhal number.

Algorithms↗

PubMed1995

Medical decision support: experience with implementing the Arden Syntax at the Columbia-Presbyterian Medical Center.

We began implementation of a medical decision support system (MDSS) at the Columbia-Presbyterian Medical Center (CPMC) using the Arden Syntax in 1992. The Clinical Event Monitor which executes the Medical Logic Modules (MLMs) runs on a mainframe computer. Data are stored in a relational database and accessed via PL/I programs known as Data Access Modules (DAMs). Currently we have 18 clinical, 12 research and 10 administrative MLMs. On average, the clinical MLMs generate 50357 simple interpretations of laboratory data and 1080 alerts each month. The number of alerts actually read varies by subject of the MLM from 32.4% to 73.5%. Most simple interpretations are not read at all. A significant problem of MLMs is maintenance, and changes in laboratory testing and message output can impair MLM execution significantly. We are now using relational database technology and coded MLM output to study the process outcome of our MDSS.

Academic Medical Centers↗

PubMed1999

Discourse structures in medical reports--watch out! The generation of referentially coherent and valid text knowledge bases in the MEDSYNDIKATE system.

The automatic analysis of medical narratives currently suffers from neglecting text structure phenomena such as referential relations between discourse units. This has unwarranted effects on the descriptional adequacy of medical knowledge bases automatically generated from texts. The resulting representation bias can be characterized in terms of incomplete, artificially fragmented and referentially invalid knowledge structures. We focus here on four basic types of textual reference relations, viz. pronominal and nominal anaphora, textual ellipsis and metonymy and show how to deal with them in an adequate text parsing device. Since the types of reference relations we discuss show an increasing dependence on conceptual background knowledge, we stress the need for formally grounded, expressive conceptual representation systems for medical knowledge. Our suggestions are based on experience with MEDSYNDIKATE, a medical text knowledge acquisition system designed to properly deal with various sorts of discourse structure phenomena.

Artificial Intelligence↗

PubMed2006

Ligand-based virtual screening, parallel solution-phase and microwave-assisted synthesis as tools to identify and synthesize new inhibitors of mycobacterium tuberculosis.

In an attempt to identify new inhibitors of the growth of Mycobacterium tuberculosis (MTB), the causative agent of tuberculosis, a procedure for the generation, design, and screening of a ligand-based virtual library was applied. This used both an in silico protocol centered on a recursive partitioning (RP) model described herein, and a pharmacophoric model for antitubercular agents previously generated by our research group. Two candidates emerged from databases of commercially available compounds, both characterized by a minimum inhibitory concentration (MIC) of 25 microg mL(-1). Based on these compounds, two series of derivatives were synthesized by both parallel solution-phase and microwave-assisted synthesis, leading to enhanced antimycobacterial activity. During both the design and synthesis, attention was focused on the efficient allocation of available resources with the aim of reducing the overall costs associated with calculation and synthesis.

Antitubercular Agents↗

PubMed1995

ICOHR: intelligent computer based oral health record.

The majority of work on computer use in the dental field has focused on non-clinical practice management information needs. Very few computer-based dental information systems provide management support of the clinical care process, particularly with respect to quality management. Traditional quality assurance methods rely on the paper record and provide only retrospective analysis. Today, proactive quality management initiatives are on the rise. Computer-based dental information systems are being integrated into the care environment, actively providing decision support as patient care is being delivered. These new systems emphasize assessment and improvement of patient care at the time of treatment, thus building internal quality management into the caregiving process. The integration of real time quality management and patient care will be expedited by the introduction of an information system architecture that emulates the gathering and storage of clinical care data currently provided by the paper record. As a proposed solution to the problems associated with existing dental record systems, the computer-based patient record has emerged as a possible alternative to the paper dental record. The Institute of Medicine (IOM) recently conducted a study on improving the efficiency and accuracy of patient record keeping. As a result of this study, the IOM advocates the development and implementation of computer-based patient records as the standard for all patient care records. This project represents the ongoing efforts of The University of Iowa College of Dentistry's collaboration with the University of Uppsala Data Center, Uppsala, Sweden, on a computer-based patient dental record model. ICOHR (Intelligent Computer Based Oral Health Record) is an information system which brings together five important parts of the patient's dental record: medical and dental history; oral status; treatment planning; progress notes; and a Patient Care Database, generated from their clinical care information (the database is also stored in the ICOHR). ICOHR is designed to be integrated into a traditional practice management system. The components of the ICOHR system support the use of various types of clinical care quality management tools, including medical alerts, clinical care guidelines, care modifiers, and diagnostic decision support. Data input is multimodal, so the user may use both voice recognition and direct input with a digitizer board to enter information into the database. ICOHR is designed to be integrated into the clinical environment in an ergonomic fashion in order to facilitate the unobtrusive and efficient acquisition of patient information. ICOHR is currently under clinical evaluation in both private practice and institutional environments. The private practice is a large general dentistry practice with over twenty sites scattered throughout a large metropolitan area. The institutional settings are a College of Dentistry and a Hospital Dentistry program. The evaluations have started in two of the sites and the other site will be phased in during the next six months. Our demonstration of the system will include both prepared presentations of the system's various functions and provide an opportunity for hands-on use of the system for interested attendees.

Artificial Intelligence↗

PubMed2002

Automated planning volume definition in soft-tissue sarcoma adjuvant brachytherapy.

In current practice, the planning volume for adjuvant brachytherapy treatment for soft-tissue sarcoma is either not determined a priori (in this case, seed locations are selected based on isodose curves conforming to a visual estimate of the planning volume), or it is derived via a tedious manual process. In either case, the process is subjective and time consuming, and is highly dependent on the human planner. The focus of the work described herein involves the development of an automated contouring algorithm to outline the planning volume. Such an automatic procedure will save time and provide a consistent and objective method for determining planning volumes. In addition, a definitive representation of the planning volume will allow for sophisticated brachytherapy treatment planning approaches to be applied when designing treatment plans, so as to maximize local tumour control and minimize normal tissue complications. An automated tumour volume contouring algorithm is developed utilizing computational geometry and numerical interpolation techniques in conjunction with an artificial intelligence method. The target volume is defined to be the slab of tissue r cm perpendicularly away from the curvilinear plane defined by the mesh of catheters. We assume that if adjacent catheters are over 2r cm apart, the tissue between the two catheters is part of the tumour bed. Input data consist of the digitized coordinates of the catheter positions in each of several cross-sectional slices of the tumour bed, and the estimated distance r from the catheters to the tumour surface. Mathematically, one can view the planning volume as the volume enclosed within a minimal smoothly-connected surface which contains a set of circles, each circle centred at a given catheter position in a given cross-sectional slice. The algorithm performs local interpolation on consecutive triplets of circles. The effectiveness of the algorithm is evaluated based on its performance on a collection of soft-tissue sarcoma tumour beds within various anatomical structures. For each of 15 patient cases considered, the algorithm takes approximately 2 min to generate the planning volume. Although the tumour shapes are rather different, the algorithm consistently generates planning volumes that visually demonstrate smooth curves compactly encapsulating the circles. This general-purpose contouring algorithm works well whether the catheters are all close together, spread far apart in the plane or arranged in a convoluted way. The automatic contouring algorithm significantly reduces labour time and provides a consistent and objective method for determining planning volumes for soft-tissue sarcoma. Further studies are needed to validate the significance of the resulting planning volumes in designing treatment plans and the role that sophisticated brachytherapy treatment planning optimization may have in producing good plans.

Adult↗

PubMed1998

Detecting errors in a scoring program: a method of double diagnosis using a computer-generated sample.

This paper discusses a new method for locating errors in diagnostic computer scoring programs for structured clinical interviews. It was proposed as a test of the accuracy of the scoring program for the Composite International Diagnostic Interview, version 1.1. The proposal was to create an independent scoring program in a different computer language but serving the same criteria. Both programs were then applied to the same large set of valid (i.e., logically consistent) computer-generated test cases, and differences in diagnostic assignments reviewed. The method described can identify the program steps that account for the sources of the errors. Corrections can be made and the programs run again on new sets of test cases until discrepancy-free results are achieved. While this method cannot discover errors that are repeated in the two programs, it does discover more of the errors in a scoring program than we have previously been able to identify. This technique provides a systematic and rigorous approach to assuring the accuracy of scoring programs based on established algorithms.

Algorithms↗

PubMed1991

A model for critiquing based on automated medical records.

We describe the design of a critiquing system, HyperCritic, that relies on automated medical records for its data input. The purpose of the system is to advise general practitioners who are treating patients who have hypertension. HyperCritic has access to the data stored in a primary-care information system that supports a fully automated medical record. Hyper-Critic relies on data in the automated medical record to critique the management of hypertensive patients, avoiding a consultation-style interaction with the user. The first step in the critiquing process involves the interpretation of the medical record in an attempt to discover the physician's actions and decisions. After detecting the relevant events in the medical record, HyperCritic views the task of critiquing as the assignment of critiquing statements to these patient-specific events. Critiquing statements are defined as recommendations involving one or more suggestions for possible modifications in the actions of the physician. The core of the model underlying HyperCritic is that the process of generating the critiquing statements is viewed as the application of a limited set of abstract critiquing tasks. We distinguish four categories of critiquing tasks: preparation tasks, selection tasks, monitoring tasks, and responding tasks. The execution of these critiquing tasks requires specific medical factual knowledge. This factual knowledge is separated from the critiquing tasks and is stored in a medical fact base. The principal advantage demonstrated by HyperCritic is the adaption of a domain-independent critiquing structure. We show how this domain-independent critiquing structure can be used to facilitate knowledge acquisition and maintenance of the system.

Artificial Intelligence↗

PubMedDate not supplied

Using dependency/association rules to find indications for computed tomography in a head trauma dataset.

Analysis of a clinical head trauma dataset was aided by the use of a new, binary-based data mining technique, termed Boolean analyzer (BA), which finds dependency/association rules. With initial guidance from a domain user or domain expert, the BA algorithm is given one or more metrics to partition the entire dataset. The weighted rules are in the form of Boolean expressions. To augment the analysis of the rules produced, we applied a probabilistic interestingness measure (PIM) to order the generated rules based on event dependency, where events are combinations of primed and unprimed variables. Interpretation of the dependency rules generated on the clinical head trauma data resulted in a set of criteria that identified minor head trauma patients needing computed tomography (CT) scans. The BA criteria contained fewer variables than were found using recursive partitioning of Chi-square values (five variables versus seven variables, respectively). The BA five-variable criteria set was more sensitive but less specific than the seven-variable Chi-square criteria set. We believe that the BA method has broad applicability in the medical domain, and hope that this paper will stimulate other creative applications of the technique.

Algorithms↗

PubMed2003

Discriminant snakes for 3D reconstruction of anatomical organs.

In this work a new statistic deformable model for 3D segmentation of anatomical organs in medical images is proposed. A statistic discriminant snake performs a supervised learning of the object boundary in an image slice to segment the next slice of the image sequence. Each part of the object boundary is projected in a feature space generated by a bank of Gaussian filters. Then, clusters corresponding to different boundary pieces are constructed by means of linear discriminant analysis. Finally, a parametric classifier is generated from each contour in the image slice and embodied into the snake energy-minimization process to guide the snake deformation in the next image slice. The discriminant snake selects and classifies image features by the parametric classifier and deforms to minimize the dissimilarity between the learned and found image features. The new approach is of particular interest for segmenting 3D images with anisotropic spatial resolution, and for tracking temporal image sequences. In particular, several anatomical organs from different imaging modalities are segmented and the results compared to expert tracings.

Algorithms↗

PubMed2007

Biomimetic approaches to the control of underwater walking machines.

We have developed a biomimetic robot based on the American lobster. The robot is designed to achieve the performance advantages of the animal model by adopting biomechanical features and neurobiological control principles. Three types of controllers are described. The first is a state machine based on the connectivity and dynamics of the lobster central pattern generator (CPG). The state machine controls myomorphic actuators based on shape memory alloys (SMAs) and responds to environmental perturbation through sensors that employ a labelled-line code. The controller supports a library of action patterns and exteroceptive reflexes to mediate tactile navigation, obstacle negotiation and adaptation to surge. We are extending this controller to neuronal network-based models. A second type of leg CPG is based on synaptic networks of electronic neurons and has been adapted to control the SMA actuated leg. A brain is being developed using layered reflexes based on discrete time map-based neurons.

Animals↗

PubMed2006

Neighborhood detection using mutual information for the identification of cellular automata.

Extracting the rules from spatio-temporal patterns generated by the evolution of cellular automata (CA) usually requires a priori information about the observed system, but in many applications little information will be known about the pattern. This paper introduces a new neighborhood detection algorithm which can determine the range of the neighborhood without any knowledge of the system by introducing a criterion based on mutual information (and an indication of over-estimation). A coarse-to-fine identification routine is then proposed to determine the CA rule from the observed pattern. Examples, including data from a real experiment, are employed to evaluate the new algorithm.

Algorithms↗

PubMed1991

Design and implementation of a rule based system for ambulatory nursing data management.

In order to effectively organize the use of nursing time during clinic check-in, we designed a forward chaining rule based program for nursing history taking, problem tracking, and documentation. The program consists of a medical logic module trigger engine which identifies relevant rules for nursing history, an interactive question manager for nursing history taking, and a rule generation shell implemented within a specially designed Medical Query Language (MQL) shcema. At clinic check-in, the engine refreshes the rule set for the patient from interaction with the computerized medical record. The interaction driver assists the nurse with tracking of elapsed time, and allows him/her to pursue questions, record data, and create or complete nursing interventions. Nursing question sets and interventions are maintained longitudinally to assure continuity of care. Nursing problems are created on the problem list within the computerized record as the rule system identifies their existence.

Academic Medical Centers↗

PubMed1994

A deductive database system for analyzing human nucleotide sequence data.

The analysis of the human genome is one of the most significant topics in both biology and medical science. There is a growing need for a well-designed database system for searching and analyzing the human genome data. We developed a deductive database system to search and analyze nucleotide sequence data derived from the GenBank primates data. A deductive database system is a next generation one and it contains an inference mechanism that can handle problems beyond the capabilities of classical database systems. Database queries are described in logical rules. These rules are simple even for molecular biologists who are not experts in computer programs because they are declarative and do not require the procedural commands that are usually used in computer programs. Furthermore, queries based on logical rules are powerful enough to express complicated biological problems. Particularly, recursive rules are suitable for examining secondary structures of nucleotide sequences. In our analysis of TfR's IRE, we noted five stem-and-loop structures.

Artificial Intelligence↗

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↗

PubMed2003

Evolutionary optimization, backpropagation, and data preparation issues in QSAR modeling of HIV inhibition by HEPT derivatives.

Artificial neural networks (ANNs) can be utilized to generate predictive models of quantitative structure-activity relationships between a set of molecular descriptors and activity. Evolutionary computation provides a means to appropriately search for the set of weights and bias terms associated with artificial neural networks that minimize selected functions of the error between the actual and desired outputs. This method is demonstrated by evolutionary training of artificial neural networks capable of predicting anti-HIV activity for a set of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) derivatives. The results of this work further confirm the growing indication that evolutionary computation can outperform backpropagation as a method of artificial neural network training. The results also indicate the degree to which bias in the initial training and testing data can affect performance and the importance of bootstrapping.

Algorithms↗

PubMed2005

Comparing protein-ligand docking programs is difficult.

There is currently great interest in comparing protein-ligand docking programs. A review of recent comparisons shows that it is difficult to draw conclusions of general applicability. Statistical hypothesis testing is required to ensure that differences in pose-prediction success rates and enrichment rates are significant. Numerical measures such as root-mean-square deviation need careful interpretation and may profitably be supplemented by interaction-based measures and visual inspection of dockings. Test sets must be of appropriate diversity and of good experimental reliability. The effects of crystal-packing interactions may be important. The method used for generating starting ligand geometries and positions may have an appreciable effect on docking results. For fair comparison, programs must be given search problems of equal complexity (e.g. binding-site regions of the same size) and approximately equal time in which to solve them. Comparisons based on rescoring require local optimization of the ligand in the space of the new objective function. Re-implementations of published scoring functions may give significantly different results from the originals. Ostensibly minor details in methodology may have a profound influence on headline success rates.

Algorithms↗