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PubMed1999

Intelligent visualization and exploration of time-oriented clinical data.

Physicians and other care providers often need to quickly browse and interpret large numbers of time-oriented clinical data. Reducing the information overload involving such tasks is a major goal for medical information systems. We describe a conceptual architecture and software implementation specific to the task of interpretation, summarization, visualization, explanation, and interactive exploration of time-oriented clinical data and the multiple levels of meaningful concepts that can be derived from these data. We build on our work on abstraction of time-oriented clinical data using a knowledge base, acquired from expert physicians, of temporal properties of the data. The core module of the new framework is called KNAVE (Knowledge-based Navigation of Abstractions for Visualization and Explanation). Health care providers can manipulate the display though several visualization and exploration operators. These operators have semantics that are domain independent but that are customized automatically for the application by access to the domain-specific knowledge base. The display, which reflects data and derived interpretations in the patient's database, changes when the user explores key relations (e.g., the dependency hierarchy) in the knowledge base of the relevant clinical domain. Preliminary assessment of the initial prototype with several clinical users has been encouraging. The KNAVE methodology has broad ramifications for reducing the load that large numbers of time-oriented clinical data put on care providers.

Artificial Intelligence↗
PubMed1996

[Individual archiving of digital angiocardiograms].

CD-R will be introduced internationally as a standardized individual archive and exchange medium allowing individual solutions for long-term archiving in a catheterization laboratory. The concept of digital archiving on two CD-R includes a long-term primary basic archive and a secondary one edited by intelligent (medical) data reduction (IDR). The basic archive is automatically composed by a background process consisting of unprocessed images or image series and is fundamental for further transfers, storage, presentations and additional studies. The digital working archive comprises a set of images and image series edited by IDR, as well as the results of morphometric studies as well as identification and documentation data. IDR is based upon the elimination of useless and redundant images series, documentation of coronary interventions on one single representative image and on the reduction of relevant images series and physiological data into an ECG-controlled representative cardiac cycle. IDR edits a redundancy-free set of 130 images (diagnostic study) or only 85 images of an interventional study. Two cardiologists and two cardiosurgeons independently studied 24 IDR-edited angiograms and the corresponding unedited digital angiograms and found no significant differences in the diagnostically relevant coronary morphology and left ventricular function. This study shows that an edited angiogram may not only serve for digital archiving but also form the basis for further evaluation or copies.

Angiocardiography↗
PubMed2005

Predicting the effect of various ISA penetration grades on pedestrian safety by simulation.

Intelligent speed adaption (ISA) is one type of vehicle-based intelligent transportation systems (ITS), which warns and regulates driving speed according to the speed limits of the roads. Early field studies showed that ISA could reduce general mean speed levels and their variances in different road environments. This paper studies the effects of various ISA penetration grades on pedestrian safety in a single lane road. A microscopic traffic simulation tool, TPMA, was further developed and used to implement different ISA penetration grades. Momentary spot speed and traffic flow data are first logged in the traffic simulation for later prediction of pedestrian safety. Then a hypothetical vehicle-pedestrian collision model is extended from early researches in order to estimate two safety indicators: probability of collision, and risk of death. Finally, Monte Carlo method is applied iteratively to compute those safety indices. The computational result shows that raising ISA penetration in traffic flow will reduce both the probability of mid-block collision between vehicle and pedestrian and the risk of death in the collision accidents. Furthermore, the decrease of the risk of death will be more prominent than that of the collision probability according to this method.

Acceleration↗
PubMed2005

An intelligent tutoring system for visual classification problem solving.

OBJECTIVE: This manuscript describes the development of a general intelligent tutoring system for teaching visual classification problem solving. MATERIALS AND METHODS: The approach is informed by cognitive theory, previous empirical work on expertise in diagnostic problem-solving, and our own prior work describing the development of expertise in pathology. The architecture incorporates aspects of cognitive tutoring system and knowledge-based system design within the framework of the unified problem-solving method description language component model. Based on the domain ontology, domain task ontology and case data, the abstract problem-solving methods of the expert model create a dynamic solution graph. Student interaction with the solution graph is filtered through an instructional layer, which is created by a second set of abstract problem-solving methods and pedagogic ontologies, in response to the current state of the student model. RESULTS: In this paper, we outline the empirically derived requirements and design principles, describe the knowledge representation and dynamic solution graph, detail the functioning of the instructional layer, and demonstrate two implemented interfaces to the system. CONCLUSION: Using the general visual classification tutor, we have created SlideTutor, a tutoring system for microscopic diagnosis of inflammatory diseases of skin.

Artificial Intelligence↗
PubMed2005

ReportTutor - an intelligent tutoring system that uses a natural language interface.

ReportTutor is an extension to our work on Intelligent Tutoring Systems for visual diagnosis. ReportTutor combines a virtual microscope and a natural language interface to allow students to visually inspect a virtual slide as they type a diagnostic report on the case. The system monitors both actions in the virtual microscope interface as well as text created by the student in the reporting interface. It provides feedback about the correctness, completeness, and style of the report. ReportTutor uses MMTx with a custom data-source created with the NCI Metathesaurus. A separate ontology of cancer specific concepts is used to structure the domain knowledge needed for evaluation of the student's input including co-reference resolution. As part of the early evaluation of the system, we collected data from 4 pathology residents who typed in their reports without the tutoring aspects of the system, and compared responses to an expert dermatopathologist. We analyzed the resulting reports to (1) identify the error rates and distribution among student reports, (2) determine the performance of the system in identifying features within student reports, and (3) measure the accuracy of the system in distinguishing between correct and incorrect report elements.

Artificial Intelligence↗
PubMed2004

Automatic eyeglasses removal from face images.

In this paper, we present an intelligent image editing and face synthesis system that automatically removes eyeglasses from an input frontal face image. Although conventional image editing tools can be used to remove eyeglasses by pixel-level editing, filling in the deleted eyeglasses region with the right content is a difficult problem. Our approach works at the object level where the eyeglasses are automatically located, removed as one piece, and the void region filled. Our system consists of three parts: eyeglasses detection, eyeglasses localization, and eyeglasses removal. First, an eye region detector, trained offline, is used to approximately locate the region of eyes, thus the region of eyeglasses. A Markov-chain Monte Carlo method is then used to accurately locate key points on the eyeglasses frame by searching for the global optimum of the posterior. Subsequently, a novel sample-based approach is used to synthesize the face image without the eyeglasses. Specifically, we adopt a statistical analysis and synthesis approach to learn the mapping between pairs of face images with and without eyeglasses from a database. Extensive experiments demonstrate that our system effectively removes eyeglasses.

Algorithms↗
PubMed1994

A knowledge-based alarm system for monitoring cardiac operated patients--assessment of clinical performance.

An intelligent alarm system for the postoperative monitoring of cardiac surgery patients, which did not require any manual data entries, was tested in two phases. A clinician monitored at bedside the patients' recovery and verified clinically abnormal physiological states. After the first test with ten patients, the system's rulebase was upgraded and then tested with an additional 15 patients. The alarm system employed two PC/ATs and was programmed to give notice of four pathological states (hyperdynamic state, hypovolemic state, hypoventilation and left ventricular failure) at two levels of urgency (alarm and alert levels). The monitoring lasted 5.4 +/- 1.7 hours per patient (mean +/- S.D.), totalling 134.7 hours. The system alarmed 27 times during the first and 73 times during the second phase of the testing. The sensitivity of the alarms was 100% in both phases, and the specificities increased from 20.0% to 73.9% and from 59.1% to 70.0% for the alarms and the alerts, respectively. This computerized decision support system based exclusively on data available in the automatically collected data base had a low false positive rate and gave early warnings about pathological states in the homogeneous group of adult postoperative cardiac patients.

Adult↗
PubMed2004

A meta-algorithm for brain extraction in MRI.

Accurate identification of brain tissue and cerebrospinal fluid (CSF) in a whole-head MRI is a critical first step in many neuroimaging studies. Automating this procedure can eliminate intra- and interrater variance and greatly increase throughput for a labor-intensive step. Many available procedures perform differently across anatomy and under different acquisition protocols. We developed the Brain Extraction Meta-Algorithm (BEMA) to address these concerns. It executes many extraction algorithms and a registration procedure in parallel to combine the results in an intelligent fashion and obtain improved results over any of the individual algorithms. Using an atlas space, BEMA performs a voxelwise analysis of training data to determine the optimal Boolean combination of extraction algorithms to produce the most accurate result for a given voxel. This allows the provided extractors to be used differentially across anatomy, increasing both the accuracy and robustness of the procedure. We tested BEMA using modified forms of BrainSuite's Brain Surface Extractor (BSE), FSL's Brain Extraction Tool (BET), AFNI's 3dIntracranial, and FreeSurfer's MRI Watershed as well as FSL's FLIRT for the registration procedure. Training was performed on T1-weighted scans of 136 subjects from five separate data sets with different acquisition parameters on separate scanners. Testing was performed on 135 separate subjects from the same data sets. BEMA outperformed the individual algorithms, as well as interrater results from a subset of the scans, when compared for the mean Dice coefficient, a rating of the similarity of output masks to the manually defined gold standards.

Adult↗
PubMed2002

Automated fault-management in a simulated spaceflight micro-world.

BACKGROUND: As human spaceflight missions extend in duration and distance from Earth, a self-sufficient crew will bear far greater onboard responsibility and authority for mission success. This will increase the need for automated fault management (FM). Human factors issues in the use of such systems include maintenance of cognitive skill, situational awareness (SA), trust in automation, and workload. This study examine the human performance consequences of operator use of intelligent FM support in interaction with an autonomous, space-related, atmospheric control system. METHODS: An expert system representing a model-base reasoning agent supported operators at a low level of automation (LOA) by a computerized fault finding guide, at a medium LOA by an automated diagnosis and recovery advisory, and at a high LOA by automate diagnosis and recovery implementation, subject to operator approval or veto. Ten percent of the experimental trials involved complete failure of FM support. RESULTS: Benefits of automation were reflected in more accurate diagnoses, shorter fault identification time, and reduced subjective operator workload. Unexpectedly, fault identification times deteriorated more at the medium than at the high LOA during automation failure. Analyses of information sampling behavior showed that offloading operators from recovery implementation during reliable automation enabled operators at high LOA to engage in fault assessment activities CONCLUSIONS: The potential threat to SA imposed by high-level automation, in which decision advisories are automatically generated, need not inevitably be counteracted by choosing a lower LOA. Instead, freeing operator cognitive resources by automatic implementation of recover plans at a higher LOA can promote better fault comprehension, so long as the automation interface is designed to support efficient information sampling.

Adult↗
PubMed2004

Adaptive user displays for intelligent tutoring software.

Intelligent tutoring software (ITS) holds great promise for K-12 instruction. Yet it is difficult to obtain rich information about users that can be used in realistic educational delivery settings--public school classrooms--in which eye tracking and other user sensing technologies are not suitable. We are pursuing three "cheap and cheerful" strategies to meet this challenge in the context of an ITS for high school math instruction. First, we use detailed representations of student cognitive skills, including tasks to assess individual users' proficiency with abstract reasoning, proficiency with simple math facts and computational skill, and spatial ability. Second, we are using data mining and machine learning algorithms to identify instructional sequences that have been effective with previous students, and to use these patterns to make decisions about current students. Third, we are integrating a simple focus-of-attention tracking system into the software, using inexpensive, web cameras. This coarse-grained information can be used to time the display of multimedia hints, explanations, and examples when the user is actually looking at the screen, and to diagnose causes of problem-solving errors. The ultimate goal is to create non-intrusive software that can adapt the display of instructional information in real time to the user's cognitive strengths, motivation, and attention.

Algorithms↗
PubMed2005

Knowledge logistics as an intelligent service for healthcare.

OBJECTIVES: The technology of grid services is developing fast. This paper presents an approach to the implementation of an intelligent grid service that configures a hospital taking advantage of the knowledge logistics idea. METHODS: The presented approach is based on synergistic integration of knowledge acquired from distributed sources in order to obtain new or complement insufficient knowledge. Presented approach uses ontologies and the formalism of object-oriented constraint networks for knowledge representation and applies ILOG to constraint-based problem solving. RESULTS: The application of the approach is illustrated via a decision support system for a fictitious case study of a hospital configuration in the Binni region. The system showed the ability to produce interrelated solutions for subtasks of the hospital configuration task based on the built ontology and input parameters. Besides, such preferences as cost or time minimization are also considered by the implemented fuzzy logic module that finds a feasible solution. CONCLUSIONS: KL would benefit from the grid, and particularly from the concept of a semantic grid. The scalable architecture of the approach enables its extension with regard to knowledge/information sources number and, thereby, with regard to factors taken into account during complex problem solving. Utilizing ontologies allows integration of the approach into existing processes and facilitates knowledge sharing with similar systems. Application of constraint networks allows rapid problem manipulation and usage of such existing efficient technologies as ILOG.

Artificial Intelligence↗
PubMed2003

Synchronized SETI-the case for "opposition".

If the signals being sought in search for extraterrestrial intelligence (SETI) programs exist but are brief (for example, they are produced intermittently to conserve energy), then it is essential to know when these signals will arrive at the Earth. Different types of transmitter/receiver synchronization schemes are possible, which vary in the relative amount of effort required by the transmitter and the receiver. The case is made for a scheme that is extremely simple for the receiver: Make observations of a target when it is at maximum angular distance from the Sun (i.e., "opposition"). This strategy requires that the transmitter has accurate knowledge of the distance and proper motion of the Sun and the orbit of the Earth. It is anticipated that within the next 10-20 years it will be possible to detect directly nearby extrasolar planets of approximately terrestrial mass. Since extraterrestrial transmitters are expected to have significantly more advanced technology, it is not unreasonable to expect that they would be able to detect the presence of the Earth and measure its orbit at even greater distances. This strategy is simple to implement, and opposition is also typically the time when observations are easiest to make. Limited opposition surveys contained in a number of all-sky surveys have already been performed. However, full-sky opposition surveys are best suited to detectors with very large fields of view.

Artificial Intelligence↗
PubMed2006

AutoNR: an automated system that measures ECAP thresholds with the Nucleus Freedom cochlear implant via machine intelligence.

OBJECTIVE: AutoNRT is an automated system that measures electrically evoked compound action potential (ECAP) thresholds from the auditory nerve with the Nucleus Freedom cochlear implant. ECAP thresholds along the electrode array are useful in objectively fitting cochlear implant systems for individual use. This paper provides the first detailed description of the AutoNRT algorithm and its expert systems, and reports the clinical success of AutoNRT to date. METHODS: AutoNRT determines thresholds by visual detection, using two decision tree expert systems that automatically recognise ECAPs. The expert systems are guided by a dataset of 5393 neural response measurements. The algorithm approaches threshold from lower stimulus levels, ensuring recipient safety during postoperative measurements. Intraoperative measurements use the same algorithm but proceed faster by beginning at stimulus levels much closer to threshold. When searching for ECAPs, AutoNRT uses a highly specific expert system (specificity of 99% during training, 96% during testing; sensitivity of 91% during training, 89% during testing). Once ECAPs are established, AutoNRT uses an unbiased expert system to determine an accurate threshold. Throughout the execution of the algorithm, recording parameters (such as implant amplifier gain) are automatically optimised when needed. RESULTS: In a study that included 29 intraoperative and 29 postoperative subjects (a total of 418 electrodes), AutoNRT determined a threshold in 93% of cases where a human expert also determined a threshold. When compared to the median threshold of multiple human observers on 77 randomly selected electrodes, AutoNRT performed as accurately as the 'average' clinician. CONCLUSIONS: AutoNRT has demonstrated a high success rate and a level of performance that is comparable with human experts. It has been used in many clinics worldwide throughout the clinical trial and commercial launch of Nucleus Custom Sound Suite, significantly streamlining the clinical procedures associated with cochlear implant use.

Algorithms↗
PubMed1999

Virtual management of radiology examinations in the virtual radiology environment using common object request broker architecture services.

In the Department of Defense (DoD), US Army Medical Command is now embarking on an extremely exciting new project--creating a virtual radiology environment (VRE) for the management of radiology examinations. The business of radiology in the military is therefore being reengineered on several fronts by the VRE Project. In the VRE Project, a set of intelligent agent algorithms determine where examinations are to routed for reading bases on a knowledge base of the entire VRE. The set of algorithms, called the Meta-Manager, is hierarchical and uses object-based communications between medical treatment facilities (MTFs) and medical centers that have digital imaging network picture archiving and communications systems (DIN-PACS) networks. The communications is based on use of common object request broker architecture (CORBA) objects and services to send patient demographics and examination images from DIN-PACS networks in the MTFs to the DIN-PACS networks at the medical centers for diagnosis. The Meta-Manager is also responsible for updating the diagnosis at the originating MTF. CORBA services are used to perform secure message communications between DIN-PACS nodes in the VRE network. The Meta-Manager has a fail-safe architecture that allows the master Meta-Manager function to float to regional Meta-Manager sites in case of server failure. A prototype of the CORBA-based Meta-Manager is being developed by the University of Arizona's Computer Engineering Research Laboratory using the unified modeling language (UML) as a design tool. The prototype will implement the main functions described in the Meta-Manager design specification. The results of this project are expected to reengineer the process of radiology in the military and have extensions to commercial radiology environments.

Algorithms↗
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↗
PubMed2005

Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractions.

OBJECTIVES: We present KNAVE-II, an intelligent interface to a distributed architecture specific to the tasks of query, knowledge-based interpretation, summarization, visualization, interactive exploration of large numbers of distributed time-oriented clinical data, and dynamic sensitivity analysis of these data. KNAVE-II main contributions to the fields of temporal reasoning and intelligent user interfaces are: (1) the capability for interactive computation and visualization of domain specific temporal abstractions, supported by ALMA--a computational engine that applies the domain knowledge base to the clinical time-oriented database. (2) Semantic (ontology-based) navigation and exploration of the data, knowledge, and temporal abstractions, supported by the IDAN mediator, a distributed architecture that enables runtime access to domain-specific knowledge bases that are maintained by expert physicians. METHODS AND MATERIALS: KNAVE-II was designed according to 12 requirements that were defined through iterative cycles of design and user-centered evaluation. The complete architecture has been implemented and evaluated in a cross-over study design that compared the KNAVE-II module versus two existing methods: paper charts and an Excel electronic spreadsheet. A small group of clinicians answered the same queries, using the domain of oncology and a set of 1000 patients followed after bone-marrow transplantation. RESULTS: The results show that users are able to perform medium to hard difficulty level queries faster and more accurately by using KNAVE-II than paper charts and Excel. Moreover, KNAVE-II was ranked first in preference by all users, along all usability dimensions. CONCLUSIONS: Initial evaluation of KNAVE-II and its supporting knowledge based temporal-mediation architecture, by applying it to a large data base of patients monitored several years after bone marrow transplantation (BMT), has produced highly encouraging results.

Artificial Intelligence↗
PubMed2005

A Bayesian approach to generating tutorial hints in a collaborative medical problem-based learning system.

OBJECTIVES: Today a great many medical schools have turned to a problem-based learning (PBL) approach to teaching. While PBL has many strengths, effective PBL requires the tutor to provide a high degree of personal attention to the students, which is difficult in the current academic environment of increasing demands on faculty time. This paper describes intelligent tutoring in a collaborative medical tutor for PBL. The main contribution of our work is the development of representational techniques and algorithms for generating tutoring hints in PBL group problem solving, as well as the implementation of these techniques in a collaborative intelligent tutoring system, COMET. The system combines concepts from computer-supported collaborative learning with those from intelligent tutoring systems. METHODS AND MATERIALS: The system uses Bayesian networks to model individual student clinical reasoning, as well as that of the group. The prototype system incorporates substantial domain knowledge in the areas of head injury, stroke and heart attack. Tutoring in PBL is particularly challenging since the tutor should provide as little guidance as possible while at the same time not allowing the students to get lost. From studies of PBL sessions at a local medical school, we have identified and implemented eight commonly used hinting strategies. In order to evaluate the appropriateness and quality of the hints generated by our system, we compared the tutoring hints generated by COMET with those of experienced human tutors. We also compared the focus of group activity chosen by COMET with that chosen by human tutors. RESULTS: On average, 74.17% of the human tutors used the same hint as COMET. The most similar human tutor agreed with COMET 83% of the time and the least similar tutor agreed 62% of the time. Our results show that COMET's hints agree with the hints of the majority of the human tutors with a high degree of statistical agreement (McNemar test, p=0.652, kappa=0.773). The focus of group activity chosen by COMET agrees with that chosen by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p=0.774, kappa=0.823). CONCLUSION: Bayesian network clinical reasoning models can be combined with generic tutoring strategies to successfully emulate human tutor hints in group medical PBL.

Artificial Intelligence↗
PubMed2005

Intelligent dental identification system (IDIS) in forensic medicine.

This study reports the design and development of the intelligent dental identification system (IDIS), including its efficiency and reliability. Five hundred patients were randomly selected from the Dental Department at Police General Hospital in Thailand to create a population of 3000 known subjects. From the original 500 patients, 100 were randomly selected to create a sample of 1000 unidentifiable subjects (400 subjects with completeness and possible alterations of dental information corresponding to natural occurrences and general dental treatments after the last clinical examination, such as missing teeth, dental caries, dental restorations, and dental prosthetics, 100 subjects with completeness and no alteration of dental information, 500 subjects with incompleteness and no alteration of dental information). Attempts were made to identify the unknown subjects utilizing IDIS. The use of IDIS advanced method resulted in consistent outstanding identification in the range of 82.61-100% with minimal error 0-1.19%. The results of this study indicate that IDIS can be used to support dental identification. It supports not only all types of dentitions: primary, mixed, and permanent but also for incomplete and altered dental information. IDIS is particularly useful in providing the huge quantity and redundancy of related documentation associated with forensic odontology. As a computerized system, IDIS can reduce the time required for identification and store dental digital images with many processing features. Furthermore, IDIS establishes enhancements of documental dental record with odontogram and identification codes, electrical dental record with dental database system, and identification methods and algorithms. IDIS was conceptualized based on the guidelines and standards of the American Board of Forensic Odontology (ABFO) and International Criminal Police Organization (INTERPOL).

Adolescent↗