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PubMed1998

Automatic tumor segmentation using knowledge-based techniques.

A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRI's) of the human brain is presented. The MRI's consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge-based (KB) techniques with multispectral analysis. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intracranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intracranial region, with region analysis used in performing the final tumor labeling. This system has been trained on three volume data sets and tested on thirteen unseen volume data sets acquired from a single MRI system. The KB tumor segmentation was compared with supervised, radiologist-labeled "ground truth" tumor volumes and supervised k-nearest neighbors tumor segmentations. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.

Algorithms↗

PubMed2004

Integration of knowledge-based metabolic predictions with liquid chromatography data-dependent tandem mass spectrometry for drug metabolism studies: application to studies on the biotransformation of indinavir.

Despite recent advances in the application of data-dependent liquid chromatography/tandem mass spectrometry (LC/MS/MS) to the identification of drug metabolites in complex biological matrixes, a prior knowledge of the likely routes of biotransformation of the therapeutic agent of interest greatly facilitates the detection and structural characterization of its metabolites. Thus, prediction of the [M + H]+ m/z values of expected metabolites allows for the construction of user-defined MS(n) protocols that frequently reveal the presence of minor drug metabolites, even in the presence of a vast excess of coeluting endogenous constituents. However, this approach suffers from inherent user bias, as a result of which additional "survey scans" (e.g., precursor ion and constant neutral loss scans) are required to ensure detection of as many drug-related components in the sample as possible. In the present study, a novel approach to this problem has been evaluated, in which knowledge-based predictions of metabolic pathways are first derived from a commercial database, the output from which is used to formulate a list-dependent LC/MS(n) data acquisition protocol. Using indinavir as a model drug, a substructure similarity search on the MDL metabolism database with a similarity index of 60% yielded 188 "hits", pointing to the possible operation of two hydrolytic, two N-dealkylation, three N-glucuronidation, one N-methylation, and several aromatic and aliphatic oxidation pathways. Integration of this information with data-dependent LC/MS(n) analysis using an ion trap mass spectrometer led to the identification of 18 metabolites of indinavir following incubation of the drug with human hepatic postmitochondrial preparations. This result was accomplished with only a single LC/MS(n) run, representing significant savings in instrument use and operator time, and afforded an accurate view of the complex in vitro metabolic profile of this drug.

Artificial Intelligence↗

PubMed1986

Knowledge-based landmarking of cephalograms.

Orthodontists have defined a certain number of characteristic points, or landmarks, on X-ray images of the human skull which are used to study growth or as a diagnostic aid. This work presents the first step toward an automatic extraction of these points. They are defined with respect to particular lines which are retrieved first. The original image is preprocessed with a prefiltering operator (median filter) followed by an edge detector (Mero-Vassy operator). A knowledge-based line-following algorithm is subsequently applied, involving a production system with organized sets of rules and a simple interpreter. The a priori knowledge implemented in the algorithm must take into account the fact that the lines represent biological shapes and can vary considerably from one patient to the next. The performance of the algorithm is judged with the help of objective quality criteria. Determination of the exact shapes of the lines allows the computation of the positions of the landmarks.

Artificial Intelligence↗

PubMed1998

A development environment for knowledge-based medical applications on the World-Wide Web.

The World-Wide Web (WWW) is increasingly being used as a platform to develop distributed applications, particularly in contexts, such as medical ones, where high usability and availability are required. In this paper we propose a methodology for the development of knowledge-based medical applications on the web, based on the use of an explicit domain ontology to automatically generate parts of the system. We describe a development environment, centred on the LISPWEB Common Lisp HTTP server, that supports this methodology, and we show how it facilitates the creation of complex web-based applications, by overcoming the limitations that normally affect the adequacy of the web for this purpose. Finally, we present an outline of a system for the management of diabetic patients built using the LISPWEB environment.

Artificial Intelligence↗

PubMed1992

Quality control in nerve conduction studies with coupled knowledge-based system approach.

Contemporary equipment used for nerve conduction studies is usually capable of computerized measurement of latency, amplitude, duration, and area of nerve and muscle action potentials and resulting conduction velocities. Abnormalities can be due to technical error or disease. Identification of technical error is a major element of quality control in electromyography, and artificial intelligence could be useful for this purpose. We have developed a coupled knowledge-based prototype system (QUALICON) to assess the correctness of recording and stimulating characteristics in routine conduction studies. QUALICON extracts numeric features from CMAPs or SNAPs, which are translated into symbolic form to drive a Bayesian network. The network uses high-level knowledge to infer the quality of stimulating and recording electrode placement as well as polarity and stimulus strength making recommendations as to the likely technical error when abnormal potentials are detected. A preliminary assessment shows that QUALICON performs as well as manual assessment performed by professionals.

Action Potentials↗

PubMed1995

Knowledge-based modeling of the D-lactate dehydrogenase three-dimensional structure.

A three-dimensional structure of the NAD-dependent D-lactate dehydrogenase of Lactobacillus bulgaricus is modeled using the structure of the formate dehydrogenase of Pseudomonas sp. as template. Both sequences share only 22% of identical residues. Regions for knowledge-based modeling are defined from the structurally conserved regions predicted by multiple alignment of a set of related protein sequences with low homology. The model of the D-LDH subunit shows, as for the formate dehydrogenase, an alpha/beta structure, with a catalytic domain and a coenzyme binding domain. It points out the catalytic histidine (His-296) and supports the hypothetical catalytic mechanism. It also suggests that the other residues involved in the active site are Arg-235, possibly involved in the binding of the carboxyl group of the pyruvate, and Phe-299, a candidate for stabilizing the methyl group of the substrate.

Amino Acid Sequence↗

PubMed1990

Knowledge-based radiologic image retrieval using axes of clinical relevance.

This paper describes an approach to computer-based intelligent retrieval of feature-coded radiographic images relevant to a specific case being evaluated. The approach involves partitioning the search space along clinically natural groups of attributes which we call "axes of clinical relevance." By embedding knowledge about the domain to help direct the search process, a clinician's needs may be met more comprehensively. Domain knowledge, supplied to the system as "axis heuristics," may make search more robust. These heuristics provide a graded, progressive relaxation of the search constraints. This approach helps show the user groups of images in order of probable relevance to a current case. AXON is a prototype knowledge-based system constructed to illustrate this approach in the domain of chest imaging. This paper describes the AXON system, demonstrates some searches which illustrate the potential utility of this approach, and discusses preliminary tests of the search strategies used by AXON.

Expert Systems↗

PubMed1997

Toxopert-I: knowledge-based automatic interpretation of serological tests for toxoplasmosis.

Primary infection with Toxoplasma gondii, a parasite found in most regions of the world, is asymptomatic in more than 80% of cases. However, primary infection with Toxoplasma gondii in a pregnant woman might cause fetal infection and severe damage. Most cases do not require treatment. This applies to women without any infection (denoted as seronegative) and women who have acquired the infection before conception (denoted as latent). In contrast, women with postconceptual infection require immediate treatment to prevent or ameliorate fetal infection. We have developed an expert system, called Toxoport-I, designed for routine laboratory work, which automatically interprets serological test results of toxoplasma infection. By using the system the clinician can also examine questionable cases by interactively exploring possible results. We used a popular method of designing expert systems applied to medical interpretation and therapy advice, the rule-based one. In order to meet the requirements of automatic interpretation in toxoplasma serology the following characteristics were introduced: the interpretation of sequences of test results, the possibility of excluding inconsistent test results and the adaptability of the knowledge base. A decision graph that covers the different kinds of infections as well as therapy and recommendations for further tests was designed, implemented and was clinically tested by carrying out a retrospective study including 1000 pregnant women. A comparison of Toxoport-I and the clinician's interpretations yielded sensitivity and specificity rates of over 99% each.

Animals↗

PubMed2005

Architecture for knowledge-based and federated search of online clinical evidence.

BACKGROUND: It is increasingly difficult for clinicians to keep up-to-date with the rapidly growing biomedical literature. Online evidence retrieval methods are now seen as a core tool to support evidence-based health practice. However, standard search engine technology is not designed to manage the many different types of evidence sources that are available or to handle the very different information needs of various clinical groups, who often work in widely different settings. OBJECTIVES: The objectives of this paper are (1) to describe the design considerations and system architecture of a wrapper-mediator approach to federate search system design, including the use of knowledge-based, meta-search filters, and (2) to analyze the implications of system design choices on performance measurements. METHODS: A trial was performed to evaluate the technical performance of a federated evidence retrieval system, which provided access to eight distinct online resources, including e-journals, PubMed, and electronic guidelines. The Quick Clinical system architecture utilized a universal query language to reformulate queries internally and utilized meta-search filters to optimize search strategies across resources. We recruited 227 family physicians from across Australia who used the system to retrieve evidence in a routine clinical setting over a 4-week period. The total search time for a query was recorded, along with the duration of individual queries sent to different online resources. RESULTS: Clinicians performed 1662 searches over the trial. The average search duration was 4.9 +/- 3.2 s (N = 1662 searches). Mean search duration to the individual sources was between 0.05 s and 4.55 s. Average system time (ie, system overhead) was 0.12 s. CONCLUSIONS: The relatively small system overhead compared to the average time it takes to perform a search for an individual source shows that the system achieves a good trade-off between performance and reliability. Furthermore, despite the additional effort required to incorporate the capabilities of each individual source (to improve the quality of search results), system maintenance requires only a small additional overhead.

Decision Making, Computer-Assisted↗

PubMed1995

Introduction of knowledge bases in patient's data management system: role of the user interface.

As the number of signals and data to be handled grows in intensive care unit, it is necessary to design more powerful computing systems that integrate and summarize all this information. The manual input of data as e.g. clinical signs and drug prescription and the synthetic representation of these data requires an ever more sophisticated user interface. The introduction of knowledge bases in the data management allows to conceive contextual interfaces. The objective of this paper is to show the importance of the design of the user interface, in the daily use of clinical information system. Then we describe a methodology that uses the man-machine interaction to capture the clinician knowledge during the clinical practice. The different steps are the audit of the user's actions, the elaboration of statistic models allowing the definition of new knowledge, and the validation that is performed before complete integration. A part of this knowledge can be used to improve the user interface. Finally, we describe the implementation of these concepts on a UNIX platform using OSF/MOTIF graphical interface.

Algorithms↗

PubMed1987

Use of research based knowledge in clinical practice.

The authors describe three research utilization projects that used a knowledge-driven approach to nursing practice. The activities involved in developing and implementing these research utilization projects included: (1) preparing nurses to read, critique, and use research; (2) identifying and reviewing research studies in a common area to develop a research base; (3) transforming the research based knowledge into a protocol to be used in the clinical area by nurses caring for patients; and (4) evaluating the protocol to see whether it is being implemented as expected and whether it is producing the predicted results. This practical utilization is discussed and illustrated in a systems theory model.

Body Temperature↗

PubMed1993

Knowledge-based generation of machine learning experiments: learning with DNA crystallography data.

Though it has been possible in the past to learn to predict DNA hydration patterns from crystallographic data, there is ambiguity in the choice of training data (both in terms of the relevant set of cases and the features needed to represent them), which limits the usefulness of standard learning techniques. Thus, we have developed a knowledge-based system to generate machine learning experiments for inducing DNA hydration pattern classifiers. The system takes as input (1) a set of classified training examples described by a large set of attributes and (2) information about a set of learning experiments that have already been run. It outputs a new learning experiment, namely a (not necessarily proper) subset of the input examples represented by a new set of features. Domain specific and domain independent knowledge is used to suggest subsets of training examples from suspected subpopulations, transform attributes in the training data or generate new ones, and choose interesting ways to substitute one experiment's set of attributes with another. Automatic hydration pattern predictors are of both theoretical and practical interest to DNA crystallographers, because they can speed up a labor intensive process, and because the extracted rules add to the knowledge of what determines DNA hydration.

Artificial Intelligence↗

PubMed1999

Constructing biological knowledge bases by extracting information from text sources.

Recently, there has been much effort in making databases for molecular biology more accessible and interoperable. However, information in text form, such as MEDLINE records, remains a greatly underutilized source of biological information. We have begun a research effort aimed at automatically mapping information from text sources into structured representations, such as knowledge bases. Our approach to this task is to use machine-learning methods to induce routines for extracting facts from text. We describe two learning methods that we have applied to this task--a statistical text classification method, and a relational learning method--and our initial experiments in learning such information-extraction routines. We also present an approach to decreasing the cost of learning information-extraction routines by learning from "weakly" labeled training data.

Artificial Intelligence↗

PubMed1993

Implementation of practice guidelines in a clinical setting using a computerized knowledge base (Iliad).

We present the implementation of the indications for surgery for three surgical operations--cholecystectomy, cataract extraction, and knee arthroscopy--in a medical expert system, called Iliad. This implementation operates in the preauthorization service of IHC Health Plans (an insurance company in Salt Lake City) as a basis for reimbursement of services. Patient data collection forms, derived from Iliad knowledge base, were used by 13 participating surgeons to document the objective patient observations that justify the surgery and, then were faxed to IHC where a trained nurse input the data in Iliad. Iliad's decisions and reports on any deviations from guidelines are communicated back to the care provider. The study evaluates the impact of the computerized implementation on process, as measured by a questionnaire, and on outcome as measured by rate of approvals, documentation level, rate of requests, and average cost. The prospective implementation of the computerized guidelines has performed reliably, has been perceived as a preferred alternative to the old preauthorization system, and, most importantly, has enhanced significantly the level of documentation permitting evaluation and determination of appropriateness before surgery.

Arthroscopy↗

PubMed2002

Development and evaluation of VIE-PNN, a knowledge-based system for calculating the parenteral nutrition of newborn infants.

Calculating the daily changing composition of parenteral nutrition for small newborn infants is troublesome and time consuming routine work in neonatal intensive care. The task needs expertise and experience and is prone to inherent calculation errors. We designed VIE-PNN (Vienna Expert System for Parenteral Nutrition of Neonates), a knowledge-based system (KBS) in order to reduce daily routine work and calculation errors. VIE-PNN was redesigned several times because the clinicians accepted the system only when it saved time. The most recent version of VIE-PNN uses an Hypertext Markup Language (HTML)-based client-server architecture and is integrated into the intranet of the local patient data management system. Since more than 3 years all parenteral nutrition plans are calculated using VIE-PNN. Evaluating the system's performance and the users contentedness, we compared 50 nutrition plans calculated in parallel using VIE-PNN or a hand-held calculator, retrospectively analyzed more than 5000 nutrition plans stored in VIE-PNNs database and evaluated a user questionnaire. Nutrition plans were calculated in a mean time of 2.4 versus 7.1min using VIE-PNN or the hand-held calculator. Errors and omissions in the nutrition plans were detected in 22% versus 56% and errors in the VIE-PNN's plans occurring only with interactively changed values. Reviews of stored plans show that a mean of 4 out of 16 parameters were interactively changed. VIE-PNN was well accepted. Most important reasons for the successful operation of VIE-PNN in the daily routine work were time savings and robustness of the system.

Artificial Intelligence↗

PubMed1985

An analysis of the knowledge base of practicing internists as measured by the 1980 recertification examination.

The performance of practicing internists on the American Board of Internal Medicine's 1980 Recertification Examination was examined in two studies. In the first study, a psychometric common-item equating technique was used to compare the performance of 1980 recertification candidates with that of 1979 certification candidates. Results showed that the knowledge base of practicing internists was similar to that of residents completing training. The second study analyzed the performance of 1980 recertification candidates to determine whether being certified or having an interest in a subspecialty affects a physician's performance on items in that area. The results showed that subspecialists do significantly better than general internists on items pertaining to their area of specialization. Similar outcomes were found for internists with a special interest in a subspecialty area. These findings establish the importance of continued periodic evaluation and support the development of an evaluation tool tailored to the physician's area of concentration.

Adult↗

PubMed1996

Temporal reasoning for diagnosis in a causal probabilistic knowledge base.

We have added temporal reasoning to the Heart Disease Program (HDP) to take advantage of the temporal constraints inherent in cardiovascular reasoning. Some processes take place over minutes while others take place over months or years and a strictly probabilistic formalism can generate hypotheses that are impossible given the temporal relationships involved. The HDP has temporal constraints on the causal relations specified in the knowledge base and temporal properties on the patient input provided by the user. These are used in two ways. First, they are used to constrain the generation of the pre-computed causal pathways through the model that speed the generation of hypotheses. Second, they are used to generate time intervals for the instantiated nodes in the hypotheses, which are matched and adjusted as nodes are added to each evolving hypothesis. This domain offers a number of challenges for temporal reasoning. Since the nature of diagnostic reasoning is inferring a causal explanation from the evidence, many of the temporal intervals have few constraints and the reasoning has to make maximum use of those that exist. Thus, the HDP uses a temporal interval representation that includes the earliest and latest beginning and ending specified by the constraints. Some of the disease states can be corrected but some of the manifestations may remain. For example, a valve disease such as aortic stenosis produces hypertrophy that remains long after the valve has been replaced. This requires multiple time intervals to account for the existing findings. This paper discusses the issues and solutions that have been developed for temporal reasoning integrated with a pseudo-Bayesian probabilistic network in this challenging domain for diagnosis.

Artificial Intelligence↗

PubMed1993

Artificial intelligence and the supervision of bioprocesses (real-time knowledge-based systems and neural networks).

The ability to supervise and control a highly non-linear and time variant bioprocess is of considerable importance to the biotechnological industries which are continually striving to obtain higher yields and improved uniformity of production. Two AI methodologies aimed at contributing to the overall intelligent monitoring and control of bioprocess operations are discussed. The development and application of a real-time knowledge-based system to provide supervisory control of fed-batch bioprocesses is reviewed. The system performs sensor validation, fault detection and diagnosis and incorporates relevant expertise and experience drawn from both bioprocess engineering and control engineering domains. A complementary approach, that of artificial neural networks is also addressed. The development of neural network modelling tools for use in bioprocess state estimation and inferential control are reviewed. An attractive characteristic of neural networks is that with the appropriate topology any non-linear functional relationship can be modelled, hence significantly reducing model-process mismatch. Results from industrial applications are presented.

Artificial Intelligence↗