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PubMed1996

A graph-grammar approach to represent causal, temporal and other contexts in an oncological patient record.

The data of a patient undergoing complex diagnostic and therapeutic procedures do not only form a simple chronology of events, but are closely related in many ways. Such data contexts include causal or temporal relationships, they express inconsistencies and revision processes, or describe patient-specific heuristics. The knowledge of data contexts supports the retrospective understanding of the medical decision-making process and is a valuable base for further treatment. Conventional data models usually neglect the problem of context knowledge, or simply use free text which is not processed by the program. In connection with the development of the knowledge-based system THEMPO (Therapy Management in Pediatric Oncology), which supports therapy and monitoring in pediatric oncology, a graph-grammar approach has been used to design and implement a graph-oriented patient model which allows the representation of non-trivial (causal, temporal, etc.) clinical contexts. For context acquisition a mouse-based tool has been developed allowing the physician to specify contexts in a comfortable graphical manner. Furthermore, the retrieval of contexts is realized with graphical tools as well.

Adverse Drug Reaction Reporting Systems↗
PubMed1980

A simple objective system for early recognition of overwhelming neonatal respiratory distress.

The early recognition of severe respiratory distress in the newborn allows for optimal care. The prompt identification of overwhelming respiratory failure may assist in the selection of candidates for new therapeutic techniques. We have evaluated, both retrospectively and prospectively, a simple scoring system for neonatal respiratory insufficiency. During the first 24 hr of life, serial inspired oxygen values (FiO2) are plotted with serial pH measurements against time on a graph. With pulmonary insufficiency, the lines cross. The severity of the insufficiency is quantified by integrating the area between the crossed lines. The 25 infants in our Regional Intensive Care Unit Nursery who died with respiratory distress syndrome (RDS) during 1976 were compared with surviving infants matched for gestational age, birth weight, and admission date; all patients received similar conventional management. The difference in the mean 24-hr cumulative scores between the two groups was significant (p < 0.01). Only 1 infant with a score over 40 U ultimately survived (96% specificity). From January 1978 through June 1979, data were graphed at the bedside on 100 neonates who required respiratory support, and analyzed without knowledge of the eventual outcome. Overall, the scoring system predicted the final outcome in 95% of the cases. False positive determinations were minimal, the system accurately selecting 86/87 ultimate survivors (98.8% specificity). These data suggest that this simple scoring system may prove useful in identifying infants with overwhelming respiratory distress. Such infants may be considered for specialized care or innovative yet unproven treatment modalities.

Humans↗
PubMed2025

MPAC: a computational framework for inferring pathway activities from multi-omic data.

MOTIVATION: Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. RESULTS: We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g. associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell composition. Our MPAC R package enables similar multi-omic analyses on new datasets. AVAILABILITY AND IMPLEMENTATION: The MPAC package is available at Bioconductor https://bioconductor.org/packages/MPAC.

Humans↗
PubMed2025

MPAC: a computational framework for inferring pathway activities from multi-omic data.

Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g., associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell compositions. Our MPAC R package, available at https://bioconductor.org/packages/MPAC, enables similar multi-omic analyses on new datasets.

Journal Article↗
PubMed1995

A conceptual graphs modeling of UMLS components.

The Unified Medical Language System (UMLS) of the U.S. National Library of Medicine is a complex collection of terms, concepts, and relationships derived from standard classifications. Potential applications would benefit from a high level representation of its components. This paper proposes a conceptual representation of both the Metathesaurus and the Semantic Network of the UMLS based on conceptual graphs. It shows that the addition of a dictionary of concepts to the UMLS knowledge base allows the capability to exploit it pertinently. This dictionary defines more precisely the core concepts and adds constraints on their use. Constraints are dedicated to guide an "intelligent" browsing of the UMLS knowledge sources.

Dictionaries as Topic↗
PubMed2024

Sparse spectral graph analysis and its application to gastric cancer drug resistance-specific molecular interplays identification.

Uncovering acquired drug resistance mechanisms has garnered considerable attention as drug resistance leads to treatment failure and death in patients with cancer. Although several bioinformatics studies developed various computational methodologies to uncover the drug resistance mechanisms in cancer chemotherapy, most studies were based on individual or differential gene expression analysis. However the single gene-based analysis is not enough, because perturbations in complex molecular networks are involved in anti-cancer drug resistance mechanisms. The main goal of this study is to reveal crucial molecular interplay that plays key roles in mechanism underlying acquired gastric cancer drug resistance. To uncover the mechanism and molecular characteristics of drug resistance, we propose a novel computational strategy that identified the differentially regulated gene networks. Our method measures dissimilarity of networks based on the eigenvalues of the Laplacian matrix. Especially, our strategy determined the networks' eigenstructure based on sparse eigen loadings, thus, the only crucial features to describe the graph structure are involved in the eigenanalysis without noise disturbance. We incorporated the network biology knowledge into eigenanalysis based on the network-constrained regularization. Therefore, we can achieve a biologically reliable interpretation of the differentially regulated gene network identification. Monte Carlo simulations show the outstanding performances of the proposed methodology for differentially regulated gene network identification. We applied our strategy to gastric cancer drug-resistant-specific molecular interplays and related markers. The identified drug resistance markers are verified through the literature. Our results suggest that the suppression and/or induction of COL4A1, PXDN and TGFBI and their molecular interplays enriched in the Extracellular-related pathways may provide crucial clues to enhance the chemosensitivity of gastric cancer. The developed strategy will be a useful tool to identify phenotype-specific molecular characteristics that can provide essential clues to uncover the complex cancer mechanism.

Stomach Neoplasms↗
PubMed1993

Developing a standard data structure for medical language--the SNOMED proposal.

The Systematized Nomenclature of Medicine, Third Edition, SNOMED International, is a comprehensive structured nomenclature of human and veterinary medicine, the terms of which are detailed, fine grained and semantically typed. Terms are assigned to eleven independent modules (fields), each of which is systematized. Terms may be linked to on another to represent complex entities or manifestations or alternately complex terms dissected into their elemental parts. Terms are illustrated utilizing a frame representation. Efforts are in progress to build both a conceptual graph and a frame-based semantic network encompassing each SNOMED term, effectively building a knowledge base. In this way, the knowledge contained in each alphanumeric representation is made explicit. SNOMED is a linked data structure capable of faithfully representing the activities, observations and diagnoses found in the medical record in a computer processable form.

Animals↗
PubMed1993

Generating MEDLINE search strategies using a librarian knowledge-based system.

We describe a librarian knowledge-based system that generates a search strategy from a query representation based on a user's information need. Together with the natural language parser AQUA, the system functions as a human/computer interface, which translates a user query from free text into a BRS Onsite search formulation, for searching the MEDLINE bibliographic database. In the system, conceptual graphs are used to represent the user's information need. The UMLS Metathesaurus and Semantic Net are used as the key knowledge sources in building the knowledge base.

Artificial Intelligence↗
PubMedDate not supplied

Knowledge-based approaches to the maintenance of a large controlled medical terminology.

OBJECTIVE: Develop a knowledge-based representation for a controlled terminology of clinical information to facilitate creation, maintenance, and use of the terminology. DESIGN: The Medical Entities Dictionary (MED) is a semantic network, based on the Unified Medical Language System (UMLS), with a directed acyclic graph to represent multiple hierarchies. Terms from four hospital systems (laboratory, electrocardiography, medical records coding, and pharmacy) were added as nodes in the network. Additional knowledge about terms, added as semantic links, was used to assist in integration, harmonization, and automated classification of disparate terminologies. RESULTS: The MED contains 32,767 terms and is in active clinical use. Automated classification was successfully applied to terms for laboratory specimens, laboratory tests, and medications. One benefit of the approach has been the automated inclusion of medications into multiple pharmacologic and allergenic classes that were not present in the pharmacy system. Another benefit has been the reduction of maintenance efforts by 90%. CONCLUSION: The MED is a hybrid of terminology and knowledge. It provides domain coverage, synonymy, consistency of views, explicit relationships, and multiple classification while preventing redundancy, ambiguity (homonymy) and misclassification.

Computer Simulation↗
PubMed1993

A tutorial introduction to stochastic simulation algorithms for belief networks.

Belief networks combine probabilistic knowledge with explicit information about conditional independence assumptions. A belief network consists of a directed acyclic graph in which the nodes represent variables and the edges express relationships of conditional dependence. When information about one variable's state is given to the network in the form of evidence, an update algorithm computes the posterior marginal probability distributions for the remaining variables in the network. Many algorithms for performing this inference task have been proposed. Exact algorithms report precise results for some classes of networks, but take exponential time (in the number of nodes) both in the worst case and for many interesting networks. Stochastic simulation algorithms estimate the posterior marginal probability distribution for many graph topologies that would require exponential time when using an exact algorithm. Nonetheless, for some belief networks, stochastic simulation algorithms are also known to have exponential worst case performance. This article describes at a tutorial level several stochastic simulation algorithms for belief networks, and illustrates them on some simple examples. In addition, the theoretical and empirical performance of the algorithms is briefly surveyed.

Algorithms↗
PubMed1992

A robust and efficient automated docking algorithm for molecular recognition.

A completely automated method is described for determining the most likely mode of binding of two (macro)molecules from the knowledge of their three-dimensional structures alone. The method is based on well-known graph theoretical techniques and has been used successfully to determine and rationalize the binding of a number of known macromolecular complexes. In this article we present results for a special case of the general molecular recognition problem--given the information concerning the particular atoms involved in the binding for one of the molecules, the algorithm can correctly identify the corresponding (contacting) atoms of the other molecule. The approach used can be easily extended to the general molecular recognition problem and requires the extraction of maximal common subgraphs. In these studies the docking of the macromolecules was achieved without the aid of computer graphics or other visual aids. The algorithm has been used to determine the correct mode of binding of a protein antigen to an antibody in approximately 100 min on a DEC micro VAX 3600.

Algorithms↗
PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗
PubMed1992

Natural language processing and semantical representation of medical texts.

For medical records, the challenge for the present decade is Natural Language Processing (NLP) of texts, and the construction of an adequate Knowledge Representation. This article describes the components of an NLP system, which is currently being developed in the Geneva Hospital, and within the European Community's AIM programme. They are: a Natural Language Analyser, a Conceptual Graphs Builder, a Data Base Storage component, a Query Processor, a Natural Language Generator and, in addition, a Translator, a Diagnosis Encoding System and a Literature Indexing System. Taking advantage of a closed domain of knowledge, defined around a medical specialty, a method called proximity processing has been developed. In this situation no parser of the initial text is needed, and the system is based on semantical information of near words in sentences. The benefits are: easy implementation, portability between languages, robustness towards badly-formed sentences, and a sound representation using conceptual graphs.

Abstracting and Indexing↗
PubMed1995

Nutrition intervention program of the Modification of Diet in Renal Disease Study: a self-management approach.

OBJECTIVE: To characterize the Modification of Diet in Renal Disease (MDRD) Study nutrition intervention program by determining the frequency of intervention strategies used by the dietitians and the usefulness of program components as rated by participants. DESIGN: Dietitians recorded which of 32 intervention strategies they used at each monthly visit. Participants rated the usefulness of 19 program components. SUBJECTS: 840 adults with renal insufficiency. INTERVENTION: Participants were assigned randomly to usual-, low-, or very-low-protein diet groups. Each eating pattern also specified a phosphorus intake goal. Each participant met monthly with a dietitian for an average of 26 months. STATISTICAL ANALYSES: Analyses of variance and chi 2 analyses. RESULTS: Dietitians used the following intervention strategies most often in all groups: providing feedback based on self-monitoring and/or food records, reviewing adherence or biochemistry data, providing low-protein foods, and reviewing graphs of adherence progress. In general, the dietitians used feedback, modeling, and support strategies more often, and knowledge and skills strategies less often, with participants who had to make the greatest reductions in protein intake and those with more advanced disease. In all groups, the dietitians' use of knowledge and skills, feedback, and modeling strategies decreased over time (P < .001), whereas use of support strategies was maintained. The type and frequency of intervention strategies used by dietitians and the usefulness ratings of participants did not vary by educational level of the participant. Both self-monitoring and dietitian support were rated as "very useful" by 88% of the participants. CONCLUSIONS: Three features were central to the MDRD Study nutrition intervention program: feedback, particularly from self-monitoring and from measures of adherence; modeling, particularly by providing low-protein food products; and dietitian support. We recommend the self-management approach.

Adult↗
PubMed1995

Dynamic characteristics of prosthetic heart valves.

The relation between flow rate (Q) and transvalvular pressure-drop (DP) is of fundamental importance for a prosthetic heart valve tested in steady flow conditions. The Q-DP plot can thus be called the static characteristic of the valve. While in pulsatile flow, with time (t) as a parameter, the instantaneous Q(t)-DP(t) relation can also be obtained. The Q-DP relation forms a phase graph on an X-Y plane during a whole cardiac cycle, and can be regarded as the dynamic characteristic, which to our knowledge has never been systematically explored before. With in vitro experiment the Q(t)-DP(t) relations are presented for five different aortic valves. Properly modelling the characteristics of heart valves is a key link in modelling the interactions between the ventricle and arterial system. Treatments for valves, such as diode analogue and orifice area assumption governed by the Gorlin formula, are found unsatisfactory. A simple one-dimensional flow equation is used to further examine the Q-DP graph, and both the dynamic resistance characteristic and the dynamic flow characteristic can be obtained. It is found that the dynamic characteristic differs from the static one not only in the inertance effect but also in the transient process, which can be quite energy-consuming and therefore important. Geometric relations of these phase graphs with the transvalvular power loss are discussed. The method of dynamic characteristics provides a new way to evaluate the performance of a tested valve.

Biomedical Engineering↗
PubMed1995

The compositional approach for representing medical concept systems.

The representation of patient-specific information in the computer-based medical record requires an expressive formalism, which supports computational services particularly with respect to subsumption. These demands are not sufficiently met by conventional medical terminology and classification systems. This paper investigates the weaknesses of conventional systems, which are primarily coding systems and contain a certain amount of implicit knowledge. The alternatives are logic-based formalisms, particularly languages of the KL-ONE-family and conceptual graphs, which are based on the formal representation of meanings. Principles of these approaches are reported and compared to the concept representation language developed in the GALEN project. Finally, an overview on the BERNWARD model is given, which aims at the formal description, classification, and composition of medical concepts. In BERNWARD subsumption and part-whole relation are treated in a symmetrical manner. There are explicit and formal criteria for supporting the inference of generic and partitive relations.

Disease↗
PubMed1996

Monocular scotomata and spinal manipulation: the step phenomenon.

OBJECTIVE: To discuss a case history wherein microvascular spasm of the optic nerve was treated by spinal manipulation. CLINICAL FEATURES: A 62-yr-old man developed a scotoma in the vision of the right eye during chiropractic treatment. INTERVENTION AND OUTCOME: Spinal manipulation treatment was continued with total resolution of the scotoma. The rate of recovery of the scotoma was mapped using computerized static perimetry. These measurements showed that significant recovery occurred at each spinal manipulation treatment, producing a stepped graph. CONCLUSION: The use of computerized static perimetry to measure the cerebral effects of spinal manipulation has increased knowledge of how chiropractic works. The further recovery of vision with each spinal adjustment suggests that more treatment may be better than less treatment in the chiropractic management of such cases.

Chiropractic↗
PubMedDate not supplied

[An educational management system for the postgraduate training of physician-radiologists].

The paper is devoted to the development of a system of program-oriented postgraduate education of radiologists based methodologically on acquiring professional knowledge and skills. Educational goals were adapted for each subject area. Matrix analysis and plotting of a logical structure graph were used for a choice of the final goals of education. The subject matter is in full accord with the educational goals, based upon the qualification characteristics of radiologists. The developed and published methodological materials make it possible to control the students' activities during extracurricular training and practical work in x-ray units and at seminars. Directed text control is used for the estimation of efficacy and correction of education.

Education, Medical, Continuing↗