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PubMed1995

Enormous knowledge base of disease diagnosis criteria.

One of the problems in the development of the medical knowledge systems is the limitations of the system's knowledge. It is a common expectation to increase the number of diseases contained in a system. Using a high density knowledge representation method designed by us, we have developed the Enormous Knowledge Base of Disease Diagnosis Criteria (EKBDDC). It contains diagnostic criteria of 1,001 diagnostic entities and describes nearly 4,000 items of diagnostic indicators. It is the core of a huge medical project--the Electronic-Brain Medical Erudite (EBME). This enormous knowledge base was implemented initially on a low-cost popular microcomputer, which can aid in the prompting of typical disease and in teaching of diagnosis. The knowledge base is easy to expand. One of the main goals of EKBDDC is to increase the number of diseases included in it as far as possible using a low-cost computer with a comparatively small storage capacity. For this, we have designed a high density knowledge representation method. Criteria of various diagnostic entities are respectively stored in different records of the knowledge base. Each diagnostic entity corresponds to a diagnostic criterion data set; each data set consists of some diagnostic criterion data values (Table 1); each data is composed of two parts: integer and decimal; the integral part is the coding number of the given diagnostic information, and the decimal part is the diagnostic value of this information to the disease indicated by corresponding record number. For example, 75.02: the integer 75 is the coding number of "hemorrhagic skin rash"; the decimal 0.02 is the diagnostic value of this manifestation for diagnosing allergic purpura. TABULAR DATA, SEE PUBLISHED ABSTRACT. The algebraic sum method, a special form of the weighted summation, is adopted as mathematical model. In EKBDDC, the diagnostic values, which represent the significance of the disease manifestations for diagnosing corresponding diseases, were determined empirically. It is of a great economical, practical, and technical significance to realize enormous knowledge bases of disease diagnosis criteria on a low-cost popular microcomputer. This is beneficial for the developing countries to popularize medical informatics. To create the enormous international computer-aided diagnosis system, one may jointly develop the unified modules of disease diagnosis criteria used to "inlay" relevant computer-aided diagnosis systems. It is just like assembling a house using prefabricated panels.

Artificial Intelligence↗

PubMed1989

A knowledge-based information system for monitoring drug levels.

The expert system shell SMR has been enhanced to include information system routines for designing data screens and providing facilities for data entry, storage, retrieval, queries and descriptive statistics. The data for inference making is abstracted from the data base record and inserted into a data array to which the knowledge base is applied to derive the appropriate advice and comments. The enhanced system has been used to develop an intelligent information system for monitoring serum drug levels which includes evaluation of temporal changes and production of specialized printed reports. The module for digoxin has been fully developed and validated. To demonstrate the extension to other drugs a module for phenytoin was constructed with only a rudimentary knowledge base. Data from the request forms together with the S-digoxin results are entered into the data base by the department secretary. The day's results are then reviewed by the clinical pharmacologist. For each case, previous results may be displayed and are taken into account by the system in the decision process. The knowledge base is applied to the data to formulate an evaluative comment on the report returned to the requestor. The report includes a semi-graphic presentation of the current and previous results and either the system's interpretation or one entered by the pharmacologist if he does not agree with it. The pharmacologist's comment is also recorded in the data base for future retrieval, analysis and possible updating of the knowledge base. The system is now undergoing testing and evaluation under routine operations in the clinical pharmacology service. It is a prototype for other applications in both laboratory and clinical medicine currently under development at Uppsala University Hospital. This system may thus provide a vehicle for a more intensive penetration of knowledge-based systems in practical medical applications.

Data Interpretation, Statistical↗

PubMed1994

Towards productive Knowledge-Based Systems in clinical organizations: a methods perspective.

This paper discusses an approach towards Knowledge-Based Systems (KBS) development which emphazises fit in the clinical organization, utility and safety. KBS design is identified as a subset of Decision Support System (DSS) design, and experiences from use of development methods is made available from the more general field. Generic and specific issues related to making KBS development result in systems used in clinical practice are discussed, and an integration of the Logic Engineering KBS technique into the Action Design DSS requirements specification method is outlined. Group-based knowledge modeling is identified as the bridge between the methods. It is concluded that while Action Design provides organizational validation (Are we building the right system?), Logic Engineering adds on KBS design verification (Are we going to build the system right?).

Artificial Intelligence↗

PubMed2004

Creation of a master table for checking indication and contraindication of medicine from a knowledge base linked with a thesaurus.

To develop a system for checking indication and contraindication of medicines in prescription order entry system, a master table consisting of the disease names corresponding to the medicines adopted in a hospital is needed. The creation of this table requires a considerable manpower. We developed a Web-based system for constructing a medicine/disease thesaurus and a knowledge base. By authority management of users, this system enables many specialists to create the thesaurus collaboratively without confusion. It supports the creation of a knowledge base using concept names by referring to the thesaurus, which is automatically converted to the check master table. When a disease name or medicine name was added to the thesaurus, the check table was automatically updated. We constructed a thesaurus and a knowledge base in the field of circulatory system disease. The knowledge base linked with the thesaurus proved to be efficient for making the check master table for indication/contraindication of medicines.

Artificial Intelligence↗

PubMed1987

Knowledge-based systems in laboratory medicine and pathology. A review and survey of the field.

Knowledge-based systems are computer systems designed to handle knowledge-intensive tasks, usually involving reasoning and inference. They are increasingly being applied to problems in laboratory medicine and pathology. In this article we provide a brief introduction to the basic concepts of knowledge-based systems, review some of their published applications, and report on an informal survey of specialists in laboratory medicine and pathology. The survey, sent to 102 individuals, indicated that 24% were involved in developing knowledge-based systems, with most systems at an early stage of development. Recent advances in knowledge-based systems research as well as survey responses suggest that this technology will have increasing value in laboratory medicine and pathology.

Artificial Intelligence↗

PubMed2003

Systems toxicology and the Chemical Effects in Biological Systems (CEBS) knowledge base.

The National Center for Toxicogenomics is developing the first public toxicogenomics knowledge base that combines molecular expression data sets from transcriptomics, proteomics, metabonomics, and conventional toxicology with metabolic, toxicologcal pathway, and gene regulatory network information relevant to environmental toxicology and human disease. It is called the Chemical Effects in Biological Systems (CEBS) knowledge base and is designed to meet the information needs of "systems toxicology," involving the study of perturbation by chemicals and stressors, monitoring changes in molecular expression and conventional toxicological parameters, and iteratively integrating biological response data to describe the functioning organism. Based upon functional genomics approaches used successfully in analyzing yeast gene expression data sets, relational and descriptive compendia will be assembled for toxicologically important genes, groups of genes, single nucleotide polymorphisms (SNPs), and mutant and knockout phenotypes. CEBS data sets will be fully documented in the experimental protocol and therefore searchable by compound, structure, toxicity end point, pathology and point, gene, gene group, SNP, pathway, and network as a function of dose, time, and the phenotype of the target tissue. A knowledge base is being developed by assimilating toxicological, biological, and chemical information from multiple public domain databases and by progressively refining that information about gene, protein, and metabolite expression for classes of chemicals and their biological effects in various species. By analogy to the GenBank database for genome sequences, researchers will globally query (or BLAST) CEBS using a transcriptome of a tissue of interest (or a list of outliers) to have the knowledge base return information on genes, groups of genes, metabolic and toxicological pathways, and contextually associated phenotypic information for compounds that display similar response profiles. With high-quality data content, CEBS will ultimately become a resource to support hypothesis-driven and discovery research that contributes effectively to drug safety and the improvement of risk assessments for chemicals in the environment. The CEBS development effort will span a decade or more.

Computational Biology↗

PubMed1995

An object-oriented model for the integration of knowledge-based systems.

This paper discusses functional integration, data integration, and knowledge integration as basic problems concerning the integration of knowledge-based systems into a hospital information system. A system model for an integrated knowledge-based system is introduced. Object-oriented models for the systems meta-database, patient database, and knowledge-base are presented. It is expected that the reader is familiar with the basic concepts of the object-oriented approach.

Artificial Intelligence↗

PubMed2000

AUDIX: a knowledge-based system for speech-therapeutic auditory discrimination exercises.

AUDIX is a knowledge-based multimedia system for auditory discrimination exercises. The aim of AUDIX is to provide patients with a computer-based therapy system which they can use between sessions with the human therapist, at home on an 'on-demand' basis. It is centered around computer based cognitive rehabilitation therapy whereas most existing programs in this area are only used for assessment. The auditory discrimination exercise system is designed for adult people who are speech-impaired as a result of a stroke. These people have auditory perceptual problems. The nature of the perceptual problem is an inability to perceive differences between phonemes. This requires a type of therapy called auditory discrimination training. The system provides computer-based auditory discrimination training. Through the knowledge-based design the domain dependent therapy knowledge is separated from the system core and provides a way for the therapist to add new knowledge, as new stimuli, or to create a new knowledge base to provide special exercises for an individual patient. The AUDIX architecture is described and the advantages of computer-based therapy are discussed.

Adult↗

PubMed2006

Novel knowledge-based mean force potential at the profile level.

BACKGROUND: The development and testing of functions for the modeling of protein energetics is an important part of current research aimed at understanding protein structure and function. Knowledge-based mean force potentials are derived from statistical analyses of interacting groups in experimentally determined protein structures. Current knowledge-based mean force potentials are developed at the atom or amino acid level. The evolutionary information contained in the profiles is not investigated. Based on these observations, a class of novel knowledge-based mean force potentials at the profile level has been presented, which uses the evolutionary information of profiles for developing more powerful statistical potentials. RESULTS: The frequency profiles are directly calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into binary profiles with a probability threshold. As a result, the protein sequences are represented as sequences of binary profiles rather than sequences of amino acids. Similar to the knowledge-based potentials at the residue level, a class of novel potentials at the profile level is introduced. We develop four types of profile-level statistical potentials including distance-dependent, contact, Phi/Psi dihedral angle and accessible surface statistical potentials. These potentials are first evaluated by the fold assessment between the correct and incorrect models generated by comparative modeling from our own and other groups. They are then used to recognize the native structures from well-constructed decoy sets. Experimental results show that all the knowledge-base mean force potentials at the profile level outperform those at the residue level. Significant improvements are obtained for the distance-dependent and accessible surface potentials (5-6%). The contact and Phi/Psi dihedral angle potential only get a slight improvement (1-2%). Decoy set evaluation results show that the distance-dependent profile-level potentials even outperform other atom-level potentials. We also demonstrate that profile-level statistical potentials can improve the performance of threading. CONCLUSION: The knowledge-base mean force potentials at the profile level can provide better discriminatory ability than those at the residue level, so they will be useful for protein structure prediction and model refinement.

Algorithms↗

PubMed2005

HYPROSP II--a knowledge-based hybrid method for protein secondary structure prediction based on local prediction confidence.

MOTIVATION: In our previous approach, we proposed a hybrid method for protein secondary structure prediction called HYPROSP, which combined our proposed knowledge-based prediction algorithm PROSP and PSIPRED. The knowledge base constructed for PROSP contains small peptides together with their secondary structural information. The hybrid strategy of HYPROSP uses a global quantitative measure, match rate, to determine whether PROSP or PSIPRED is to be used for the prediction of a target protein. HYPROSP made slight improvement of Q(3) over PSIPRED because PROSP predicted well for proteins with match rate >80%. As the portion of proteins with match rate >80% is quite small and as the performance of PSIPRED also improves, the advantage of HYPROSP is diluted. To overcome this limitation and further improve the hybrid prediction method, we present in this paper a new hybrid strategy HYPROSP II that is based on a new quantitative measure called local match rate. RESULTS: Local match rate indicates the amount of structural information that each amino acid can extract from the knowledge base. With the local match rate, we are able to define a confidence level of the PROSP prediction results for each amino acid. Our new hybrid approach, HYPROSP II, is proposed as follows: for each amino acid in a target protein, we combine the prediction results of PROSP and PSIPRED using a hybrid function defined on their respective confidence levels. Two datasets in nrDSSP and EVA are used to perform a 10-fold cross validation. The average Q(3) of HYPROSP II is 81.8% and 80.7% on nrDSSP and EVA datasets, respectively, which is 2.0% and 1.1% better than that of PSIPRED. For local structures with match rate >80%, the average Q(3) improvement is 4.4% on the nrDSSP dataset. The use of local match rate improves the accuracy better than global match rate. There has been a long history of attempts to improve secondary structure prediction. We believe that HYPROSP II has greatly utilized the power of peptide knowledge base and raised the prediction accuracy to a new high. The method we developed in this paper could have a profound effect on the general use of knowledge base techniques for various predictionalgorithms. AVAILABILITY: The Linux executable file of HYPROSP II, as well as both nrDSSP and EVA datasets can be downloaded from http://bioinformatics.iis.sinica.edu.tw/HYPROSPII/.

Algorithms↗

PubMed1992

A knowledge-based computer-aided design and manufacturing system for total hip replacement.

A knowledge-based computer-aided design and manufacturing system (CAD-CAM) has been developed for total hip replacement. Knowledge-based refers to the fact that the design process is a computer program that has been provided with preprogrammed design rules. Compared with conventional CAD-CAM systems, the knowledge-based system is automated, requires less designer intervention, and increases the accuracy of the design process. The capabilities of the system make it ideal for the design of standard and custom total hip replacement. A full-fill, press-fit custom total hip replacement has been designed using the knowledge-based system. The early clinical results of a series of 37 replacements in 31 patients is described in this paper.

Adult↗

PubMed1989

Creation of realistic appearing simulated patient cases using the INTERNIST-1/QMR knowledge base and interrelationship properties of manifestations.

The Internist-1/Quick Medical Reference (QMR) knowledge base (KB) describes the clinical manifestations of some 600 diseases in the domain of internal medicine. This KB, while not representing deep causal modelling of disease processes, is nonetheless effective in providing medical diagnostic assistance through the QMR medical decision support system. One potential application of this extensive KB is the generation of simulated patient cases for use in educating health professionals. However, the "flat" KB is not adequate for this because the clinical manifestations used in the disease descriptions are not mutually independent. While it is theoretically possible to construct disease descriptions which embody pathophysiologic mechanisms of disease causality, it is not practical from the standpoint of resource utilization. Short of constructing a causal knowledge base, the authors herein describe the generation of realistic appearing simulated patient case data using existing information in the knowledge base. This existing information in the KB is in the form of properties which represent a shallow form of interrelationships of the manifestations. The authors conclude that this ability to generate simulated cases represents another view in which to look at an extensive knowledge base, as well as having application to constructing intelligent tutoring systems for health professionals in training.

Artificial Intelligence↗

PubMed1994

Information technology factors in transferability of knowledge based systems in medicine.

The history of knowledge based systems in medicine has been that they are generally very localised, serving a special need in a single setting. Very few have proven to be capable of transfer to a distant environment. With the advent of tele-medical services and the associated transfer of data and knowledge in such services, the ability of medical KBS to transfer will be crucial to the success of tele-medical services. Differences in knowledge acquisition methods, knowledge representation techniques and in the epidemiological composition of training databases may influence viable transfer of knowledge based systems. Through experiments we demonstrate how rule-based systems may impose inflexible demands on data, how different knowledge acquisition techniques acquire different aspects of knowledge, though trained on a common training database, and how different knowledge acquisition techniques show varying degrees of robustness to slight changes in training databases.

Algorithms↗

PubMed1987

Spinning fantasy: themes, structure, and the knowledge base.

The influence of the child's knowledge base, in terms of event schemas, on symbolic play behavior was investigated. The pretend play behavior of 10 mother-child (2-0 to 2-4) dyads was observed in 2 play contexts. Play was examined for thematic content and the following structural components: self-other relations, substitute/imaginary objects, action integration, and planfulness. The highest levels of symbolic play behavior emerged in pretense episodes whose thematic content was event based. Additionally, thematic content affected the respective roles of mother and child in the construction of pretense. In pretense activity based on themes with which the child was familiar (e.g., routine events), the child, as well as the mother, participated in advanced levels of symbolic play activity, coconstructing pretense. In pretense based on themes unfamiliar to the child, the mother was almost exclusively responsible for the pretense. Thus, the development of child symbolic play appears to be related to the knowledge base in that its emergence is domain-specific--limited to themes for which the child has knowledge--before being more widely manifested.

Child, Preschool↗

PubMed1986

Promoting women's health: redefining the knowledge base and strategies for change.

Promoting women's health involves undertaking a critical gender-based analysis of women's health status and health needs and the knowledge bases which underlie health promotion action. The authors argue that professional and lay definitions of health problems often differ and that these differences stem from a differential emphasis on existing knowledge bases. Here the authors explore the focus of epidemiological, clinical, and experiential knowledge and suggest ways in which each does or does not address many key health issues which women themselves identify as important. Attention is also directed towards women's own suppressed and devalued knowledge as embodied in traditional folk practices and alternative care forms. Recommendations are made to improve existing knowledge bases by transforming some of the value orientations, priorities, methods and the social organization of research. The authors suggest that positive health promotion strategies must be based on an improved knowledge base and must incorporate three key concepts which women emphasize as central--self determination, women-centred values, and a gender-based political analysis. Strategies and methods to achieve these ends are suggested for health educators and policy-makers who wish to develop more positive approaches to promoting women's health.

Female↗

PubMed1994

[P.A.I.S., a personal medical information system. A comprehensive medical knowledge base].

The electronic medical knowledge data base DOPIS is a compliation of knowledge from various special fields of medicine. Using uniform nomenclature, the data are presented on demand as they would be in a book chapter. Concise updates can be performed at low cost. The primary structure of the concept is the division of medical knowledge into data banks on diagnosis, literature, medication and pharmacology, as well as so-called electronic textbooks. All data banks and electronic textbooks are connected associatively. Visual information is obtained via the image data bank connected to the diagnosis data bank and the electronic books. Moreover, DOPIS has an integrated patient findings system, as well as an image processing and archiving system with research values enabling research functions. The diagnosis and literature data banks can be modified by the user or author, or fed with their own data (a so-called Expert System Shell). For authors from special fields working on the project, an extra Medical Electronic Publishing System has been developed and made available for the electronic textbooks. The model for the knowledge data base has been developed in the field of ENT, the programme implemented and initially ENT data have been stored.

Computer Systems↗

PubMed1998

Facilitating Knowledge-Based Inferences in Less-Skilled Readers

The present study replicated Long, Oppy, and Seely's (1994) finding that skilled readers make knowledge-based inferences spontaneously during reading whereas less-skilled readers do not. However, the study also showed that less-skilled readers can make knowledge-based inferences with appropriate textual support. Evidence for knowledge-based inferences was obtained by examining whether readers were faster to make lexical decision responses to theme-appropriate targets (e.g., burglar) than to theme-inappropriate targets (e.g., blueprint), when reading short passages (e.g., The old woman awoke to a sound from downstairs. She reached into her purse and found only a file.). Whereas skilled readers generated knowledge-based inferences under all text conditions, less-skilled readers only showed evidence of having generated knowledge-based inferences when the text incorporated a question inviting the inference (e.g., The old woman awoke and said, 'Why is there a sound downstairs?' She reached into her purse and found only a file.) and text-presentation speed was slower. Copyright 1998 Academic Press.

Journal Article↗

PubMed2003

A knowledge-based approach to information extraction from surgical pathology reports.

We describe the development of a prototype system for knowledge-based information extraction from surgical pathology reports. The current system includes abstract problem solving methods and a frame-based knowledge representation of body parts, procedures, diseases, and findings for prostate and breast cases. The system currently extracts the organ, procedure, and diagnoses, and sets an agenda of goals for further processing. A potential advantage of this approach is the ability to increase specificity of information extraction.

Artificial Intelligence↗