bluuebunny/crossref_metadata_2025 huggingface dataset:
Developing an NLP and IR-based Algorithm for Analyzing Gene-disease Relationships
Authors: Yen Y. T. , Chen B. , Chiu H. W. , Lee Y. C. , Li Y. C. , Hsu C. Y.
Published: March 2018

Objectives:
High-throughput techniques such as cDNA microarray, oligonucleotide arrays, and serial analysis of gene expression (SAGE) have been developed and used to automatically screen huge amounts of gene expression data. However, researchers usually spend lots of time and money on discovering gene-disease relationships by utilizing these techniques. We prototypically implemented an algorithm that can provide some kind of predicted results for biological researchers before they proceed with experiments, and it is very helpful for them to discover gene-disease relationships more efficiently.

Methods:
Due to the fast development of computer technology, many information retrieval techniques have been applied to analyze huge digital biomedical databases available worldwide. Therefore we highly expect that we can apply information retrieval (IR) technique to extract useful information for the relationship of specific diseases and genes from MEDLINE articles. Furthermore, we also applied natural language processing (NLP) methods to do the semantic analysis for the relevant articles to discover the relationships between genes and diseases.

Results:
We have extracted gene symbols from our literature collection according to disease MeSH classifications. We have also built an IR-based retrieval system, “Biomedical Literature Retrieval System (BLRS)“ and applied the N-gram model to extract the relationship features which can reveal the relationship between genes and diseases. Finally, a relationship network of a specific disease has been built to represent the gene-disease relationships.

Conclusions:
A relationship feature is a functional word that can reveal the relationship between one single gene and a disease. By incorporating many modern IR techniques, we found that BLRS is a very powerful information discovery tool for literature searching. A relationship network which contains the information on gene symbol, relationship feature, and disease MeSH term can provide an integrated view to discover gene-disease relationships.
A Global Socio-economic-medico-legal Model for the Sustainability of Longitudinal Electronic Health Records
Authors: Shabo A.
Published: March 2018

Objectives:
This paper pursues the challenge of sustaining lifetime electronic health records (EHRs) based on a comprehensive socio-economic-medico-legal model. The notion of a lifetime EHR extends the emerging concept of a longitudinal and cross-institutional EHR and is invaluable information for increasing patient safety and quality of care.

Methods:
The challenge is how to compile and sustain a coherent EHR across the lifetime of an individual. Several existing and hypothetical models are described, analyzed and compared in an attempt to suggest a preferred approach.

Results:
The vision is that lifetime EHRs should be sustained by new players in the healthcare arena, who will function as independent health record banks (IHRBs). Multiple competing IHRBs would be established and regulated following preemptive legislation. They should be neither owned by healthcare providers nor by health insurer/payers or government agencies. The new legislation should also stipulate that the records located in these banks be considered the medico-legal copies of an individual’s records, and that healthcare providers no longer serve as the legal record keepers.

Conclusions:
The proposed model is not centered on any of the current players in the field; instead, it is focussed on the objective service of sustaining individual EHRs, much like financial banks maintain and manage financial assets. This revolutionary structure provides two main benefits: 1) Healthcare organizations will be able to cut the costs of long-term record keeping, and 2) healthcare providers will be able to provide better care based on the availability of a lifelong EHR of their new patients.
A Closed Queueing Network Approach to the Analysis of Patient Flow in Health Care Systems
Authors: Xie H. , Millard P. , Chaussalet T. J.
Published: March 2018

Objectives:
To model patient flow in health care systems with bed capacity constraints in order to provide a useful decision aid for health service managers.

Methods:
We model the patient flow of health care systems using a closed queueing network framework with the assumption that the system is always full. Key performance measures of the health care system are also derived.

Results:
Using parameters taken from a study of a geriatric department in the UK, we show that the model is useful in helping service managers to gain better understanding of the behaviour of the system. In addition, we demonstrate that the model could help improving decision-making by allowing managers to explore different options and evaluate their impacts on performance. Our findings highlight the importance of policy makers taking into account the interactions between different phases of care.

Conclusions:
We have developed a novel approach to modelling the flow of patients through health care systems with constrained bed capacity.
The Common Data Elements for Cancer Research: Remarks on Functions and Structure
Authors: Brandt C. A. , Nadkarni P. M.
Published: March 2018

Objectives:
The National Cancer Institute (NCI) has developed the Common Data Elements (CDE) to serve as a controlled vocabulary of data descriptors for cancer research, to facilitate data interchange and inter-oper-ability between cancer research centers. We evaluated CDE’s structure to see whether it could represent the elements necessary to support its intended purpose, and whether it could prevent errors and inconsistencies from being accidentally introduced. We also performed automated checks for certain types of content errors that provided a rough measure of curation quality.

Methods:
Evaluation was performed on CDE content downloaded via the NCI’s CDE Browser, and transformed into relational database form. Evaluation was performed under three categories: 1) compatibility with the ISO/IEC 11179 metadata model, on which CDE structure is based, 2) features necessary for controlled vocabulary support, and 3) support for a stated NCI goal, set up of data collection forms for cancer research.

Results:
Various limitations were identified both with respect to content (inconsistency, insufficient definition of elements, redundancy) as well as structure – particularly the need for term and relationship support, as well as the need for metadata supporting the explicit representation of electronic forms that utilize sets of common data elements.

Conclusions:
While there are numerous positive aspects to the CDE effort, there is considerable opportunity for improvement. Our recommendations include review of existing content by diverse experts in the cancer community; integration with the NCI thesaurus to take advantage of the latter’s links to nationally used controlled vocabularies, and various schema enhancements required for electronic form support.
A Method for Classification of Transient Events in EEG Recordings: Application to Epilepsy Diagnosis
Authors: Tzallas A. T. , Karvelis P. S. , Katsis C. D. , Giannopoulos S. , Konitsiotis S. , Fotiadis D. I.
Published: March 2018

Objectives:
The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method.

Methods:
A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity.

Results:
The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases.

Conclusions:
The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.
Recurrent Neural Networks for Predicting Outcomes after Liver Transplantation: Representing Temporal Sequence of Clinical Observations
Authors: Doyle H. R. , Parmanto B.
Published: March 2018

Objectives:
This paper investigates a version of recurrent neural network with the backpropagation through time (BPTT) algorithm for predicting liver transplant graft failure based on a time series sequence of clinical observations. The objective is to improve upon the current approaches to liver transplant outcome prediction by developing a more complete model that takes into account not only the preoperative risk assessment, but also the early postoperative history.

Methods:
A 6-fold cross-validation procedure was used to measure the performance of the networks. The data set was divided into a learning set and a test set by maintaining the same proportion of positive and negative cases in the original set. The effects of network complexity on overfitting were investigated by constructing two types of networks with different numbers of hidden units. For each type of network, 10 individual networks were trained on the learning set and used to form a committee. The performance of the networks was measured exhaustively with respect to both the entire training and test sets.

Results:
The networks were capable of learning the time series problem and achieved good performances of 90% correct classification on the learning set and 78% on the test set. The prediction accuracy increases as more information becomes progressively available after the operation with the daily improvement of 10% on the learning set and 5% on the test set.

Conclusions:
Recurrent neural networks trained with BPTT algorithm are capable of learning to represent temporal behavior of the time series prediction task. This model is an improvement upon the current model that does not take into account postoperative temporal information.
The CardioOP-Data Clas (CDC)
Authors: Klas W. , Westermann U. , Rose T. , Tremper J. , Stracke S. , Gödje O. , Hannekum A. , Preisack M. B. , Friedl R.
Published: March 2018

Objectives: Self-directed and customized medical education programs are gaining importance in health care instruction. We prototypically implemented a repository-driven online computer system (CardioOP) for teleteaching in Heart Surgery. It supports authoring and multiple re-use of multimedia data for different user groups in different instructional applications and therefore requires a process of content management.

Methods: We defined objectives for a terminological system to support semantic, cross-media type annotation and retrieval of learning objects: domain completeness, German (natural) language processing, multi-user concepts, extensibility and maintenance, content based annotation and technical implementation. Existing terminologies (ICD10, READ V3, Snomed III, UMLS 1997, MESH) have been analysed according to these objectives.

Results: We found that the analysed terminologies did not meet our criteria sufficiently. Therefore, we developed a domain-specific thesaurus, the CardioOPDataClas (CDC). The application of the CDC within a database-driven authoring process using specifically developed tools is reported.

Conclusions: Metadata play an important role in the effective discovery and search, access, integration and management of educational multimedia data in medicine but so far, there is no terminology to support content management for instructional multimedia. We prototypically designed and applied a thesaurus for the CardioOP educational system. Additional work is needed to evaluate the system in terms of user-friendliness, concept coverage and information retrieval performance.
On the Design of a Generic and Scalable Multilayer Software Architecture for Data Flow Management in the Intensive Care Unit
Authors: De Turck F. , Vanhaste S. , Vandermeulen F. , Demeester P. , de Moor G. , Decruyenaere J.
Published: March 2018

Objectives: The current Intensive Care Information Systems (IC-ISs) collect and store monitoring data in an automated way and can replace all paper forms by an electronic equivalent, resulting in a paperless ICU. Future development of IC-ISs will now have to focus on bedside clinical decision support. The current IC-ISs are data-driven systems, with a two-layer software architecture. This software architecture is hardly maintainable and probably not the most optimal architecture to make the transition towards future systems with decision support. The aim of this research was to address the design of an alternative software architecture based on new paradigms.

Methods: State-of-the art component, middleware and agent technology were deployed to design and implement a software architecture for ICU data flow management.

Results: An advanced multi-layer architecture for efficient data flow management in the ICU has been designed. The architecture is both generic and scalable, which means that it neither depends on a particular ICU nor on the deployed monitoring devices. Automatic device detection and Graphical User Interface generation are taken into account. Furthermore, a demonstrator has been developed as a proof that the proposed conceptual software architecture is feasible in practice. The core of the new architecture consists of Bed Decision Agents (BDAs). The introduction of BDAs, who perform specific dedicated tasks, improves the adaptability and maintainability of the future very complex IC-ISs.

Conclusions: A software architecture, based on component, middleware and agent technology, is feasible and offers important advantages over the currently used two-layer software architecture.
The Micro-Macro Spectrum of Medical Informatics Challenges: From Molecular Medicine to Transforming Health Care in a Globalizing Society
Authors: Kulikowski C. A.
Published: March 2018

Background: Medical informatics has always encompassed a very broad spectrum of techniques for clinical and biomedical research, education and practice. There has been a concomitant variety of depth of specialization, ranging from the routine application of information processing methods to cutting-edge research on fundamental problems of computer-based systems and their relations to cognition and perception in biomedicine.

Objectives: Challenges for the field can be placed in perspective by considering the scale of each – from the highly detailed scientific problems in bioinformatics and emerging molecular medicine to the broad and complex social problems of introducing medical informatics into web-related global settings. Methods: The scale of an informatics problem is not only determined by the inherent physical space in which it exists, but also by the conceptual complexity that it involves, reinforcing the need to investigate the semantic web within which medical informatics is defined.

Results and Conclusion: Bioinformatics, biomedical imaging and language understanding provide examples that anchor research and practice in biomedical informatics at the detailed, scientific end of the spectrum. Traditional concerns of medical informatics in the clinical arena make up the broad mid-range of the spectrum, while novel social interaction models of competition and cooperation will be needed to understand the implications of distributed health information technology for individual and societal change in an increasingly interconnected world.
Challenges in Medical Informatics: Perspectives of an International Medical Informatics Organization
Authors: Lun K. C.
Published: March 2018

Objective: As an international organization with the missions to promote informatics in health care and biomedical research, advance international cooperation, stimulate research, development and education, and disseminate and exchange information, the International Medical Informatics Association (IMIA) must be constantly cognizant of new developments in medical informatics and address the challenges to the discipline. From an international organization standpoint, it perceives three major challenges viz. the Identity, Organizational and Leadership challenges.

Method: This paper attempts to identify and discuss these challenges and to offer ways to overcome them through the activities of an international organization for medical informatics.

Results and Conclusion: From an international organization standpoint, IMIA can help overcome these organizational challenges by ensuring strong leadership throughout its echelon, actively promoting its goals and objectives worldwide through its national and institutional members as well as its regional groups and encouraging strategic partnerships between its many Working Groups and Special Interest Group on Nursing with other international organizations and industry to further promote the awareness and the perception of the relevance of medical informatics to health and medicine by the international community.
Terminology Tools: State of the Art and Practical Lessons
Authors: Cimino J. J.
Published: March 2018

Objectives:
As controlled medical terminologies evolve from simple code-name-hierarchy arrangements, into rich, knowledge-based ontologies of medical concepts, increased demands are placed on both the developers and users of the terminologies. In response, researchers have begun developing tools to address their needs. The aims of this article are to review previous work done to develop these tools and then to describe work done at Columbia University and New York Presbyterian Hospital (NYPH).

Methods:
Researchers working with the Systematized Nomenclature of Medicine (SNOMED), the Unified Medical Language System (UMLS), and NYPH’s Medical Entities Dictionary (MED) have created a wide variety of terminology browsers, editors and servers to facilitate creation, maintenance and use of these terminologies.

Results:
Although much work has been done, no generally available tools have yet emerged. Consensus on requirement for tool functions, especially terminology servers is emerging. Tools at NYPH have been used successfully to support the integration of clinical applications and the merger of health care institutions.

Conclusions:
Significant advancement has occurred over the past fifteen years in the development of sophisticated controlled terminologies and the tools to support them. The tool set at NYPH provides a case study to demonstrate one feasible architecture.
RAGs: A Novel Approach to Computerized Genetic Risk Assessment and Decision Support from Pedigrees
Authors: Coulson A. S. , Glasspool D. W. , Emery J. , Fox J.
Published: March 2018

Objectives:
To assist general practitioners in evaluating patients’ genetic risk of cancer on the basis of family history data.

Methods:
A new computer application, RAGs (Risk Assessment in Genetics), has been developed to help doctors create graphical family trees and assess the genetic risk of breast and colorectal cancer. RAGs possesses two features that distinguish it from similar software: (i) a user-centred design, which takes into account the requirements of the doctor-patient encounter; (ii) effective and accessible risk reporting by employing qualitative evidence for or against increased risk, which is more easily understood than numerical probabilities. The system allows any rule-based genetic risk guideline to be implemented, and may be readily modified to cater for the varying degrees of information required by different specialists.

Results:
RAGs permits fast, accurate data entry, and results in more appropriate management decisions than those made via other techniques. In addition, RAGs enables both the clinician and the patient to understand how it arrives at its conclusions, since the use of qualitative evidence allows the program to provide explanations for its reasoning.

Conclusions:
The RAGs system promises to help practitioners be more effective gatekeepers to genetic services. It may empower doctors both to make an informed choice when deciding to refer patients who are at increased genetic risk of breast or colorectal cancer, and to reassure those who are at low risk.
What is Bioinformatics? A Proposed Definition and Overview of the Field
Authors: Luscombe N. M. , Greenbaum D. , Gerstein M.
Published: March 2018

Background:
The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science.

Objectives:
Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems.

Methods:
Our definition is as follows: Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying “informatics” techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale.

Results and Conclusions:
Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (eg expression data). Additional information includes the text of scientific papers and “relationship data” from metabolic pathways, taxonomy trees, and protein-protein interaction networks. Bioinformatics employs a wide range of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering. The emphasis is on approaches integrating a variety of computational methods and heterogeneous data sources. Finally, bioinformatics is a practical discipline. We survey some representative applications, such as finding homologues, designing drugs, and performing large-scale censuses. Additional information pertinent to the review is available over the web at http://bioinfo.mbb.yale.edu/what-is-it.
Domain Ontologies in Software Engineering: Use of Protégé with the EON Architecture
Authors: Musen M. A.
Published: March 2018

Domain ontologies are formal descriptions of the classes of concepts and the relationships among those concepts that describe an application area. The Protege software-engineering methodology provides a clear division between domain ontologies and domain-independent problemsolvers that, when mapped to domain ontologies, can solve application tasks. The Protege approach allows domain ontologies to inform the total software-engineering process, and for ontologies to be shared among a variety of problem-solving components. We illustrate the approach by describing the development of EON, a set of middleware components that automate various aspects of protocol-directed therapy. Our work illustrates the organizing effect that domain ontologies can have on the software-development process. Ontologies, like all formal representations, have limitations in their ability to capture the semantics of application areas. Nevertheless, the capability of ontologies to encode clinical distinctions not usually captured by controlled medical terminologies provides significant advantages for developers and maintainers of clinical software applications.
Modeling Concepts in Medicine for Medical Language Understanding
Authors: Miller R. A. , Baud R. H. , Scherrer J.-R. , Rassinoux A.-M.
Published: March 2018

Over the past two decades, the construction of models for medical concept representation and for understanding of the deep meaning of medical narrative texts have been challenging areas of medical informatics research. This review highlights how these two inter-related domains have evolved, emphasizing aspects of medical modeling as a tool for medical language understanding. A representation schema, which balances partially but accurately with complete but complex representations of domainspecific knowledge, must be developed to facilitate language understanding. Representative examples are drawn from two major independent efforts undertaken by the authors: the elaboration and the subsequent adjustment of the RECIT multilingual analyzer to include a robust medical concept model, and the recasting of a frame-based interlingua system, originally developed to map equivalent concepts between controlled clinical vocabularies, to invoke a similar concept model.
Alternative Ways for Knowledge Collection, Indexing and Robust Language Retrieval
Authors: Lovis C. , Rassinoux A.-M. , Scherrer J.-R. , Baud R. H.
Published: March 2018

Definitions are provided of the key entities in knowledge representation for Natural Language Processing (NLP). Starting from the words, which are the natural components of any sentence, both the role of expressions and the decomposition of words into their parts are emphasized. This leads to the notion of concepts, which are either primitive or composite depending on the model where they are created. The problem of finding the most adequate degree of granularity for a concept is studied. From this reflection on basic Natural Language Processing components, four categories of linguistic knowledge are recognized, that are considered to be the building blocks of a Medical Linguistic Knowledge Base (MLKB). Following on the tracks of a recent experience in building a natural language-based patient encoding browser, a robust method for conceptual indexing and query of medical texts is presented with particular attention to the scheme of knowledge representation.
Conceptual Graph Grammar – A Simple Formalism for Sublanguage
Authors: Johnson S. B.
Published: March 2018

There are a wide variety of computer applications that deal with various aspects of medical language: concept representation, controlled vocabulary, natural language processing, and information retrieval. While technical and theoretical methods appear to differ, all approaches investigate different aspects of the same phenomenon: medical sub language. This paper surveys the properties of medical sublanguage from a formal perspective, based on detailed analyses cited in the literature. A review of several computer systems based on sublanguage approaches shows some of the difficulties in addressing the interaction between the syntactic and semantic aspects of sUblanguage. A formalism called Conceptual Graph Grammar is presented that attempts to combine both syntax and semantics into a single notation by extending standard Conceptual Graph notation. Examples from the domain of pathology diagnoses are provided to illustrate the use of this formalism in medical language analysis. The strengths and weaknesses of the approach are then considered. Conceptual Graph Grammar is an attempt to synthesize the common properties of different approaches to sublanguage into a single formalism, and to begin to define a common foundation for language-related research in medical informatics.
Classification Systems in Nursing: Formalizing Nursing Knowledge and Implications for Nursing Information Systems
Authors: Epping P. J. M. M. , Abraham I. L. , Goossen W. T. F.
Published: March 2018

The development of nursing information systems (NIS) is often hampered by the fact that nursing lacks a unified nursing terminology and classification system. Currently there exist various initiatives in this area. We address the question as to how current initiatives in the development of nursing terminology and classification systems can contribute towards the development of NIS. First, the rationale behind the formalization of nursing knowledge is discussed. Next, using a framework for nursing information processing, the most important developments in the field of nursing on formalization, terminology and classification are critically reviewed. The initiatives discussed include nursing terminology projects in several countries, and the International Classification of Nursing Practice. Suggestions for further developments in the area are discussed. Finally, implications for NIS are presented, as well as the relationships of these components to other sections of an integrated computerized patient record.
Determinants of Homonym and Synonym Rates of Record Linkage in Disease Registration
Authors: Schmidtmann I. , Brenner H.
Published: March 2018

Reliable record linkage is an essential component of the quality of population-based disease registration. Quality assessment of disease registries should, therefore, include quantitative approaches to describe the extent of record-linkage errors. The homonym and synonym rates have been proposed for this purpose. The homonym rate quantifies the proportion of distinct patients excluded from registration due to erroneous linkage with other patients. The synonym rate quantifies the proportion of unrecognized duplicate notifications on patients already registered in the registry. This paper provides an algebraic assessment of the determinants of both rates. It is shown how the homonym and the synonym rate are determined by the discriminating power provided by the personal identifiers and the record linkage procedure on the one hand and the specific circumstances of disease registration on the other hand, such as the number of patients in the registry or the number of notifications per case. All these factors should be taken into account when reporting and interpreting results of record-linkage studies, particularly if comparisons are made between the performance of record-linkage procedures in different environments.
Formal Descriptions and Adaptive Mechanisms for Changes in Controlled Medical Vocabularies
Authors: Cimino J. J.
Published: March 2018

Standard controlled medical vocabularies are typically based on a coding scheme, while medical informatics applications tend to have a more formal conceptual foundation. When such applications attempt to use data coded with standard vocabularies, problems can arise when the standard vocabulary changes over time. A formal taxonomy is presented for describing the semantic changes which can occur in a vocabulary, such as simple addition, refinement, precoordination, disambiguation, redundancy, obsolescence, discovered redundancy, major name changes, minor name changes, code reuse, and changed codes. The taxonomy is described that used to effect change in one concept-based vocabulary (the Medical Entities Dictionary), and the utility of the approach is demonstrated by applying it to the changes appearing in the 1994 release of the International Classification of Diseases, Ninth Edition, with Clinical Modifications (ICD-9-CM).
Extended Telemedical Consultation using Arden Syntax Based Decision Support, Hypertext and WWW Technique
Authors: Ekdahl C. , Wigertz O. , Shahsavar N. , Gill H. , Forsum U. , Karlsson D.
Published: March 2018

There is an obvious need for geographic distribution of expert knowledge among several health care units without increasing the cost of on-site expertise in locations where health care is provided. This paper describes the design of a knowledge-based decision-support system for extended consultation in clinical medicine. The system is based on Arden Syntax for Medical Logic Modules and hypertext using World Wide Web technology. It provides advice and explanations regarding the given advice. The explanations are presented in a hypertext format allowing the user to browse related information and to verify the relevance of the given advice. The system is intended to be used in a closed local network. With special precautions regarding issues of safety and patient security, the system can be used over wider areas such as in rural medicine. A prototype has been developed in the field of clinical microbiology and infectious diseases regarding infective endocarditis.
Extraction of Rules for Tuberculosis Diagnosis Using an Artificial Neural Network
Authors: Cloete I. , Beyers N. , Viktor H. L.
Published: March 2018

The treatment of tuberculosis (TB) is a major challenge throughout the world. The Western Cape Region of South Africa has the highest occurrence of TB in the world. Here, TB is increasing due to improperly managed treatment programmes and inadequate facilities. The development of rules to aid medical practitioners in the early and accurate diagnosis of tuberculosis should prove worthwhile. A method to extract such diagnostic rules from an artificial neural network is presented. These rules accurately represent the knowledge embedded in the “raw” TB data.
Philosophy Into Practice: A Health Informatics Course Proposal
Authors: Ball M. J. , Douglas J. V.
Published: March 2018

International health informaticians acknowledge the critical importance of education and training to the successful implementation of information technology in the health-care setting. As access to the Internet grows, so does the richness of resources the Internet can supply. With the changes in health care brought by communication, telematics will become the successor to informatics. The authors present the philosophy of telematics training and propose a short training course designed to help health professionals in developing nations take advantage of telematics, providing conceptual understanding and hands-on training.
User Attitudes to a Heritage Hospital Information System
Authors: Lakay D. , Isaacs S.
Published: March 2018

A heritage system is one that has exceeded its lifespan but continues to be used for various reasons, despite the problems it may be causing. Groote Schuur Hospital has a Hospital Information System that is more than 25 years old. The present study reports the result of a survey on users’ attitude and sense of involvement in this system. An attempt is made to determine whether users feel the need for change and whether a sense of involvement is related to this need.
Using Connectionist Modules for Decision Support
Authors: Hripcsak G.
Published: March 2018

A connectionist model for decision support was constructed out of several back-propagation modules. Manifestations serve as input to the model; they may be real-valued, and the confidence in their measurement may be specified. The model produces as its output the posterior probability of disease. The model was trained on 1,000 cases taken from a simulated underlying population with three conditionally independent manifestations. The first manifestation had a linear relationship between value and posterior probability of disease, the second had a stepped relationship, and the third was normally distributed. An independent test set of 30,000 cases showed that the model was better able to estimate the posterior probability of disease (the standard deviation of residuals was 0.046, with a 95% confidence interval of 0.046-0.047) than a model constructed using logistic regression (with a standard deviation of residuals of 0.062, with a 95% confidence interval of 0.062-0.063). The model fitted the normal and stepped manifestations better than the linear one. It accommodated intermediate levels of confidence well.
Interpretation of Short ECGs with a Personal Computer: The Porto Program
Authors: Abreu-Lima C. , Marques de Sá J. P.
Published: March 2018

A computer program for ECG analysis and interpretation developed at the University of Porto, Portugal is presented. The program runs on a microcomputer and employs the three-lead Frank VCG. The signals are sampled at 250 Hz with 8-bit precision during 5.5 s. Details on signal conditioning and wave recognition and measurement techniques are given. The diagnostic part of the program uses decision-tree logic. The decision rules are mainly derived from the Washington Code. The diagnostic accuracy of four classes (normal, left and right ventricular hypertrophies and myocardial infarction), evaluated in a sample of 1,075 pediatric and adult patients and classified by ECG-independent means was 76%. The program is currently being evaluated on the CSE database.
Signal Analysis for ECG Interpretation
Authors: van Bemmel J. H. , Zywietz Chr. , Kors J. A.
Published: March 2018

In ECG interpretation usually two main areas are discerned: the signal analysis and the diagnostic classification. This article reviews the major developments in the first area. ECG signal analysis itself is subdivided into the stages data acquisition, data transformation, feature selection, and data reduction. These stages are consecutively reviewed, while in the data transformation stage digital filtering, detection, wave typing, beat selection, and boundary recognition are discussed.
A Versatile Test for Clustering and a Proximity Analysis of Neurons
Authors: Grimson R.C. , Rose R.D.
Published: March 2018

A test for nonrandom patterns in populations of “cells” of binary attributes is formulated for applications in settings where it is impossible or impractical to completely specify the adjacency matrix, as in circumstances involving enormous numbers of cells and/or censored data. Applications are made to problems concerning spatial distributions of specially labeled cells in nervous system research.
Diagnostic Consultations: a Speech-Act Theoretical Reconstruction for the Design of Consultation Systems
Authors: Müller-Kolck U.
Published: March 2018

In this paper the theory of speech acts is applied for a structural reconstruction of medical diagnostic consultations. The reconstruction provides the conceptual model for a research prototype of a consultation tool which is expected to play a mediating role between the participants in the professional diagnostic discourse. Preliminary results of the design will be outlined.
WING – Entering a New Phase of Electronic Data Processing at the Gießen University Hospital
Authors: Dudeck J. , Junghans G. , Marquardt K. , Sebald P. , Michel A. , Prokosch H. U.
Published: March 2018

At the Gielßen University Hospital electronic data processing systems have been in routine use since 1975. In the early years developments were focused on ADT functions (admission/discharge/transfer) and laboratory systems. In the next decade additional systems were introduced supporting various functional departments. In the mid-eighties the need to stop the ongoing trend towards more and more separated standalone systems was realized and it was decided to launch a strategic evaluation and planning process which sets the foundation for an integrated hospital information system (HIS). The evaluation of the HELP system for its portability into the German hospital environment was the first step in this process. Despite its recognized capabilities in integrating decision support and communication technologies, and its powerful HIS development tools, the large differences between American and German hospital organization, influencing all existing HELP applications, and the incompatibility of the HELP tools with modern software standards were two important factors forcing the investigation of alternative solutions. With the HELP experience in mind, a HIS concept for the Gießen University Hospital was developed. This new concept centers on the idea of a centralized relational patient database on a highly reliable database server, and clinical front-end applications which might be running on various other computer systems (mainframes, departmental UNIX satellites or PCs in a LAN) integrated into a comprehensive open HIS network. The first step towards this integrated approach was performed with the implementation of ADT and results reporting functions on care units.
Natural Language Processing and Semantical Representation of Medical Texts
Authors: Baud R. H. , Rassinoux A.-M. , Scherrer J.-R.
Published: March 2018

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.
Graphical Access to Medical Expert Systems: IV. Experiments to Determine the Role of Spoken Input
Authors: Isaacs E. , Wulfman C. E. , Rohn J. A. , Lane C. D. , Fagan L. M.
Published: March 2018

The goal of our research is to design improved interfaces for medical expert systems. Previously, the use of graphical techniques was explored to improve the acceptance by clinicians of the user interface. Now that devices that accept spoken input are available, we wish to design interfaces that take advantage of this potentially more natural modality for interaction. To understand how clinicians might want to speak to a medical decision-support system, we carried out an experiment that simulated the availability of a spoken interface to the ONCOCIN medical expert system. ONCOCIN provides therapy advice for patients on complex cancer therapy protocols based on a description of the patient’s current medical status and laboratory-test values. In the experiment, we had oncologists present a clinical case while observing the ONCOCIN flowsheet display. A project member listened to the presentation and filled in values for the flowsheet, as well as introducing purposeful misunderstandings of the input. The results suggest that each individual developed a stereotypical grammar for communicating with the program. Our experience with the purposeful miscommunications suggests particular ways to tailor requests for repetition based on the part of the utterance that was not understood.
Modelling In-Patient Bed Usage Behaviour in a Department of Geriatric Medicine
Authors: Millard P. , McClean S.
Published: March 2018

The flow of patients through geriatric hospitals has been previously described in terms of acute and long-stay states where the bed occupancy at a census point is modelled by a mixed exponential model. Using data for sixteen years the model was fitted to successive annual census points, in order to provide a description of temporal trends. While the number of acute patients has remained fairly stable during the period, the model shows that there has been a decrease in the number of long-stay patients. Mean lengths of stay in our geriatric hospital before death or discharge have decreased during the study period for both acute and long-stay patients.

Using these fits of the mixed exponential model to census data, a method is provided for predicting future turnover of patients. These predictions are reasonably good, except when the turnover patterns go through a period of flux in which assumption of stability no longer holds. Overall, a methodology is presented which relates census analysis to the behaviour of admission cohorts, thus producing a means of predicting future behaviour of patients and identifying where there is a change in patterns.
Semantic Analysis of Medical Records
Authors: Bassøe C.-F. , Rasmussen J.-E.
Published: March 2018

A program (LogStory) is described that was developed for the automatic semantic analysis of clinical narratives, stored in a computerized problem-oriented medical record (PROMED). The diagnoses were written in a free-text format during consultation, and later collected into diagnostic classes, e.g., diseases. A lexical parser automatically created dictionaries from the clinical narrative associated with each disease. Automatic (fuzzy) set operations were performed on the words associated with each class. The manifestations of 16 diseases were automatically extracted by pairwise operations on the word sets. The correlation between diseases and corresponding signs, symptoms and treatment was highly significant (p <0.001). Applying the difference operation on diseases with disjunct sets of clinical findings allowed the recovery of disease-specific knowledge. The evolution of a disease was accounted for, and the system was able to generalize its findings. The PROMED-LogStory concept enables the processing of natural language and may be a powerful tool for knowledge acquisition and clinical research.
The Development and Practical Use of Information Systems in the Pharmaceutical Industry
Authors: Negi H. , Tamura M. , Kawai T. , Yoda I. , Kawasaki T. , Hirata T. , Sugimura Y. , Okada S. , Yamamoto M.
Published: March 2018

To provide information on the efficacy, safety, and quality of medicine promptly and accurately, we have developed two databases: a literature database with numerical data, and a Drug Information database. By an easy-to-use, company-wide information network system, over 1,000 medical representatives in more than 80 offices throughout Japan can retrieve information. Information derived from the system is displayed on the terminal and the original documents are automatically output through a facsimile.
A Physician-Based Architecture for the Construction and Use of Statistical Models
Authors: Shachter R. D. , Lehmann H. P.
Published: March 2018

Physicians need specially tailored computer tools to take advantage of published research results. We present a knowledge-based computer framework -the physician-based (PB) architecture - for constructing such tools, and we use the problem of physicians’ interpretation of two-arm parallel randomized clinical trials (TAPRCT) as a working example. Statistical models are represented by influence diagrams. The interpretation of influence-diagram elements are mapped into users’ language in a domain-specific, physician-based user interface, called a patient-flow diagram. Statistical-model transformations that maintain the semantic relationships of the model and that embody clinical-epidemiological knowledge are encoded in a mediating structure called the cohort-state diagram. The algorithm that coordinates the interactions among the knowledge representations uses modular actions called construction steps. This architecture has been implemented in a Bayesian system, called THOMAS, that supports physician decision making in light of TAPRCT data. This support entails assessing clinical significance, prior beliefs, and methodological concerns. We suggest that the PB architecture applies to a wide range of statistical tools and users.
Application of Capture-Recapture Methods for Disease Monitoring: Potential Effects of Imperfect Record Linkage
Authors: Brenner H.
Published: March 2018

Capture-recapture methods are increasingly employed to correct for underascertainment of cases in disease monitoring. Routine systems of disease monitoring, such as morbidity registries, are often prone to specific threats of validity, such as in imperfect diagnoses or imperfect record linkage. A quantitative assessment is given of the performance of the two-source capture-recapture method for disease monitoring in the presence of imperfect record linkage. The capture-recapture approach can eliminate underestimation of the number of eligible cases, which is typical for most disease monitoring systems, under certain conditions, including independence of sources of case ascertainment and perfect record linkage. Correction for underestimation remains less than perfect, however, in the case of false-positive matches, whereas application of capture-recapture methods leads to overestimation of case numbers in the presence of false-negative matches. A simple correction procedure to overcome potential overestimation by false-negative matches is outlined.