2,524 research outputs found
An overview of decision table literature 1982-1995.
This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.
Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review
Background: Natural Language Processing (NLP) is widely used to extract
clinical insights from Electronic Health Records (EHRs). However, the lack of
annotated data, automated tools, and other challenges hinder the full
utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL)
and NLP techniques are studied and compared to understand the limitations and
opportunities in this space comprehensively.
Methodology: After screening 261 articles from 11 databases, we included 127
papers for full-text review covering seven categories of articles: 1) medical
note classification, 2) clinical entity recognition, 3) text summarisation, 4)
deep learning (DL) and transfer learning architecture, 5) information
extraction, 6) Medical language translation and 7) other NLP applications. This
study follows the Preferred Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA) guidelines.
Result and Discussion: EHR was the most commonly used data type among the
selected articles, and the datasets were primarily unstructured. Various ML and
DL methods were used, with prediction or classification being the most common
application of ML or DL. The most common use cases were: the International
Classification of Diseases, Ninth Revision (ICD-9) classification, clinical
note analysis, and named entity recognition (NER) for clinical descriptions and
research on psychiatric disorders.
Conclusion: We find that the adopted ML models were not adequately assessed.
In addition, the data imbalance problem is quite important, yet we must find
techniques to address this underlining problem. Future studies should address
key limitations in studies, primarily identifying Lupus Nephritis, Suicide
Attempts, perinatal self-harmed and ICD-9 classification
An ontology-based approach for modelling and querying Alzheimer’s disease data
Background The recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being "standardized" so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. This is the case of neurodegenerative diseases and the Alzheimer's Disease (AD) in whose context specialized data collections such as the one by the Alzheimer's Disease Neuroimaging Initiative (ADNI) are maintained.Methods Ontologies are controlled vocabularies that allow the semantics of data and their relationships in a given domain to be represented. They are often exploited to aid knowledge and data management in healthcare research. Computational Ontologies are the result of the combination of data management systems and traditional ontologies. Our approach is i) to define a computational ontology representing a logic-based formal conceptual model of the ADNI data collection and ii) to provide a means for populating the ontology with the actual data in the Alzheimer Disease Neuroimaging Initiative (ADNI). These two components make it possible to semantically query the ADNI database in order to support data extraction in a more intuitive manner.Results We developed: i) a detailed computational ontology for clinical multimodal datasets from the ADNI repository in order to simplify the access to these data; ii) a means for populating this ontology with the actual ADNI data. Such computational ontology immediately makes it possible to facilitate complex queries to the ADNI files, obtaining new diagnostic knowledge about Alzheimer's disease.Conclusions The proposed ontology will improve the access to the ADNI dataset, allowing queries to extract multivariate datasets to perform multidimensional and longitudinal statistical analyses. Moreover, the proposed ontology can be a candidate for supporting the design and implementation of new information systems for the collection and management of AD data and metadata, and for being a reference point for harmonizing or integrating data residing in different sources
Structuring Clinical Decision Support rules for drug safety using Natural Language Processing
Drug safety is an important aspect in healthcare, resulting in a number of inadvertent events, which may harm the patients. IT based Clinical Decision Support (CDS), integrated in electronic-prescription or Electronic Health Records (EHR) systems, can provide a means for checking prescriptions for errors. This requires expressing prescription guidelines in a way that can be interpreted by IT systems. The paper uses Natural Language Processing (NLP), to interpret drug guidelines by the UK NICE BNF offered in free text. The employed NLP component, MetaMap, identifies the concepts in the instructions and interprets their semantic meaning. The UMLS semantic types that correspond to these concepts are then processed, in order to understand the concepts that are needed to be implemented in software engineering for a CDS engine
GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning
Despite the remarkable progress in the development of predictive models for
healthcare, applying these algorithms on a large scale has been challenging.
Algorithms trained on a particular task, based on specific data formats
available in a set of medical records, tend to not generalize well to other
tasks or databases in which the data fields may differ. To address this
challenge, we propose General Healthcare Predictive Framework (GenHPF), which
is applicable to any EHR with minimal preprocessing for multiple prediction
tasks. GenHPF resolves heterogeneity in medical codes and schemas by converting
EHRs into a hierarchical textual representation while incorporating as many
features as possible. To evaluate the efficacy of GenHPF, we conduct multi-task
learning experiments with single-source and multi-source settings, on three
publicly available EHR datasets with different schemas for 12 clinically
meaningful prediction tasks. Our framework significantly outperforms baseline
models that utilize domain knowledge in multi-source learning, improving
average AUROC by 1.2%P in pooled learning and 2.6%P in transfer learning while
also showing comparable results when trained on a single EHR dataset.
Furthermore, we demonstrate that self-supervised pretraining using multi-source
datasets is effective when combined with GenHPF, resulting in a 0.6%P AUROC
improvement compared to models without pretraining. By eliminating the need for
preprocessing and feature engineering, we believe that this work offers a solid
framework for multi-task and multi-source learning that can be leveraged to
speed up the scaling and usage of predictive algorithms in healthcare.Comment: Accepted by IEEE Journal of Biomedical and Health Informatic
A Survey on Recognizing Textual Entailment as an NLP Evaluation
Recognizing Textual Entailment (RTE) was proposed as a unified evaluation
framework to compare semantic understanding of different NLP systems. In this
survey paper, we provide an overview of different approaches for evaluating and
understanding the reasoning capabilities of NLP systems. We then focus our
discussion on RTE by highlighting prominent RTE datasets as well as advances in
RTE dataset that focus on specific linguistic phenomena that can be used to
evaluate NLP systems on a fine-grained level. We conclude by arguing that when
evaluating NLP systems, the community should utilize newly introduced RTE
datasets that focus on specific linguistic phenomena.Comment: 1st Workshop on Evaluation and Comparison for NLP systems (Eval4NLP)
at EMNLP 2020; 18 page
Research and Development Workstation Environment: the new class of Current Research Information Systems
Against the backdrop of the development of modern technologies in the field
of scientific research the new class of Current Research Information Systems
(CRIS) and related intelligent information technologies has arisen. It was
called - Research and Development Workstation Environment (RDWE) - the
comprehensive problem-oriented information systems for scientific research and
development lifecycle support. The given paper describes design and development
fundamentals of the RDWE class systems. The RDWE class system's generalized
information model is represented in the article as a three-tuple composite web
service that include: a set of atomic web services, each of them can be
designed and developed as a microservice or a desktop application, that allows
them to be used as an independent software separately; a set of functions, the
functional filling-up of the Research and Development Workstation Environment;
a subset of atomic web services that are required to implement function of
composite web service. In accordance with the fundamental information model of
the RDWE class the system for supporting research in the field of ontology
engineering - the automated building of applied ontology in an arbitrary domain
area, scientific and technical creativity - the automated preparation of
application documents for patenting inventions in Ukraine was developed. It was
called - Personal Research Information System. A distinctive feature of such
systems is the possibility of their problematic orientation to various types of
scientific activities by combining on a variety of functional services and
adding new ones within the cloud integrated environment. The main results of
our work are focused on enhancing the effectiveness of the scientist's research
and development lifecycle in the arbitrary domain area.Comment: In English, 13 pages, 1 figure, 1 table, added references in Russian.
Published. Prepared for special issue (UkrPROG 2018 conference) of the
scientific journal "Problems of programming" (Founder: National Academy of
Sciences of Ukraine, Institute of Software Systems of NAS Ukraine
An Interoperable Clinical Cardiology Electronic Health Record System - a standards based approach for Clinical Practice and Research with Data Reuse
Currently in hospitals, several information systems manage, very often autonomously, the patient’s personal, clinical and diagnostic data. This originates a clinical information management system consisting of a myriad of independent subsystems which, although efficient in their specific purpose, make the integration of the whole system very difficult and limit the use of clinical data, especially as regards the reuse of these data for research purposes. Mainly for these reasons, the management of the Genoese ASL3 decided to commission the University of Genoa to set up a medical record system that could be easily integrated with the rest of the information system already present, but which offered solid interoperability features, and which could support the research skills of hospital health workers. My PhD work aimed to develop an electronic health record system for a cardiology ward, obtaining a prototype which is functional and usable in a hospital ward. The choice of cardiology was due to the wide availability of the staff of the cardiology department to support me in the development and in the test phase. The resulting medical record system has been designed “ab initio” to be fully integrated into the hospital information system and to exchange data with the regional health information infrastructure. In order to achieve interoperability the system is based on the Health Level Seven standards for exchanging information between medical information systems. These standards are widely deployed and allow for the exchange of information in several functional domains. Specific decision support sections for particular aspects of the clinical life were also included. The data collected by this system were the basis for examples of secondary use for the development of two models based on machine learning algorithms. The first model allows to predict mortality in patients with heart failure within 6 months from their admission, and the second is focused on the discrimination between heart failure versus chronic ischemic heart disease in the elderly population, which is the widest population section served by the cardiological ward
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