348 research outputs found
Using openEHR Archetypes for Automated Extraction of Numerical Information from Clinical Narratives
Up to 80% of medical information is documented by unstructured data such as clinical reports written in natural language. Such data is called unstructured because the information it contains cannot be retrieved automatically as straightforward as from structured data. However, we assume that the use of this flexible kind of documentation will remain a substantial part of a patient’s medical record, so that clinical information systems have to deal appropriately with this type of information description. On the other hand, there are efforts to achieve semantic interoperability between clinical application systems through information modelling concepts like HL7 FHIR or openEHR. Considering this, we propose an approach to transform unstructured documented information into openEHR archetypes. Furthermore, we aim to support the field of clinical text mining by recognizing and publishing the connections between openEHR archetypes and heterogeneous phrasings. We have evaluated our method by extracting the values to three openEHR archetypes from unstructured documents in English and German language
Detecting automatically the layout of clinical documents to enhance the performances of downstream natural language processing
Objective:Develop and validate an algorithm for analyzing the layout of PDF
clinical documents to improve the performance of downstream natural language
processing tasks. Materials and Methods: We designed an algorithm to process
clinical PDF documents and extract only clinically relevant text. The algorithm
consists of several steps: initial text extraction using a PDF parser, followed
by classification into categories such as body text, left notes, and footers
using a Transformer deep neural network architecture, and finally an
aggregation step to compile the lines of a given label in the text. We
evaluated the technical performance of the body text extraction algorithm by
applying it to a random sample of documents that were annotated. Medical
performance was evaluated by examining the extraction of medical concepts of
interest from the text in their respective sections. Finally, we tested an
end-to-end system on a medical use case of automatic detection of acute
infection described in the hospital report. Results:Our algorithm achieved
per-line precision, recall, and F1 score of 98.4, 97.0, and 97.7, respectively,
for body line extraction. The precision, recall, and F1 score per document for
the acute infection detection algorithm were 82.54 (95CI 72.86-91.60), 85.24
(95CI 76.61-93.70), 83.87 (95CI 76, 92-90.08) with exploitation of the results
of the advanced body extraction algorithm, respectively. Conclusion:We have
developed and validated a system for extracting body text from clinical
documents in PDF format by identifying their layout. We were able to
demonstrate that this preprocessing allowed us to obtain better performances
for a common downstream task, i.e., the extraction of medical concepts in their
respective sections, thus proving the interest of this method on a clinical use
case.Comment: 22 pages, 5 figure
Clinical foundations and information architecture for the implementation of a federated health record service
Clinical care increasingly requires healthcare professionals to access patient record information that
may be distributed across multiple sites, held in a variety of paper and electronic formats, and
represented as mixtures of narrative, structured, coded and multi-media entries. A longitudinal
person-centred electronic health record (EHR) is a much-anticipated solution to this problem, but
its realisation is proving to be a long and complex journey.
This Thesis explores the history and evolution of clinical information systems, and establishes a set
of clinical and ethico-legal requirements for a generic EHR server. A federation approach (FHR) to
harmonising distributed heterogeneous electronic clinical databases is advocated as the basis for
meeting these requirements.
A set of information models and middleware services, needed to implement a Federated Health
Record server, are then described, thereby supporting access by clinical applications to a distributed
set of feeder systems holding patient record information. The overall information architecture thus
defined provides a generic means of combining such feeder system data to create a virtual
electronic health record. Active collaboration in a wide range of clinical contexts, across the whole
of Europe, has been central to the evolution of the approach taken.
A federated health record server based on this architecture has been implemented by the author
and colleagues and deployed in a live clinical environment in the Department of Cardiovascular
Medicine at the Whittington Hospital in North London. This implementation experience has fed
back into the conceptual development of the approach and has provided "proof-of-concept"
verification of its completeness and practical utility.
This research has benefited from collaboration with a wide range of healthcare sites, informatics
organisations and industry across Europe though several EU Health Telematics projects: GEHR,
Synapses, EHCR-SupA, SynEx, Medicate and 6WINIT.
The information models published here have been placed in the public domain and have
substantially contributed to two generations of CEN health informatics standards, including CEN
TC/251 ENV 13606
Front-Line Physicians' Satisfaction with Information Systems in Hospitals
Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe
COHORT IDENTIFICATION FROM FREE-TEXT CLINICAL NOTES USING SNOMED CT’S SEMANTIC RELATIONS
In this paper, a new cohort identification framework that exploits the semantic hierarchy of SNOMED CT is proposed to overcome the limitations of supervised machine learning-based approaches. Eligibility criteria descriptions and free-text clinical notes from the 2018 National NLP Clinical Challenge (n2c2) were processed to map to relevant SNOMED CT concepts and to measure semantic similarity between the eligibility criteria and patients. The eligibility of a patient was determined if the patient had a similarity score higher than a threshold cut-off value, which was established where the best F1 score could be achieved. The performance of the proposed system was evaluated for three eligibility criteria. The current framework’s macro-average F1 score across three eligibility criteria was higher than the previously reported results of the 2018 n2c2 (0.933 vs. 0.889). This study demonstrated that SNOMED CT alone can be leveraged for cohort identification tasks without referring to external textual sources for training.Doctor of Philosoph
New Fundamental Technologies in Data Mining
The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining
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