Enhancing Patient Safety through Human-Computer Information Retrieval on the Example of German-Speaking Surgical Reports

Stocker, Christof; Marzi, Leopold-Michael; Matula, Christian; Schantl, Johannes; Prohaska, Gottfried; Brabenetz, Aberto; Holzinger, Andreas

doi:10.1109/dexa.2014.53

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…This approach is operated by systems strategies and new technological developments for disease diagnostics, therapeutics and prevention, coupled for digitalization of the health sector [180]. Mobile Medical Doctors Assistants: Mobile Medical Doctors Assistants is a project that allows interaction between medical professionals and biomedical data [181,182].…”

Section: Application Layermentioning

confidence: 99%

Analysis of factors affecting IoT-based smart hospital design

2020

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Currently, rapidly developing digital technological innovations affect and change the integrated information management processes of all sectors. The high efficiency of these innovations has inevitably pushed the health sector into a digital transformation process to optimize the technologies and methodologies used to optimize healthcare management systems. In this transformation, the Internet of Things (IoT) technology plays an important role, which enables many devices to connect and work together. IoT allows systems to work together using sensors, connection methods, internet protocols, databases, cloud computing, and analytic as infrastructure. In this respect, it is necessary to establish the necessary technical infrastructure and a suitable environment for the development of smart hospitals. This study points out the optimization factors, challenges, available technologies, and opportunities, as well as the system architecture that come about by employing IoT technology in smart hospital environments. In order to do that, the required technical infrastructure is divided into five layers and the system infrastructure, constraints, and methods needed in each layer are specified, which also includes the smart hospital’s dimensions and extent of intelligent computing and real-time big data analytic. As a result of the study, the deficiencies that may arise in each layer for the smart hospital design model and the factors that should be taken into account to eliminate them are explained. It is expected to provide a road map to managers, system developers, and researchers interested in optimization of the design of the smart hospital system.

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Section: Application Layermentioning

confidence: 99%

Analysis of factors affecting IoT-based smart hospital design

2020

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“…Clinical text classification techniques have been employed on different types of clinical records such as surgical reports (Stocker et al, 2014;Raja et al, 2012), radiology reports (Mendona et al, 2005), autopsy reports (Mujtaba et al, 2018), death certificates (Koopman et al, 2015), clini-cal narratives (Meystre and Haug, 2006;Friedlin and McDonald, 2008), progress notes (Frost et al, 2005), laboratory reports (Friedlin and McDonald, 2008;Liu et al, 2012), admission notes and patient summaries (Jensen et al, 2012), pathology reports (Imler et al, 2013), and unstructured electronic text (Portet et al, 2009). In this research, we aim to primarily use clinical discharge summaries as the input text data.…”

Section: Types Of Clinical Recordmentioning

confidence: 99%

Distributed Knowledge Based Clinical Auto-Coding System

Kaur

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) methods and techniques to resolve the problem of manual coding of clinical narratives. Most of the studies are focused on classification systems relevant to the U.S and there is a scarcity of studies relevant to Australian classification systems such as ICD-10-AM and ACHI. Therefore, we aim to develop a knowledge-based clinical auto-coding system, that utilise appropriate NLP and ML techniques to assign ICD-10-AM and ACHI codes to clinical records, while adhering to both local coding standards (Australian Coding Standard) and international guidelines that get updated and validated continuously.

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“…SESARF focuses on the unstructured part of electronic health records (EHRs) because they may contain hidden knowledge that is essential for predicting a VTE diagnosis. These unstructured data contain surgical reports (Ip et al, 2015;Stocker et al, 2014), narrative texts (Hripcsak, Bakken, Stetson, & Patel, 2003), and laboratory reports (Wagholikar et al, 2012) in addition to narrative notes and report texts (Hripcsak et al, 2003;Roberts, Gaizauskas, & Hepple, 2008;Matheny et al, 2012).…”

mentioning

confidence: 99%

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

et al. 2019

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Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence‐based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment‐based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component‐based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.

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Enhancing Patient Safety through Human-Computer Information Retrieval on the Example of German-Speaking Surgical Reports

Cited by 7 publications

References 21 publications

Analysis of factors affecting IoT-based smart hospital design

Analysis of factors affecting IoT-based smart hospital design

Distributed Knowledge Based Clinical Auto-Coding System

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

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