A clinical decision support tool to screen health records for contraindications to stroke thrombolysis–a pilot study

Sun, Mu‐Chien; Chan, Jo-Ann

doi:10.1186/s12911-015-0229-4

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Of the research groups working on CDS in the stroke domain, some identify and display contraindications to thrombolysis at the point of care (5,6), whilst others focus on predicting outcomes in the case that thrombolysis is administered versus not administered (7)(8)(9). A recent review of machine learning methods for selecting patients who might benefit most from thrombolysis treatment is provided in (10).…”

Section: Existing Thrombolysis Cds Systems For Ehr Datamentioning

confidence: 99%

Acute stroke CDS: automatic retrieval of thrombolysis contraindications from unstructured clinical letters

Cutforth

Watson

Brown

et al. 2023

Front. Digit. Health

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IntroductionThrombolysis treatment for acute ischaemic stroke can lead to better outcomes if administered early enough. However, contraindications exist which put the patient at greater risk of a bleed (e.g. recent major surgery, anticoagulant medication). Therefore, clinicians must check a patient's past medical history before proceeding with treatment. In this work we present a machine learning approach for accurate automatic detection of this information in unstructured text documents such as discharge letters or referral letters, to support the clinician in making a decision about whether to administer thrombolysis.MethodsWe consulted local and national guidelines for thrombolysis eligibility, identifying 86 entities which are relevant to the thrombolysis decision. A total of 8,067 documents from 2,912 patients were manually annotated with these entities by medical students and clinicians. Using this data, we trained and validated several transformer-based named entity recognition (NER) models, focusing on transformer models which have been pre-trained on a biomedical corpus as these have shown most promise in the biomedical NER literature.ResultsOur best model was a PubMedBERT-based approach, which obtained a lenient micro/macro F1 score of 0.829/0.723. Ensembling 5 variants of this model gave a significant boost to precision, obtaining micro/macro F1 of 0.846/0.734 which approaches the human annotator performance of 0.847/0.839. We further propose numeric definitions for the concepts of name regularity (similarity of all spans which refer to an entity) and context regularity (similarity of all context surrounding mentions of an entity), using these to analyse the types of errors made by the system and finding that the name regularity of an entity is a stronger predictor of model performance than raw training set frequency.DiscussionOverall, this work shows the potential of machine learning to provide clinical decision support (CDS) for the time-critical decision of thrombolysis administration in ischaemic stroke by quickly surfacing relevant information, leading to prompt treatment and hence to better patient outcomes.

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Section: Existing Thrombolysis Cds Systems For Ehr Datamentioning

confidence: 99%

Acute stroke CDS: automatic retrieval of thrombolysis contraindications from unstructured clinical letters

Cutforth

Watson

Brown

et al. 2023

Front. Digit. Health

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“…Academic research in Taiwan has contributed to CDSS development including AI-based CDSSs to screen for contraindications [34], predict medical conditions [35,36], recommend medications [37] and support diagnosis [15].…”

Section: Advancing Healthcare Withmentioning

confidence: 99%

Realizing the promise of big data: how Taiwan can help the world reduce medical errors and advance precision medicine

Wang

2022

ACI

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PurposeThe study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.Design/methodology/approachThis study is a narrative review of the literature.FindingsThe body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.Originality/valueWhile research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

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Integrated System for Clinical Decision Support in Emergency Stroke Care

Przelaskowski

Sobieszczuk

Jóźwiak

et al. 2016

Advances in Intelligent Systems and Computing

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A clinical decision support tool to screen health records for contraindications to stroke thrombolysis–a pilot study

Cited by 4 publications

References 14 publications

Acute stroke CDS: automatic retrieval of thrombolysis contraindications from unstructured clinical letters

Acute stroke CDS: automatic retrieval of thrombolysis contraindications from unstructured clinical letters

Realizing the promise of big data: how Taiwan can help the world reduce medical errors and advance precision medicine

Integrated System for Clinical Decision Support in Emergency Stroke Care

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