Method for Designing Semantic Annotation of Sepsis Signs in Clinical Text

Annotated clinical text corpora are essential for machine learning studies that model and predict care processes and disease progression. However, few studies describe the necessary experimental design of the annotation guideline and annotation phases. This makes replication, reuse, and adoption challenging. Using clinical questions about sepsis, we designed a semantic annotation guideline to capture sepsis signs from clinical text. The clinical questions aid guideline design, application, and evaluation. Our method incrementally evaluates each change in the guideline by testing the resulting annotated corpus using clinical questions. Additionally, our method uses inter-annotator agreement to judge the annotator compliance and quality of the guideline. We show that the method, combined with controlled design increments, is simple and allows the development and measurable improvement of a purpose-built semantic annotation guideline. We believe that our approach is useful for incremental design of semantic annotation guidelines in general

Terminology and ontology development for semantic annotation : A use case on sepsis and adverse events

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Intensive care nurses’ experiences with organ donors at non-donor hospitals

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Predicting in-hospital death from derived EHR trajectory features

Medical histories of patients can provide insight into the immediate future of a patient. While most studies propose to predict survival from vital signs and hospital tests within one episode of care, we carry out selective feature engineering from longitudinal historical medical records in this study to develop a dataset with derived features. We then train multiple machine learning models for the binary prediction whether an episode of care will culminate in death among patients suspected of bloodstream infections. The machine learning classifier performance is evaluated and compared and the feature importance impacting the model output is explored. The findings indicated that the logistic regression model achieved the best performance for predicting death in the next hospital episode with an accuracy of 98% and an almost perfect area under the receiver operating characteristic curve. Exploring the feature importance reveals that time to and severity of the last episode and previous history of sepsis episodes were the most critical features

The bidirectional association between depressive symptoms, assessed by the HADS, and albuminuria-A longitudinal population-based cohort study with repeated measures from the HUNT2 and HUNT3 Study

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Direct and indirect effects of socioeconomic status on sepsis risk and mortality : a mediation analysis of the HUNT Study

Author's accepted version (postprint).This is an Accepted Manuscript of an article published by BMJ in Journal of Epidemiology and Community Health on 9/2/2023.Available online: doi.org/10.1136/jech-2022-219825acceptedVersio

Insomnia symptoms and risk of bloodstream infections : prospective data from the prospective population-based Nord-Trøndelag Health Study (HUNT), Norway

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Anxiety and depression symptoms, albuminuria and risk of acute myocardial infarction in the Norwegian HUNT cohort study

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Trends in mortality after a sepsis hospitalization : a nationwide prospective registry study from 2008 to 2021

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