J Biomed Inform - Automatic detection of patients with invasive fungal disease from free-text computed tomography (CT) scans.

Tópicos

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Resumo

CKGROUND: Invasive fungal diseases (IFDs) are associated with considerable health and economic costs. Surveillance of the more diagnostically challenging invasive fungal diseases, specifically of the sino-pulmonary system, is not feasible for many hospitals because case finding is a costly and labour intensive exercise. We developed text classifiers for detecting such IFDs from free-text radiology (CT) reports, using machine-learning techniques.METHOD: We obtained free-text reports of CT scans performed over a specific hospitalisation period (2003-2011), for 264 IFD and 289 control patients from three tertiary hospitals. We analysed IFD evidence at patient, report, and sentence levels. Three infectious disease experts annotated the reports of 73 IFD-positive patients for language suggestive of IFD at sentence level, and graded the sentences as to whether they suggested or excluded the presence of IFD. Reliable agreement between annotators was obtained and this was used as training data for our classifiers. We tested a variety of Machine Learning (ML), rule based, and hybrid systems, with feature types including bags of words, bags of phrases, and bags of concepts, as well as report-level structured features. Evaluation was carried out over a robust framework with separate Development and Held-Out datasets.RESULTS: The best systems (using Support Vector Machines) achieved very high recall at report- and patient-levels over unseen data: 95% and 100% respectively. Precision at report-level over held-out data was 71%; however, most of the associated false-positive reports (53%) belonged to patients who had a previous positive report appropriately flagged by the classifier, reducing negative impact in practice.CONCLUSIONS: Our machine learning application holds the potential for developing systematic IFD surveillance systems for hospital populations.

Resumo Limpo

ckground invas fungal diseas ifd associ consider health econom cost surveil diagnost challeng invas fungal diseas specif sinopulmonari system feasibl mani hospit case find cost labour intens exercis develop text classifi detect ifd freetext radiolog ct report use machinelearn techniquesmethod obtain freetext report ct scan perform specif hospitalis period ifd control patient three tertiari hospit analys ifd evid patient report sentenc level three infecti diseas expert annot report ifdposit patient languag suggest ifd sentenc level grade sentenc whether suggest exclud presenc ifd reliabl agreement annot obtain use train data classifi test varieti machin learn ml rule base hybrid system featur type includ bag word bag phrase bag concept well reportlevel structur featur evalu carri robust framework separ develop heldout datasetsresult best system use support vector machin achiev high recal report patientlevel unseen data respect precis reportlevel heldout data howev associ falseposit report belong patient previous posit report appropri flag classifi reduc negat impact practiceconclus machin learn applic hold potenti develop systemat ifd surveil system hospit popul

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