J Am Med Inform Assoc - Supervised machine learning and active learning in classification of radiology reports.

Tópicos

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Resumo

JECTIVE: This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry.MATERIALS AND METHODS: In addition to traditional supervised learning methods such as conditional random fields and support vector machines, active learning (AL) approaches were investigated to optimize training production and further improve classification performance. The project involved two pilot sites in Victoria, Australia (Lake Imaging (Ballarat) and Peter MacCallum Cancer Centre (Melbourne)) and, in collaboration with the NSW Central Registry, one pilot site at Westmead Hospital (Sydney).RESULTS: The reportability classifier performance achieved 98.25% sensitivity and 96.14% specificity on the cancer registry's held-out test set. Up to 92% of training data needed for supervised machine learning can be saved by AL.DISCUSSION: AL is a promising method for optimizing the supervised training production used in classification of radiology reports. When an AL strategy is applied during the data selection process, the cost of manual classification can be reduced significantly.CONCLUSIONS: The most important practical application of the reportability classifier is that it can dramatically reduce human effort in identifying relevant reports from the large imaging pool for further investigation of cancer. The classifier is built on a large real-world dataset and can achieve high performance in filtering relevant reports to support cancer registries.

Resumo Limpo

jectiv paper present autom system classifi result imag examin ct mri positron emiss tomographi report nonreport cancer case system part industrialstrength process pipelin built extract content radiolog report use victorian cancer registrymateri method addit tradit supervis learn method condit random field support vector machin activ learn al approach investig optim train product improv classif perform project involv two pilot site victoria australia lake imag ballarat peter maccallum cancer centr melbourn collabor nsw central registri one pilot site westmead hospit sydneyresult report classifi perform achiev sensit specif cancer registri heldout test set train data need supervis machin learn can save aldiscuss al promis method optim supervis train product use classif radiolog report al strategi appli data select process cost manual classif can reduc significantlyconclus import practic applic report classifi can dramat reduc human effort identifi relev report larg imag pool investig cancer classifi built larg realworld dataset can achiev high perform filter relev report support cancer registri

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