Methods Inf Med - Automated classification of free-text pathology reports for registration of incident cases of cancer.

Tópicos

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Resumo

JECTIVE: Our study aimed to construct and evaluate functions called "classifiers", produced by supervised machine learning techniques, in order to categorize automatically pathology reports using solely their content.METHODS: Patients from the Poitou-Charentes Cancer Registry having at least one pathology report and a single non-metastatic invasive neoplasm were included. A descriptor weighting function accounting for the distribution of terms among targeted classes was developed and compared to classic methods based on inverse document frequencies. The classification was performed with support vector machine (SVM) and Naive Bayes classifiers. Two levels of granularity were tested for both the topographical and the morphological axes of the ICD-O3 code. The ability to correctly attribute a precise ICD-O3 code and the ability to attribute the broad category defined by the International Agency for Research on Cancer (IARC) for the multiple primary cancer registration rules were evaluated using F1-measures.RESULTS: 5121 pathology reports produced by 35 pathologists were selected. The best performance was achieved by our class-weighted descriptor, associated with a SVM classifier. Using this method, the pathology reports were properly classified in the IARC categories with F1-measures of 0.967 for both topography and morphology. The ICD-O3 code attribution had lower performance with a 0.715 F1-measure for topography and 0.854 for morphology.CONCLUSION: These results suggest that free-text pathology reports could be useful as a data source for automated systems in order to identify and notify new cases of cancer. Future work is needed to evaluate the improvement in performance obtained from the use of natural language processing, including the case of multiple tumor description and possible incorporation of other medical documents such as surgical reports.

Resumo Limpo

jectiv studi aim construct evalu function call classifi produc supervis machin learn techniqu order categor automat patholog report use sole contentmethod patient poitoucharent cancer registri least one patholog report singl nonmetastat invas neoplasm includ descriptor weight function account distribut term among target class develop compar classic method base invers document frequenc classif perform support vector machin svm naiv bay classifi two level granular test topograph morpholog axe icdo code abil correct attribut precis icdo code abil attribut broad categori defin intern agenc research cancer iarc multipl primari cancer registr rule evalu use fmeasuresresult patholog report produc pathologist select best perform achiev classweight descriptor associ svm classifi use method patholog report proper classifi iarc categori fmeasur topographi morpholog icdo code attribut lower perform fmeasur topographi morphologyconclus result suggest freetext patholog report use data sourc autom system order identifi notifi new case cancer futur work need evalu improv perform obtain use natur languag process includ case multipl tumor descript possibl incorpor medic document surgic report

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