The SYNODOS Project: System for the Normalization and Organization of Textual Medical Data for Observation in Healthcare

Bouvry, Côme; Tvardik, Nastassia; Kergourlay, Ivan; Bittar, André; Arnod-Prin, P.; Segond, Frédérique; Dini, Luca; Darmoni, Stéfan Jacques; Metzger, M.-H.

doi:10.1016/j.irbm.2016.03.002

Cited by 4 publications

(1 citation statement)

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“…Afterward, tokenization was carried out. At this phase, continuous text, breaking down into linguistic units (tokens) such as sentences or words[23,41]. After identifying the tokens, the words divided in to three types; Unigrams (i.e.…”

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confidence: 99%

Automated classification of BI-RADS in textual mammography reports

Boroumandzadeh¹,

Parvinnia²

2021

Turk J Elec Eng & Comp Sci

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The main purpose of this paper is to process key information in medical text records and also classify patients, per different levels of Breast Imaging-Reporting and Data system (BI-RADS). The BI-RADS is a scheme for the standardization of breast imaging reports. Therefore, medical text mining is employed to classify mammography reports supported BI-RADS. In this research, a new method is proposed for automated BI-RADS classifications extraction from textual reports and improves the therapeutic procedures. At first, a mammography lexicon is employed for choosing keywords from medical text reports. Word2vec and Term Frequency Inverse Document Frequency (TFIDF) techniques are used for extracting features, finally, they're combined with the Hospital Information System (HIS) reports and called With-HIS. The different classifiers like multi-class Support Vector Machine (SVM), Naïve Bayesian (NB), Extreme Gradient Boosting (XGBoost), and Multi-Level Fuzzy Min-Max Neural Network (MLF) are used so as to compare the accuracy of With-HIS and Without HIS (called Without-HIS). The results are confirmed that using HIS beside the proposed approach (Word2vec +TFIDF) encompasses a significant effect on the accuracy of medical text classification. Accuracy within the proposed method with MLF classifier is 0.89% but without-HIS is 0.85%.

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confidence: 99%