Presentation is loading. Please wait.

Presentation is loading. Please wait.

A Process Based on Paragraph for Treatment Extraction in Scientific Papers of the Biomedical Domain Juliana Duque, Pablo Matos, Cristina Ciferri, Thiago.

Similar presentations


Presentation on theme: "A Process Based on Paragraph for Treatment Extraction in Scientific Papers of the Biomedical Domain Juliana Duque, Pablo Matos, Cristina Ciferri, Thiago."— Presentation transcript:

1 A Process Based on Paragraph for Treatment Extraction in Scientific Papers of the Biomedical Domain Juliana Duque, Pablo Matos, Cristina Ciferri, Thiago Pardo and Ricardo Ciferri presented by Juliana Duque UFSCar Database Group and USP Natural Language Processing Group São Carlos, BR

2 http://gbd.dc.ufscar.br Context and Motivation A lot of electronic documents that report experiments treatment adopted patients with some kind of disease number of patients enrolled in the treatment symptoms and risk factors positive and negative effects Nowadays, researchers and doctors are not able to process this huge number of documents A Process for Treatment Extraction 2/16

3 http://gbd.dc.ufscar.br Context and Motivation These documents are in unstructured format, i.e., in plain textual form, specially in PDF It is necessary to transform these data from unstructured to structured format in order to submit it to an automatic knowledge discovery process A Process for Treatment Extraction 3/16

4 http://gbd.dc.ufscar.br Goal Identify and extract treatments Drugs, therapies and procedures Process by paragraph Empirical analysis of papers from Sickle Cell Anemia Treatments mainly occurs in sentences with complications or in sentences very near in the same paragraph Approaches for Extracting Information Machine Learning Rules Dictionary A Process for Treatment Extraction 4/16

5 http://gbd.dc.ufscar.br Contributions Theoretical: Domain Knowledge Methodology of Information Extraction Practical: Resources: collection of documents, dictionary and rules Tools: Information Extraction A Process for Treatment Extraction 5/16

6 http://gbd.dc.ufscar.br Extraction Process for Treatment A Process for Treatment Extraction 6/16 Final goal: data mining!

7 http://gbd.dc.ufscar.br Sentence Classification This result is both clinically meaningful and statistically significant. Hydroxyurea (HU) is considered to be the most successful drug therapy for severe sickle cell disease (SCD). The HU dose was given orally once a day, initially at 20 mg/kg. ML Algorithm ML Algorithm Others This result is both …… Treatment Hydroxyurea (HU) is ….. The HU dose was… A Process for Treatment Extraction 7/16

8 http://gbd.dc.ufscar.br Sentence Classification Process: training and testing phase 1/2 Bag-of-words model AVM configuration: Minimum Frequency = 2 Attribute Selection: 1, for the case the n-gram occurs in the sentence (present) 0 otherwise (absent) Attributes: 1 to 3-grams Not considered: stopwords removal and stemming Partitioning Method: 10-fold cross-validation Removed parentheses, brackets and apostrophes A Process for Treatment Extraction 8/16

9 http://gbd.dc.ufscar.br Filter pre-processing: 1) No Filter 2) Randomize 3) Remove Misclassified - remove noise 4) Resample - balancing of the classes Algorithms: Support Vector Machine and Naïve Bayes Best result: SVM - Remove Misclassified – Resample C1: 95.01% accuracy C2: 96.62% accuracy A Process for Treatment Extraction Sentence Classification Process: training and testing phase 2/2 9/16

10 http://gbd.dc.ufscar.br Results - Automatic Classification SVM Algorithm A Process for Treatment Extraction ClassifierQuant.SentencesPrecisionRecall F- measure Accuracy C1120Complication85%64%73%79% C2107Treatment88%51%64.5%71% 10/16

11 http://gbd.dc.ufscar.br Rules Sentence without POS: Fourteen patients had brain MRI and MRA evaluation after 4 years of hydroxyurea therapy. Sentence with POS: Fourteen_CD patients_NNS had_VBD brain_NN MRI_NNP and_CC MRA_NNP evaluation_NN after_IN 4_CD years_NNS of_IN hydroxyurea_NN therapy_NN._. Sentence of Treatment [\w\-]*_IN (?:[\w-/\\]* )?([\w\-]*_NN|[\w\-]*_NNP|[\w\-]*_NNS) (?:treatment_NN|therapy_NN) Rule - word representative + POS A Process for Treatment Extraction 11/16

12 http://gbd.dc.ufscar.br Rules Sentence without POS: All patients were treated with antibiotics and,on average, became afebrile after a mean of two days of hospitalization. Sentence with POS: All_DT patients_NNS were_VBD treated_VBN with_IN antibiotics_NNS and_CC,_, on_IN average_NN,_, became_VBD afebrile_JJ after_IN a_DT mean_NN of_IN two_CD days_NNS of_IN hospitalization_NN._. Sentence of Treatment (?:[\w\-]*_VBD|[\w\-]*_VBN) (?:[\w\-]*_IN )?([\w\-]*_NN| [\w\-]*_NNP|[\w\-]*_NNS) Rule – only POS A Process for Treatment Extraction 12/16

13 http://gbd.dc.ufscar.br Dictionary Biomedical Database In the MSH study, 299 adults were randomized to receive HU or placebo for a period of approximately 2 years. These results confirm the benefit of HU, even in very young children, and its possible role in primary stroke prevention. In the MSH study, 299 adults were randomized to receive HU or placebo for a period of approximately 2 years. These results confirm the benefit of HU, even in very young children, and its possible role in primary stroke prevention. Term: Hydroxyurea Variation: HU A Process for Treatment Extraction 13/16

14 http://gbd.dc.ufscar.br Conclusions Classification 79% accuracy – Classifier C1 – Complication 71% accuracy – Classifier C2 – Treatment Rules 45% precision 70% recall New experiments: 59% precision and 75% recall Dictionary 100% precision - known occurrences of treatments Variations of terms and synonyms A Process for Treatment Extraction 14/16

15 http://gbd.dc.ufscar.br Future Work Apply the proposed process to others terms in the context of Sickle Cell Anemia Investigate the identification of treatment and symptoms information in scientific papers of other diseases Using indexes to speed up the identification of terms Other biomedical areas may also benefit from our text mining approach A Process for Treatment Extraction 15/16

16 A Process Based on Paragraph for Treatment Extraction in Scientific Papers of the Biomedical Domain Questions? UFSCar Database Group and USP Natural Language Processing Group São Carlos, BR

17 http://gbd.dc.ufscar.br References Ananiadou, S.; McNaught, J. (2006) (Ed.). Text mining for biology and biomedicine. Norwood, MA: Artech House, 302 p. Cohen, K. B.; Hunter, L. (2008) Getting started in text mining. PLoS Computational Biology, v. 4, n. 1, p. 1-3. Matos, P. F. (2010) Metodologia de pré-processamento textual para extração de informação sobre efeitos de doenças em artigos científicos do domínio biomédico. 159 f. Dissertação (Mestrado em Ciência de Computação) – Departamento de Computação, Universidade Federal de São Carlos, São Carlos. Matos, P. F. et al. (2010) An environment for data analysis in biomedical domain: information extraction for decision support systems. In: García- Pedrajas, N. et al. (Eds.). IEA-AIE. 23th. Heidelberg: Springer, p. 306-316. Tsuruoka, Y.; Tsujiii, J. I. (2004) Improving the performance of dictionary- based approaches in protein name recognition. Journal of Biomedical Informatics, v. 37, n. 6, p. 461-470. A Process for Treatment Extraction 17/16

18 http://gbd.dc.ufscar.br Formula Precision: TP / (TP + FP) Recall: TP / (TP + FN) F-measure: (2 x Prec x Rec) / (Prec + Rec) Accuracy: TP + TN / (TP + TN + FN + FP) A Process for Treatment Extraction 18/16


Download ppt "A Process Based on Paragraph for Treatment Extraction in Scientific Papers of the Biomedical Domain Juliana Duque, Pablo Matos, Cristina Ciferri, Thiago."

Similar presentations


Ads by Google