Information extraction system that labels the following named entities given a natural language description of the dosage for a farmaceutical drug:

• quantity (DOS)
• measuring unit (UNIT)
• to whom the treatement is directed (WHO)
• frequency (FREQ)
• period (PER)

The model is based on python's CRF implementation, crfsuite. Part-ofspeech tags were obtained with nltk. For more details, read the pdf in the Report folder.

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

You will need python3 to run this application. The following modules need to be installed:

nltk

sklearn

sklearn-crfsuite

Assuming you have python3 and pip3 already installed, run the following commands to obtain the needed dependencies:

pip3 install nltk

pip3 install sklearn

pip3 install sklearn-crfsuite

To run the application, open a terminal session and issue the following commands:

git clone https://github.com/costimasca/farmaceuticalNLP.git

cd farmaceuticalNLP

chmod +x dosage.py

./model.py "The recommended oral dosage for adults is 300 mg once daily at bedtime."

You can specify any sentence using different quantities (1, two, 3 to 5, 0.5 etc.) and different measuring units (drops, ml, miligrams, tablespoons etc.) Feel free to choose one from the corpus.

In the above example, the output is the following label list: (['O', 'O', 'O', 'O', 'O', 'WHO', 'O', 'DOS', 'UNIT', 'FREQ', 'FREQ', 'O', 'O', 'O'])

Using 10-fold cross validation:

 	precision	recall	f1-measure
PER 	0.950		0.918	0.933
WHO 	0.944		0.934	0.938
UNIT 	0.983		0.956	0.969
DOS 	0.981		0.962	0.971
FREQ 	0.987		0.982	0.985
avg 	0.969		0.942	0.955