Medical-Notes-Entities-Extraction-and-Network-Generation

Using ScispaCy to extract and identify entities in medical texts and generate networks visualizations

Data Source---https://www.kaggle.com/c/medical-notes/data

NLP package used--ScispaCy
Website: https://spacy.io/universe/project/scispacy

Data format--text

Text Data Example:

B>STUDY: A trial of Passy-Muir valve was completed to allow the patient to achieve hands-free voicing and also to improve his secretion management. A clinical swallow evaluation was not completed due to the severity of the patient's mucus and lack of saliva control.

The patient's laryngeal area was palpated during a dry swallow and he does have significantly reduced laryngeal elevation and radiation fibrosis. The further evaluate of his swallowing function is safety; a modified barium swallow study needs to be concluded to objectively evaluate his swallow safety, and to rule out aspiration. A trial of neuromuscular electrical stimulation therapy was completed to determine if this therapy protocol will be beneficial and improving the patient's swallowing function and safety.

For his neuromuscular electrical stimulation therapy, the type was BMR with a single mode cycle time is 4 seconds and 12 seconds off with frequency was 60 __________ with a ramp of 2 seconds, phase duration was 220 with an output of 99 milliamps. Electrodes were placed on the suprahyoid/submandibular triangle with an upright body position, trial length was 10 minutes. On a pain scale, the patient reported no pain with the electrical stimulation therapy.

Project Consists of: 2 jupyter notebook files

1. NLP: from medical note text data, extract entities informaiton to edges and nodes dataframe and stored in csv --- ScispaCy

2. Network Visualization: build network and sub-networks to visualize the entities relationship --- NetworkX

Some network graphs: