Problem Importance
Conversations on social media are known to be casual, informal and open across several topics including medical related topics. Even though social media are good sources of information, its informal nature and the use of pseudonyms have made information extraction difficult for medical related applications.
This problem is faced by everyone building a solution that requires medical(clinical) data from social media.
Solution Description
This solution extracts medical information(entity) such as symptoms, diseases, drugs, organisms and others medical related entities from social media text which can be used for NLP applications such as information extraction, summarization, and data mining.
Solution output description
The output of the solution is a highlight/ list of all health-related words and the class of information(entity) present in a given text.
The solution was trained to identify 14 entities namely:
PERSON (Any Human)
SYMPTOM (Symptom of any disease)
MEDICAL FIELD (Medical speciality)
DRUG (Medicinal product)
FOOD (Edible and source of Nutrients)
DOSAGE (Dosage of Medication)
BODY PART(Part of the body)
PLACE (Location, Town, City)
MEDICAL PROCEDURE (Medical Procedure and processes)
DISEASE (Illnesses)
ORGANISM (Causative organism or disease vector)
INJURY (Breakage in skin continuity)
PHYSIOLOGIC PROCESS (Biological Processes)
ADVERSE REACTION (Unintended consequences of medication or food)
Solution Usage
The intended use of the solution
The intended use is extract medical information from social media text.
The key procedures followed while using the solution
A user feeds the model with a text and the text is returned on the screen with all the medical related entities highlighted with the class entity (such as Person, Symptom, Drug etc.) on the screen.
Steps to reproduce the solution
To reproduce the solution:
To reproduce the solution:
- Download all files from here
- Run all the cells in the notebook at /Nairaland_NER.ipynb
To reproduce the solution:
- Download all files from here
- Run all the cells in the notebook at /Nairaland_NER.ipynb