EventEpi is a framework that shows how natural language processing and machine learning can help improve disease surveillance tasks (more specifically event-based surveillance).
To run the code and reproduce the results in the paper, you have to install the necessary libraries and ontologies.
- Install environment
conda env create -f environment.yml - Install external ressources
python -m nltk.downloader allpython -m spacy download en_core_web_mdpython -m epitator.importers.import_geonamespython -m epitator.importers.import_disease_ontologypython -m epitator.importers.import_wikidata
If you got the embeddings from figshare you can use them like this:
from gensim.models import KeyedVectors
embeddings = KeyedVectors.load("embeddings_300").wvIn the following I will briefly introduce the modules of this project by describing what they do and what they output.
The two main external data sources for this project are WHO's Disease Outbreak News and ProMED Mail events. The relevant texts can be downloaded using the eventepi/scraper.py. They will be saved under data/corpus/.
The scraped articles need to be preprocessed to feed them into the machine learning pipeline. The scraped data can be transformed to a corpus using eventepi/corpus_reader.py. It will create a corpus using NLTK's API where the articles are processed and serialized. The final corpus is saved under data/corpus_processed/
The eventepi/idb.py module contains the code to preprocess the incidence data base (IDB) that contains relevant outbreak events that were collected as part of event-based surveillance. It is a table that contains information on disease outbreaks and their respective key information The IDB itself is located under data/idb.csv.
eventepi/idb.py is a module that reads and cleans the IDB. This preprocessing of the IDB allows us to later run our machine learning pipeline. The final processed IDB is saved under data/idb_processed.csv.
This project contains custom word embeddings. To build them, we will combine the scraped news articles and Wikipedia articles to train a word2vec model. First, we will download the latest Wikipedia dump and extract it by running eventepi/wiki_util.py. The dump and the extracted corpus will be saved under data/wikipedia/.
Second, we will use the WHO/ProMED corpus and the Wikipedia corpus to train a word2vec model. To do this, run eventepi/embed.py. The result will be saved under data\embeddings.
This whole process takes several days and I recommend using our final embeddings from figshare.
Finally, we can feed the corpus, the IDB, and the embeddings into our machine learning pipeline.
The whole pipeline to train the relevance scoring and entity recognition of this project is run by executing python eventepi/classification.py.
EventEpi's whole functionality is wrapped into main.py. This will start downloading news articles from WHO and ProMED Mail and the most recent Wikipedia dump. Afterwards, it generates a corpus out of both sources that is used to train word embeddings which are later used to apply relevance scoring and key entity extraction on epidemiological texts.
There are also some notebooks that help you understand this repo. You can eyeball the IDB (notebooks/IDB_eyeballing.ipynb), by looking at how we plotted the data and how the corpus can be accessed using the corpus reader (notebooks/corpus_functionality.ipynb).
You can see the base line for the entity extraction task in (notebooks/disease_country_performance.ipynb) which is build on top of eventepi/summarize.py.
notebooks/cnn.ipynb contains the code to train the convolutional neural network and display the layer-wise relevance propagation.
If you have any questions, please don't hesitate to get in touch by contacting me here at GitHub!
