sympscraper is a Java command line program that allows you to scrape and extract symptom entities from medical web pages.
sympscraper accepts urls with its -u flag. Multiple urls can be processed if a file containing them is passed using the -i flag.
$ java -jar sympscraper-0.0.1.jar -u http://www.nhs.uk/conditions/sleep-paralysis/Pages/Introduction.aspx
condition: sleep paralysis
url: http://www.nhs.uk/conditions/sleep-paralysis/Pages/Introduction.aspx
symptoms:
[1] paralysis
[2] hallucination
[3] frightened
$ java -jar sympscraper-0.0.1.jar -i my_urls.txt
...
- Download this repository to a local machine.
- cd into
symscraper/ - run
mvn package - cd into 'target/'
- Run
$ java -jar sympscraper-0.0.1-jar-with-dependencies.jar -u http://www.nhs.uk/Conditions/Glue-ear/Pages/Symptoms.aspx.1 - Download this repository to a local machine.
sympscraper can be thought of as a scraping framework and information extraction skeleton framework.
scrape -> clean -> extract
Its three main components are scrapers which scrape content from web pages into some intermediate form, cleaners which clean the output of the scrapers and extractors which extract information.
All three components are decoupled to allow for great extensibility. We describe each in turn:
scrape
Scrapers all extend the Scraper interface which defines a scrape(url) method. Each scraper belongs to a 'category' or 'type' of scrapers defined in the ScraperType enum. The category of a scraper defines which URLs it can be used for.
The matchScraperType(url) method in ScraperUtil() returns the correct category of scrapers.
All scrapers are initialised by the ScraperFactory which defines a method make(scraperType) which returns the correct scraper given the type.
The rationale behind this is that (a) one might want to develop different scrapers for different domains (e.g. we implement a NhsSimpleScraper which scrapes NHS web pages), and - it follows - (b) there should be a smart way of matching the correct scraper to a url.
clean
Cleaner methods are currently implemented as static methods in CleanerUtil. For example simpleCleaner() removes anything other than alphanumeric characters.
extract
Extractors all extend the Extractor interface which defines a extract() method. We defined a SimpleSymptomExtractor which matches text scraped a cleaned from a web page to a list of known symptoms (found via wikipedia).
The problem with symptom entities is that:
- they can be multi-word
- they have many synonyms
- they are tied to the underlying condition to a varying degree
A more principled solution would deduplicate symptoms using something akin to WordNet synsets.
A way of training a model to detect symptoms in text might be to use a list of symptoms to highlight symptoms in as much text as possible and thus 'seed' a training set with with a model could be trained to extract symptoms in a way similar to named entity recognition.
Existing Stanford Core NLP NER tags do not capture medical symptoms. Nor is there any discernible parse pattern that could be used to extract symptoms.