cnlp-transformers 0.1.0

Transformers for Clinical NLP

Clinical NLP Transformers (cnlp_transformers)

Transformers for Clinical NLP

This library was created to add abstractions on top of the Huggingface Transformers library for many clinical NLP research use cases. Primary use cases include 1) simplifying multiple tasks related to fine-tuning of transformers for building models for clinical NLP, and 2) creating inference APIs that will allow downstream researchers easier access to clinical NLP outputs.

Fine-tuning

The main entry point for fine-tuning is the train_system.py script. Run with no arguments to show an extensive list of options that are allowed, inheriting from and extending the Huggingface training options.

Workflow

To use the library for fine-tuning, you'll need to take the following steps:

1. Write your dataset to a convenient format in a folder with train, dev, and test files.
2. Create a new entry for your dataset in cnlp_processors.py in the following places:
   1. Create a unique task_name for your task.
   2. cnlp_output_modes -- Add a mapping from a task name to a task type. Currently supported task types are sentence classification, tagging, relation extraction, and multi-task sentence classification.
   3. Processor class -- Create a subclass of DataProcessor for your data source. There are multiple examples to base off of, including intermediate abstractions like LabeledSentenceProcessor, RelationProcessor, SequenceProcessor, that simplify the implementation.
   4. cnlp_processors -- Add a mapping from your task name to the "processor" class you created in the last step.
3. Run train_system.py with the --task_name argument from Step 2.1 and the --data-dir argument from Step 1.

End-to-end example

transform_uci_drug.py to preprocess the data from Drug Review Dataset (Drugs.com) Data Set

Running existing APIs

There are existing APIs in the api folder for a few important clinical NLP tasks:

1. Negation detection
2. Time expression tagging (spans + time classes)
3. Event detection (spans + document creation time relation)
4. End-to-end temporal relation extraction (event spans+DTR+timex spans+time classes+narrative container [CONTAINS] relation extraction)

Negation API

To demo the negation API:

1. Install the cnlp-transformers package.
2. Run cnlpt_negation_rest [-p PORT].
3. Open a python console and run the following commands:

## Setup variables
>>> import requests
>>> init_url = 'http://hostname:8000/negation/initialize'  ## Replace hostname with your host name
>>> process_url = 'http://hostname:8000/negation/process'  ## Replace hostname with your host name

## Load the model
>>> r = requests.post(init_url)
>>> r.status_code
# should return 200

## Prepare the document
>>> sent = 'The patient has a sore knee and headache but denies nausea and has no anosmia.'
>>> ents = [[18, 27], [32, 40], [52, 58], [70, 77]]
>>> doc = {'doc_text':sent, 'entities':ents}

## Process the document
>>> r = requests.post(process_url, json=doc)
>>> r.json()
# Output: {'statuses': [-1, -1, -1, 1]}
# The model thinks only one of the entities is negated (anosmia). It missed "nausea" for some reason.

Temporal API (End-to-end temporal information extraction)

To demo the temporal API:

1. Install the cnlp-transformers package.
2. Run cnlpt_temporal_rest [-p PORT]
3. Open a python console and run the following commands to test:

## Setup variables
>>> import requests
>>> from pprint import pprint
>>> init_url = 'http://hostname:8000/temporal/initialize'  ## Replace hostname with your host name
>>> process_url = 'http://hostname:8000/temporal/process_sentence'  ## Replace hostname with your host name

## Load the model
>>> r = requests.post(init_url)
>>> r.status_code
# should return 200

## Prepare and process the document
>>> sent = 'The patient was diagnosed with adenocarcinoma March 3, 2010 and will be returning for chemotherapy next week.'
>>> r = requests.post(process_url, json={'sentence':sent})
>>> pprint(r.json())

# should return:
{'events': [[{'begin': 3, 'dtr': 'BEFORE', 'end': 3},
             {'begin': 5, 'dtr': 'BEFORE', 'end': 5},
             {'begin': 13, 'dtr': 'AFTER', 'end': 13},
             {'begin': 15, 'dtr': 'AFTER', 'end': 15}]],
 'relations': [[{'arg1': 'TIMEX-0', 'arg2': 'EVENT-0', 'category': 'CONTAINS'},
                {'arg1': 'EVENT-2', 'arg2': 'EVENT-3', 'category': 'CONTAINS'},
                {'arg1': 'TIMEX-1', 'arg2': 'EVENT-2', 'category': 'CONTAINS'},
                {'arg1': 'TIMEX-1',
                 'arg2': 'EVENT-3',
                 'category': 'CONTAINS'}]],
 'timexes': [[{'begin': 6, 'end': 9, 'timeClass': 'DATE'},
              {'begin': 16, 'end': 17, 'timeClass': 'DATE'}]]}

# This output indicates the token spans of events and timexes, and relations between events and timexes, where the suffixes are indices into the respective arrays (e.g., TIMEX-0 in a relation refers to the 0th time expression found, which begins at token 6 and ends at token 9 -- ["March 3, 2010"])

To run only the time expression or event taggers, change the run command to:

uvicorn api.timex_rest:app --host 0.0.0.0 or

uvicorn api.event_rest:app --host 0.0.0.0

then run the same init and process commands as above. You will get similar json output, but only one of the dictionary elements (timexes or events) will be populated.