Perform Named Entity Recognition - NLP

Question

I am trying to learn how to perform Named Entity Recognition.

I have a set of discharge summaries containing medical information about patients. I converted my unstructured data into structured data. Now, I have a DataFrame that looks like this:

Text                        |   Target
normal coronary arteries...     R060

The Text column contains information about the diagnosis of a patient, and the Target column contains the code that will need to be predicted in a further task.

I have also constructed a dictionary that looks like this:

Code (Key) | Term (Value)
A00          Cholera

This dictionary brings information about each diagnosis and the afferent code. The term column will be used to identify the clinical entities in the corpus.

I will need to train a classifier and predict the code in order to automate the process of assigning codes for the discharge summaries (I am explaining this to have an idea about the task I'm performing).

Until now I have converted my data into a structured one. I am trying to understand how I should perform Named Entity Recognition to label the medical terminology. I would like to try direct matching and fuzzy matching but I am not sure what are the previous steps. Should I perform tokenizing, stemming, lemmatizing before? Or firstly should I find the medical terminology as clinical named entities are often multi-token terms with nested structures that include other named entities inside them? Also what packages or tools are you recommending me to use in Python?

I am new in this field so any help will be appreciated! Thanks!

Answer 1

If you are asking for building a classification model, then you should go for deep learning. Deep learning is highly efficient in classification.

While dealing with such type of language processing tasks, I recommend you to first tokenize your text and do padding. Basic tokenization should be enough, but you can go for more preprocessing like basic string processing because proper preprocessing can improve your model accuracy upto 3% or 4%. For basic string processing, you can use regex(built-in package called re) in python.

https://docs.python.org/3/library/re.html

I think, you are doing mapping after preprocessing. Mapping should be enough for tasks like classification, but I recommend you to learn about word embeddings. Word embedding will improve your model.

For all these tasks, i recommend you to use tensorflow. Tensorflow is famous tool for machine learning, language processing, image processing, and much more. You can learn natural language processing from official tensorflow documentation. They have provided all learning material in tensorflow tutorial section.

https://www.tensorflow.org/tutorials/

I think, this will help you. All the best for your work!!!!

Thank you.