Comparison of natural language processing algorithms for medical texts

It’s not just social media that can use NLP to its benefit. There are a wide range of additional business use cases for NLP, from customer service applications to user experience improvements . One field where NLP presents an especially big opportunity is finance, where many businesses are using it to automate manual processes and generate additional business value. Develop data science models faster, increase productivity, and deliver impactful business results.

Deep Learning has enabled us to write programs to perform things like language translation, semantic understanding and text summarization. While these technical terms may not mean much to a non-technical person, they do in fact have some real-world applications. Preprocessing text data is an important step in the process of building various NLP models — here the principle of GIGO (“garbage in, garbage out”) is true more than anywhere else. The main stages of text preprocessing include tokenization methods, normalization methods , and removal of stopwords. Often this also includes methods for extracting phrases that commonly co-occur (in NLP terminology — n-grams or collocations) and compiling a dictionary of tokens, but we distinguish them into a separate stage.

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But natural language processing algorithm also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes. This system uses carefully designed linguistic rules. This approach was used early on in the development of natural language processing, and is still used. We are in the process of writing and adding new material exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. In this article we have reviewed a number of different Natural Language Processing concepts that allow to analyze the text and to solve a number of practical tasks.

semantic analysis

This is when words are reduced to their root forms to process. The model predicts the probability of a word by its context. So, NLP-model will train by vectors of words in such a way that the probability assigned by the model to a word will be close to the probability of its matching in a given context . The Naive Bayesian Analysis is a classification algorithm that is based on the Bayesian Theorem, with the hypothesis on the feature’s independence. The stemming and lemmatization object is to convert different word forms, and sometimes derived words, into a common basic form. The results of the same algorithm for three simple sentences with the TF-IDF technique are shown below.

Extraction of n-grams and compilation of a dictionary of tokens

You often only have to type a few letters of a word, and the texting app will suggest the correct one for you. And the more you text, the more accurate it becomes, often recognizing commonly used words and names faster than you can type them. This example is useful to see how the lemmatization changes the sentence using its base form (e.g., the word “feet”” was changed to “foot”).

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Stemming and Lemmatization is Text Normalization techniques in the field of Natural Language Processing that are used to prepare text, words, and documents for further processing. Stemming and Lemmatization have been studied, and algorithms have been developed in Computer Science since the 1960s. As a branch of AI, NLP helps computers understand the human language and derive meaning from it. There are increasing breakthroughs in NLP lately, which extends to a range of other disciplines, but before jumping to use cases, how exactly do computers come to understand the language?


We extracted 65,024 specimen, 65,251 procedure, and 65,215 pathology keywords by BERT from 36,014 reports that were not used to train or test the model. After removing the duplicates, we prepared unique keyword sets. Fine-tuning for the keyword extraction of pathology reports Cross-entropy loss on the training and test sets according to the training step F1 score on the test set according to the training step. The pathology report is the fundamental evidence for the diagnosis of a patient. All kinds of specimens from all operations and biopsy procedures are examined and described in the pathology report by the pathologist. As a document that contains detailed pathological information, the pathology report is required in all clinical departments of the hospital.

  • Although there are rules to language, none are written in stone, and they are subject to change over time.
  • By feeding injury reports across all of the planets oil wells into this basic algorithm you might discover that falling debris injuries are clustered around oil wells in the Gulf of Mexico.
  • In the next sentence prediction, two sentences are given, and then the model learns to classify whether the sentences are precedent relation.
  • These tools are also great for anyone who doesn’t want to invest time coding, or in extra resources.
  • The top-down, language-first approach to natural language processing was replaced with a more statistical approach, because advancements in computing made this a more efficient way of developing NLP technology.
  • What computational principle leads these deep language models to generate brain-like activations?

The Stanford NLP Group has made available several resources and tools for major NLP problems. In particular, the Stanford CoreNLP is a broad range integrated framework that has been a standard in the field for years. It is developed in Java, but they have some Python wrappers like Stanza.

Getting the vocabulary

It has many applications across a wide range of industries. By feeding injury reports across all of the planets oil wells into this basic algorithm you might discover that falling debris injuries are clustered around oil wells in the Gulf of Mexico. You might then know about a new piece of machinery, environmental factor or something else that is causing injuries., thus allowing them to be prevented. Is in the form of text like repair manuals, injury reports and notes jotted down by technicians. However, due to its size and structure, it has largely been invisible to analytics teams.


For the specimen + pathology type, we found 38 zero similarities compared with both vocabulary sets among 9084 extracted keywords. The keywords that showed zero similarity included terms that were incorrectly extracted, terms with no relation with such vocabulary sets, and terms extracted from typos. Our model managed to extract the proper keywords from the misrepresented text. To evaluate the language processing performance of the networks, we computed their performance (top-1 accuracy on word prediction given the context) using a test dataset of 180,883 words from Dutch Wikipedia. The list of architectures and their final performance at next-word prerdiction is provided in Supplementary Table2.

How to get started with natural language processing

Text processing – define all the proximity of words that are near to some text objects. Proceedings of the EACL 2009 Workshop on the Interaction between Linguistics and Computational Linguistics. On this Wikipedia the language links are at the top of the page across from the article title. Summarizer is finally used to identify the key sentences. Sentiment Analysis is then used to identify if the article is positive, negative, or neutral. These libraries provide the algorithmic building blocks of NLP in real-world applications.

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These topics usually require understanding the words being used and their context in a conversation. As another example, a sentence can change meaning depending on which word or syllable the speaker puts stress on. NLP algorithms may miss the subtle, but important, tone changes in a person’s voice when performing speech recognition. The tone and inflection of speech may also vary between different accents, which can be challenging for an algorithm to parse. Current approaches to natural language processing are based on deep learning, a type of AI that examines and uses patterns in data to improve a program’s understanding.

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