MEDICAL REPORT USING NER & OCR WITH EASYOCR
Aug 9, 2022
Healthcare organizations around the country are turning to optical character recognition software to Become Paperless and improve patient care. Claims Capture is an intelligent, accurate and highly scalable data capture and document processing solution that drastically reduces an organization’s commitment to paper-based processes and the errors associated with manual data entry
Medical records are important resources in which patients’ diagnosis and treatment activities in hospitals are documented. In recent years, many medical institutions have done significant work in archiving electronic medical records. Handwritten medical records are gradually being replaced by digital ones. Many researchers strive for extracting medical knowledge from digital data, using medical knowledge to help medical professionals understand potential causes of various symptoms, and building medical decision support systems.
Medical named entity recognition (NER) is an important technique that has recently received attention in medical communities in extracting named entities from medical texts, such as diseases, drugs, surgery reports, anatomical parts, and examination documents.
In this article we gonna describe the manner to extract text from images files related to covid-19 and recognize three entities (PATHOGEN,MEDICAL CONDITION ,MEDICINE ) from this unstructured text using fine-tuning with spacy transformers,to generate finally a summary including all this informations about this disease.
Named Entity Recognition :
Named Entity Recognition is a common problem in NLP dealing with identifying and classifying named entities.
A named entity is a real life object which has an identification and can be defined by a name. A place, person, countries or organizations can be a named entity. For example, Microsoft is an organization and Asia is a geographic entity.
A raw or instructed data is processed and by using the help of named entity recognition, one can label and classify the data as different entities. A NER system is developed with the help of linguistic approaches and statical methods.
A NER model begins with identifying an entity and categorizes into the most suitable class.
Named Entity Recognition with spaCy :
SpaCy is an open source Natural processing library with fast statistical entity recognition system. The methods that are available in SpaCy for NER assigns a label to the text data and classifies the same as defined above.
Spacy also provides us an option to add arbitrary classes to entity recognition systems and update the model to include new examples. We can train our own data for business-specific needs and prepare the model as necessary.
Spacy Transformers :
Transformers are a particular architecture for deep learning models that revolutionized natural language processing. The defining characteristic for a Transformer is the self-attention mechanism. Using it, each word learns how related it is to the other words in a sequence.
Transformers are a family of neural network architectures that compute dense, context-sensitive representations for the tokens in your documents. Downstream models in your pipeline can then use these representations as input features to improve their predictions. You can connect multiple components to a single transformer model, with any or all of those components giving feedback to the transformer to fine-tune it to your tasks.
spaCy’s transformer support interoperates with PyTorch and the HuggingFace transformers library, giving you access to thousands of pretrained models for your pipelines. There are many great guides to transformer models, but for practical purposes, you can simply think of them as drop-in replacements that let you achieve higher accuracy in exchange for higher training and runtime costs.
Optical Character Recognition (OCR) :
Optical character recognition (OCR) is referred to as text recognition. An OCR program extracts and repurposes data from scanned documents, camera images and image-only pdfs. OCR software singles out letters on the image, puts them into words and then puts the words into sentences, thus enabling access to and editing of the original content. It also eliminates the need for manual data entry.
EasyOCR, is a Python package that allows computer vision developers to effortlessly perform Optical Character Recognition.