Identify ICD code from doctor-patient conversation using AI/ML models
- Develop Natural language processing (NLP) and Machine learning-based model to
predict ICD codes from the medical records obtained in the form of pdf, doc, Docx,
images (png, jpeg, tiff), audio (doctor-patient conversation in .wav format
- Converted sample Doctor Notes, nurse notes, Laboratory reports of different formats
(pdf, docs, png, .wav) into text.
- Converted audio (speech recognition) to text and generated medical report in doc
format based on the entities extracted.
- Text is extracted from images using Optical Character Recognition (OCR)
- Extracted all entities from text using Named-Entity Recognition (NER) and
- The medical description is being generated based on the entities and conditions
- Predicted ICD codes by measuring the similarity between the medical description
generated and the description against each ICD code. This is achieved by using
cosine similarity and the code with maximum distance score is predicted.
- Developed a web application where one can upload the medical report in different
formats (audio/image/document) and the corresponding ICD code will be generated and