Automated Medical Recommendation System using Machine Learning Techniques & Natural Language Processing

Abstract

The suggested work in this paper relates to the automation in the medical field in terms of how the diagnosis of irregularities has been going on in the industry and what the future in the said field looks like with the advancing technology. With the advent of application of machine learning approaches, we have observed a significant rise in the performance of detection and prediction with each passing year. With the current norms being that a patient is to perform the necessary tests and wait for a significant period of time to gain a consultation with an esteemed doctor. The methodologies used by other authors from the research papers that we gathered were either using image corpus or using machine learning techniques to detect and identify cancer, while others have used Natural Language Processing techniques to extract information from handwritten or printed texts and have developed models based on it. We have addressed the problem with a unified approach that combines both machine learning and Natural Language Processing techniques in tandem to extract data and correlations which may have been ignored by the other models. The image corpus and handwritten notes would together give better insights and help improve the accuracy of the model. Bearing in mind the objective of relieving novice doctors from feeling perplexed, in addition to answering questions that patients might have, our model aims to allow such medical professionals make better informed decisions in their diagnosis and prognosis procedures. The chatbot, to serve this purpose, will be based on Bidirectional Encoder Representations from Transformers (BERT), which has demonstrated ground-breaking performance in tasks involving natural language understanding, including sentiment analysis, semantic role labelling, sentence classification, and the disambiguation of polysemous words. We aim to not only successfully diagnose and provide prognosis for cancer, but also to detect the chances of remission. Our systematic approach for Auto Detection of critical issues from reports is a cost effective and hybrid approach that combines object oriented modelling and image analysis with entity extraction from medical reports through natural language requirements.

Ähnliche Arbeiten