Disease Inference using LSTM and Sparse Neural Network – Health Data

Despite the fact that high-quality solutions for solving biomedical problems utilizing artificial intelligence and deep learning algorithms are available, there is no precise way to reach end-users. Bots can deliver health-care solutions, but they can’t be totally trusted unless users provide an accurate description of a medical condition. The difficulty of terminology is rooted here in the fact that not all users have the medical knowledge to provide an accurate description of their ailment, and expecting the same is also nonsensical, thus this issue is critical. Strong NLU (natural language understanding) is required, which can’t be accomplished with basic RNNs. Solving graph-based problems is an art form. For resolving such a challenge, NLP has spawned a new avenue. This paper considers the words in the dataset as vertices and use dense subgraphs to uniquely identify the disease using medical dictionary data. But identifying the the dataset containing medical descriptions and language used by common users has been the tough part. Fortunately, health care forums all across the world had the exactly required information, along with valuable solutions/answers offered by authorized medical specialists. With the data publicly available, the only thing left to do was develop our language model, which will translate community data into valid medical data, which was made possible by the extraction of medical signatures from Snomed via global data mining. This is the first study to combine recurrent language modeling with graph-based language modeling.