Long Short-Term Memory (LSTM) Networks

When dealing with textual data, such as patient symptoms, a specific type of deep learning architecture called a Long Short-Term Memory (LSTM) network is often used. LSTM networks are well-suited for tasks involving sequences of data, as they can learn long-term dependencies between elements in the sequence.

For example, consider a patient describing their symptoms as ” I’ve experienced a loss of appetite and don’t enjoy food anymore, followed by fatigue and muscle weakness.” An LSTM network can understand the importance of the order of these symptoms (“loss of appetite” followed by “fatigue and muscle weakness”) to make an accurate diagnosis.

Imagine getting the disease predictions and medication prescriptions in seconds with the help of artificial intelligence (AI). This dream can become a reality with the power of deep learning. Deep learning is a type of artificial intelligence that can learn on its own by analyzing vast amounts of data.

In the field of medicine, Deep Learning can be used to train models to predict diseases and recommend medications based on a patient’s symptoms.

This article will explore how Deep Learning can be used to build a medical diagnosis model.

Deep Learning has already shown remarkable success in many industries by helping us to automate the processes. Now let’s try to use this technology in the field of medicine. We will build a deep learning model that will be trained on Patient’s Problems which will be textual data, then our model will give the predicted Disease and will recommend Medicine to treat the patient’s problem as an output.

This is clearly an application of Recurrent Neural Network (RNN). This is because we need a model that will store the information from the previous text and use it later to predict the output. Hence, we will use the Long Short-Term Memory (LSTM) algorithm with Tensorflow to train our model.