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Research Topics in Deep Learning for Disease Prediction

Research Topics in Deep Learning for Disease Prediction

   The disease prediction at an earlier stage becomes a vital task, but the accurate prediction based on symptoms becomes too difficult. To overcome this problem, deep learning plays an important role in predicting the disease. Disease prediction aims to predict the risk probability of a person from the disease in the future. Prediction is based on the medical information from basic patient information, electronic health record, electronic medical record, medical image, and medical instrument data. Deep learning uses the medical data to train and test the model with a specific deep learning algorithm based on diseases.

   Deep learning can be a potent tool to identify patterns of certain conditions that develop in our body, a lot quicker than clinician medical imaging. Artificial neural Networks include Convolutional Neural networks, Recurrent Neural networks, Autoencoders are the deep learning algorithms used for disease prediction. Some of the disease classification and prediction applications of deep learning are pneumonia, cardiovascular disease, Alzheimer disease, cancer, diabetes mellitus, hepatitis, infectious disease, and kidney disease. Future advancements of disease prediction in deep learning are deep learning drives precision medicine, chronic disease prediction, biological network analysis, among others.

   • Among the many methods of studying complex data, deep learning is a deep structured learning or hierarchical learning that comprises multiple processing layers which automatically learn multiple levels of abstract representations of data for disease prediction and classification.

   • Numerous advances in the medical field and the rise of a new generation of computers will promote the emergence of new disease prediction methods. Deep learning methods such as CNN, RNN, and LSTM assist doctors process medical information more comprehensively.

   • Due to its unique feature processing method, deep learning methods process the high compatible medical data based on the patient information, electronic health record, electronic medical record, medical image, and medical instrument data.

   • In recent years, deep learning applications in disease prediction have attained more people’s attention and achieved many impressive results.

   • While applying the deep learning method to the real disease prediction and healthcare system, it is necessary to solve the basic credibility problem and make the model convincing.

   • In addition, a high degree of stability is necessary to apply deep learning to clinical applications.

   • Deep Learning Drives Precision Medicine (PM) Precision medicine is extremely significant in future research, which provides patients with more effective and timely medical services.