The current research is more inclined towards the preference of deep learning models. In this context, I want to know about the various Deep nets which will be useful for designing recommendation systems.
Hybrid models give better results than single model. Look for 100 percent accuracy.
If it is an image based reccommendation, you can use CNN
else you can try with RNN and RBM
Artificial Neural Network for prediction of the disease in a person. Convolution Neural Network for prediction and classification of the diseases. Reinforcement Neural Network for Healthcare Robots. FCN and RCNN masking for masking the disease or tumor.
well, the question is too generic to answer. The healthcare data could be in the form of text or images or else like time series etc. The choice of model also depends on the type of the data that you will use for your problem. If the data is text I would recommend RNNs, if the data is image then CNNs or if the data is combination of various types of data then an ensemble method can be used
1) If data is in the form of healthcare images (e.g. MRI results, X-rays, CT Scan Images etc.) then CNN is the best choice. See the following recent top-level experimental evidences:
i. According to a study published in June 2018 in “Annals of Oncology” [1] , a research work was carried out by a team of experts from German institutions. The goal was early detection of Melanoma (the most serious type of skin cancer) through dermoscopic images. They trained and tested a deep learning CNN for differentiating dermoscopic images of melanoma and benign marks. The team set out to compare its diagnostic performance to that of 58 dermatologists of various levels of expertise. The convolutional neural network (CNN) scored 10% higher in terms of specificity than human experts.
ii. According to an article published in “Nature Medicine” in August 2018 [2-3], researchers at the Mount Sinai Icahn School of Medicine (USA) trained CNN for early detection of acute neurological events (like stroke, hemorrhage and hydrocephalus etc.). They used 37,236 CT scan images for its training and their algorithm detected disease in CT scan images in just 1.2 seconds that was 150 times faster than human radiologists.
Also, there are many more experimental studies in this context and you can see them if you want to work using deep learning in such healthcare data applications.
2) If the data is in the form of text (like medical reports or any other health care documentation) then those deep learning models will be more suitable that had already given good results in NLP (natural language processing) applications because such algorithms can understand the complex semantic meaning in the text. Such algorithms are like LSTM (which is in fact the special type of RNN) as these algorithms are capable of learning long-term dependencies and good in sequence prediction problems. Hence they are well suited for classifying, processing and making predictions regarding text analytics which demands understanding textual data based on the sentential context.
[1] https://healthitanalytics.com/news/deep-learning-tool-tops-dermatologists-in-melanoma-detection
[2] https://healthitanalytics.com/news/deep-learning-ids-neurological-scans-150-times-faster-than-humans
[3] https://www.newswise.com/articles/artificial-intelligence-platform-screens-for-acute-neurological-illnesses-at-mount-sinai?sc=dwhr&xy=10022482
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