Computational Phenotyping is the process of transforming the noisy, massive Electronic Health Record (EHR) data into meaningful medical concepts that can be used to predict the risk of disease for an individual, or the response to drug therapy.
Conventional patch-based haze removal algorithms (e. g. the Dark Channel prior) usually performs dehazing with a fixed patch size.
In addition, to further enhance the performance of the method for haze removal, a patch-map-based DCP has been embedded into the network, and this module has been trained with the atmospheric light generator, patch map selection module, and refined module simultaneously.
Such methods generally require an input tensor describing the inter-modal interactions to be pre-established; however, the correspondence between different modalities (e. g., correspondence between medications and diagnoses) can often be missing in practice.
With large volumes of health care data comes the research area of computational phenotyping, making use of techniques such as machine learning to describe illnesses and other clinical concepts from the data itself.