About

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.

Source: Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis

Benchmarks

No evaluation results yet. Help compare methods by submit evaluation metrics.

Greatest papers with code

PMHLD: Patch Map Based Hybrid Learning DehazeNet for Single Image Haze Removal

IEEE Transaction on Image Processing 2020 weitingchen83/Dehazing-PMHLD-Patch-Map-Based-Hybrid-Learning-DehazeNet-for-Single-Image-Haze-Removal-TIP-2020

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.

COMPUTATIONAL PHENOTYPING DENOISING IMAGE DEHAZING IMAGE RESTORATION NONHOMOGENEOUS IMAGE DEHAZING SINGLE IMAGE DERAINING SINGLE IMAGE HAZE REMOVAL

Learning Inter-Modal Correspondence and Phenotypes from Multi-Modal Electronic Health Records

12 Nov 2020jakeykj/cHITF

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.

COMPUTATIONAL PHENOTYPING

Unsupervised Learning for Computational Phenotyping

26 Dec 2016Hodapp87/mimic3_phenotyping

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.

COMPUTATIONAL PHENOTYPING TIME SERIES