Computational Phenotyping

6 papers with code • 0 benchmarks • 1 datasets

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

Most implemented papers

Multitask learning and benchmarking with clinical time series data

yerevann/mimic3-benchmarks 22 Mar 2017

Health care is one of the most exciting frontiers in data mining and machine learning.

Unsupervised Learning for Computational Phenotyping

Hodapp87/mimic3_phenotyping 26 Dec 2016

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.

PMS-Net: Robust Haze Removal Based on Patch Map for Single Images

weitingchen83/PMS-Net CVPR 2019

Conventional patch-based haze removal algorithms (e. g. the Dark Channel prior) usually performs dehazing with a fixed patch size.

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

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

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

jakeykj/cHITF 12 Nov 2020

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.