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Computational Phenotyping

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

Papers

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Showing 1–18 of 18 papers

TitleStatusHype
PMHLD: Patch Map Based Hybrid Learning DehazeNet for Single Image Haze RemovalCode1
Multitask learning and benchmarking with clinical time series dataCode1
Supervised Coupled Matrix-Tensor Factorization (SCMTF) for Computational Phenotyping of Patient Reported Outcomes in Ulcerative ColitisCode0
SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping—0
PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping—0
Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions—0
Communication Efficient Generalized Tensor Factorization for Decentralized Healthcare Networks—0
Learning Inter-Modal Correspondence and Phenotypes from Multi-Modal Electronic Health RecordsCode0
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis—0
Analysis | OPEN | Published: 17 June 2019 Multitask learning and benchmarking with clinical time series dataCode0
PMS-Net: Robust Haze Removal Based on Patch Map for Single ImagesCode0
Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective—0
PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization—0
Natural Language Processing for EHR-Based Computational Phenotyping—0
Using Clinical Narratives and Structured Data to Identify Distant Recurrences in Breast Cancer—0
Federated Tensor Factorization for Computational Phenotyping—0
Unsupervised Learning for Computational PhenotypingCode0
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain—0
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