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