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
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Rare Diseases Mentions in MIMIC-III
Latest papers with no code
Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions
Low-rank data approximation methods such as matrix (e. g., non-negative matrix factorization) and tensor decompositions (e. g., CANDECOMP/PARAFAC) have demonstrated that they can provide such transparent and interpretable insights.
Communication Efficient Generalized Tensor Factorization for Decentralized Healthcare Networks
Tensor factorization has been proved as an efficient unsupervised learning approach for health data analysis, especially for computational phenotyping, where the high-dimensional Electronic Health Records (EHRs) with patients' history of medical procedures, medications, diagnosis, lab tests, etc., are converted to meaningful and interpretable medical concepts.
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis
We propose DPFact, a privacy-preserving collaborative tensor factorization method for computational phenotyping using EHR.
Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective
We also developed a utility to convert an inline annotation format to stand-off annotations to enable the reuse of clinical text datasets with inline annotations.
PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization
It has been recently shown that sparse, nonnegative tensor factorization of multi-modal electronic health record data is a promising approach to high-throughput computational phenotyping.
Using Clinical Narratives and Structured Data to Identify Distant Recurrences in Breast Cancer
Our model can accurately and efficiently identify distant recurrences in breast cancer by combining features extracted from unstructured clinical narratives and structured clinical data.
Natural Language Processing for EHR-Based Computational Phenotyping
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping.
Federated Tensor Factorization for Computational Phenotyping
In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data.
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain
Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research.
