Papers Computational Phenotyping
“Computational Phenotyping” 태그가 달린 논문 18편 · 필터 해제
Supervised Coupled Matrix-Tensor Factorization (SCMTF) for Computational Phenotyping of Patient Reported Outcomes in Ulcerative Colitis
Phenotyping is the process of distinguishing groups of patients to identify different types of disease progression. A recent trend employs low-rank matrix and tensor factorization methods for their capability of dealing …
Computational PhenotypingSHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
Objective: Computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications. However, it is time-intensive because of manual data review, limited automation, an…
Computational PhenotypingPrompt EngineeringSpecificitytext-classification+1PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping
Computational phenotyping is essential for biomedical research but often requires significant time and resources, especially since traditional methods typically involve extensive manual data review. While machine learnin…
Computational PhenotypingLanguage ModelingLanguage ModellingLarge Language Model+1Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions
Computational phenotyping allows for unsupervised discovery of subgroups of patients as well as corresponding co-occurring medical conditions from electronic health records (EHR). Typically, EHR data contains demographic…
Computational PhenotypingCommunication 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 pat…
Computational PhenotypingLearning Inter-Modal Correspondence and Phenotypes from Multi-Modal Electronic Health Records
Non-negative tensor factorization has been shown a practical solution to automatically discover phenotypes from the electronic health records (EHR) with minimal human supervision. Such methods generally require an input …
Computational PhenotypingPMHLD: Patch Map Based Hybrid Learning DehazeNet for Single Image Haze Removal
Images captured in a hazy environment usually suffer from bad visibility and missing information. Over many years, learning-based and handcrafted prior-based dehazing algorithms have been rigorously developed. However, b…
Computational PhenotypingDenoisingGenerative Adversarial NetworkImage Dehazing+5Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis
Tensor factorization has been demonstrated as an efficient approach for computational phenotyping, where massive electronic health records (EHRs) are converted to concise and meaningful clinical concepts. While distribut…
Computational PhenotypingPrivacy PreservingAnalysis | OPEN | Published: 17 June 2019 Multitask learning and benchmarking with clinical time series data
Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but pro…
BenchmarkingBIG-bench Machine LearningComputational PhenotypingLength-of-Stay prediction+3PMS-Net: Robust Haze Removal Based on Patch Map for Single Images
In this paper, we proposed a novel haze removal algorithm based on a new feature called the patch map. Conventional patch-based haze removal algorithms (e.g. the Dark Channel prior) usually performs dehazing with a fixed…
Computational PhenotypingDenoisingImage DehazingImage Restoration+5Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective
This paper presents a Lisp architecture for a portable NLP system, termed LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard, customized and in-house developed NLP tools. Our system facilitates po…
Computational PhenotypingDomain AdaptationRelation ExtractionPIVETed-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. However, such approaches typical…
Computational PhenotypingUsing Clinical Narratives and Structured Data to Identify Distant Recurrences in Breast Cancer
Accurately identifying distant recurrences in breast cancer from the Electronic Health Records (EHR) is important for both clinical care and secondary analysis. Although multiple applications have been developed for comp…
Computational PhenotypingNatural 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. NLP-based computational phenotyping has numerous applications includin…
Computational PhenotypingFederated Tensor Factorization for Computational Phenotyping
Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples t…
Computational PhenotypingMultitask learning and benchmarking with clinical time series data
Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but pro…
BenchmarkingBIG-bench Machine LearningComputational PhenotypingGeneral Classification+5Unsupervised Learning for Computational 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. The "…
Computational PhenotypingTime SeriesTime Series AnalysisDistilling 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 r…
Computational PhenotypingDecision MakingDeep LearningDenoising+3