Disease phenotyping algorithms process observational clinical data to identify patients with specific diseases. Supervised phenotyping methods require significant quantities of expert-labeled data, while unsupervised methods may learn non-disease phenotypes. To address these limitations, we propose the Semi-Supervised …
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48 results for “Phenotyping”
Electronic phenotyping is the task of ascertaining whether an individual has a medical condition of interest by analyzing their medical record and is foundational in clinical informatics. Increasingly, electronic phenotyping is performed via supervised learning. We investigate the effectiveness of multitask learning fo…
VBphenoR uses variational Bayes for EHR-based patient phenotyping.
problem Phenotyping patients from EHR data for targeted treatments.
method Variational Bayes Gaussian Mixture Model (GMM) and logistic regression.
result Closed-form inference for efficient patient phenotype determination.
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 typically do not leverage available domain knowledge while extracting the phenotypes; hence, some of the su…
The linking genotype to phenotype is the fundamental aim of modern genetics. We focus on study of links between gene expression data and phenotype data through integrative analysis. We propose three approaches. 1) The inherent complexity of phenotypes makes high-throughput phenotype profiling a very difficult and labor…
A New Statistical Framework for Genetic Pleiotropic Analysis of High Dimensional Phenotype Datastat.ML
The widely used genetic pleiotropic analysis of multiple phenotypes are often designed for examining the relationship between common variants and a few phenotypes. They are not suited for both high dimensional phenotypes and high dimensional genotype (next-generation sequencing) data. To overcome these limitations, we …
Bayesian model enhances phenotype discovery in asthma EHRs.
problem Lack of interpretability in unsupervised learning phenotyping of EHR data.
method Operationalized a Bayesian latent class framework with clinical knowledge priors.
result Identified an asthma sub-phenotype with elevated eosinophil levels and allergy markers.
Model identifies key problems in HIV patients' records.
problem Complex and time-consuming task of identifying patient problems from electronic health records.
method Unsupervised phenotyping approach that jointly learns phenotypes from structured and unstructured data.
result Learned phenotypes and their relatedness are clinically valid and surpass existing methods.
This paper reviews methods for discovering patient subgroups from EHR data.
problem Discovering subgroups of patients and co-occurring medical conditions from EHR data.
method Low-rank data approximation methods like matrix and tensor decompositions.
result These methods provide transparent and interpretable insights into patient phenotypes.
Objective: We investigate whether deep learning techniques for natural language processing (NLP) can be used efficiently for patient phenotyping. Patient phenotyping is a classification task for determining whether a patient has a medical condition, and is a crucial part of secondary analysis of healthcare data. We ass…
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 to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospi…
Develops methods for GWAS of high dimensional phenotypes using summary statistics.
problem Lack of methods to model pleiotropy in multi-phenotype GWAS.
method Bayesian inference model using summary statistics, fast computation, and biologically informed priors.
result Demonstrates utility in metabolite GWAS with interpretable pathway-level inference.
ODBAE detects complex phenotypes in biological data.
problem Challenges in identifying complex phenotypes from high-dimensional biological data.
method ODBAE (Outlier Detection using Balanced Autoencoders) identifies influential and high leverage points in latent relationships among multiple physiological parameters.
result ODBAE reveals novel metabolism-related genes and uncovers coordinated abnormalities across metabolic indicators.
A looming question that must be solved before robotic plant phenotyping capabilities can have significant impact to crop improvement programs is scalability. High Throughput Phenotyping (HTP) uses robotic technologies to analyze crops in order to determine species with favorable traits, however, the current practices r…
Traditional anatomical analyses captured only a fraction of real phenomic information. Here, we apply deep learning to quantify total phenotypic similarity across 2468 butterfly photographs, covering 38 subspecies from the polymorphic mimicry complex of and . E…
SWoTTeD discovers hidden temporal patterns in EHR data.
problem Complex temporal patterns in EHR data.
method Sliding Window for Temporal Tensor Decomposition (SWoTTeD) with constraints and regularizations.
result SWoTTeD achieves at least as accurate reconstruction as state-of-the-art models and extracts meaningful temporal phenotypes.
WEST uses EHRs and expert cases to improve rare disease phenotyping.
problem Limited labeled data for rare diseases.
method Weakly supervised transformer model trained on probabilistic silver-standard labels.
result WEST outperforms existing methods in phenotype classification and subphenotyping.
OMTL uses ontology to learn from imbalanced EHR data.
problem Imbalanced and small usable data for phenotypes in EHRs.
method Ontology-driven multi-task learning framework.
result Improved learning performance on phenotypes.
We develop a model to cluster time-series data with interval censoring, improving disease phenotyping.
problem Noise and interval censoring hinder clustering in disease phenotyping.
method Deep generative, continuous-time model that clusters time-series data while correcting for censorship.
result Our model corrects for interval censoring and recovers known clinical subtypes.
Study develops electronic phenotypes of ICU patient acuity.
problem Limited time for patient acuity assessments and imprecise clinical trajectory prediction.
method Developed electronic phenotypes using automated variable retrieval in electronic health records.
result Identified three phenotypes: persistently stable, persistently unstable, and transitioning from unstable to stable.
Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a large number of hospitalized patients. Accurate early prediction of AKI can enable in-time interventions and treatments. However, AKI is hig…
Unsupervised learning with GLRM feature selection reveals novel traumatic brain injury phenotypescs.LG
Baseline injury categorization is important to traumatic brain injury (TBI) research and treatment. Current categorization is dominated by symptom-based scores that insufficiently capture injury heterogeneity. In this work, we apply unsupervised clustering to identify novel TBI phenotypes. Our approach uses a generaliz…
Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of patients in those groups. Tensor factorization has been an effective tool for phenotyping. Most of the existing works assume either a static …
The increased affordability of whole genome sequencing has motivated its use for phenotypic studies. We address the problem of learning interpretable models for discrete phenotypes from whole genomes. We propose a general approach that relies on the Set Covering Machine and a k-mer representation of the genomes. We sho…
Using binary silver labels in electronic health records-based computable phenotyping algorithmsstat.ME
Binary PheNorm extends phenotype labeling for EHRs using binary silver labels.
problem Lack of gold-standard phenotype labels in EHR studies.
method Proposes Binary PheNorm, an extension that uses binary silver labels directly in phenotype scoring.
result Binary PheNorm achieved strong discrimination using binary labels alone and improved performance when combined with count labels.
Understanding the phenotypic drug response on cancer cell lines plays a vital rule in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to test their models and methods. Previously, most research in these ar…
New method phenotypes sleep apnea patients using time series analysis.
problem Traditional diagnosis of sleep apnea is insufficient for capturing its multi-faceted outcomes.
method Fuzzy clustering in time and frequency domains, and persistent homology for topological analysis.
result Phenotyping patients improves understanding of sleep apnea.
Machine learning predicts plant phenotypes from soil microbiome data.
problem Predicting plant phenotypes from soil microbiome data.
method Two models (random forest and Bayesian neural network) were used to predict plant phenotypes from soil properties and microbial population density.
result Human decisions and normalization strategies significantly impact model performance.
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. Deep Learning models have shown superior performance for robust prediction in computational…
Computable Phenotypes to Characterize Changing Patient Brain Dysfunction in the Intensive Care Unitq-bio.QM
Study developed phenotypes for ICU patients' brain dysfunction states.
problem Underdiagnosis of acute brain dysfunction in ICU patients.
method Created algorithms to quantify and cluster brain dysfunction states.
result Developed three phenotypes of ICU patients' brain dysfunction states.
As an increasing number of genome-wide association studies reveal the limitations of attempting to explain phenotypic heritability by single genetic loci, there is growing interest for associating complex phenotypes with sets of genetic loci. While several methods for multi-locus mapping have been proposed, it is often…
The paper presents a systematic review of state-of-the-art approaches to identify patient cohorts using electronic health records. It gives a comprehensive overview of the most commonly de-tected phenotypes and its underlying data sets. Special attention is given to preprocessing of in-put data and the different modeli…
Probabilistically Sampled and Spectrally Clustered Plant Genotypes using Phenotypic Characteristicscs.LG
New algorithm improves plant breeding by clustering soybean genotypes more accurately and efficiently.
problem Low accuracy and high computational complexity in clustering plant genotypes.
method Spectral Clustering with Pivotal Sampling for phenotypic data.
result Our algorithm achieves substantially more accuracy than existing methods.
This work proposes a new algorithm for automated and simultaneous phenotyping of multiple co-occurring medical conditions, also referred as comorbidities, using clinical notes from the electronic health records (EHRs). A basic latent factor estimation technique of non-negative matrix factorization (NMF) is augmented wi…
We propose a non-parametric regression methodology, Random Forests on Distance Matrices (RFDM), for detecting genetic variants associated to quantitative phenotypes representing the human brain's structure or function, and obtained using neuroimaging techniques. RFDM, which is an extension of decision forests, requires…
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 "traditional" approach of using supervised learning relies on a domain expert, and has two main limit…
In this work, we utilize Machine Learning for early recognition of patients at high risk of acute respiratory distress syndrome (ARDS), which is critical for successful prevention strategies for this devastating syndrome. The difficulty in early ARDS recognition stems from its complex and heterogenous nature. In this s…
Second generation sequencing technologies are being increasingly used for genetic association studies, where the main research interest is to identify sets of genetic variants that contribute to various phenotype. The phenotype can be univariate disease status, multivariate responses and even high-dimensional outcomes.…
A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correctioncs.LG
While linear mixed model (LMM) has shown a competitive performance in correcting spurious associations raised by population stratification, family structures, and cryptic relatedness, more challenges are still to be addressed regarding the complex structure of genotypic and phenotypic data. For example, geneticists hav…
Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, while simultaneously correcting for various confounding factors such as age, ethnicity and population structure…
Paper models treatment effects by clustering patients with distinct survival characteristics.
problem Estimating treatment efficacy in clinical settings with censored outcomes.
method Latent variable approach to model heterogeneous treatment effects.
result The latent structure can mediate base survival rates and reveal actionable phenotypes.
New method identifies key genes affecting phenotypes in biological systems.
problem Identifying genes that drive specific phenotypes in complex biological systems.
method Data-driven observability decomposition using Koopman operators.
result Koopman operator representation identifies genes that drive phenotypes.
auton-survival simplifies survival analysis for healthcare data.
problem Handling censored time-to-event data in healthcare.
method Open-source package for survival regression, adjustment, counterfactual estimation, phenotyping, and treatment effects.
result Demonstrates auton-survival's ability to support complex health and epidemiological questions.
Deep learning clusters patient time-series data for better prognosis.
problem Clustering time-series data for patient phenotyping and prognosis.
method Deep predictive clustering with novel loss functions for future outcome distribution.
result Model achieves superior clustering performance and identifies meaningful patient subgroups.
The opioid epidemic in the United States claims over 40,000 lives per year, and it is estimated that well over two million Americans have an opioid use disorder. Over-prescription and misuse of prescription opioids play an important role in the epidemic. Individuals who are prescribed opioids, and who are diagnosed wit…
Probabilistic ML improves healthcare data analysis.
problem Insufficient understanding and incomplete data in healthcare.
method Examination of probabilistic machine learning models for healthcare challenges.
result Probabilistic models enhance healthcare data analysis and model building.
Objective: To evaluate unsupervised clustering methods for identifying individual-level behavioral-clinical phenotypes that relate personal biomarkers and behavioral traits in type 2 diabetes (T2DM) self-monitoring data. Materials and Methods: We used hierarchical clustering (HC) to identify groups of meals with simila…
Bayesian model predicts phenotype effects from multi-environmental factors.
problem Predict phenotype effects from multi-environmental trials.
method Bayesian tensor regression with spike-and-slab structure.
result Model outperforms previous methods in simulation and real-world data.