PheCAP - High-Throughput Phenotyping with EHR using a Common Automated Pipeline

Implement surrogate-assisted feature extraction (SAFE) and common machine learning approaches to train and validate phenotyping models. Background and details about the methods can be found at Zhang et al. (2019) <doi:10.1038/s41596-019-0227-6>, Yu et al. (2017) <doi:10.1093/jamia/ocw135>, and Liao et al. (2015) <doi:10.1136/bmj.h1885>.

Last updated 4 years ago

6.02 score 21 stars 8 scripts 149 downloads

ZIM - Zero-Inflated Models (ZIM) for Count Time Series with Excess Zeros

Analyze count time series with excess zeros. Two types of statistical models are supported: Markov regression by Yang et al. (2013) <doi:10.1016/j.stamet.2013.02.001> and state-space models by Yang et al. (2015) <doi:10.1177/1471082X14535530>. They are also known as observation-driven and parameter-driven models respectively in the time series literature. The functions used for Markov regression or observation-driven models can also be used to fit ordinary regression models with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) assumption. Besides, the package contains some miscellaneous functions to compute density, distribution, quantile, and generate random numbers from ZIP and ZINB distributions.

Last updated 1 years ago

5.91 score 7 stars 33 scripts 244 downloads

phecodemap - Visualization for Phecode Mapping with ICD-9 and ICD-10-cm Codes

Phecodemap builds a shiny app to visualize the hierarchy of Phecode Mapping with ICD. The same Phecode hierarchy is displayed in two ways: as a sunburst plot and as a tree.

Last updated 9 months ago

5.61 score 3 stars 13 scripts 160 downloads

PheNorm - Unsupervised Gold-Standard Label Free Phenotyping Algorithm for EHR Data

The algorithm combines the most predictive variable, such as count of the main International Classification of Diseases (ICD) codes, and other Electronic Health Record (EHR) features (e.g. health utilization and processed clinical note data), to obtain a score for accurate risk prediction and disease classification. In particular, it normalizes the surrogate to resemble gaussian mixture and leverages the remaining features through random corruption denoising. Background and details about the method can be found at Yu et al. (2018) <doi:10.1093/jamia/ocx111>.

Last updated 4 years ago

4.70 score 5 stars 7 scripts 128 downloads

SCORNET - Semi-Supervised Calibration of Risk with Noisy Event Times

A consistent, semi-supervised, non-parametric survival curve estimator optimized for efficient use of Electronic Health Record (EHR) data with a limited number of current status labels. See van der Laan and Robins (1997) <doi:10.2307/2670119>.

Last updated 4 years ago

4.48 score 3 stars 3 scripts 123 downloads

SAMGEP - A Semi-Supervised Method for Prediction of Phenotype Event Times

A novel semi-supervised machine learning algorithm to predict phenotype event times using Electronic Health Record (EHR) data.

Last updated 4 years ago

4.30 score 2 stars 2 scripts 165 downloads

kesernetwork - Visualization of the KESER Network

A shiny app to visualize the knowledge networks for the code concepts. Using co-occurrence matrices of EHR codes from Veterans Affairs (VA) and Massachusetts General Brigham (MGB), the knowledge extraction via sparse embedding regression (KESER) algorithm was used to construct knowledge networks for the code concepts. Background and details about the method can be found at Chuan et al. (2021) <doi:10.1038/s41746-021-00519-z>.

Last updated 2 years ago

4.00 score 1 stars 7 scripts 196 downloads

sureLDA - A Novel Multi-Disease Automated Phenotyping Method for the Electronic Health Record

A Novel Multi-Disease Automated Phenotyping Method for the Electronic Health Record.

Last updated 4 years ago

3.60 score 4 stars 3 scripts 120 downloads