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HCUPtools - Access and Work with HCUP Resources and Datasets

A comprehensive R package for accessing and working with publicly available and free resources from the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP). The package provides streamlined access to HCUP's Clinical Classifications Software Refined (CCSR) mapping files and Summary Trend Tables, enabling researchers and analysts to efficiently map ICD-10-CM diagnosis codes and ICD-10-PCS procedure codes to CCSR categories and access HCUP statistical reports. Key features include: direct download from HCUP website, multiple output formats (long/wide/default), cross-classification support, version management, citation generation, and intelligent caching. The package does not redistribute HCUP data files but facilitates direct download from the official HCUP website, ensuring users always have access to the latest versions and maintain compliance with HCUP data use policies. This package only accesses free public tools and reports; it does NOT access HCUP databases (NIS, KID, SID, NEDS, etc.) that require purchase. For more information, see <https://hcup-us.ahrq.gov/>.

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4.60 score 2 stars 472 downloads

privacyR - Privacy-Preserving Data Anonymization

Tools for anonymizing sensitive patient and research data. Helps protect privacy while keeping data useful for analysis. Anonymizes IDs, names, dates, locations, and ages while maintaining referential integrity. Methods based on: Sweeney (2002) <doi:10.1142/S0218488502001648>, Dwork et al. (2006) <doi:10.1007/11681878_14>, El Emam et al. (2011) <doi:10.1371/journal.pone.0028071>, Fung et al. (2010) <doi:10.1145/1749603.1749605>.

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4.00 score 2 stars 170 downloads

autoFlagR - AI-Driven Anomaly Detection for Data Quality

Automated data quality auditing using unsupervised machine learning. Provides AI-driven anomaly detection for data quality assessment, primarily designed for Electronic Health Records (EHR) data, with benchmarking capabilities for validation and publication. Methods based on: Liu et al. (2008) <doi:10.1109/ICDM.2008.17>, Breunig et al. (2000) <doi:10.1145/342009.335388>.

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2.70 score 512 downloads