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Utilities for loading and validating clinical assessment metadata so physiological sessions can be linked with EDC/EHR variables using subject_id and visit_id. Read clinical metadata from CSV

Usage

readClinicalMetadataCSV(
  path,
  col_map = NULL,
  required_cols = c("subject_id", "visit_id", "scale_name", "scale_score"),
  date_cols = c("assessment_date", "visit_date"),
  validate = TRUE,
  sep = ",",
  header = TRUE,
  ...
)

Arguments

path

Path to the CSV/TSV file.

col_map

Optional named character vector for renaming columns. Names are source columns and values are target column names.

required_cols

Required columns for validation.

date_cols

Columns to parse as Date (YYYY-MM-DD) when present.

validate

Logical; run validateClinicalMetadata() when TRUE.

sep

Field separator, default ",".

header

Logical, default TRUE.

...

Additional arguments passed to utils::read.csv().

Value

Data frame containing standardized clinical metadata.

References

Goldberger AL, et al. (2000). "PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals." Circulation, 101(23), e215-e220. doi:10.1161/01.CIR.101.23.e215

Examples

tmp <- tempfile(fileext = ".csv")
write.csv(data.frame(
  sid = "S01",
  vid = "V01",
  scale_name = "FIM",
  scale_score = 90,
  assessment_date = "2026-01-10"
), tmp, row.names = FALSE)

df <- readClinicalMetadataCSV(
  tmp,
  col_map = c(sid = "subject_id", vid = "visit_id")
)
unlink(tmp)