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Diagnose via a Single Algorithm

diagnose_pdd_case.Rd

Applies a specified set of diagnostic criteria to a data matrix representing patient-level data to determine whether each patient meets the criteria for probable Parkinson’s disease dementia (PDD). This function is intended to be used internally by the wrapper function diagnose_pdd_sample.

Usage

diagnose_pdd_case(
  x,
  c,
  p = c("moca_7", "moca_cloc", "vf_k", "moca_5words", "moca_cube", "moca_abs",
    "moca_anim", "mmse_7", "cloc", "vf_s", "mmse_3words", "mmse_pent")
)

Arguments

x

A data frame or matrix containing patient-level variables used for diagnosis.

c

A named vector (or single-row data frame) specifying the algorithms to be applied for PDD diagnosis.

p

A character vector containing variable names to be used later during projective prediction feature selection. Defaults to all Level I screenings subtask defined in the algorithm set.

Value

A logical vector indicating probable PDD diagnosis for each patient (TRUE = diagnosed, FALSE = not diagnosed).

See also

diagnose_pdd_sample() wraps the function to diagnose all patients.

Examples

if (FALSE) { # \dontrun{
p <- data_paths("data-raw")
data <- prepare_data(p)
crit <- specify_algorithms()
pdd  <- diagnose_pdd_case(data, crit[1, ])
} # }