Package 'CRBHSF'

Clean healthcare surveillance data

Description

Clean healthcare surveillance data

Usage

clean_health_data(data, provider_col, time_col)

Arguments

data	A data frame.
provider_col	Character string giving the column name.
time_col	Character string giving the column name.

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Compare simple machine-learning benchmarks

Description

Compare simple machine-learning benchmarks

Usage

compare_ml_benchmarks(data, outcome_col, predictor_cols)

Arguments

data	A data frame.
outcome_col	Character string giving the column name.
predictor_cols	Character vector of predictor column names.

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Compute CRBHSF risk score

Description

Compute CRBHSF risk score

Usage

compute_crbhsf_risk(
  data,
  bayes_risk_col = "risk_bayes",
  trust_col = "latent_trust",
  lambda = 0.2
)

Arguments

data	A data frame.
bayes_risk_col	Character string giving the column name.
trust_col	Character string giving the column name.
lambda	Numeric parameter.

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Compute Cyber-Resilience Pressure Ratio

Description

Compute Cyber-Resilience Pressure Ratio

Usage

compute_crpr(data, bayes_risk_col = "risk_bayes", trust_col = "latent_trust")

Arguments

data	A data frame.
bayes_risk_col	Character string giving the column name.
trust_col	Character string giving the column name.

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Create future deterioration outcome

Description

Create future deterioration outcome

Usage

create_deterioration_outcome(
  data,
  provider_col,
  time_col,
  value_col,
  threshold = 1
)

Arguments

data	A data frame.
provider_col	Character string giving the column name.
time_col	Character string giving the column name.
value_col	Character string giving the column name.
threshold	Numeric parameter.

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Estimate expected value of intervention benefit

Description

Estimate expected value of intervention benefit

Usage

estimate_evib(
  data,
  risk_col = "risk_crbhsf",
  review_cost = 1,
  miss_cost = 10,
  threshold = 0.5
)

Arguments

data	A data frame.
risk_col	Character string giving the column name.
review_cost	Numeric parameter.
miss_cost	Numeric parameter.
threshold	Numeric parameter.

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Estimate latent organisational trust

Description

Estimate latent organisational trust

Usage

estimate_latent_trust(
  data,
  anomaly_col = NULL,
  corruption_col = NULL,
  cyber_col = NULL,
  missing_col = NULL,
  delay_col = NULL
)

Arguments

data	A data frame.
anomaly_col	Character string giving the column name.
corruption_col	Character string giving the column name.
cyber_col	Character string giving the column name.
missing_col	Character string giving the column name.
delay_col	Character string giving the column name.

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Fit lightweight Bayesian surveillance model

Description

Fit lightweight Bayesian surveillance model

Usage

fit_bayesian_surveillance(data, y_col, n_col, alpha0 = 2, beta0 = 2)

Arguments

data	A data frame.
y_col	Character string giving the column name.
n_col	Character string giving the column name.
alpha0	Numeric parameter.
beta0	Numeric parameter.

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Generate simple surveillance report

Description

Generate simple surveillance report

Usage

generate_surveillance_report(validation_results, deployment_results = NULL)

Arguments

validation_results	Validation results returned by validate_surveillance().
deployment_results	Optional deployment results returned by simulate_digital_twin().

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Plot ablation AUC

Description

Plot ablation AUC

Usage

plot_ablation_auc(ablation_results)

Arguments

ablation_results

Function argument.

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Plot digital-twin deployment impact

Description

Plot digital-twin deployment impact

Usage

plot_deployment_impact(deployment_results)

Arguments

deployment_results

Optional deployment results returned by simulate_digital_twin().

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Plot risk distribution

Description

Plot risk distribution

Usage

plot_risk_distribution(data, risk_col = "risk_crbhsf")

Arguments

data	A data frame.
risk_col	Character string giving the column name.

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Run CRBHSF ablation study

Description

Evaluates the incremental predictive contribution of multiple surveillance risk scores by calculating validation metrics for each score against a common binary outcome.

Usage

run_ablation_study(
  data,
  outcome_col,
  score_cols = c("risk_bayes", "latent_trust", "crpr", "risk_crbhsf")
)

Arguments

data	A data frame containing the outcome and score columns.
outcome_col	Character string giving the binary outcome column.
score_cols	Character vector giving the surveillance score columns to evaluate. Defaults to risk_bayes, latent_trust, crpr, and risk_crbhsf.

Value

A tibble containing validation metrics for each score column, including sample size, event count, event rate, AUC, and Brier score.

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Simulate digital-twin deployment

Description

Simulate digital-twin deployment

Usage

simulate_digital_twin(
  data,
  outcome_col,
  score_col,
  capacity = 0.1,
  intervention_effect = 0.6,
  miss_cost = 15,
  review_cost = 1
)

Arguments

data	A data frame containing outcome and score columns.
outcome_col	Character string giving the binary deterioration outcome column.
score_col	Character string giving the risk score column used for prioritisation.
capacity	Proportion of observations selected for review.
intervention_effect	Proportion of reviewed deterioration events assumed preventable.
miss_cost	Cost assigned to a missed deterioration event.
review_cost	Cost assigned to each review.

Value

A tibble summarising reviews, prevented deteriorations, losses, and loss reduction.

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Validate surveillance score

Description

Validate surveillance score

Usage

validate_surveillance(data, outcome_col, score_col)

Arguments

data	A data frame containing the outcome and score columns.
outcome_col	Character string giving the binary outcome column.
score_col	Character string giving the risk score column.

Value

A tibble containing sample size, event count, event rate, AUC, and Brier score.

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