Differential Diagnosis Assistance provides Bayesian diagnostic reasoning with multi-factorial clinical presentation analysis to generate ranked differential diagnoses with likelihood ratios and diagnostic probabilities for evidence-based clinical decision support and diagnostic accuracy optimization.
Usage
differentialdiagnosis(
data,
clinicalFindings,
confirmedDiagnosis,
demographicVars,
labResults,
imagingFindings,
diagnostic_probability = TRUE,
likelihood_ratios = TRUE,
prevalence_adjustment = TRUE,
differential_ranking = TRUE,
bayesian_network = FALSE,
reasoning_method = "naive_bayes",
prevalence_source = "population_based",
confidence_threshold = 0.5,
max_diagnoses = 10,
clinical_context = TRUE,
uncertainty_analysis = TRUE,
sensitivity_analysis = FALSE,
confidence_level = 0.95,
diagnostic_plots = TRUE,
network_diagram = FALSE,
probability_heatmap = FALSE,
clinical_guidelines = TRUE,
differential_explanation = TRUE
)Arguments
- data
.
- clinicalFindings
Clinical findings, symptoms, signs, and test results for diagnostic reasoning
- confirmedDiagnosis
Confirmed diagnosis variable for model training and validation (optional)
- demographicVars
Patient demographic variables (age, gender, ethnicity) affecting disease prevalence
- labResults
Laboratory test results for diagnostic probability calculation (optional)
- imagingFindings
Imaging findings and radiological results (optional)
- diagnostic_probability
Calculate Bayesian diagnostic probabilities for differential diagnoses
- likelihood_ratios
Calculate positive and negative likelihood ratios for diagnostic tests
- prevalence_adjustment
Adjust probabilities based on disease prevalence and demographics
- differential_ranking
Rank differential diagnoses by probability and clinical significance
- bayesian_network
Advanced Bayesian network modeling for complex diagnostic relationships
- reasoning_method
Statistical method for diagnostic probability calculation
- prevalence_source
Source of disease prevalence estimates for prior probability calculation
- confidence_threshold
Minimum confidence threshold for diagnostic recommendations
- max_diagnoses
Maximum number of differential diagnoses to display in results
- clinical_context
Integrate clinical context and patient history in diagnostic reasoning
- uncertainty_analysis
Analyze and report diagnostic uncertainty and confidence intervals
- sensitivity_analysis
Perform sensitivity analysis for key diagnostic parameters
- confidence_level
Confidence level for diagnostic probability intervals
- diagnostic_plots
Create diagnostic probability and likelihood ratio visualizations
- network_diagram
Create network diagram of diagnostic relationships
- probability_heatmap
Create heatmap of diagnostic probabilities across findings
- clinical_guidelines
Integrate evidence-based diagnostic guidelines and recommendations
- differential_explanation
Provide detailed explanation of diagnostic reasoning process
Value
A results object containing:
results$todo | a html | ||||
results$summary | a html | ||||
results$diagnosticProbabilities | a table | ||||
results$likelihoodRatios | a table | ||||
results$bayesianAnalysis | a table | ||||
results$uncertaintyAnalysis | a table | ||||
results$clinicalContext | a table | ||||
results$sensitivityAnalysis | a table | ||||
results$modelPerformance | a table | ||||
results$diagnosticPlots | an image | ||||
results$networkDiagram | an image | ||||
results$probabilityHeatmap | an image | ||||
results$clinicalGuidelines | a html | ||||
results$differentialExplanation | a html | ||||
results$technicalNotes | a html |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$diagnosticProbabilities$asDF
as.data.frame(results$diagnosticProbabilities)