Treatment Optimization Framework provides personalized treatment selection, drug interaction screening, and dose optimization based on individual patient characteristics, medical history, and evidence-based clinical guidelines for optimal therapeutic decision-making.
Usage
treatmentoptim(
data,
patientVars,
treatmentOptions,
responseVar,
medicationVars,
labVars,
comorbidityVars,
treatment_selection = TRUE,
drug_interaction = TRUE,
dose_optimization = FALSE,
safety_assessment = TRUE,
treatment_comparison = TRUE,
prediction_model = "logistic",
interaction_severity = "major_critical",
dose_adjustment = TRUE,
confidence_level = 0.95,
treatment_plots = TRUE,
interaction_network = FALSE,
dose_response_plots = FALSE,
clinical_guidelines = TRUE,
pharmacokinetic_model = FALSE,
population_model = TRUE,
individual_factors = TRUE,
evidence_level = "high_moderate",
clinical_interpretation = TRUE
)Arguments
- data
.
- patientVars
Patient characteristics for personalized treatment (age, gender, comorbidities, biomarkers)
- treatmentOptions
Available treatment options for comparison and selection
- responseVar
Treatment response or outcome variable (optional, for model training)
- medicationVars
Current medications for drug interaction screening (optional)
- labVars
Laboratory values for dose optimization and safety assessment (optional)
- comorbidityVars
Comorbidity variables for treatment safety assessment (optional)
- treatment_selection
Perform personalized treatment selection analysis
- drug_interaction
Perform comprehensive drug interaction screening
- dose_optimization
Perform dose optimization based on patient characteristics
- safety_assessment
Perform comprehensive safety assessment and contraindication checking
- treatment_comparison
Compare multiple treatment options with risk-benefit analysis
- prediction_model
Machine learning model for treatment response prediction
- interaction_severity
Filter drug interactions by clinical significance level
- dose_adjustment
Consider dose adjustment for age, weight, organ function
- confidence_level
Confidence level for prediction intervals and safety assessments
- treatment_plots
Create treatment comparison and outcome prediction plots
- interaction_network
Create network visualization of drug interactions
- dose_response_plots
Create dose-response relationship plots
- clinical_guidelines
Integrate evidence-based clinical practice guidelines
- pharmacokinetic_model
Apply pharmacokinetic models for dose optimization
- population_model
Use population-based treatment response models
- individual_factors
Consider individual patient factors in treatment selection
- evidence_level
Filter recommendations by evidence quality level
- clinical_interpretation
Provide clinical interpretation and implementation guidance
Value
A results object containing:
results$todo | a html | ||||
results$summary | a html | ||||
results$treatmentSelection | a table | ||||
results$drugInteractions | a table | ||||
results$doseOptimization | a table | ||||
results$safetyAssessment | a table | ||||
results$treatmentComparison | a table | ||||
results$predictionModel | a table | ||||
results$treatmentPlots | an image | ||||
results$interactionNetwork | an image | ||||
results$doseResponsePlots | an image | ||||
results$clinicalGuidelines | a html | ||||
results$clinicalInterpretation | a html | ||||
results$pharmacokineticModel | a html | ||||
results$technicalNotes | a html |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$treatmentSelection$asDF
as.data.frame(results$treatmentSelection)