Computes a personalized injury risk profile for all muscles based on MSK network topology and patient characteristics.
Arguments
- patient_data
A list with patient characteristics:
- age
Numeric, patient age in years (required)
- bmi
Numeric, body mass index (optional)
- activity_level
Character: "sedentary", "moderate", "active", "elite" (optional)
- prior_injuries
Character vector of previously injured muscle names (optional)
- hg
An MSKHypergraph object (NULL loads default).
- sim
An MSKSimulation object (NULL creates default).
Value
An S3 object of class "MSKInjuryRiskProfile" with:
- risk_scores
Data frame with muscle name, base risk, adjusted risk
- patient_data
Input patient data
- factors
Applied adjustment factors
Clinical Validity
Patient factor adjustments (age, BMI, activity level) are heuristic multipliers, not derived from validated epidemiological models. This is a research exploration tool, not a clinical diagnostic.
Examples
C <- matrix(c(1,1,0,0,0, 0,1,1,0,0, 0,0,1,1,0, 0,0,0,1,1, 1,0,0,0,1, 0,1,0,1,0),
nrow = 6, byrow = TRUE,
dimnames = list(paste0("bone", 1:6),
c("Biceps Brachii", "Deltoid", "Trapezius",
"Triceps Brachii", "Brachialis")))
hg <- MSKHypergraph(C)
profile <- mskInjuryRiskProfile(list(age = 45, activity_level = "active"), hg = hg)
profile
#> MSK Injury Risk Profile
#> =======================
#> Patient: age = 45, activity = active
#> Adjustment factors: age = 1.2 , activity = 1.2 , BMI = 1
#>
#> Top 10 Risk Muscles:
#> 1. Deltoid risk = 1.0000
#> 2. Triceps Brachii risk = 1.0000
#> 3. Biceps Brachii risk = 0.1667
#> 4. Brachialis risk = 0.1667
#> 5. Trapezius risk = 0.0000