Table 1 –

Contrasting statistical and clinical prediction of suicide risk

DISTINGUISHING CHARACTERISTICS	STATISTICAL PREDICTION	CLINICAL PREDICTION
INPUTS
Data sources	EHRs and other computerized databases	Clinical observations, clinician review of available records
Number of possible predictors	Hundreds or thousands	Typically fewer than 10
PROCESS
Combining and weighting predictors	Statistical optimization, often involving machine learning	Clinician judgment regarding relevance and weighting of risk factors
Accommodating heterogeneity	Interactions or tree-based approaches to identify subgroup-specific predictors	Clinician judgment regarding applicability of specific risk factors to individuals
Balancing sensitivity and specificity	Explicit assessment of performance at varying thresholds (May also evaluate alternative loss or cost functions)	Clinician judgment regarding importance of false positive and false negative errors
OUTPUTS
Product	Unidimensional prediction – often a continuous score	Clinical formulation
Use in treatment decisions	Not intended to identify causal relationships or treatment targets	Aims to identify causal relationships and treatment targets
IMPLEMENTATION
Timing	Typically computed prior to clinical encounters	Formulated during clinical encounters
Scale	Batch calculation for large populations	Distinct assessments for each individual