Table 2.

Prediction models based on LASSO logistic regression analysis

		Training	Test
		AUC	Sensitivity	Specificity	AUC
	Baseline model*	0.66	0.73	0.54	0.68
Theory-driven	Literature-based†‡	0.70	0.61	0.68	0.71
	Free text†§	0.68	0.70	0.56	0.71
	Combined*	0.69	0.73	0.60	0.71
Non-temporal data-driven	Symptoms/diseases†¶	0.68	0.72	0.57	0.70
	Medications†**	0.69	0.76	0.58	0.70
	Referrals†††	0.66	0.71	0.55	0.69
	Combined†	0.70	0.57	0.72	0.71
Temporal data-driven	Lab contextualisation†‡‡	0.67	0.73	0.58	0.70
	Sequential patterns†§§	0.66	0.83	0.43	0.69
	Combined†	0.68	0.73	0.58	0.70
	Full model†¶¶	0.70	0.72	0.60	0.72

For a detailed description of the models, see online supplemental table S1.

*Gender, age and consultation frequency.

†It includes baseline model.

‡Variables selected based on literature search of risk factors in the general population.

§Word search through free journal text.

¶ICPC codes categorised according to the WONCA categorisation (dichotomised).

**ATC-3: therapeutic/pharmacological subgroup (dichotomised).

††Outgoing correspondence to medical specialists (dichotomised).

‡‡Relative grounded lab-results (stable, increase, decrease; dichotomised).

§§Order of ICPC, ATC and referrals over time, patterns identified with the SPADE algorithm (see online supplemental table S3).

¶¶All available candidate predictors combined; For a detailed description of the models, see online supplemental table S1.

ATC, anatomical therapeutic chemical; AUC, area under the receiver operating characteristic curve; ICPC, International Classification of Primary Care; LASSO, least absolute shrinkage and selection operator.