Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study

Table 1 Baseline characteristics of study cohort (n = 146)

Characteristic	n (%)
Age, years, median (IQR)	36 (25-50)
Sex
Female	76 (52)
Male	70 (48)
Smoker (active)	33 (23)
CD behavior
B1: Non-stricturing, non-penetrating	75 (51)
B2: Stricturing	56 (38)
B3: Penetrating/fistulizing	15 (10)
CD location
L1: Ileal	41 (28)
L2: Colonic	43 (29)
L3: Ileocolonic	62 (42)
L4: Isolated UGI	0 (0)
Perianal involvement	20 (21)
Initial anti-TNF commenced
Infliximab	84 (58)
Adalimumab	62 (42)
Baseline thiopurine	99 (68)
Baseline methotrexate	27 (18)
Baseline corticosteroids	64 (44)
Baseline aminosalicylates	48 (33)
Prior anti-TNF	22 (15)
Prior intestinal surgery	41 (28)
Disease duration, yr, median (IQR)	5 (1-12)
Baseline investigations
CRP, mg/L, median (IQR)	3 (2-8)
Albumin, g/L, median (IQR)	37 (36-41)

IQR: Inter-quartile range; CD: Crohn's disease; CRP: C-reactive protein; TNF: Tumor necrosis factor; UGI: Upper gastrointestinal.