Novel Use of Natural Language Processing (NLP) to Predict Suicidal Ideation and Psychiatric Symptoms in a Text-Based Mental Health Intervention in Madrid
Table 1
Baseline characteristics by suicidal ideation status and average GHQ-12 ≥ 4 (,453).
Mean (SD) or %
Suicidality
GHQ-12
Never suicidal
Ever suicidal
value
Avg GHQ < 4
Avg GHQ ≥ 4
value
Age (cont)
40.0 (13.8)
41.6 (13.9)
<0.001
40.9 (0.5)
40.2 (0.5)
0.316
Percent female
59.3
69.3
<0.001
59.7
69.7
<0.001
Average nightly sleep (hours)
7.3 (1.6)
6.9 (1.9)
<0.001
7.4 (1.4)
6.8 (2.0)
<0.001
Self-rated sleep quality (0–100)
72.4 (24.1)
55.0 (25.1)
<0.001
73.6 (21.2)
52.8 (26.2)
<0.001
Self-rated anger rarely (0–100)
83.8 (16.3)
67.0 (23.5)
<0.001
83.1 (15.6)
66.6 (24.3)
<0.001
Self-rated changes in appetite (0–100)
55.0 (18.8)
50.0 (23.0)
<0.001
56.2 (17.8)
48.7 (23.5)
<0.001
Medication adherence (0–100)
76.5 (36.7)
75.2 (35.1)
0.005
77.1 (35.4)
74.5 (36.0)
0.012
Average WHO-5
61.6 (21.1)
38.3 (20.0)
<0.001
63.3 (18.9)
35.0 (18.6)
<0.001
Significance was assessed using 2-sample -tests for continuous normal variables, Wilcoxon rank-sum (Mann-Whitney) tests for continuous skewed variables, and Pearson chi-square test for binary variables.