Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Model Calculation
3.2. Determinants of Aggressive Inpatient Behavior
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Characteristics | Total n/N (%) | No Aggression n/N (%) | Aggression n/N (%) |
---|---|---|---|
Male sex | 327/352 (92.9) | 219/239 (91.6) | 108/113 (95.6) |
Age at admission (mean, SD) | 33.98 (10.206) | 34.62 (10.014) | 32.64 (10.519) |
Native Country Switzerland | 156/352 (44.3) | 106/239 (44.4) | 50/113 (44.2) |
Single (at offense) | 285/346 (82.4) | 188/233 (80.7) | 97/113 (85.8) |
Statistical Procedure | Balanced Accuracy (%) | AUC | Sensitivity (%) | Specificity (%) | PPV (%) | NPV (%) |
---|---|---|---|---|---|---|
Logistic Regression | 74.9 | 0.85 | 77.8 | 72.1 | 85.7 | 60.6 |
Tree | 74.7 | 0.80 | 72.6 | 76.8 | 86.2 | 56.7 |
Random Forest | 75.3 | 0.83 | 74.9 | 74.9 | 87.3 | 59.9 |
Gradient Boosting | ||||||
KNN | 77.7 | 0.85 | 78.6 | 76.8 | 88.0 | 63.1 |
SVM | 77.6 | 0.87 | 78.2 | 66.9 | 87.3 | 66.3 |
Naive Bayes | 75.9 | 0.85 | 87.9 | 76.1 | 87.8 | 59.8 |
Variable Code | Variable Description | Aggressive Incidents | No Aggressive Incidents |
---|---|---|---|
DZ1 | Did the patient complain about the hospital staff? | 73/111 (65.8) | 45/238 (18.9) |
DZ2 | Did the patient show negative behavior toward other patients? | 76/112 (67.9) | 40/237 (16.9) |
DZ7 | Did the patient show dis/antisocial behavior? | 90/111 (81.1) | 73/238 (30.7) |
DZ10 | Did the patient break the rules of the ward (e.g., substance abuse)? | 61/112 (54.5) | 36/238 (15.1) |
R22c (mean, SD) | Time spent at a high-security level during current forensic hospitalization | 48.36 (59.65)) | 33.84 (45.22) |
PA_A (mean, SD) | Adapted PANSS at admission: Total score | 30.19 (12.34) | 22.05 (11.35) |
PA7 | Adapted PANSS at admission: Hostility | ||
symptom absent | 27/113 (23.9) | 160/238 (67.2) | |
symptom discreet | 22/113 (19.5) | 45/238 (18.9) | |
symptom substantial | 64/113 (56.6) | 33/238 (13.9) | |
PA18 | Adapted PANSS at admission: Tension | ||
symptom absent | 25/113 (22.1) | 131/238 (55) | |
symptom discreet | 25/113 (22.1) | 54/238 (22.7) | |
symptom substantial | 63/113 (55.8) | 53/238 (22.3) | |
PA22 | Adapted PANSS at admission: Uncooperativeness | ||
symptom absent | 22/113 (19.5) | 144/238 (60.5) | |
symptom discreet | 38/113 (33.6) | 58/238 (24.4) | |
symptom substantial | 53/113 (46.9) | 36/238 (15.1) | |
PA28 | Adapted PANSS at admission: Poor impulse control | ||
symptom absent | 17/113 (15) | 155/238 (65.1) | |
symptom discreet | 33/113 (29.2) | 40/238 (16.8) | |
symptom substantial | 63/113 (55.8) | 43/238 (18.1) |
Performance Measures | % (95% CI) |
---|---|
Balanced Accuracy | 73.5 (64.4–82.1) |
AUC | 0.84 (0.75–0.93) |
Sensitivity | 83.5 (83.3–83.8) |
Specificity | 59.4 (58.8–59.9) |
PPV | 83.5 (83.2–83.8) |
NPV | 59.4 (58.8–59.9) |
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Hofmann, L.A.; Lau, S.; Kirchebner, J. Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia. Appl. Sci. 2022, 12, 819. https://doi.org/10.3390/app12020819
Hofmann LA, Lau S, Kirchebner J. Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia. Applied Sciences. 2022; 12(2):819. https://doi.org/10.3390/app12020819
Chicago/Turabian StyleHofmann, Lena A., Steffen Lau, and Johannes Kirchebner. 2022. "Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia" Applied Sciences 12, no. 2: 819. https://doi.org/10.3390/app12020819
APA StyleHofmann, L. A., Lau, S., & Kirchebner, J. (2022). Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia. Applied Sciences, 12(2), 819. https://doi.org/10.3390/app12020819