nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
1 |
Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study
|
Winterburn, Julie L. |
|
|
214 |
C |
p. 3-10 |
artikel |
2 |
Editorial Board
|
|
|
|
214 |
C |
p. IFC |
artikel |
3 |
Effective connectivity within a triple network brain system discriminates schizophrenia spectrum disorders from psychotic bipolar disorder at the single-subject level
|
Palaniyappan, Lena |
|
|
214 |
C |
p. 24-33 |
artikel |
4 |
Guest editorial: Special issue on machine learning in schizophrenia
|
Chakravarty, M. Mallar |
|
|
214 |
C |
p. 1-2 |
artikel |
5 |
Identifying schizophrenia subgroups using clustering and supervised learning
|
Talpalaru, Alexandra |
|
|
214 |
C |
p. 51-59 |
artikel |
6 |
Improving individual predictions: Machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases)
|
Schnack, Hugo G. |
|
|
214 |
C |
p. 34-42 |
artikel |
7 |
Individualized prediction of psychosis in subjects with an at-risk mental state
|
Zarogianni, Eleni |
|
|
214 |
C |
p. 18-23 |
artikel |
8 |
Machine learning improved classification of psychoses using clinical and biological stratification: Update from the bipolar-schizophrenia network for intermediate phenotypes (B-SNIP)
|
Mothi, Suraj Sarvode |
|
|
214 |
C |
p. 60-69 |
artikel |
9 |
Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods
|
Honnorat, Nicolas |
|
|
214 |
C |
p. 43-50 |
artikel |
10 |
Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI
|
Xiao, Yuan |
|
|
214 |
C |
p. 11-17 |
artikel |
11 |
Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype
|
Tandon, Neeraj |
|
|
214 |
C |
p. 70-75 |
artikel |