Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment

Table 3 Studies evaluating antidepressant drug response using machine learning predictive models

Psychiatric disorder	Machine learning method	Datatypes	Dataset features	Findings	Ref.
Bipolar disorder	Decision tree, random forest	Gene expression	RBPMS2, LILRA5 (male responders); ABRACL, FHL3, NBPF14 (female responders)	Predicted lithium responders in bipolar patients with AUC = 0.92	Eugene et al[36]
Major depressive disorder	ARPNet model-linear regression	SNPs, DNA methylation, demographic	Neuroimaging biomarkers, Genetic variants, DNA methylation, demographic information	Predicted the most effective antidepressant with 84% accuracy	Chang et al[37]
Major depressive disorder	Deep learning-MFNNs	SNPs, demographic, clinical	Genome-wide associations, marital status, age, sex, suicide attempt status, baseline hamilton rating scale for depression score, depressive episodes	Conducted GWAS to identify SNP associations with antidepressant treatment response and remission. MFNN models achieved high accuracy (AUC = 0.82 for response, AUC = 0.81 for remission).	Lin et al[39]
Major depressive disorder	Tree-based ensemble structure	Clinical, demographic	Clinical variables (patients with depression from STAR*D)	Predicted clinical antidepressant remission with 59% accuracy	Chekroud et al[40]
Major depressive disorder	Elastic net	Clinical, demographic	Clinical variables: Patients with major depressive disorder (GENDEN participants)	Forecasted antidepressant response with AUC = 0.72	Iniesta et al[41]
Treatment-resistant depression	Random forest	SNPs, clinical	SNP (rs6265 (BDNF gene), rs6313 (HTR2A gene), rs7430 (PPP3CC gene), Clinical variable - Melancholia	Predicted antidepressant treatment outcome with 25% accuracy	Kautzky et al[42]
Major depressive disorder	SVM, decision trees	SNPs	rs2036270 SNP (RARB gene), rs7037011 SNP (LOC105375971 gene)	Estimated antidepressant treatment response with 52% accuracy	Maciukiewicz et al[43]
Bipolar disorder	Random forest	Clinical	Clinical variables (patients with bipolar disorder treated primarily with lithium)	Predicted responders for lithium treatment outcome with AUC = 0.8	Nunes et al[44]
Late-life depression	Alternating decision tree	Clinical, demographic	Mini-mental status examination scores, age, structural imaging	Predicted antidepressant treatment response with 89% accuracy	Patel et al[45]
Major depressive disorder	Random forest	SNPs	SNPs (rs5743467, rs2741130, rs2702877, rs696692, rs17137566, rs10516436)	Predicted antidepressant therapy response with AUC > 0.7 and accuracy > 69%	Athreya et al[46]

AUC: Area under the receiver operating characteristic curve; SNP: Single nucleotide polymorphism.