Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry

Abstract

Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry.

Breaking Barriers—The Intersection of AI and Assistive Technology in Autism Care: A Narrative Review

“…In [70], 11 systematic reviews [28,[71][72][73][74][75][76][77][78][79][80] focused on the impact of AI on autism. Collectively, these reviews tell a compelling story of AI emerging as a powerful ally in autism research.…”

“…Collectively, these reviews tell a compelling story of AI emerging as a powerful ally in autism research. Themes explored include precision psychiatry [71], virtual reality-based techniques for health improvement [72], bibliometric analysis of AI in autism treatment [73], hybridization of medical tests [74], triage and priority-based healthcare diagnosis [75], mobile and wearable AI in child and adolescent psychiatry [76], robot-assisted therapy [28], machine-learning models in behavioral assessment [77], deep learning in psychiatric disorders classification [78], the impact of technology on ASD [79], and deep learning in neurology [80]. Each systematic review contributes to a nuanced exploration of AI within the realm of autism research, shedding light on technology's intersections with neurodevelopmental disorders.…”

An Umbrella Review of the Fusion of fMRI and AI in Autism

“…A fast search in Pubmed using the composite key "("artificial intelligence"[Title/Abstract] AND ("autism s"[All Fields] OR "autisms"[All Fields] OR "autistic disorder"[MeSH Terms] OR ("autistic"[All Fields] AND "disorder"[All Fields]) OR "autistic disorder"[All Fields] OR "autism"[All Fields])) AND (systematicreview [Filter])" identify 11 systematic reviews [45][46][47][48][49][50][51][52][53][54][55] focused on the impact of AI on autism.…”

“…The first systematic review [45] ushers us into a realm where machine learning converges with pharmacogenomics, envisioning a future of precision psychiatry. This integration holds promise for tailoring psychiatric interventions to individual genetic profiles, potentially revolutionizing the treatment landscape for individuals on the autism spectrum.…”