20 research outputs found
Combining Kernel and Model Based Learning for HIV Therapy Selection
We present a mixture-of-experts approach for HIV therapy selection. The heterogeneity in patient data makes it difficult for one particular model to succeed at providing suitable therapy predictions for all patients. An appropriate means for addressing this heterogeneity is through combining kernel and model-based techniques. These methods capture different kinds of information: kernel-based methods are able to identify clusters of similar patients, and work well when modelling the viral response for these groups. In contrast, model-based methods capture the sequential process of decision making, and are able to find simpler, yet accurate patterns in response for patients outside these groups. We take advantage of this information by proposing a mixture-of-experts model that automatically selects between the methods in order to assign the most appropriate therapy choice to an individual. Overall, we verify that therapy combinations proposed using this approach significantly outperform previous methods
Personalized rTMS for Depression: A Review
Personalized treatments are gaining momentum across all fields of medicine.
Precision medicine can be applied to neuromodulatory techniques, where focused
brain stimulation treatments such as repetitive transcranial magnetic
stimulation (rTMS) are used to modulate brain circuits and alleviate clinical
symptoms. rTMS is well-tolerated and clinically effective for
treatment-resistant depression (TRD) and other neuropsychiatric disorders.
However, despite its wide stimulation parameter space (location, angle,
pattern, frequency, and intensity can be adjusted), rTMS is currently applied
in a one-size-fits-all manner, potentially contributing to its suboptimal
clinical response (~50%). In this review, we examine components of rTMS that
can be optimized to account for inter-individual variability in neural function
and anatomy. We discuss current treatment options for TRD, the neural
mechanisms thought to underlie treatment, differences in FDA-cleared devices,
targeting strategies, stimulation parameter selection, and adaptive closed-loop
rTMS to improve treatment outcomes. We suggest that better understanding of the
wide and modifiable parameter space of rTMS will greatly improve clinical
outcome