Motor symptoms in Parkinson's disease (PD) correlate with an excess in synchrony in the beta frequency band (13-30Hz) of local field potentials recorded from basal ganglia circuits. Recent results have suggested that this abnormal activity arises as a result of changes in specific dynamical features of the underlying neural signatures. In particular, patterns of activity in the beta band have been shown to be structured in bursts of longer durations and higher amplitudes in untreated patients with PD. Closed-loop deep brain stimulation (DBS) paradigms that specifically target these pathological bursts of activity hold promises to help trim, and thus normalize, their abnormal behavior in real-time. Here, we developed classification algorithms that predict pathological beta bursts based on ongoing changes in LFP frequency dynamics. We then compared simulations of prediction-based DBS profiles with existing 'adaptive DBS' alternatives. We show that model-driven stimulation profiles are more precise in restricting the delivery of stimulation to bursts that are considered pathological, while preserving physiological ones. The overall stimulation time required is also diminished, thus supporting longer battery life. These results represent a conceptual and algorithmic framework for the development of more precise DBS strategies that are selectively tailored to the electrophysiological profile of each patient.