Objective: Frailty is a prevalent risk factor for adverse outcomes among patients with chronic lung disease. However, identifying frail patients who may benefit from interventions is challenging using standard data sour- ces. We therefore sought to identify phrases in clinical notes in the electronic health record (EHR) that describe actionable frailty syndromes. Materials and Methods: We used an active learning strategy to select notes from the EHR and annotated each sentence for 4 actionable aspects of frailty: respiratory impairment, musculoskeletal problems, fall risk, and nu- tritional deficiencies. We compared the performance of regression, tree-based, and neural network models to predict the labels for each sentence. We evaluated performance with the scaled Brier score (SBS), where 1 is perfect and 0 is uninformative, and the positive predictive value (PPV). Results: We manually annotated 155 952 sentences from 326 patients. Elastic net regression had the best perfor- mance across all 4 frailty aspects (SBS 0.52, 95% confidence interval [CI] 0.49–0.54) followed by random forests (SBS 0.49, 95% CI 0.47–0.51), and multi-task neural networks (SBS 0.39, 95% CI 0.37–0.42). For the elastic net model, the PPV for identifying the presence of respiratory impairment was 54.8% (95% CI 53.3%–56.6%) at a sensitivity of 80%. Discussion: Classification models using EHR notes can effectively identify actionable aspects of frailty among patients living with chronic lung disease. Regression performed better than random forest and neural network models. Conclusions: NLP-based models offer promising support to population health management programs that seek to identify and refer community-dwelling patients with frailty for evidence-based interventions.