Table 3.
Top-performing ML model on the OASIS dataset, as the baseline, along with the combination of clinical notes and audio-recorded patient-nurse verbal communication.
| Feature generation methods | Best performing algorithm | AUC-ROC | F1-score |
|---|---|---|---|
| Sample: N = 47 | |||
| Baseline dataset | |||
| OASIS dataset | XG-boost | 67.63 | 48.01 |
| Combination of OASIS and clinical notes and audio-recorded encounter for the most recent encounter | |||
| OASIS+features extracted from clinical notes | SVM-RBF | 79.17 | 73.68 |
| OASIS+features extracted from clinical notes+features extracted from the patient’s speech during an encounter | SVM-RBF | 94.55 | 85.72 |
| OASIS+features extracted from clinical notes+features extracted from the patient’s speech during an encounter+the nurse’s speech during an encounter | SVM-RBF | 96.15 | 87.5 |
| Combination of OASIS and clinical notes and audio-recorded encounters for all available encounters | |||
| OASIS+features extracted from clinical notes | SVM-RBF | 86.54 | 75.01 |
| OASIS+features extracted from clinical notes + features extracted from the patient’s speech during an encounter | XGB | 96.79 | 87.5 |
| OASIS+features extracted from clinical notes+features extracted from the patient’s speech during an encounter+the nurse’s speech during an encounter | SVM-RBF | 99.68 | 94.12 |