## 4 Discussion  
The findings presented above indicate that acoustic speech features have the potential to distinguish ADHF patients' pre- and post-treatment speech samples with an accuracy up to 69%, sensitivity up to 71%, and specificity up to 67% (Table 4). The acoustic features that best predicted that a sample was after treatment were higher MPT, more stable phonation (decreased F0 SD in sustained vowels, increased CPP, and decreased CPP SD), increased crack percent, faster speech rate, and reduced pausing compared to voice and speech  
Figure 3: Day-to-day discharge probabilities for each patient based on the microphone-based MPT–model, with daily recording sessions on the horizontal axis of each subplot. Red squares indicate admission predictions (discharge probabilities below 0.5 threshold [dashed line]), and black dots indicate discharge predictions (discharge probabilities > 0.5). Participants are ordered (**left** to **right**, **top** to **bottom**) from the greatest to least first-to-last-day change in discharge probability.  
characteristics before treatment. These features are hypothesized to reflect physiological changes in the larynx and lungs due to fluid accumulation from ADHF. This interpretation is tempered by the presence of false positives and false negatives output by the applied machine learning models. However, these sorts of models can be used as a screening tool that can aid in quantifying the potential for a heart-related condition/episode in conjunction with other symptoms that are either self-reported by an individual or measured by clinical staff. Future work with augmented data sets could benefit from ensemble learning in which different models could be learned from data stratified by patient severity.  
Importantly, these results are based on speakers who were recovering from ADHF and were receiving inpatient treatment. The long-term goal of this work is to monitor stable HF patients at home to predict and prevent ADHF episodes before they require hospitalization. Therefore, it is important to determine whether these voice changes (or similar ones) are also present in speakers who are developing ADHF. If they are, then patients and clinicians could use at-home voice monitoring to assess ADHF risk and guide outpatient treatment. These results are also based on a short set of speech tasks (i.e., reading and sustained vowel tasks), most of which did not involve spontaneous speech. Future work might reveal that longer or more natural speech samples provide better information about vocal function and ADHF risk. In that case, a neck-surface ACC sensor could be used for ambulatory voice monitoring to evaluate ADHF risk based on real-world voice use [43].  
Due to the sample size, the statistical approach applied was one of supervised machine learning with leave-one-out cross-validation. This approach was selected as an initial window into the performance of the features and models investigated that reflect voice and speech properties of patients with acute decompensated heart failure (Table 4). Future work would benefit from a much larger sample size to allow for a separate held-out data set for testing model performance.