## 2 Materials and Methods
### Statistics and Machine Learning
The models' performances were evaluated based on their accuracy, area under the receiver operating characteristic curve, true admission rate (sensitivity), true discharge rate (specificity), admission predictive value (positive predictive value), and discharge predictive value (negative predictive value). \"Admission\" and \"discharge\" are used in place of \"positive\" and \"negative\" in the names of these metrics to increase clarity. Performance metrics for the five models are summarized in Table 4.  
The MPT\(-\) and MPT+ models, which were trained on all of the participants' data, were also used to generate the probability of discharge for every daily recording from each individual rather than simply the recordings from admission and discharge days. This follow-up visualization generated a series of day-to-day discharge probabilities for each participant. These trajectories are shown in Figure 3. Note that three participants only yielded recordings from a single day due to either patient non-compliance or scheduling \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multicolumn{1}{c}{\(\boldsymbol{\lambda}\)} & \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**AUC**} & \multicolumn{1}{c}{**TAR**} & \multicolumn{1}{c}{**TDR**} & \multicolumn{1}{c}{**APV**} & \multicolumn{1}{c}{**DPV**} \\ \hline N/A & MPT\(-\) & 0.53 (0.53) & 0.49 (0.53) & 0.51 (0.49) & 0.55 (0.57) & 0.55 (0.54) & 0.51 (0.52) \\ N/A & MPT+ & 0.52 (0.58) & 0.46 (0.54) & 0.48 (0.50) & 0.57 (0.65) & 0.53 (0.59) & 0.52 (0.57) \\ N/A & MPT-only & 0.64 (0.58) & 0.61 (0.60) & 0.63 (0.52) & 0.65 (0.64) & 0.63 (0.57) & 0.65 (0.59) \\ L1 & MPT\(-\) & 0.62 (0.56) & 0.66 (0.51) & 0.70 (0.60) & 0.52 (0.52) & 0.61 (0.56) & 0.62 (0.56) \\ L1 & MPT+ & 0.69 (0.68) & 0.65 (0.63) & 0.71 (0.70) & 0.67 (0.65) & 0.68 (0.67) & 0.70 (0.68) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification performance metrics reported for each of five logistic classifier models using microphone (accelerometer) recordings. \begin{table}
\begin{tabular}{l l l c} \hline \hline \multicolumn{1}{c}{**Feature**} & \multicolumn{1}{c}{**Task**} & \multicolumn{1}{c}{**Odds Ratio (MIC)**} & \multicolumn{1}{c}{**Odds Ratio (ACC)**} \\ \hline Creak \%: 0.3 threshold & Sentences & 1.92 & \\ F0 mean & Vowel & 1.40 & 1.08 \\ CPP median & Spontaneous & 1.37 & \\ Phrase duration median & Spontaneous & 1.27 & \\ Phrase duration mean & Spontaneous & 1.25 & 1.13 \\ Phrase \% & Rainbow Passage & 1.18 & \\ Creek \%: 0.02 threshold & Sentences & & 1.12 \\ CPP median & Vowel & 1.08 & \\ Phrase \% & Spontaneous & & 1.08 \\ Harmonics-to-noise ratio & Vowel & & 1.07 \\ CPP SD & Rainbow Passage & 0.92 & \\ Phrase count & Rainbow Passage & & 0.91 \\ CPP mean & Sentences & & 0.90 \\ Phrase duration SD & Rainbow Passage & 0.87 & \\ F0 SD & Vowel & 0.85 & \\ F0 SD & Spontaneous & 0.79 & \\ Total phrase duration & Rainbow Passage & 0.70 & 0.92 \\ CPP SD & Sentences & 0.61 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Odds ratios for the MPT\(-\) models derived separately for the microphone (MIC) and neck-surface accelerometer (ACC) sensors. \begin{table}
\begin{tabular}{l l c c} \hline \hline \multicolumn{1}{c}{**Feature**} & \multicolumn{1}{c}{**Task**} & \multicolumn{1}{c}{**Odds Ratio (MIC)**} & \multicolumn{1}{c}{**Odds Ratio (ACC)**} \\ \hline Phonation time & Max phonation & 1.34 & 1.03 \\ F0 SD & Sentences & 1.25 & 1.02 \\ Phrase duration median & Spontaneous & 1.11 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Feature weights and odds ratios for the MPT+ model. issues. Other participants yielded multiple days or recordings according to their treatment schedule. The trajectories from the 52 participants are ordered from the greatest to least first-to-last-day change in discharge probability. Within each plot, a participant's recordings are ordered sequentially from admission to discharge. If the probability of discharge for a point was below 0.5, the point was classified as \"admission\" and plotted in red.