## 2 Proposed Digital Health Platform
### Advanced Analytics
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Chi-Squared between Each Indicator Variable and the Baseline for the Model**} \\ \hline
**Feature** & **Description** & **Dummy** & \(p\)**-Value** & **Score** \\ \hline \multirow{2}{*}{**GENDER**} & **Male** & **GENDER\_1** & 0.1416446 & 2.160001 \\  & **Female** & **GENDER\_2** & 0.1450268 & 2.123795 \\ \hline \multirow{6}{*}{**AGERANGE**} & **20–30** & **AGERANGE\_1** & 0.0000001 & 560.890568 \\  & **31–40** & **AGERANGE\_2** & 0.0000001 & 299.675698 \\  & **41–50** & **AGERANGE\_3** & 0.0000001 & 98.221463 \\  & **51–60** & **AGERANGE\_4** & 0.0000035 & 21.520345 \\  & **61–70** & **AGERANGE\_5** & 0.0000001 & 342.879412 \\  & **71–80** & **AGERANGE\_6** & 0.0000001 & 1037.137074 \\ \hline \multirow{6}{*}{**RACE**} & **Mexican American** & **RACE\_1** & 0.0067797 & 7.330429 \\  & **Other Hispanic** & **RACE\_2** & 0.0275756 & 4.854409 \\  & **Non-Hispanic White** & **RACE\_3** & 0.0455912 & 3.996636 \\  & **Non-Hispanic Black** & **RACE\_4** & 0.0000001 & 91.264812 \\  & **Other Race** & **RACE\_5** & 0.0000278 & 17.562718 \\ \hline \multirow{6}{*}{**BMIANGE**} & **Underweight = \textless{18.5} & **BMIANGE\_1** & 0.6730361 & 0.178071 \\  & **Normal weight = 18.5–24.9** & **BMIANGE\_2** & 0.000033 & 17.234712 \\  & **Overweight = 25–29.9** & **BMIANGE\_3** & 0.9174572 & 0.010741 \\  & **Obesity = BMI of 30 or greater** & **BMIANGE\_4** & 0.0006362 & 11.666854 \\ \hline \multirow{2}{*}{**KIDNEY**} & Yes & **KIDNEY\_1** & 0.0000001 & 58.963059 \\  & **No** & **KIDNEY\_2** & 0.1872889 & 1.738816 \\ \hline \multirow{2}{*}{**SMOKE**} & Yes & **SMOKE\_1** & 0.0021759 & 9.394891 \\  & **No** & **SMOKE\_2** & 0.0053461 & 7.758468 \\ \hline \multirow{2}{*}{**DIABETES**} & Yes & **DIABETES\_1** & 0.0000001 & 217.214128 \\  & **No** & **DIABETES\_2** & 0.0000001 & 39.351672 \\  & **Borderline** & **DIABETES\_3** & 0.0000051 & 20.798905 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Chi2 test and \(p\)-value for the independent variables. Table 3 shows the parameters of the architecture. Labels X1-X7 are informative only, and the input size is 27 variables. The model used an anonymous dataset from a private medical institution in Cartagena, Colombia, from 2016 to 2017. Demographic, laboratory data, blood pressure, and body measures data were part of the dataset. This dataset includes cases of live newborns of ages inferior to 72 h with a diagnosis of early neonatal sepsis by clinical criteria and laboratory blood cultures. Control cases were part of the dataset including all newborns healthy by clinical diagnosis and who returned healthy for a follow up at 72 h.  
This retrospective study includes 186 cases and 368 controls based on a case-control relationship of 1:2 with a 95% trust factor and power of 80%. Bivariate analysis and logistic regression were performed to detect the variables associated with early sepsis, and the statistical significance was considered at the alpha level of 0.05. Figure 3: Multilayer Perceptron Architecture. \begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{2}{c}{**Model Architecture Parameters**} \\ \hline
**Parameter** & **Value** \\ \hline Input Dimension & 27 \\ Num Output classes & 2 \\ Num Hidden Layers & 3 \\ Hidden Layer1 Dimension & 674 \\ Activation Func Layer1 & Relu \\ Hidden Layer2 Dimension & 336 \\ Activation Func Layer2 & Relu \\ Hidden Layer3 Dimension & 168 \\ Activation Func Layer3 & Relu \\ Minibatch size & 8 \\ Num samples to train & 388 \\ Num minibatches to train & 48 \\ Loss Function & cross entropy with softmax \\ Eval Error & Classification error \\ Learner for parameters & momentum sgd \\ Eval Metrics & Confusion Matrix, AUC \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model architecture parameters. This model considered nine sociodemographic, fourteen obstetric, nine neonatal, and four maternal infectious related pathology variables.