# Midwifery Learning and Forecasting: Predicting Content Demand with User-
Generated Logs

Anna Guitart  ,  Ana Fernández del Río  ,  África Periáñez<EMAIL_ADDRESS>benshi.aiPasseig de Gracia, 74Barcelona08008Spain and  Lauren Bellhouse
<EMAIL_ADDRESS>Maternity FoundationForbindelsesvej 3, 2.
floorCopenhagen2100Denmark

###### Abstract.

Every day, 800 women and 6,700 newborns die from complications related to
pregnancy or childbirth. A well-trained midwife can prevent most of these
maternal and newborn deaths. Data science models together with logs generated
by users of online learning applications for midwives can help to improve
their learning competencies. The goal is to use these rich behavioral data to
push digital learning towards personalized content and to provide an adaptive
learning journey. In this work, we evaluate various forecasting methods to
determine the interest of future users on the different kind of contents
available in the app, broken down by profession and region.

user logs, personalization

††conference: 2021 KDD Workshop on Applied Data Science for Healthcare; August
14–18, 2021; ††ccs: Forecasting Healthcare††ccs: Neural Networks Deep
Learning††ccs: Time series User behavior

##  1\. Introduction

The rapid expansion of mobile health applications in low- and middle-income
countries, and the large volume of data generated by their users, has created
unprecedented opportunities for applying artificial intelligence (AI) to
improve individual and population health (Hosny and Aerts, 2019; Wahl et al.,
2018). The application of data science models to the digital tools’ behavioral
logs of frontline healthcare workers and patients can lead to improvements in
clinical research and practice, and health service delivery. And public health
can use big datasets to promote healthy habits and ameliorate self-management,
by providing people with health and well-being plans based on their particular
medical and social circumstances (O’Connor, 2018; Marsch, 2021).

Every year 300,000 women and 5 million newborns die of causes related to
pregnancy and childbirth (Fund, 2020; for Child Mortality Estimation, 2020).
Additionally, for every maternal death approximately 20 women suffer serious
birth injuries (World Health Organization and Bank, 2019). Nearly all of these
deaths and disabilities occur in low- and middle-income countries, and almost
90% of them could be prevented if the woman gave birth with qualified
assistance from a skilled midwife (Nove et al., 2021). Additionally, 80% of
all newborn deaths result from conditions which are preventable and treatable,
and for which proven, cost-efficient interventions exist (Organization and
UNICEF, 2014). Almost all intrapartum and many antepartum stillbirths could be
prevented with quality essential childbirth care and antenatal care (Lawn et
al., 2016).

Here we show an analysis of the logs from skilled birth attendants using the
_Safe Delivery App_ (Foundation, 2021b), a digital training and learning
mobile application developed by the Maternity Foundation—a non-profit that
develops digital learning tools to ensure all women and newborns have a safe
childbirth (Foundation, 2021a). This work represents a step towards content
personalization for midwives, in a sector that has traditionally been left out
of big technological developments. Forecasting the demand for learning content
by profession and region can lead to a better understanding of user habits and
improve the management of campaigns (Xu and Chan, 2019). We apply several
forecasting methods to evaluate their accuracy and production feasibility with
the aim of using the outcomes for future experimentation and incentive
analysis. Previous studies using similar methodologies and user logs can be
found in (Guitart et al., 2018; del Río et al., 2021).

Figure 1. _Nurses_ module usage. Number of daily users who accessed a
particular _Safe Delivery App_ module, for India _nurses_.

###  1.1. Forecasting models

We compare the performance of different time series forecasting methods in
predicting the daily demand (in number of users) per type of content (module,
in the app’s language) and user’s profession. Training and prediction were
performed using the gluonTS (Alexandrov et al., 2020), keras (Chollet et al.,
2015) and mxnet (Chen et al., 2015) Python libraries and the Forecast R
package (Hyndman and Khandakar, 2008).

####  1.1.1. Seasonal naïve forecaster

The naïve forecast model (Alexandrov et al., 2020) was used as a benchmark.
Its forecasts are given by the exact values at the equivalent time points of
the previous season. For prediction lengths larger than a season, the season
is repeated multiple times, whereas for time series shorter than a season, the
mean observed value is used as the prediction.

####  1.1.2. Seasonal Autorregressive Integrated Moving Average (SARIMA)
forecaster

The SARIMA model (Box and Jenkins, 1990) was used as an additional benchmark,
as it is one of the best performing and most widely used classical approaches
to time series analysis and forecasting. At each time step, the time series
value is a combination of regular and seasonal autoregressive (where the value
depends on the previous values) and moving average (where the value depends on
the previous errors) polynomials. In addition, one can take as many
differences as needed in the original time series to make it stationary.

####  1.1.3. Neural networks with categorical embedding

The last decade has seen the rapid growth of deep neural network architectures
to tackle a great variety of problems (LeCun et al., 2015), due to increasing
computational resources and data availability, as well as improved
methodology. One shortcoming of this approach is its difficulty to include
categorical features, due to their lack of continuity. Entity embedding (Guo
and Berkhahn, 2016) can be used to effectively learn the representation of
categorical variables in multidimensional spaces, increasing their continuity
and thus providing an intelligent way of using them as features in deep
learning models. In particular, it overcomes the problems faced by the more
traditional approach of one-hot encoding, namely the need for excessive
computational power and its tendency to overfitting.

####  1.1.4. Autoregressive recurrent networks (DeepAR)

The use of autoregressive recurrent networks to simultaneously predict many
time series was introduced in (Salinas et al., 2020). The method trains either
long short-term memory (LSTM) or gated recurrent unit (GRU) networks, where
the inputs at each time step are the covariates, the target value from the
previous time step (which makes it autoregressive) and the previous output of
the network (which makes it recurrent). A global model is learned from all the
time series that can be used to generate probabilistic forecasts for the
individual time series, each with its own individual distribution. This
technique has been previously used in connection with healthcare in
(Papastefanopoulos et al., 2020).

####  1.1.5. Low-rank Gaussian copula processes

This is a multivariate approach with deep learning elements, described as GP-
Copula in (Salinas et al., 2019). It combines a time series model based on
autoregressive recurrent newtworks with a Gaussian copula process to
parametrize the output distribution. This copula has low-rank structure in
order to keep the number of parameters and computational complexity within
reasonable bounds.

Table 1. Error metrics for the different forecasting models evaluated. The final errors are an average over the individual predictions using a rolling window from 2020-06-01 to 2020-12-01, with a forecasting horizon of 30 days. Model | MASE | MAPE | sMAPE | RMSE | MSE
---|---|---|---|---|---
Seasonal Naïve | 1.3221 | 0.7945 | 0.7294 | 8.2179 | 84.2975
ARIMA | 0.8752 | 0.5569 | 0.7416 | 4.5020 | 21.2377
NN with categorical embeddings | 1.0381 | 0.5283 | 0.6547 | 12.4945 | 250.0235
DeepAR | 0.8110 | 0.4373 | 0.5689 | 4.4461 | 22.1728
GP-Copula | 0.8209 | 0.4285 | 0.6564 | 4.4088 | 21.5326
Figure 2. Mean Absolute Percentage Error (MAPE) boxplot distribution from
individual time series displayed by model and by prediction month.

##  2\. Modeling

###  2.1. Dataset

Our dataset comprised user logs extracted from Maternity Foundation’s _Safe
Delivery App_. This app targets skilled birth attendants around the world,
empowering them to provide a safer birth for mothers and newborns through
evidence-based and up-to-date clinical guidelines on maternal and neonatal
care, including the core components or ”signal functions” of Basic Emergency
Obstetric and Newborn Care.

_Safe Delivery App_ is organized in several learning topics, known as modules:
_Active Management of Third Stage Labour_ , _Female Genital Mutilation_ ,
_Hypertension_ , _Infection Prevention_ , _Manual Removal of Placenta_ ,
_Maternal Sepsis_ , _Neonatal Resuscitation_ , _Newborn Management_ , _Post
Abortion Care_ and _Post Partum Hemorrhage_. The possible professions of the
app users (as self-reported by them on first login) are: _Midwife_ , _Nurse_ ,
_Other skilled birth attendant_ , _Physician_ or _Student_.

Data were collected from 2019-05-01 to 2021-01-14, and the results shown
correspond to a sample of 20,422 users from India. We built daily time series
of module usage per profession, showing the number of professionals that
accessed a particular module per day, and took them as a proxy for the demand
for that specific content. Figure 1 presents the time series for various
modules in the case of  _nurses_. Even though all modules show a similar
overall usage trend, each series exhibits different scale and usage patterns.
Similar series are obtained for the other professions. Our goal is to predict
the app usage per profession, in order to personalize the content and get a
better grasp of usage dynamics.

###  2.2. Model specification

The goal was to predict the daily values of the usage time series for each
month and each module-profession combination, training the model with all the
available data until the end of the previous month. Cross-validation was
performed using a rolling window (Racine, 2000; Cerqueira et al., 2020) from
2020-06-01 to 2020-12-01,considering all historical data before the prediction
date for the training samples and 30 days of data before the prediction date
for the test sample (training samples were split into training and validation
sets). The final configuration was selected as the one that got the best
results in the cross-validation rolling-window process. All models used the
profession and module as categorical features, and the day of the month, day
of the week, month and year as covariates. The inclusion of COVID-19-related
covariates and information on the _Safe Delivery App_ training for healthcare
professionals was tested but did not result in any clear improvement.

Regarding the specifications of each method, the SARIMA forecasting was
performed using the auto-ARIMA functionality, which means that all
combinations of regular polynomials up to degree 5, seasonal polynomials up to
degree 2, up to 2 regular and up to 1 seasonal differences were tried, using
the Akaike Information Criterion to select the best of them. Our neural
network with categorical embeddings had 3 fully connected layers with 1000,
500 and 1 cells; the activation function for the dense layers was ReLU for the
first and a sigmoid for the second layer, and we used the mean absolute error
as the loss function and Adam as the optimization method. The best performing
DeepAR model was found to be that using 20 2-layer LSTM cells, a negative
binomial distribution, a dropout rate of 0.01, 300 epochs and a training batch
size of 30. The selected GP-Copula variant had exactly the same settings,
except that only 5 epochs were considered—as this method is much more
computationally intensive and the model was already reaching convergence.

###  2.3. Results

Most of the forecasting models evaluated were able to capture the trend of the
time series, with results differing mainly in the estimation of the daily
patterns specific to each time series. Results are summarized in Table 1,
which shows several error metrics averaged over across all monthly
predictions. These are: Mean Absolute Scaled Error (MASE), Mean Absolute
Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (sMAPE),
Root Mean Squared Error (RMSE) and Mean Squared Error (MSE) (Botchkarev,
2019). In Figure 2 the boxplot distribution of the individual MASE scores are
displayed for each model and for each prediction month. We can observe that
for more recent months, the scores tend to be lower, partially due to the
enlargement of the historic data used in the training sample.

Figures 3 and 4 shows an example of the forecasts for each model. It
illustrates that, while the performance of the DeepAR and GP-Copula methods is
relatively similar (left panel), the former shows a tighter 50% confidence
prediction interval that fits better the shape of the actual series. The ARIMA
model also produced remarkably accurate predictions, which accounts for its
extended use even nowadays that more sophisticated methods are available,
although it shows a larger forecast uncertainty (as shown by the wider
confidence interval). The forecasts for the other evaluated models are
displayed in the right panel. The GP-Copula model trained over just 5 epochs
performs similarly than the DeepAR model trained over 300 epochs, though it
still needs more time and resources. For that reason, DeepAR would be the
preferred option in a production environment. However, if higher accuracy were
critical and there were no constraints on computational time and resources,
the use of GP-Copula with an increased number of epochs would be justified.

Figure 3. Real vs. forecasted values for the _Infection Prevention_ module
demand by _midwifes_ on December 2020. The predictions and 50% confidence
intervals of the DeepAR, GP-Copula, ARIMA, NN with categorical embeddings and
seasonal naïve models are shown.

Figure 4. Real vs. forecasted values for the _Active Management of Third Stage
Labour_ module demand by _physicians_ on November 2020. The predictions and
50% confidence intervals of the DeepAR, GP-Copula, ARIMA, NN with categorical
embeddings and seasonal naïve models are shown.

##  3\. Summary and conclusion

Overall, we found that the DeepAR and GP-Copula deep learning models are the
most accurate for daily forecasting of the content demand. This result holds
across different contents (modules) and user types (professions), as these two
models show less error variability in the overall results of each individual
time series. Other models such as Facebook’s Prophet (Taylor and Letham, 2018)
and DeepVAR (the simplest multivariate extension of DeepAR) (Salinas et al.,
2019), both with their default settings, were also tested but performed worse
than the ARIMA benchmark, so they were not included in the analysis.

Although the evaluated dataset corresponds to India, this methodology shows
potential to be applied to different countries or geographical areas, and also
to additional contents. DeepAR constitutes a generalizable model that can
correctly capture the trend behavior of the time series and anticipate user
demand for a particular content depending on the user profile. We provided a
solution that could be used in operational settings to get real-time demand
estimates, due to the flexibility and speed of the model implementation.

## Data

All data used in this analysis comes from the Safe Delivery App logs and
belongs to the Maternity Foundation. For inquiries regarding its use, please
contact them at<EMAIL_ADDRESS>
###### Acknowledgements.

The authors wish to thank Javier Grande and Wei Xiang Low for their careful
review of the manuscript. This work was supported, in whole or in part, by the
Bill & Melinda Gates Foundation INV-022480. Under the grant conditions of the
Foundation, a Creative Commons Attribution 4.0 Generic License has already
been assigned to the Author Accepted Manuscript version that might arise from
this submission.

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