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.

1. Midwifery Learning and Forecasting: Predicting
Content Demand with User-Generated Logs
August 15th, 2021
KDD 2021 Health Day
2021 KDD Workshop on Applied Data Science for Healthcare
Anna Guitart Atienza, Ana Fernández del Río, África Periáñez
and Lauren Bellhouse

2. ai for equitable healthcare
Accelerating and democratizing behavioral machinelearning for
low- and middle- incomecountries to reduce health inequalities

3. Mission
▸ Provide real-time and at the right time personalized incentives and
recommendations to frontline health workers and patients
▸ Using the latest behavioral machine learning and personalization techniques to
provide equitable healthcare

4. ▸ Operationalize large-scale digital traces from mobile health devices
▸ Turn behavioral logs into robust personalized scientific results
▸ Dealing with online/offline connection logs information
(particularlyrelevant in low-income countries)
Challenges

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

6. Midwiferylearning importance
▸ Every day, 800 women and 6,700 newborns die from complications related to
pregnancy or childbirth.
▸ Well-trained midwifes are of utmost importance as they can prevent most of these
maternal and newborn deaths.
▸ Expansion of mobile health applications in low- and middle-income countries.
▸ Providing robust techologies from Data Science and Machine Learning is a must,
torwards personalized content and to provide an adaptive training journey.

7. Dataset
User logs extracted from Maternity Foundation’s
Safe DeliveryApp: https://www.maternity.dk/safe-delivery-app/
- Midwife, Nurse, Other skilledbirth attendant, Physician or Student.
- Data collected from 2019-05-01 to 2021-01-14 (20,422 users from India)
All data used in this analysis comes from the Safe
Delivery App logsand belongs to the Maternity
Foundation. For inquiries regardingits use, please contact
them at mail@maternity.dk.
▸ More than half of all maternal and newborn deaths
happening due to poor quality of care
▸ Safe Delivery App, an online learning app, to improve
skilled birth attendant training and support

8. Goal
▸ DEMAND FORECASTING
Predicting the dependencies between
observations from multiple time series
▸ PERSONALIZATION
Determine the interest of future users on the
different kind of contents available in the app,
broken down by profession and region.
- Neural networks with categorical embeddings [1]
- Autoregressive recurrent neural networks (DeepAR) [2]
- Low-rank Gaussian copula processes (GP-Copula) [3]
Benchmark Models
Deep Learning
- Seasonal Autoregressive Integrated Moving Average
(SARIMA) [4]
- Seasonal Naïve
▸ FORECASTING MODELS
[1] Cheng Guoand Felix Berkhahn. 2016. Entity Embeddings of Categorical Variables.
[2] David Salinas et. al. 2020. DeepAR: Probabilistic forecastingwith autoregressive recurrentnetworks. International Journal of Forecasting.
[3] David Salinas, et. al., 2019. High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes. In Advances in Neural Information Processing.
[4] George Edward Pelham Box and Gwilym Jenkins. 1990. Time Series Analysis, Forecastingand Control. Holden-Day, Inc., USA.

9. Modeling
All data used in this analysis comes from the Safe
Delivery App logsand belongs to the Maternity
Foundation. For inquiries regardingits use, please contact
them at mail@maternity.dk.
App usage per module-profession
Target: Number of users
from a specific
Profession that use a
particular app
Module per day
Features: day, month,
year, profession, module
Forcasting horizon:
30 days

10. Results – error metrics
Cross-validation:
rolling window from
2020-06-01 to 2020-12-01

11. Infection prevention
Midwifes
December forecast
Active Management of Third Stage Labour
Physicians
November forecast

12. Summary and Conclusions
▸ DeepAR and GP-Copula
Most accurate for daily forecasting of content demand.
Results holds across different contents and user types.
Potential to be applied to different geographical areas and additional contents.
▸ Operational settings
DeepAR most suitable for real-time demand estimates, due to the flexibility
and speed of the model implementation.

13. THANK YOU!
guitart@benshi.ai
@benshi_ai
linkedin.com/company/benshi-ai/mycompany/
benshi.ai