Use Deep Learning and System Dynamics together for predict the number of deaths of patients over 50 years old in hospital admissions, in Brazil - AIH

1. Automation of Artificial
Intelligence Models
Guttenberg Ferreira Passos
Deep Learning and System Dynamics for
Time Series forecasts of
deaths in hospital admissions - AIH

2. Propósito
• Automação dos Modelos de
Inteligência Artificial para
reduzir os erros nos testes
com algoritmos de Machine
Learning e Deep Learning.
Fonte: https://www.analyticsinsight.net/top-automl-platforms-to-
look-out-for-in-2020/

3. Artificial Intelligence
• Artificial Intelligence (AI) is
the study of how to make
computers perform tasks in
which, at the moment, people
are better.

4. Fonte: https://goo.gl/p2SjrV Por Stefan Kojouharov
• Machine Learning is a
branch of Artificial
Intelligence based on the
idea that systems can
learn from data, identify
patterns and make
decisions with minimal
human intervention.

5. Fonte: https://goo.gl/p2SjrV Por Stefan Kojouharov

6. Deep Learning
• Deep Learning is a type of
Machine Learning that trains
computers to perform tasks
like humans, which includes
speech recognition, image
identification and predictions.

7. AutoML
• AutoML is the automation
of Machine Learning and
Deep Learning models
through the automatic
creation of model codes.
AutoML is in its infancy
and its future will
determine the future of
Artificial Intelligence itself.
Fonte: http://mentoriadsalc.com/2020/08/09/automl-automacao-do-trabalho-na-ia/#Resumo

8. Deaths in hospital admissions – AIH
• The present project aims to predict the number of deaths of
patients over 50 years of age in hospital admissions - AIH, through
the study of Artificial Intelligence models, using time series, for the
comparison of results and the analysis of predictions suggesting
the best option with the least error.
• Data were downloaded from the Datasus website, SIHSUS module,
file type RD- AIH Reduced, years 2010 to 2020, UF MG, every
month :
• http://www2.datasus.gov.br/DATASUS/index.php?area=0901&item
=1&acao=25

9. Deaths in hospital admissions – AIH
• 129 files were downloaded containing the number of
hospitalizations of people over 50 years of age who died.
• These files were grouped with 5,231,829 records used to
prepare a csv file.
• The hospitalization data were totaled, making a total of 3,896
daily records.
• The daily records were regrouped in 556 weekly records with
date, number and total value of hospitalizations, data since
2010.

10. Challenge
• Use AutoML to perform the automation of
Predictive Systems, using as a basis the data
the prediction of the number of deaths of
patients over 50 years in hospital
admissions - AIH, allowing a better accuracy
in the forecast and reducing errors in tests
with Machine Learning algorithms and Deep
Learning.
Prototype :
https://pt2.slideshare.net/guttenbergpasso
s/inteligncia-artificial-em-sries-temporais-
na-arrecadao

11. Purpose of AutoML: decrease errors
RSME RSME RSME
Nº Arquitetura Modelo Setup treino validação teste
1 Métodos Estatísticos BASE 11 Método Naive 86,1777
2 Métodos Estatísticos Forecasting 12 Exponential Smoothing v1 92,9691
3 Métodos Estatísticos Forecasting 12 Exponential Smoothing v2 76,6730
4 Métodos Estatísticos ARIMA 13 ARIMA LOG (1, 0, 1) 149,2452
5 Métodos Estatísticos ARIMA 14 ARIMA LOG (1, 1, 1) 79,8950
6 Métodos Estatísticos ARMA 15 ARMA (1, 0) 150,6672
7 Métodos Estatísticos ARMA 15 ARMA (5, 5) 144,1510
8 Métodos Estatísticos ARMA 15 ARMA (12, 9) 100,0858
9 Métodos Estatísticos ARIMA 16 ARIMA (3, 1, 3) Forecast 80,4460
10 Métodos Estatísticos SARIMAX 17 SARIMAX (2, 1, 2) 2, 1, 0, 12) 197,0291
11 Métodos Estatísticos SARIMAX 18 SARIMAX (0, 1, 1) (0, 1, 1, 12) 81,8482
12 IA - Deep Learning LSTM 22 LSTM (3 repetições) 130,1589
13 IA - Deep Learning LSTM 22 LSTM (5 repetições) 120,1346
14 IA - Deep Learning LSTM 23 LSTM Otimizado 98,5724
15 IA - Deep Learning LSTM 24 Stacked LSTM 100,2641
16 IA - Deep Learning LSTM 25 LSTM Bidirecional 86,4822
17 IA - Deep Learning DeepAR 26 DeepAR 934,1830
18 Inteligência Artificial - IA RNA - MLP 1 MLP Vanilla 51,5066 57,1090
19 Inteligência Artificial - IA RNA - MLP 2 MLP e Método Window 47,8556 51,3619
20 IA - Deep Learning RNN - LSTM 1 LSTM Vanilla - 130 epochs 45,6769 67,4191 63,9893
21 IA - Deep Learning RNN - LSTM 1 LSTM Vanilla - 200 epochs 45,2500 63,4594 61,6839
22 IA - Deep Learning RNN - LSTM 2 LSTM e Método Window 48,5169 62,8968 69,1200
23 IA - Deep Learning RNN - LSTM 3 LSTM e Time Steps 46,1317 70,3126 73,4725
24 IA - Deep Learning RNN - LSTM 4 LSTM e Stateful 42,0199 60,9425 57,2971
25 IA - Deep Learning RNN - LSTM 5 LSTM e Stacked 41,7801 73,1018 72,6560
Scores de Óbitos em Internações Hospitalares - AIH

12. Purpose of AutoML: decrease errors

13. AWS AutoPilot
• With the AWS SageMaker
Studio service we can use
AutoPilot to automate
Predictive Systems and
publish the results on the
Data Science Academy - DSA
team line to share the results.

14. Invitation to challenge
• We invite everyone to take
part in this challenge using
AutoML to automate
Predictive Systems and
improve accuracy in
predicting models by
decreasing test errors.

15. Next challenge
Next year we have a new challenge :
Depois de uma camada oculta no Deep Learning é aplicada uma função de ativação para promover a não
linearidade, imagine se fosse possível aplicar uma função derivada de um modelo de System Dynamics sobre
COVID-19 nessa etapa!
Fonte: https://www.systemdynamics.org/covid-19 Fonte: http://deeplearningbook.com.br/