Pornlapat Saligupta, 6105077,4th year, Department of Physics, Faculty of Science, Mahidol University

Fatality Prediction of COVID-19 Patients Using Artificial Intelligent.

1

Image: https://github.com/softnami/Autoencoder

/20

Outline

2

• Introduction • Autoencoder • Rare Event Detection • Data Sets • Methodology • Threshold Selection • Results • Conclusion • References

/20

Introduction

3

• Limited medical resources • Prioritising Covid-19 patients • Fatality Prediction using

rare event detection autoencoder

/20

Autoencoder

4

A neural network that learns effective representation of the input data (encoding) and then reconstructs the representative codes back into the original input (decoding).

Image: https://github.com/softnami/Autoencoder

/20

Autoencoder

5 Image: Li, Y. et al (2020)

“compress” into lower dimension

“copy” of the input

Reconstruction

/20

Rare Event Detection

6

• Learnt normal data • Detect anomaly data • Reconstruction error • Ex: Cat (normal), Dog (anomaly)

/20

Data Sets

7

Including features like • Age • Symtoms • Chronic diseases • Death outcome • Etc.

train70%

test30%

Validation30%

Train70%

/20

Data Sets

8

Train data • Normal data set • All alive cases

Train70%

/20

Data Sets

9

Validation data • Threshold selection • Differentiates alive and

dead cases

Validation30%

/20

Data Sets

10

test30%

Test set • Dataset with both

alive and dead cases • “In real life data”

/20

Methodology

11 Image: Li, Y. et al (2020)/20

Threshold Selection

12

• Precision • Recall • F1

Image: Li, Y. et al (2020)/20

Threshold Selection

13

How many selected elements are relevant

precision =true positive

true positive + false positive

Image(2): https://en.wikipedia.org/wiki/F-score/20

image(1):https://dmcommunity.org/2020/09/09/how-to-evaluate-performance-of-machine-learning-models/

(1)(2)

Threshold Selection

14

How many relevant elements are selected

recall =true positive

true positive + false negative

/20(1)

(2)

image(1):https://dmcommunity.org/2020/09/09/how-to-evaluate-performance-of-machine-learning-models/ Image(2): https://en.wikipedia.org/wiki/F-score

Threshold Selection

15

• Harmonic mean of precision and recall

• How good is the algorithm

F1 = 2 ×precision × recallprecision + recall

Image: https://en.wikipedia.org/wiki/F-score/20

Threshold Selection

16

Threshold Selection • Highest F1 score • Threshold = 2.5

Image: Li, Y. et al (2020)/20

Results

17

• Only death cases are above the threshold line

• Only true positives are above the threshold line

• Predicted36/37 deaths (Test dataset)

Image: Li, Y. et al (2020)

True positive and false positive

Threshold

/20

Conclusion

18

• Using an Autoencoder for Rare Event Detection

• Alive cases as normal dataDead cases as anomaly data

• Reconstruction error

Normal Dataset70%

/20

Conclusion

19

• Selecting Threshold using F1 score

• Using the threshold to differentiate alive and dead cases

Images: Li, Y. et al (2020)/20

References

20

• Li, Y., Horowitz, M. A., Liu, J., Chew, A., Lan, H., Liu, Q., … Yang, C. (2020). Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods. Frontiers in Public Health, 8(September), 1–12.

• Intro to Autoencoders | TensorFlow Core. (n.d.). TensorFlow. Retrieved September 14, 2021, from https://www.tensorflow.org/tutorials/generative/autoencoder

• Koehrsen, W. (2021, August 6). Beyond Accuracy: Precision and Recall - Towards Data Science. Medium. https://towardsdatascience.com/beyond-accuracy-precision-and-recall-3da06bea9f6c

/20