1. An Initial Study of Survival Analysis using Deep Learning
TOSHIBA Corp.
Corporate Solutions Development Center Elemental Technology Dept. Group 4
Weiling Chen (陳未羚)

2. Self introduction 陳未羚（チェン ウェイリン）
陳未羚（チェン　ウェイリン）
Nanyang Technological University (Aug 2014 – Present)
School of Computer Science and Engineering (PhD)

3. Research interests
My research interests include machine learning and data mining with applications to social media and other related fields. More specifically, my research mainly focuses on extracting knowledge from social media using text analysis, behavioral pattern recognition and other techniques so as to build prediction and/or detection models – in that way merging insights both from data mining and machine learning. I also have interest and experience in knowledge representation and reasoning especially in contextual reasoning.
Research projects:
Stock Index Prediction Using News Content (Jan 2016 – Apr 2017)
Rumor Detection on Sina Weibo (Jan 2016 – Apr 2017)
Low-quality content filtering on Twitter (Jul 2014 – Dec 2015)

4. Contents Background introduction DeepSurv framework
Experimental results
Future work
Others
What I have learnt?
What I have done?
How I would make use of my internship experience in the future?
Work and life in Japan
機械学習・深層学習

5. Survival Analysis
A branch of statistics for analyzing the expected duration of time until one or more events happen, such as death in biological organisms and failure in mechanical systems.
Other applications – Web mining
To predict the degree of information propagation
To predict the probability of losing users
機械学習・深層学習

6. Survival Data
Three elements: a patient's baseline data x, a failure event time T, and an event indicator E.
age
sex
body-mass-index (BMI)
left heart failure complications (CHF)
order of MI (MIORD).
0:Censored, １:Death
observation time
teacher label
input data
機械学習・深層学習

7. 𝜆(𝑡│𝑥)=𝜆_0 (𝑡)∗𝑒^(ℎ(𝑥))
Survival Data
Two fundamental functions in survival analysis
The survival function which signifies the probability that an individual has “survived” beyond time t:
𝑆(𝑡)=𝑃𝑟(𝑇>𝑡)
The hazard function is a measure of risk at time t. A greater hazard signifies a greater risk of death.
𝜆(𝑡│𝑥)=𝜆_0 (𝑡)∗𝑒^(ℎ(𝑥))
機械学習・深層学習

8. DeepSurv
A multi-layer perceptron, which predicts a patient’s risk of death.
The output of the network is a single node, which estimates the risk function ℎ 𝜃 𝑥 𝑖 parameterized by the weights of the network𝜃.
the number of output node = 1
Loss function:
𝑙 θ =− 𝑖: 𝐸 𝑖 =1 ( ℎ 𝜃 𝑥 𝑖 −𝑙𝑜𝑔 𝑗∈𝑅( 𝑇 𝑖 ) 𝑒 ℎ 𝜃 𝑥 𝑖 )
the number of input node : the dim of input data
機械学習・深層学習

9. Experiment Results – Task 1
Results in the DeepSurv paper
Reproduced experimental results
Experiment
CPH
DeepSurv
RSF
Simulated Linear
(0.773, 0.776)
(0.773, 0.775)
(0.762, 0.765)
Simulated Nonlinear
(0.502, 0.506)
(0.646, 0.650)
(0.641, 0.645)
WHAS
(0.813, 0.820)
(0.851, 0.858)
(0.891, 0.895)
機械学習・深層学習

10. Experiment Results – Task 2
CPH
DeepSurv
RSF
Data size
Input dim
Simulated Linear
(0.773, 0.776)
(0.773, 0.775)
(0.762, 0.765)
6000 10
Simulated Nonlinear
(0.502, 0.506)
(0.646, 0.650)
(0.641, 0.645)
WHAS
(0.813, 0.820)
(0.851, 0.858)
(0.891, 0.895)
1638 6
Colon1
(0.661, 0.673)
(0.635, 0.647)
(0.654, 0.665)
929 11
Colon2
(0.670 ,0.680)
(0.660 ,0.670)
(0.641 ,0.654)
MGUS2
(0.649, 0.659)
(0.664 ,0.674)
(0.657, 0.666)
1384
Flchain
(0.803, 0.808)
(0.803, 0.807)
(0.790, 0.795)
7874 8
↑Experimental results on other datasets
機械学習・深層学習

11. Experiment Results – Task 3
How many data is enough for survival analysis?
機械学習・深層学習

12. Experiment Results – Task 3
How many data is enough for survival analysis?
機械学習・深層学習

13. Future work To understand how much do features affect performance
To explore recurrent event survival analysis
To apply the method to other tasks like human capital management
機械学習・深層学習

14. Others
Work
Life
Shopping (秋葉原、新宿、アメ横商店街)
Sightseeing (川崎大師、三溪園、横浜中華街、増上寺、NHK放送博物館、愛宕神社、etc)
I would like to take the chance to give my sincerest gratitude to my mentors and other colleagues at ET開 as well as hr assistants from Toshiba global recruiting center for their precious and warm help.
機械学習・深層学習

15. Q&A