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) 2018-02-06

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)

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)

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

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

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

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

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

Experiment Results – Task 1 Results in the DeepSurv paper Reproduced experimental results Experiment CPH DeepSurv RSF Simulated Linear 0.774873 (0.773, 0.776) 0.774039 (0.773, 0.775) 0.763409 (0.762, 0.765) Simulated Nonlinear 0.506174 (0.502, 0.506) 0.648758 (0.646, 0.650) 0.641302 (0.641, 0.645) WHAS 0.815868 (0.813, 0.820) 0.854865 (0.851, 0.858) 0.892279 (0.891, 0.895) 機械学習・深層学習

Experiment Results – Task 2 CPH DeepSurv RSF Data size Input dim Simulated Linear 0.774873 (0.773, 0.776) 0.774039 (0.773, 0.775) 0.763409 (0.762, 0.765) 6000 10 Simulated Nonlinear 0.506174 (0.502, 0.506) 0.648758 (0.646, 0.650) 0.641302 (0.641, 0.645) WHAS 0.815868 (0.813, 0.820) 0.854865 (0.851, 0.858) 0.892279 (0.891, 0.895) 1638 6 Colon1 0.665948 (0.661, 0.673) 0.642555 (0.635, 0.647) 0.655358 (0.654, 0.665) 929 11 Colon2 0.672573 (0.670 ,0.680) 0.665919 (0.660 ,0.670) 0.643205 (0.641 ,0.654) MGUS2 0.655456 (0.649, 0.659) 0.667541 (0.664 ,0.674) 0.663652 (0.657, 0.666) 1384 Flchain 0.804169 (0.803, 0.808) 0.804770 (0.803, 0.807) 0.791600 (0.790, 0.795) 7874 8 ↑Experimental results on other datasets 機械学習・深層学習

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

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

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

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

Q&A wchen015@e.ntu.edu.sg