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Mattias Ohlsson

Mattias Ohlsson


Mattias Ohlsson

Neural Network Approaches To Survival Analysis


  • Jonas Kalderstam

Summary, in English

Predicting the probable survival for a patient can be very challenging

for many diseases. In many forms of cancer, the choice of treatment

can be directly impacted by the estimated risk for the patient. This

thesis explores different methods to predict the patient's survival

chances using artificial neural networks (ANN).

ANN is a machine learning technique inspired by how

neurons in the brain function. It is capable of learning to recognize

patterns by looking at labeled examples, so-called supervised

learning. Certain characteristics of medical data make it difficult to

use ANN methods and the articles in this thesis investigates different

methods of overcoming those difficulties.

One of the most prominent difficulties is the missing

data known as censoring. Survival data usually originates from medical

studies, which only are conducted during a limited time period for

example during five years. During this time, some patients will leave

the study for various reasons like death by unrelated causes. Some

patients will also survive the study without experiencing cancer

recurrence or death. These patients provide partial information about

the survival characteristics of the disease but are challenging to

include in statistical models.

Articles 1-3, and 5 utilize a genetic algorithm to train ANN

models to maximize (or minimize) non-differentiable functions, which

are impossible to combine with traditional ANN training techniques

which rely on gradient information. One of these functions is the

concordance index, which compares survival predictions in a pair-wise

fashion. This function is often used to compare prognostic models in

survival analysis, and is maximized directly using the genetic

algorithm approach. In contrast, Article 5

tries to produce the best grouping of the patients into low,

intermediate, or high risk by maximizing, or minimizing the area under

the survival curve.

Article 4 does not use a genetic

algorithm approach but instead takes the approach to modify the

underlying data. Regular gradient methods are used to train ANNs on

survival data where censored times are estimated in a maximum

likelihood framework.


  • Computational Biology and Biological Physics

Publishing year




Document type



Department of Astronomy and Theoretical Physics, Lund University


  • Biophysics
  • Physical Sciences


  • Survival Analysis
  • Artificial Neural Networks
  • Machine Learning
  • Genetic Algorithms
  • Evolutionary Algorithms
  • Fysicumarkivet:2015:Kalderstam




  • Mattias Ohlsson


  • ISBN: 978-91-7623-307-8
  • ISBN: 978-91-7623-308-5

Defence date

29 May 2015

Defence time


Defence place

Sal F, Fysikum, Sölvegatan 14A, 221 00 Lund


  • Azzam Taktak