Early-stage event prediction for longitudinal data

Mahtab J. Fard, Sanjay Chawla, Chandan K. Reddy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Predicting event occurrence at an early stage in longitudinal studies is an important problem which has high practical value. As opposed to the standard classification and regression problems where a domain expert can provide the labels for the data in a reasonably short period of time, training data in such longitudinal studies must be obtained only by waiting for the occurrence of sufficient number of events. The main objective of this work is to predict the event occurrence in the future for a particular subject in the study using the data collected at the initial stages of a longitudinal study. In this paper, we propose a novel Early Stage Prediction (ESP) framework for building event prediction models which are trained at early stages of longitudinal studies. More specifically, we develop two probabilistic algorithms based on Naive Bayes and Tree-Augmented Naive Bayes (TAN), called ESP-NB and ESP-TAN, respectively, for early stage event prediction by modifying the posterior probability of event occurrence using different extrapolations that are based on Weibull and Lognormal distributions. The proposed framework is evaluated using a wide range of synthetic and real-world benchmark datasets. Our extensive set of experiments show that the proposed ESP framework is able to more accurately predict future event occurrences using only a limited amount of training data compared to the other alternative approaches.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings
EditorsRuili Wang, James Bailey, Takashi Washio, Joshua Zhexue Huang, Latifur Khan, Gillian Dobbie
PublisherSpringer Verlag
Pages139-151
Number of pages13
ISBN (Print)9783319317526
DOIs
StatePublished - 2016
Externally publishedYes
Event20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016 - Auckland, New Zealand
Duration: Apr 19 2016Apr 22 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9651
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016
Country/TerritoryNew Zealand
CityAuckland
Period04/19/1604/22/16

Keywords

  • Longitudinal data
  • Prediction
  • Regression
  • Survival analysis

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