Identification of patient prescribing predicting cancer diagnosis using boosted decision trees

Josephine French, Cong Chen, Katherine Henson, Brian Shand, Patrick Ferris, Josh Pencheon, Sally Vernon, Meena Rafiq, David Howe, Georgios Lyratzopoulos, Jem Rashbass

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

Abstract

Machine learning has potential to identify patterns in pre-diagnostic prescribing that act as an early signal of cancer diagnosis. Danish studies using classical regression models have shown that prescribing of particular drugs increases in the months prior to lung and colorectal cancer diagnosis. The aim of this case-control study is to assess the potential for machine learning to extend these findings to identify combinations of prescriptions that might act as pre-cancer signals. We use a boosted trees approach to analyse prescriptions data from NHS Business Services Authority linked to English cancer registry data to classify individuals into two classes: cancer patients and controls. We then identify the drugs that contributed the most to the classification decisions in the models. To the best of our knowledge, this is the first study utilising machine learning to find pre-diagnostic primary-care-prescription-related indicators of cancer diagnosis in England. We assess two feature selection approaches using text categorisation methods alone and in combination with clinical domain knowledge. Matched samples of controls (ten controls for each patient) to control for age are used throughout. We train models for matched cohorts of 6,770 lung cancer patients and 5,869 colorectal cancer patients starting the cancer pathway for the first time between January and March 2016. The models outperform classical methods by AUC, AUC-PR, and F 0.5 score, showing strong potential for using machine learning to extract signals from this dataset to aid earlier diagnosis. Our findings confirm the Danish studies.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings
EditorsDavid Riaño, Szymon Wilk, Annette ten Teije
PublisherSpringer Verlag
Pages328-333
Number of pages6
ISBN (Print)9783030216412
DOIs
Publication statusPublished - 2019
Event17th Conference on Artificial Intelligence in Medicine, AIME 2019 - Poznan, Poland
Duration: 26 Jun 201929 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11526 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th Conference on Artificial Intelligence in Medicine, AIME 2019
Country/TerritoryPoland
CityPoznan
Period26/06/1929/06/19

Keywords

  • Boosted trees
  • Cancer
  • Clinical input
  • Feature selection

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