Accelerating High-Dimensional Temporal Modelling Using Graphics Processing Units for Pharmacovigilance Signal Detection on Real-Life Data...32nd Medical Informatics Europe Conference (MIE2022), 27-30 May, 2022, Nice, France.
Adverse drug reaction is a major public health issue. The increasing availability of medico-administrative databases offers major opportunities to detect real-life pharmacovigilance signals. We have recently adapted a pharmacoepidemiological method to the large dimension, the WCE (Weigthed Cumulativ...
| Published in: | Studies in Health Technology & Informatics Vol. 294; pp. 83 - 88 |
|---|---|
| Main Authors: | , , |
| Format: | proceedings research Journal Article |
| Published: |
Sage Publications Inc.
2022
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157268371&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157268371 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2022 vid: 294 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 157268371 157268371 157268371 10.3233/SHTI220401 157268371 ppf: 83 ppct: 5 formats: tig: atl: Accelerating High-Dimensional Temporal Modelling Using Graphics Processing Units for Pharmacovigilance Signal Detection on Real-Life Data...32nd Medical Informatics Europe Conference (MIE2022), 27-30 May, 2022, Nice, France. aug: au: SABATIER, Pierre FEYDY, Jean JANNOT, Anne-Sophie affil: AP-HP.Centre, Université de Paris, Paris, France. sug: subj: Models, Biological Computer Graphics Pharmacovigilance Image Processing, Computer Assisted Adverse Drug Event Human Descriptive Statistics Technology Computers and Computerization Congresses and Conferences France France ab: Adverse drug reaction is a major public health issue. The increasing availability of medico-administrative databases offers major opportunities to detect real-life pharmacovigilance signals. We have recently adapted a pharmacoepidemiological method to the large dimension, the WCE (Weigthed Cumulative Exposure) statistical model, which makes it possible to model the temporal relationship between the prescription of a drug and the appearance of a side effect without any a priori hypothesis. Unfortunately, this method faces a computational time problem. The objective of this paper is to describe the implementation of the WCE statistical model using Graphics Processing Unit (GPU) programming as a tool to obtain the spectrum of adverse drug reactions from medico-administrative databases. The process is divided into three steps: pre-processing of care pathways using the Python library Panda, calculation of temporal co-variables using the Python library "KeOps", estimation of the model parameters using the Python library "PyTorch" - standard in deep learning. Programming the WCE method by distributing the heaviest portions (notably spline calculation) on the GPU makes it possible to accelerate the time required for this method by 1000 times using a computer graphics card and up to 10,000 times with a GPU server. This implementation makes it possible to use WCE on all the drugs on the market to study their spectrum of adverse effects, to highlight new vigilance signals and thus to have a global vigilance tool on medico-administrative database. This is a proof of concept for the use of this technology in epidemiology. pubtype: Academic Journal doctype: proceedings research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|