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Data · dataset · 2023

Machine Learning for Descriptive Epidemiology

Listed in Borealis

The dataset is an imputed, de-identified cross-sectional data of LGBTQI2S+ young adults who was living in Ontario and Quebec in Canada during the pandemic and responded to a range of questions regarding their mental health and substance use status and related factors.

Description

This data is used as an illustrative example on how to use machine learning for descriptive epidemiology. Codes can be found on the project github page

Links

Where it is published

Catalogue records · 1

Topics

Inferred from text
Epidemiological modelling 75%
Provenance · 1 source records, 10 field assertions
SourceKeyLast seenRaw
Borealisdoi:10.5683/SP3/OPQZWQ13 d agoJSON v1
FieldAssertionExtractorEvidence
concepts[field].anzsrc:field:420205enrichment · borealisdata cataxonomy-embedding@1.1.0title+keywords+description (75%)
concepts[field].dataverse_subject:medicine-health-and-life-sciencessource · borealisdata caconnector:borealisdata_ca@1.0.0/subjects
concepts[field].local:field:life-sciencesmapping · borealisdata caconnector:borealisdata_ca@1.0.0/subjects
concepts[field].local:field:medicine-healthmapping · borealisdata caconnector:borealisdata_ca@1.0.0/subjects
created_datesource · borealisdata caconnector:borealisdata_ca@1.0.0
descriptionsource · borealisdata caconnector:borealisdata_ca@1.0.0/description
publication_datesource · borealisdata caconnector:borealisdata_ca@1.0.0
titlesource · borealisdata caconnector:borealisdata_ca@1.0.0/name
updated_datesource · borealisdata caconnector:borealisdata_ca@1.0.0
version_labelsource · borealisdata caconnector:borealisdata_ca@1.0.0