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Titel:

MedSat: A Public Health Dataset for England Featuring Medical Prescriptions and Satellite Imagery

Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
03.11.2023
Verantwortlich:
Obadic, Ivica
Autorinnen / Autoren:
Sanja*,Šćepanović; Ivica*, Obadic; Sagar, Joglekar; Laura, Giustarini; Christiano, Nattero; Daniele, Quercia; Xiaoxiang, Zhu
Institutionszugehörigkeit:
Technical University of Munich: Obadic Ivica, Zhu Xiaoxiang
Nokia Bell Labs: Šćepanović Sanja, Quercia Daniele, Joglekar Sagar
WASDI: Giustarini Laura, Nattero Cristiano
Herausgeber:
TUM
Identifikator:
doi:10.14459/2023mp1714817
Enddatum der Datenerzeugung:
14.06.2023
Fachgebiet:
DAT Datenverarbeitung, Informatik; MED Medizin
zusätzliche Fachgebiete:
Public and Population Health
Quellen der Daten:
Abbildungen von Objekten / image of objects; Statistik und Referenzdaten / statistics and reference data
Datentyp:
Bilder / images; Tabellen / tables
Methode der Datenerhebung:
The environmental variables were collected using the Google Earth Engine platform. The sociodemographic features were derived from the latest UK census in 2021. The Sentinel-2 image tiles were collected from the WASDI platform. The prescription data was provided by the UK National Health Service and we used the DrugBank database to match the prescriptions to specific medical conditions.
Beschreibung:
As extreme weather events become more frequent, understanding their impact on human health becomes increasingly crucial. However, the utilization of Earth Observation to effectively analyze the environmental context in relation to health remains limited. This limitation is primarily due to the lack of fine-grained spatial and temporal data in public and population health studies, hindering a comprehensive understanding of health outcomes. For the years 2019 (pre-COVID) and 2020 (COVID), we colle...     »
Schlagworte:
prescription prediction; public health modelling; environmental impact on health
Technische Hinweise:
Representative Dataset: View and download (25 GB total, 35 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1714817.rep):
rsync rsync://m1714817.rep@dataserv.ub.tum.de/m1714817.rep/

Entire Dataset: View (1,01 TB total, 309 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1714817):
rsync rsync://m1714817@dataserv.ub.tum.de/m1714817/
Sprache:
en
Rechte:
by-sa, http://creativecommons.org/licenses/by-sa/4.0
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