All LIT Search News Topics Latest news Media statements Media contacts Topics Latest news Media statements Media contacts LIT Impact Latest news 2021 October Health hackathon win puts students on road to commercial success PhD students' innovation to help end-of-life prognosis 11/10/2021 | 3 MINS A predictive model that improves end of life prognosis for patients in their final stage of life has taken out a state-wide competition aimed at finding innovative solutions to real world healthcare changes. The 2021 WA Health Hackathon brought together a diverse group of data professionals, digital specialists and students from around the state to tackle 18 unique clinical challenges provided by WA hospitals and health care service providers. Sixty-one participants in 16 teams were given just one week to come up with digitally-driven solutions to their tasks, which were pitched to a judging panel and scored against a range of criteria. Representing the LIT Medical Physics group, PhD students Brani Rusanov, Jake Kendrick and Nathaniel Barry formed the SCGH (Sir Charles Gairdner Hospital) Machine Learning Group and were assigned the ΓÇÿEnd of Life PrognosisΓÇÖ dataset. Medical Physics program c
All LIT Search News Topics Latest news Media statements Media contacts Topics Latest news Media statements Media contacts LIT Impact Latest news 2021 October Health hackathon win puts students on road to commercial success PhD students' innovation to help end-of-life prognosis 11/10/2021 | 3 MINS A predictive model that improves end of life prognosis for patients in their final stage of life has taken out a state-wide competition aimed at finding innovative solutions to real world healthcare changes.
The 2021 WA Health Hackathon brought together a diverse group of data professionals, digital specialists and students from around the state to tackle 18 unique clinical challenges provided by WA hospitals and health care service providers.
Lombok Institute of Technology
Sixty-one participants in 16 teams were given just one week to come up with digitally-driven solutions to their tasks, which were pitched to a judging panel and scored against a range of criteria.
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Representing the LIT Medical Physics group, PhD students Brani Rusanov, Jake Kendrick and Nathaniel Barry formed the SCGH (Sir Charles Gairdner Hospital) Machine Learning Group and were assigned the ΓÇÿEnd of Life PrognosisΓÇÖ dataset.
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