1 code implementation • 28 May 2024 • Zhiyao Luo, Yangchen Pan, Peter Watkinson, Tingting Zhu
In the rapidly changing healthcare landscape, the implementation of offline reinforcement learning (RL) in dynamic treatment regimes (DTRs) presents a mix of unprecedented opportunities and challenges.
no code implementations • 28 Oct 2023 • Munib Mesinovic, Peter Watkinson, Tingting Zhu
Survival analysis focuses on estimating time-to-event distributions which can help in dynamic risk prediction in healthcare.
no code implementations • 16 Aug 2023 • Munib Mesinovic, Peter Watkinson, Tingting Zhu
To ensure trust and reliability in AI systems, especially in clinical risk prediction models, explainability becomes crucial.
no code implementations • 10 May 2023 • Munib Mesinovic, Peter Watkinson, Tingting Zhu
Heart attack remain one of the greatest contributors to mortality in the United States and globally.
no code implementations • 29 Sep 2021 • Henrique Aguiar, Mauro Santos, Peter Watkinson, Tingting Zhu
The recent availability of Electronic Health Records (EHR) has allowed for the development of algorithms predicting inpatient risk of deterioration and trajectory evolution.
no code implementations • 17 Nov 2020 • Henrique Aguiar, Mauro Santos, Peter Watkinson, Tingting Zhu
Recent years have seen an increased focus into the tasks of predicting hospital inpatient risk of deterioration and trajectory evolution due to the availability of electronic patient data.
no code implementations • ICML 2020 • Rasheed el-Bouri, David Eyre, Peter Watkinson, Tingting Zhu, David Clifton
Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing with patients who come from the emergency department.