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Data Analysis: Prediction of Acute Hypotensive Episodes (Remote Supervision)
We aim to predict accurately when a person may next have a hypotensive episode
Keywords: Time series analysis, biosignal analysis, data science, medical technologies, and digital health
The purpose of this study is to predict the incidence of an acute hypotensive episode before its onset, to enable medical professionals to immediately begin administering care to their patients. Different time-series prediction methods commonly used for forecasting financial data can be applied to arterial blood pressure recordings to solve the acute hypotensive episode prediction problem.
The purpose of this study is to predict the incidence of an acute hypotensive episode before its onset, to enable medical professionals to immediately begin administering care to their patients. Different time-series prediction methods commonly used for forecasting financial data can be applied to arterial blood pressure recordings to solve the acute hypotensive episode prediction problem.
- Implement ARIMA model, EMA crossover, neural networks, or any other time series algorithms
- Testing and comparing trends and prediction accuracy among different algorithms
Tasks
- Literature review (10%)
- Data analysis (loading data, data filtering) (20%)
- Design and implement a time series prediction model (20%)
- Design and implement a basic machine learning algorithm (20%)
- Test, compare and evaluate results (10%)
- Report and present (20%)
- Implement ARIMA model, EMA crossover, neural networks, or any other time series algorithms - Testing and comparing trends and prediction accuracy among different algorithms
Tasks
- Literature review (10%) - Data analysis (loading data, data filtering) (20%) - Design and implement a time series prediction model (20%) - Design and implement a basic machine learning algorithm (20%) - Test, compare and evaluate results (10%) - Report and present (20%)
Dr Moe Elgendi (moe.elgendi@hest.ethz.ch) will supervise the student at the Biomedical and Mobile Health Technology Research Group in ETH Zurich’s D-HEST Department of Health Sciences and Technology.
Dr Moe Elgendi (moe.elgendi@hest.ethz.ch) will supervise the student at the Biomedical and Mobile Health Technology Research Group in ETH Zurich’s D-HEST Department of Health Sciences and Technology.