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Novel Digital Biomarker for Chronic Obstructive Pulmonary Disease (COPD)
This thesis centers around the evaluation of a Digital Biomarker for COPD.
Keywords: machinel learning, health, acoustics, smartphone, digital biomarker, COPD
New possibilities to support the treatment of patients with chronic diseases have arisen from affordable personal informatics technologies such as smartphones and smartwatches. In particular, smartphones enable recording and monitoring of symptoms in near real-time and therefore provide a low-cost and scalable scope for a sensing application, which can trigger evidence-based health interventions for people with chronic diseases.
In a previous study, we detected cough counts of COPD patients hospitalized for an exacerbation by smartphone until hospital discharge.
The successful candidate will analyze these cough counts and explore the correlation and predictability of cough counts for hospital discharge.
New possibilities to support the treatment of patients with chronic diseases have arisen from affordable personal informatics technologies such as smartphones and smartwatches. In particular, smartphones enable recording and monitoring of symptoms in near real-time and therefore provide a low-cost and scalable scope for a sensing application, which can trigger evidence-based health interventions for people with chronic diseases.
In a previous study, we detected cough counts of COPD patients hospitalized for an exacerbation by smartphone until hospital discharge.
The successful candidate will analyze these cough counts and explore the correlation and predictability of cough counts for hospital discharge.
In the current project, we focus on the preprocessing and data analysis of smartphone-dectected cough counts. This works builds upon previous works and focuses on building an efficient pipeline in Python.
For the application, you should meet the following requirements:
- Experience in programming skills or the interest to learn the basics of machine learning
- Interest in acoustics and mobile technology
- ETH / UZH students are preferred
In the current project, we focus on the preprocessing and data analysis of smartphone-dectected cough counts. This works builds upon previous works and focuses on building an efficient pipeline in Python.
For the application, you should meet the following requirements: - Experience in programming skills or the interest to learn the basics of machine learning - Interest in acoustics and mobile technology - ETH / UZH students are preferred
Please send your transcript of records and CV to Filipe Barata (fbarata@ethz.ch). More information about research and projects can be found on the website: https://adamma-cdhi-eth-zurich.github.io/.
If you have any further questions or comments, please do not hesitate to contact me.
Please send your transcript of records and CV to Filipe Barata (fbarata@ethz.ch). More information about research and projects can be found on the website: https://adamma-cdhi-eth-zurich.github.io/.
If you have any further questions or comments, please do not hesitate to contact me.