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pAIn-sense: An Artificial Intelligence based telemonitoring tool to measure chronic pain in at-home settings
This project aims to develop interpretable machine learning algorithms to understand the relationship between physical and psychological aspects of pain, through the identification of reliable biomarkers that consider pain in all its multidimensional aspects, towards an optimal diagnosis and personalized therapies.
Chronic Pain patients will be monitored during at-home multiday experimental protocol. During this time, they will be asked to wear a wearable device collecting multiple physiological data and to complete comprehensive assessments through psychological and life-quality questionnaires. Machine Learning models will be applied to disentangle the physical and emotional components and to correlate pain perception with sleep quality and medications.
Chronic pain is a highly prevalent medical condition and affects around 20% of the general adult population. Pain is a subjective and not fully understood experience typically measured through patient’s self-report. Pain has also** emotional and cognitive** components that impact on its subjective experience. Nowadays there is no reliable and universal model of pain prediction that considers all its multifaceted aspects. It is necessary to understand if the perceived pain is more physical or psychological to propose **personalized therapies** that are more efficient in the long term, tackling the opioid crisis and decreasing the cost on healthcare and society.
Chronic pain is a highly prevalent medical condition and affects around 20% of the general adult population. Pain is a subjective and not fully understood experience typically measured through patient’s self-report. Pain has also** emotional and cognitive** components that impact on its subjective experience. Nowadays there is no reliable and universal model of pain prediction that considers all its multifaceted aspects. It is necessary to understand if the perceived pain is more physical or psychological to propose **personalized therapies** that are more efficient in the long term, tackling the opioid crisis and decreasing the cost on healthcare and society.
The student will be guided in understanding the principal cause of chronic pain, its effects and meaning in terms of reduction of quality of life and consequences on the healthcare system. They will also be introduced to state of the art of pain perception and AI with scientific literature readings. In the project, starting from longitudinal data that will be collected using a wearable on chronic pain patients i.e., physiological responses to pain (HRV, BVP, temperature and SC) and a complete psychological profile of chronic pain patients, the aim is to discover the most relevant biomarkers of pain.
The major goals for the student will be:
1) Finalization of a phone application for behavioural and psychological data collection
2) Contacting of patients and supervising of experimental protocol
3) Machine Learning pipeline including: signal processing and extraction of the most relevant features as known by the literature, development of new machine learning models which are able to consider the complexity of pain and find the most meaningful biomarkers (i.e., features) on data collected
4) Interpret the results and exploit these biomarkers to understand the relative contribution of physical vs psychological components in the pain experience
5) understand what the best measurements are to perform during the diagnosis to propose a more efficient and cost-effective diagnosis
**Requested skills:** Knowledge of Python and/or Matlab, knowledge of AI and Machine Learning algorithms, Signal Processing and Statistical Analysis. Good Programming experience. Highly motivated, prone to interact and work with patients.
Extra skills: App development, Knowledge of Flutter, Computational neuroscience, neuroscience of Pain, Deep Learning, Explainable AI
The student will be guided in understanding the principal cause of chronic pain, its effects and meaning in terms of reduction of quality of life and consequences on the healthcare system. They will also be introduced to state of the art of pain perception and AI with scientific literature readings. In the project, starting from longitudinal data that will be collected using a wearable on chronic pain patients i.e., physiological responses to pain (HRV, BVP, temperature and SC) and a complete psychological profile of chronic pain patients, the aim is to discover the most relevant biomarkers of pain. The major goals for the student will be:
1) Finalization of a phone application for behavioural and psychological data collection
2) Contacting of patients and supervising of experimental protocol
3) Machine Learning pipeline including: signal processing and extraction of the most relevant features as known by the literature, development of new machine learning models which are able to consider the complexity of pain and find the most meaningful biomarkers (i.e., features) on data collected
4) Interpret the results and exploit these biomarkers to understand the relative contribution of physical vs psychological components in the pain experience
5) understand what the best measurements are to perform during the diagnosis to propose a more efficient and cost-effective diagnosis
**Requested skills:** Knowledge of Python and/or Matlab, knowledge of AI and Machine Learning algorithms, Signal Processing and Statistical Analysis. Good Programming experience. Highly motivated, prone to interact and work with patients.
Extra skills: App development, Knowledge of Flutter, Computational neuroscience, neuroscience of Pain, Deep Learning, Explainable AI
Dr. Stanisa Raspopovic, Assistant Professor Neuroengineering laboratory, Head ETH Zurich, Switzerland Email: stanisa.raspopovic@hest.ethz.ch
Noemi Gozzi, PhD Student at the Neuroengineering laboratory, Email: noemi.gozzi@hest.ethz.ch
Greta Preatoni, PhD Student at the Neuroengineering laboratory, Email: greta.preatoni@hest.ethz.ch
Dr. Stanisa Raspopovic, Assistant Professor Neuroengineering laboratory, Head ETH Zurich, Switzerland Email: stanisa.raspopovic@hest.ethz.ch
Noemi Gozzi, PhD Student at the Neuroengineering laboratory, Email: noemi.gozzi@hest.ethz.ch
Greta Preatoni, PhD Student at the Neuroengineering laboratory, Email: greta.preatoni@hest.ethz.ch