Project Description:

This project centers on training a machine learning model to evaluate and extract biological values derived from biological signals acquisitions. The student will have access to both existing datasets and the medical devices necessary for data acquisition, enabling the development of a proof-of-concept model.

Main Goal:

The primary objective of this project is to train a machine learning model using data acquired from medical device to evaluate and predict certain other metrics values in patient monitoring.

Project Scope and Expectations:

The project will involve data acquisition from medical devices and existing datasets. The student's tasks will encompass data labeling, thorough data analysis, and the development of an algorithm to validate the proof-of-concept model.

Company Overview:

Displaid pioneers cutting-edge healthcare technology, prioritizing patient empowerment. Our innovative solutions provide accessible and user-friendly tools, offering comprehensive insights for heart failure patients. By harnessing advanced medical imaging technology, we enable individuals to proactively manage their well-being, fostering informed decision-making about their treatment.

At Displaid, we're dedicated to revolutionizing healthcare by putting the control in patients' hands, promoting greater autonomy and improved health outcomes.

Project Details:

1. Acquire and preprocess data from medical devices and existing datasets.
2. Label datasets to establish supervised learning for biometric values.
3. Extract and select pertinent features from biological data for model development.
4. Develop machine learning algorithms for predicting biological metrics.
5. Validate the model's accuracy and performance metrics through comprehensive analysis.
6. Optimize the algorithm for enhanced predictive capabilities.

Final Deliverables:

1. A machine learning model capable of evaluating specific metrics values from medical device acquisitions.
2. Documentation detailing the methodologies, model architecture, and performance analysis.
3. Recommendations for future enhancements or applications of the developed algorithm.

Benefits for Students:

1. Exposure to cutting-edge medical technology and machine learning in a real-world application.
2. Hands-on experience in data labeling, analysis, and algorithm development.
3. Opportunity to contribute to healthcare innovation and diagnostics.

This project provides a unique chance for students to dive into advanced technologies in healthcare, applying machine learning to improve patient monitoring and diagnostics while gaining valuable practical experience in the field.

Employer Commitment:

Our company is committed to providing a minimum of 5 hours of mentorship per team. This mentorship will be tailored to address specific project challenges, offer career advice, and foster a supportive learning environment. The mentorship sessions aim to complement the technical guidance and ensure that students not only complete the project successfully but also gain valuable insights into real-world applications of their skills.