AI Healthcare Research Laboratory

Research for prospective detection, diagnosis, and intervention

The main goal of this research group is to deploy AI technologies for prospective detection, diagnosis, and to recommend interventions. This group is a hub of interdisciplinary researchers pioneering healthcare technology solutions, user behavior analysis, and recommender systems. We are drawn together by our conviction to exchange knowledge between methods developers and application-driven researchers. In our active projects, we handle and analyze large, diverse, and time-sensitive data sets, especially in the biomedical field. We collaborate with biomedical researchers, different types of doctors, and medical specialists to address technical and non-technical research issues. We analyze data from cardiovascular patients, create algorithms for malaria detection, and develop models for patient health states and early warning systems. Our work is a part of the broader field of data-driven medicine, where we utilize machine learning to leverage clinical data, generating new biomedical insights and building precise predictive models for disease outcomes and treatment efficacy.

Faculty

Carine Mukamakuza

Instructor

Carine Pierrette Mukamakuza holds a bachelor’s and master’s degree in computer science from Central South University, China. She completed her Ph.D. studies at Vienna Technical University in the Vienna Ph.D. School of Informatics.

Mukamakuza is a lecturer, researcher, and entrepreneur. Her research focuses on digital healthcare solutions, business intelligence, data science (specifically machine learning, where she has centered her attention on recommender systems), online social network behavior, and personalization. Using publicly available datasets, she has investigated the extent to which social connections influence user rating behavior over time. Driven by her interest in digital healthcare solutions, Mukamakuza is creating a digital detection project. She is currently focused on Rwanda as a case study, and her long-term goals for the interface are much broader where she envisions creating a system that could be utilized across countries in Africa, and across diseases. Overall, her goal is to construct an effective data management system that has wide usability and high reliability.

Office: D108 Regional ICT Center of Excellence Bldg

Phone: +250.781.986076

Email: cmukamak@andrew.cmu.edu

Google Scholar: Carine Mukamakuza

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Guest speaker events

About

"My passion for healthcare research stems from a deep desire to improve lives. Witnessing the impact of limited healthcare resources firsthand has fueled my commitment to this field. This drive, coupled with a fascination for technology, led me to explore how information and communication technology (ICT) can revolutionize healthcare delivery.

For aspiring ICT experts in the field of health, especially women, my advice is to embrace your unique viewpoints and champion them. The ability to see challenges from different angles is a powerful asset in this field. Seek mentorship, build strong networks and leverage your skills to make a significant impact. Remember, you are not just building technology, you are shaping the future of healthcare delivery. By harnessing the power of ICT, we can create a more equitable and and accessible healthcare system for all."

-Carine Mukamakuza

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Projects

research image showing connection between data management system, repository, public, and medical centers

Digital malaria control for the developing world

This project aims to transform the way healthcare providers can prevent, diagnose, and treat malaria in the hardest-hit regions. Malaria remains a pressing public health concern, especially in resource-constrained settings, so the project holds immense promise as it harnesses digital technologies like artificial intelligence (AI) and machine learning (ML).

At its core, the project seeks to revolutionize the traditional approach to diagnosing malaria by leveraging advanced AI and ML algorithms. By integrating these technologies with microscopy—a widely used method for malaria diagnosis—the project aims to automate and streamline diagnoses. This automation will ensure they are accurate and reliable, which are critical factors in effectively managing disease.

Furthermore, this project represents a significant advancement in diagnosing malaria by incorporating a recommender system.

Powered by AI, this system analyzes diagnostic data and patient information to generate personalized treatment recommendations. It tailors treatment plans to individual patients based on their unique characteristics and the specifics of their malaria infection, which enhances the precision and effectiveness of treatment strategies.

Collaborator and funding agency: The African Engineering and Technology Network (Afretec) and the Upanzi Network

research image showing how cardiac health is assessed

AI systems for cardiovascular disease screening in Africa

This project aims to revolutionize how cardiac health is assessed by simultaneously acquiring and analyzing electrocardiogram (ECG) and phonocardiogram (PCG) signals. It aims to understand in detail how cardiac activity functions, as well as to establish a secure, anonymized database of cardiovascular clinical profiles. The project addresses the challenges faced by individuals with cardiovascular disease in Rwanda, such as limited access to essential services and a lack of cardiologists. The study employs an artificial intelligence (AI) model for analyzing ECG and PCG signals, utilizing advanced algorithms like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. This approach aims to significantly improve how cardiovascular diseases are diagnosed and treated, particularly in resource-limited settings like Rwanda. The project also formed a team of clinical experts in congenital heart defects (CHD) to provide valuable insights on various aspects related to cardiovascular screening tools.

Collaborator and funding agency: Rwanda Biomedical Center

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Research team

Eugenia Mawuenya Akpo

Masters

Research interests: AI applied to medical imaging and healthcare
Email: eakpo@andrew.cmu.edu

Yamlak Asrat Bogale

Masters

Research interests: Artificial intelligence in healthcare (in areas such as computer vision and image processing) and the intersection of automatic speech recognition tailored for low-resource language medical settings.
Email: ybogale@andrew.cmu.edu

Bogale's website

Alain Destin Nishimwe Karasira

Masters

Research interests: Application of AI in various domains including healthcare
Email: anishimw@andrew.cmu.edu

Eric Maniraguha

Research Engineer

Research interests: Advancing technology solutions in Africa

Chukwuemeka Malachi Ugwu

Masters

Research interests: Digital health, recommender systems, and natural language processing
Email: mugwuchu@andrew.cmu.edu

Ugwu's GitHub

Past students

Rose Happiness (MSIT '23)

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table with chairs and microscopes

The AI Healthcare Research Laboratory at CMU-Africa

Media mentions

CMU Engineering

$3.3 million awarded to advance digital technology in Africa

Afretec has awarded 11 grants, each led by a multi-university research team, to build research capacity and work toward achieving the UN Sustainable Development Goals (SDGs) in Africa.

Carnegie Mellon University Africa

Building an accessible digital model for malaria screening

Carine Pierrette Mukamakuza is creating an automated malaria screening tool that could revolutionize data accessibility and treatment in Africa.

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Courses

In Mukamakuza's teaching, she focuses on fostering inclusivity within the context of CMU-Africa. This involves the establishment of a learning environment characterized by fairness, equal access, and opportunities for all students to thrive and develop. The diverse student population, which encompasses variations in education, language, culture, and professional skills, necessitates the creation of an inclusive atmosphere that nurtures a sense of belonging.

In addition to the below list, Mukamakuza advises the independent studies projects focused on AI and healthcare.

Course	Course Name	Location	Units	Semester Offered
04-613	ICT Business Economics and Finance	Africa	12	Spring
04-800-B	Recommender Systems	Africa	12	Fall
04-654	Introduction to Probabilistic Graphical Model	Africa	12	Fall

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Publications

2024

Ugwu, C. M., Pierrette Mukamakuza, C., & Tuyishimire, E. (2024). ECG-Signals-based Heartbeat Classification: A Comparative Study of Artificial Neural Network and Support Vector Machine Classifiers. In 2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI). 2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE. https://doi.org/10.1109/sami60510.2024.10432834

2023

Mary, H. R., Mukamakuza, C. P., & Tuyishimire, E. (2023). A Data Management Model for Malaria Control: A Case of Rwanda. In 2023 IEEE AFRICON. 2023 IEEE AFRICON. IEEE. https://doi.org/10.1109/africon55910.2023.10293671

Tuyishimire, E., Mukamakuza, C. P., Mbituyumuremy, A., Brown, T. X., Iradukunda, D., Phuti, O., & Mary, H. R. (2023). IT-Aided Forecasting Model for Malaria Spread for the Developing World. In 2023 Conference on Information Communications Technology and Society (ICTAS). 2023 Conference on Information Communications Technology and Society (ICTAS). IEEE. https://doi.org/10.1109/ictas56421.2023.10082725

2020

Sacharidis, D., Mukamakuza, C. P., & Werthner, H. (2020). Fairness and Diversity in Social-Based Recommender Systems. In Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization. UMAP ’20: 28th ACM Conference on User Modeling, Adaptation and Personalization. ACM. https://doi.org/10.1145/3386392.3397603

2019

Mukamakuza, C. P., Sacharidis, D., & Werthner, H. (2019). The Role of Activity and Similarity in Rating and Social Behavior in Social Recommender Systems. In International Journal on Artificial Intelligence Tools (Vol. 28, Issue 06, p. 1960004). World Scientific Pub Co Pte Lt. https://doi.org/10.1142/s0218213019600042

Mukamakuza, C. P., Sacharidis, D., & Werthner, H. (2019). The Impact of Social Connections in Personalization. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization. UMAP ’19: 27th Conference on User Modeling, Adaptation and Personalization. ACM. https://doi.org/10.1145/3314183.3323675

2018

Mukamakuza, C., Sacharidis, D., & Werthner, H. (2018). Mining User Behavior in Social Recommender Systems. In Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics. WIMS ’18: 8th International Conference on Web Intelligence, Mining and Semantics. ACM. https://doi.org/10.1145/3227609.3227651

2017

Mukamakuza, C. P. (2017). Analyzing the Impact of Social Connections on Rating Behavior in Social Recommender Systems. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. UMAP ’17: 25th Conference on User Modeling, Adaptation and Personalization. ACM. https://doi.org/10.1145/3079628.3079706

2014

Dukuzumuremyi, J. P., Zou, B., Mukamakuza, C. P., Hanyurwimfura, D., & Masabo, E. (2014). Discrete Cosine Coefficients as Images features for Fire Detection based on Computer Vision. In Journal of Computers (Vol. 9, Issue 2). International Academy Publishing (IAP). https://doi.org/10.4304/jcp.9.2.295-300

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