Welcome!

Thank you for visiting my research website. I am currently an Assistant Professor at OHSU working in the Artificial Intelligence for Medical Systems (AIMS) Lab. Prior to joining the BME Department at OHSU, I was a Senior Imaging Scientist at Intel Corporation. I obtained my Ph.D. in Electrical Engineering from the University of Texas at San Antonio where I worked with Dr. Sos S. Agaian.

Resume [PDF]

  • Email

    mosquera [at] ohsu [dot] edu

Current research interests

Computational biology ▪ artificial intelligence (AI) ▪ data-driven modeling ▪ physiology-guided machine learning ▪ biomedical image analysis ▪ diabetes technologies ▪ computer-aided diagnosis ▪ computer vision.

News

New paper
11/2023
P. G. Jacobs et al., “Artificial intelligence and machine learning for improving glycemic control in diabetes: best practices, pitfalls and opportunities,” IEEE Rev. Biomed. Eng., pp. 1–19, 2023, doi: 10.1109/RBME.2023.3331297.
The DiabetesMine Innovation Summit
11/2023
Clara presents the work of the AIMS Lab on automated meal detection for closed loop in San Diego, CA.
Virtual Diabetes Technology Meeting
11/2023
Clara presents the work of the AIMS Lab on uncertainty-aware nocturnal hypoglycemia prediction.
New paper
10/2023
C. Mosquera-Lopez et al., “Combining uncertainty-aware predictive modeling and a bedtime Smart Snack intervention to prevent nocturnal hypoglycemia in people with type 1 diabetes on multiple daily injections,” Journal of the American Medical Informatics Association, p. ocad196, Oct. 2023, doi: 10.1093/jamia/ocad196.
New paper
09/2023
T. Kushner, C. Mosquera-Lopez, A. Hildebrand, M. H. Cameron, and P. G. Jacobs, “Risky movement: Assessing fall risk in people with multiple sclerosis with wearable sensors and beacon-based smart-home monitoring,” Multiple Sclerosis and Related Disorders, vol. 79, p. 105019, Nov. 2023, doi: 10.1016/j.msard.2023.105019.
New paper
03/2023
C. Mosquera-Lopez et al., “Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence,” npj Digit. Med., vol. 6, no. 1, Art. no. 1, Mar. 2023, doi: 10.1038/s41746-023-00783-1.
New paper (Recognized as paper of the month by OHSU School of Medicine)
02/2023
C. Mosquera-Lopez, K. L. Ramsey, V. Roquemen-Echeverri, and P. G. Jacobs, “Modeling risk of hypoglycemia during and following physical activity in people with type 1 diabetes using explainable mixed-effects machine learning,” Computers in Biology and Medicine, vol. 155, p. 106670, Mar. 2023, doi: 10.1016/j.compbiomed.2023.106670.
New QSB award
02/2023
The BE-In-AIMS internship program will be supported by the 2023 OHSU Quantitative and Systems Biology (QSB) award; with Dr. Jacobs
New award
12/2022
Valentina Roquemen-Echeverri was awarded a Professional Development Scholarship from Women in Science Portland. Congratulations!
Colombian Endocrinology Association (ACE) DITEC 1.0 virtual course
10/2022
Clara gave a talk on AI and its applications in diabetes management hosted by Dr. Alex Ramirez-Rincon
New JDRF award
09/2022
"Development of an open-source AI-based digital twin generator for replicating personalized glucose dynamics in people with type 1 and type 2 diabetes"; with AIMS Lab team
New paper
08/2022
J. R. Castle et al., “Assessment of a Decision Support System for Adults with Type 1 Diabetes on Multiple Daily Insulin Injections,” Diabetes Technology & Therapeutics, Aug. 2022, doi: 10.1089/dia.2022.0252.
ATTD Conference
04/2022
Clara presents the work of the AIMS Lab on (1) automated meal detection for closed loop and (2) hypoglycemia risk during and following physical activity at the 15th ATTD Conference in Barcelona, Spain.
New student
03/2022
Valentina Roquemen-Echeverri joins the AIMS Lab. Welcome Valentina!
New paper
03/2022
N. S. Tyler, C. Mosquera-Lopez, G. M. Young, J. El Youssef, J. R. Castle, and P. G. Jacobs, “Quantifying the impact of physical activity on future glucose trends using machine learning,” iScience, vol. 25, no. 3, p. 103888, Mar. 2022, doi: 10.1016/j.isci.2022.103888.
New OHSU REI + BME award
11/2021
"Bias Equity and Inclusion in Artificial Intelligence for Medical Systems Internship (BE-In-AIMS)"; with Dr. Jacobs
Oregon Bioengineering Symp.
11/2021
Clara gives a presentation on some of the work in the AIMS Lab.
New OCTRI IDEA Gap award
"Development of a Novel Smart-Phone-Based Stethoscope Device that uses Machine Learning to Detect, Diagnose, and Determine the Severity of Cardiac and Pulmonary Disease"; with Dr. Schulman, Dr. Heitner, and Dr. Jacobs
New NIH/NIDDK R21 award
09/2021
"Development and Evaluation of Personalized Explainable Machine Learning Models to Predict and Prevent Nocturnal Hypoglycemia in Type 1 Diabetes"; with AIMS Lab team

Research projects

As a research faculty in the AIMS Lab, I am involved in multiple research projects in the general field of computational biology. Below are selected projects in which I am a principal investigator (PI or co-PI).

Modeling hypoglycemia risk in T1D

The goal of this project is to build mixed effects machine learning models to study the effect of different forms of exercise on hypoglycemia risk during and up to 24 hours after physical activity.

Online hypoglycemia risk assessment tool

Explainable AI for prediction and prevention of nocturnal hypoglycemia in T1D

The goal of this project is to develop and evaluate (in a clinical trial) the effect of a personalized machine-learning-based recommender system that analyzes glucose profile and physical activity to predict at bedtime the likelihood of overnight hypoglycemia.

Bias Equity and Inclusion in Artificial Intelligence for Medical Systems Internship (BE-In-AIMS)

With Co-PI Dr. Jacobs

BE-In-AIMS creates opportunities for students from diverse backgrounds or underrepresented groups to participate in world-class research at the intersection of AI and medical systems design and development. The specific focus of this program is to explore how racial, gender, or other bias impacts the performance of machine learning algorithms.

AI-powered diabetes simulation platform

The goal of this project is to build an open-source AI-based digital twin generator for replicating personalized glucose dynamics in people with type 1 and type 2 diabetes.

Device and algorithms for structural heart disease diagnosis (Telluscope)

With Tellunostics LLC

The goal of this project is to build a new AI-powered cardiac auscultation device for improved structural heart murmur diagnosis.

Students

Valentina Roquemen-Echeverri, BSc

PhD student, BME|OHSU, Co-advised with Dr. Jacobs

Spring 2022 ▸

Valentina holds a BSc in Physics from the Universidad de Antioquia (COL). Her research focuses on physiology-guided AI and its applications in computational cardiology and diabetes technologies. Learn more

Clara Escorihuela-Altaba, MSc

PhD student, University of Bern, Co-advised with Dr. Garcia-Tirado

Spring 2023 ▸

Clara holds a BSc in Biomedical Engineering from the University of Barcelona (ESP) and an MSc in Medical Engineering (Informatics) from the KTH Royal Institute of Technology (SWE). Her research involves combining mathematical physiological models with artificial intelligence to develop innovative solutions that can help individuals with diabetes manage their condition more effectively. GitHub / Learn more

Interns/Volunteers

Jenny Wang: Summer 2023
Mary Loeb: Summer 2023
Tomas Londono-Murillo: Fall 2022 ▸ Fall 2023
Dylan Jacobs: Summer 2021,2022
Clara Escorihuela-Altaba, MSc: Summer 2022

Collaborations

Below is a list of collaborators and researchers I work with, who are not members of the AIMS Lab.

Dr. Melanie B. Gillingham Molecular and Medical Genetics, OHSU (USA)
Dr. Jhonattan Cordoba-Ramirez Dept. of Electrical Eng., Federal University of Minas Gerais - UFMG (Brazil)
Dr. Jorge Mahecha-Gomez Institute of Physics, Universidad de Antioquia (Colombia)
Dr. Edison Montoya Institute of Mathematics, Universidad de Antioquia (Colombia)
Dr. Taisa Kushner Galois, Inc. (USA)
Dr. Jose Garcia-Tirado University Clinic of Diabetes, Endocrinology, and Metabolism, University of Bern (Switzerland)

Teaching

Fall 2021, Winter 2022, Spring 2022, Summer 2022, Fall 2022, Winter 2023, Fall 2023 OHSU | BME 610NN/605 | Machine Learning/Mathematical Modeling Group, with Dr. Jacobs
Spring 2023 OHSU | BME 680 | Digital Signal Processing, with Dr. Jacobs

Selected publications

T1D: type 1 diabetes | MS: multiple sclerosis | BioSen: biosensors | PCa: prostate cancer | DS: data science

    [T1D]
    C. Mosquera-Lopez et al., “Combining uncertainty-aware predictive modeling and a bedtime Smart Snack intervention to prevent nocturnal hypoglycemia in people with type 1 diabetes on multiple daily injections,” Journal of the American Medical Informatics Association, p. ocad196, Oct. 2023, doi: 10.1093/jamia/ocad196.
    [MS]
    T. Kushner, C. Mosquera-Lopez, A. Hildebrand, M. H. Cameron, and P. G. Jacobs, “Risky movement: Assessing fall risk in people with multiple sclerosis with wearable sensors and beacon-based smart-home monitoring,” Multiple Sclerosis and Related Disorders, vol. 79, p. 105019, Nov. 2023, doi: 10.1016/j.msard.2023.105019.
    [DS]
    E. N. Erickson, N. Gotlieb, L. M. Pereira, L. Myatt, C. Mosquera-Lopez, and P. G. Jacobs, “Predicting labor onset relative to the estimated date of delivery using smart ring physiological data,” npj Digit. Med., vol. 6, no. 1, Art. no. 1, Aug. 2023, doi: 10.1038/s41746-023-00902-y.
    [T1D]
    P. G. Jacobs et al., “Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial,” The Lancet Digital Health, p. S2589750023001127, Aug. 2023, doi: 10.1016/S2589-7500(23)00112-7.
    [T1D]
    C. Mosquera-Lopez et al., “Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence,” npj Digit. Med., vol. 6, no. 1, Art. no. 1, Mar. 2023, doi: 10.1038/s41746-023-00783-1.
    [T1D]
    C. Mosquera-Lopez, K. L. Ramsey, V. Roquemen-Echeverri, and P. G. Jacobs, “Modeling risk of hypoglycemia during and following physical activity in people with type 1 diabetes using explainable mixed-effects machine learning,” Computers in Biology and Medicine, vol. 155, p. 106670, Mar. 2023, doi: 10.1016/j.compbiomed.2023.106670.
    [T1D]
    N. S. Tyler, C. Mosquera-Lopez, G. M. Young, J. El Youssef, J. R. Castle, and P. G. Jacobs, “Quantifying the impact of physical activity on future glucose trends using machine learning,” iScience, vol. 25, no. 3, p. 103888, Mar. 2022, doi: 10.1016/j.isci.2022.103888.
    [T1D]
    C. Mosquera-Lopez and P. G. Jacobs, “Incorporating Glucose Variability into Glucose Forecasting Accuracy Assessment Using the New Glucose Variability Impact Index and the Prediction Consistency Index: An LSTM Case Example,” J Diabetes Sci Technol, vol. 16, no. 1, pp. 7–18, Jan. 2022, doi: 10.1177/19322968211042621.
    [MS]
    C. Mosquera-Lopez et al., “Automated Detection of Real-World Falls: Modeled From People With Multiple Sclerosis,” IEEE J. Biomed. Health Inform., vol. 25, no. 6, pp. 1975–1984, Jun. 2021, doi: 10.1109/JBHI.2020.3041035.
    [T1D]
    C. Mosquera-Lopez et al., “Predicting and Preventing Nocturnal Hypoglycemia in Type 1 Diabetes Using Big Data Analytics and Decision Theoretic Analysis,” Diabetes Technology & Therapeutics, vol. 22, no. 11, pp. 801–811, Nov. 2020, doi: 10.1089/dia.2019.0458.
    [BioSen]
    P. G. Jacobs et al., “Measuring glucose at the site of insulin delivery with a redox-mediated sensor,” Biosensors and Bioelectronics, vol. 165, p. 112221, Oct. 2020, doi: 10.1016/j.bios.2020.112221.
    [T1D]
    N. S. Tyler et al., “An artificial intelligence decision support system for the management of type 1 diabetes,” Nat Metab, vol. 2, no. 7, pp. 612–619, Jul. 2020, doi: 10.1038/s42255-020-0212-y.
    [Sleep]
    C. Mosquera-Lopez et al., “Design and Evaluation of a Non-Contact Bed-Mounted Sensing Device for Automated In-Home Detection of Obstructive Sleep Apnea: A Pilot Study,” Biosensors, vol. 9, no. 3, p. 90, Jul. 2019, doi: 10.3390/bios9030090.
    [PCa]
    C. Mosquera-Lopez, S. Agaian, A. Velez-Hoyos, and I. Thompson, “Computer-Aided Prostate Cancer Diagnosis From Digitized Histopathology: A Review on Texture-Based Systems,” IEEE Rev Biomed Eng, vol. 8, pp. 98–113, 2015, doi: 10.1109/RBME.2014.2340401.