Peer-reviewed journal articles
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.
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.
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.
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.
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.
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.
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.
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, p. dia.2022.0252, Aug. 2022, doi: 10.1089/dia.2022.0252.
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.
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.
A. Hildebrand, P. G. Jacobs, J. G. Folsom, C. Mosquera-Lopez, E. Wan, and M. H. Cameron, “Comparing fall detection methods in people with multiple sclerosis: A prospective observational cohort study,” Multiple Sclerosis and Related Disorders, vol. 56, p. 103270, Nov. 2021, doi: 10.1016/j.msard.2021.103270.
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.
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.
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.
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.
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.
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.
Peer-reviewed conference papers
V. Roquemen-Echeverri et al., “An AI-Powered Tool for Automatic Heart Sound Quality Assessment and Segmentation,” in 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Houston, TX, USA, Dec. 2021, pp. 3065–3074. doi: 10.1109/BIBM52615.2021.9669514.
C. Mosquera-Lopez, J. Leitschuh, J. Condon, C. C. Hagen, C. Hanks, and P. G. Jacobs, “In-Home Sleep Apnea Severity Classification using Contact-free Load Cells and an AdaBoosted Decision Tree Algorithm,” in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, Jul. 2018, pp. 6044–6047. doi: 10.1109/EMBC.2018.8513602.
P. G. Jacobs et al., “Design and evaluation of a portable smart-phone based peripheral neuropathy test platform,” in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, Jul. 2018, pp. 1–4. doi: 10.1109/EMBC.2018.8513100.
C. Mosquera-Lopez, R. Escobar, and S. Agaian, “Modeling human-perceived quality for the assessment of digitized histopathology color standardization,” in 2015 IEEE International Conference on Imaging Systems and Techniques (IST), Macau, China, Sep. 2015, pp. 1–6. doi: 10.1109/IST.2015.7294526.
C. Mosquera-Lopez and D. Pack, “Comparative Out-of-Sequence Estimation Techniques for Multi-Sensor Target Tracking,” in Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing, San Antonio, Texas, USA, Oct. 2014, p. V002T26A001. doi: 10.1115/DSCC2014-5863.
C. Mosquera-Lopez, S. Agaian, and A. Velez-Hoyos, “The development of a multi-stage learning scheme using new tissue descriptors for automatic grading of prostatic carcinoma,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, May 2014, pp. 3586–3590. doi: 10.1109/ICASSP.2014.6854269.
A. Greenblatt, C. Mosquera-Lopez, and S. Agaian, “Quaternion Neural Networks Applied to Prostate Cancer Gleason Grading,” in 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, Oct. 2013, pp. 1144–1149. doi: 10.1109/SMC.2013.199.
C. Mosquera-Lopez and S. Agaian, “Iterative local color normalization using fuzzy image clustering,” Baltimore, Maryland, USA, May 2013, p. 875518. doi: 10.1117/12.2016051.
C. Mosquera Lopez and S. Agaian, “A new set of wavelet- and fractals-based features for Gleason grading of prostate cancer histopathology images,” Burlingame, California, USA, Feb. 2013, p. 865516. doi: 10.1117/12.1000193.
C. M. Lopez et al., “Exploration of efficacy of gland morphology and architectural features in prostate cancer gleason grading,” in 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Seoul, Korea (South), Oct. 2012, pp. 2849–2854. doi: 10.1109/ICSMC.2012.6378181.
C. Mosquera-Lopez, S. Agaian, A. Velez-Hoyos, and I. Thompson, “Computer-Aided Prostate Cancer Diagnosis: Principles, Recent Advances, and Future Prospective,” in Computer-Aided Cancer Detection and Diagnosis: Recent Advances, vol. PM240, Bellingham, Washington: SPIE Press, 2014, pp. 229–268.
L. M. Wilson et al., “291-OR: Using the Performance in Exercise and Knowledge (PEAK) Guidelines Incorporated in a Smartphone-Based Decision Support System Improves Glucose Outcomes during Free-Living Exercise,” in Diabetes, Jun. 2022, vol. 71, pp. 291-OR. doi: 10.2337/db22-291-OR.
J. R. Castle et al., “771-P: Acceptance of Decision Support Recommendations Improves Time in Range for People Living with Type 1 Diabetes on Multiple Daily Injections,” in Diabetes, Jun. 2022, vol. 71, pp. 771-P. doi: 10.2337/db22-771-P.
P. Jacobs et al., “Integrating metabolic expenditure data from wearable sensors into an automated insulin delivery system: Clinical study results.,” 2022.
T. Kushner et al., “DIABLOCKS: Multivariate pattern detection for patient-specific identification and targeted adjustment of problem patterns in diabetes care management.,” 2022.
C. Mosquera-Lopez, K. Ramsey, L. Wilson, G. Young, J. Castle, and P. Jacobs, “Predicting hypoglycemia and identifying risk factors during and following physical activity in type 1 diabetes using explainable models,” presented at the 15th Conference on Advanced Technologies & Treatments for Diabetes (ATTD), 2022.
C. Mosquera-Lopez, W. Hilts, L. Wilson, J. Castle, and P. Jacobs, “Meal detection and size estimation using machine learning: towards fully automated insulin delivery systems.,” presented at the 15th Conference on Advanced Technologies & Treatments for Diabetes (ATTD), 2022.
N. Tyler et al., “Comparison of artificial-intelligence decision support for multiple daily injection therapy with automated insulin delivery after 3 months of use in silico,” presented at the American Diabetes Association (ADA), 2020.
P. Jacobs et al., “Using machine learning to predict glucose changes during aerobic, anaerobic, and mixed forms of exercise in patients with type 1 diabetes,” presented at the 13th Conference on Advanced Technologies & Treatments for Diabetes (ATTD), 2020.
N. Tyler et al., “Improving glycemic outcomes in t1d using an automated decision support recommender system: evaluation in silico and compared with physician recommendations,” presented at the American Diabetes Association (ADA), 2019.
C. Mosquera-Lopez, R. Dodier, N. Tyler, N. Resalat, and P. Jacobs, “Leveraging a Big Dataset to Develop a Recurrent Neural Network to Predict Adverse Glycemic Events in Type 1 Diabetes,” presented at the 12th Conference on Advanced Technologies & Treatments for Diabetes (ATTD), 2019.
C. Mosquera Lopez, R. Dodier, N. S. Tyler, and P. G. Jacobs, “Using a support vector regression model to predict nocturnal hypoglycemia in patients with type 1 diabetes.,” presented at the 12th Conference on Advanced Technologies & Treatments for Diabetes (ATTD), 2019.
C. Mosquera-Lopez et al., “Automated Detection of Real-World Falls: Modeled From People With Multiple Sclerosis,” presented at the 8th International Symposium on Gait and Balance in Multiple Sclerosis, 2018.
C. Mosquera-Lopez, S. Agaian, and A. Velez-Hoyos, “Diagnóstico de cancer de próstata asistido por computador usando imágenes digitalizadas de biopsias.,” presented at Congreso Colombiano de Patología, Cartagena de Indias, Colombia, 2014.
C. Mosquera-Lopez, “Digital health technologies,” presented at the Congreso Internacional Derecho, Tecnología e Innovación DTI, Medellin, Colombia, 2023.
C. Mosquera-Lopez, “Artificial Intelligence applied to decision support systems in type 1 diabetes,” presented at the INFORMS Español, 2021.
C. Mosquera Lopez, “Leveraging in-silico data and large free-living glucose management datasets to predict glucose and detect meal intake in type 1 diabetes.,” presented at the Oregon Bioengineering Symposium – Regenerative Medicine, Rehabilitation, and Artificial Intelligence, Oregon Health & Science University, University of Oregon, and Oregon State University. Portland, OR, USA, 2021.
C. Mosquera-Lopez, “Artificial Intelligence in multiple sclerosis and type 1 diabetes.,” presented at the Research Seminar – Mathematical Modeling Group, Universidad EAFIT, Medellin, Colombia, 2020.