Affiliations
Associate Professor of Computer Science
School of Computing College of Engineering, Computing and Applied Sciences Clemson University
Dr. Gary Spitzer Endowed Distinguished Professor of Genomics
Center for Human Genetics Clemson University
Contact Information
315 McAdams Hall Clemson, SC 29634 amasino@clemson.edu
Education
PhD (Applied Mathematics) University of Central Florida, Orlando, 2004 MEng (Aerospace Engineering) University of Colorado, Colorado Springs, 1999 BA (Mathematics) Rutgers University, New Brunswick, 1997
Research
My research interests include the development and application of artificial intelligence, data science, bioinformatics, and biomedical informatics methods for biomedical research. A primary focus of my current research is the integration of structured ontology and multiomic data in deep learning models to increase knowledge of rare genetic disorders through phenotype discovery, in-silico variant pathogenicity prediction, and clinical diagnostic planning with large language model interfaces. I am also interested in development and application of related methods for clinical decision support systems in settings that require continuous patient monitoring such as early sepsis recognition in the NICU and remote patient monitoring. For more informaiton, visit the Masino Lab Website.
Selected Publications
Kark SM, Worthington MA, Christie RH, Masino AJ: Opportunities for Digital Health Technology: Identifying Unmet Needs for Bipolar Misdiagnosis and Depression Care Management. Frontiers in Digital Health 2023; 5:1221754. doi: 10.3389/fdgth.2023.1221754
Epifano JR, Ramachandran RP, Masino AJ, Rasool G: Revisiting the Fragility of Influence Functions. Neural Networks 162: 581-588, 2023
Campbell EA, Maltenfort MG, Shults J, Forrest CB, Masino AJ: Characterizing clinical pediatric obesity subtypes using electronic health record data. PLOS Digital Health 1.8 e0000073, 2022
Huang BH, Wang R, Masino AJ, Obstfeld O: Aiding Clinical Assessment of Neonatal Sepsis Using Hematological Analyzer Data with Machine Learning Techniques. International Journal of Laboratory Hematology May, 2021.
Bose S, Kenyon CC, Masino AJ: Personalized prediction of early childhood asthma persistence: a machine learning approach. PLOS One 16(3): e0247784, March 2021.
Folweiler KA, Sandsmark DK, Diaz-Arrastia R, Cohen AS, Masino AJ: Unsupervised machine learning reveals novel traumatic brain injury patient phenotypes with distinct acute injury profiles and long-term outcomes. Journal of Neurotrauma 37(12): 1431-1444, June 2020.
Masino AJ, Forsyth D, Nuske H, Herrington J, Pennington J, Kushleyeva Y, Bonafide CP: m-Health and Autism: Recognizing Stress and Anxiety with Machine Learning and Wearables Data. Proceedings of 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems Page: 714-719, 2019.
Masino AJ, Harris MC, Forsyth D, Ostapenko S, Srinivasan L, Bonafide CP, Balamuth F, Schmatz M, Grundmeier, RW: Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLoS One 14(2), February 2019.
Masino AJ, Forsyth D, Fiks AG: Detecting Adverse Drug Reactions on Twitter with Convolutional Neural Networks and Word Embedding Features. Journal of Healthcare Informatics Research 2: 25-43, June 2018 Notes: https://doi.org/10.1007/s41666-018-0018-9
Cocos A, Qian T, Callison-Burch C, Masino AJ: Crowd control: Effectively utilizing unscreened crowd workers for biomedical data annotation. Journal of Biomedical Informatics 69: 86-92, 2017.
Cocos A, Fiks AG, Masino AJ: Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts. Journal of the American Medical Informatics Association 24(4): 813-821, 2017
Masino AJ, Dechene ET, Dulik M, Spinner NB, Krantz ID, Wilkens A, Pennington JW, White PS: Clinical gene-phenotype variant prioritization: A semantic similarity approach using the Human Phenotype Ontology. BMC Bioinformatics 15: 248, 2014.