Evaluation of AIML + HDR—A Course to Enhance Data Science Workforce Capacity for Hispanic Biomedical Researchers

Frances Heredia-Negron, Natalie Alamo-Rodriguez, Lenamari Oyola-Velazquez, Brenda Nieves, Kelvin Carrasquillo, Harry Hochheiser, Brian Fristensky, Istoni Daluz-Santana, Emma Fernandez-Repollet and Abiel Roche-Lima ()
Additional contact information
Frances Heredia-Negron: RCMI-CCRHD Program, Medical Sciences Campus, University of Puerto Rico, San Juan 00934, Puerto Rico
Natalie Alamo-Rodriguez: RCMI-CCRHD Program, Medical Sciences Campus, University of Puerto Rico, San Juan 00934, Puerto Rico
Lenamari Oyola-Velazquez: Department of Public Health, Medical Sciences Campus, University of Puerto Rico, San Juan 00934, Puerto Rico
Brenda Nieves: RCMI-CCRHD Program, Medical Sciences Campus, University of Puerto Rico, San Juan 00934, Puerto Rico
Kelvin Carrasquillo: RCMI-CCRHD Program, Medical Sciences Campus, University of Puerto Rico, San Juan 00934, Puerto Rico
Harry Hochheiser: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15260, USA
Brian Fristensky: Department of Plant Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
Istoni Daluz-Santana: Department of Biostatistics and Epidemiology, Medical Sciences Campus, University of Puerto Rico, San Juan 00934, Puerto Rico
Emma Fernandez-Repollet: RCMI-CCRHD Program, Medical Sciences Campus, University of Puerto Rico, San Juan 00934, Puerto Rico
Abiel Roche-Lima: RCMI-CCRHD Program, Medical Sciences Campus, University of Puerto Rico, San Juan 00934, Puerto Rico

IJERPH, 2023, vol. 20, issue 3, 1-13

Abstract: Artificial intelligence (AI) and machine learning (ML) facilitate the creation of revolutionary medical techniques. Unfortunately, biases in current AI and ML approaches are perpetuating minority health inequity. One of the strategies to solve this problem is training a diverse workforce. For this reason, we created the course “Artificial Intelligence and Machine Learning applied to Health Disparities Research (AIML + HDR)” which applied general Data Science (DS) approaches to health disparities research with an emphasis on Hispanic populations. Some technical topics covered included the Jupyter Notebook Framework, coding with R and Python to manipulate data, and ML libraries to create predictive models. Some health disparities topics covered included Electronic Health Records, Social Determinants of Health, and Bias in Data. As a result, the course was taught to 34 selected Hispanic participants and evaluated by a survey on a Likert scale (0–4). The surveys showed high satisfaction (more than 80% of participants agreed) regarding the course organization, activities, and covered topics. The students strongly agreed that the activities were relevant to the course and promoted their learning (3.71 ± 0.21). The students strongly agreed that the course was helpful for their professional development (3.76 ± 0.18). The open question was quantitatively analyzed and showed that seventy-five percent of the comments received from the participants confirmed their great satisfaction.

Keywords: data science; artificial intelligence; machine learning; health disparities; hispanic biomedical research (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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