Charles Senteio
Biographic Data
| ID | 183437 |
|---|---|
| NAME | Charles Senteio |
| GIVEN NAMES | Charles |
| FAMILY NAME | Senteio |
| SIGNATURE | SENTEIO C |
| AFFILIATIONS | University of Michigan, Ann Arbor, Michigan, USA |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 8 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2013 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 2 |
A scientometric analysis of fairness in health AI literature
Artificial intelligence (AI) and machine learning are central components of today’s medical environment. The fairness of AI, i.e. the ability of AI to be free from bias, has repeatedly come into question. This study investigates the diversity of members of academia whose scholarship poses questions about the fairness of AI. The articles that combine the topics of fairness, artificial intelligence, and medicine were selected from Pubmed, Google Sc…
Trying to Make Things Right
Adherence to treatment recommendations for chronic diseases is notoriously low across all patient populations. But African American patients, who are more likely to live in low-income neighborhoods and to have multiple chronic conditions, are even less likely to follow medical recommendations. Yet we know little about their contextually embedded, adherence-related experiences. We interviewed individuals ( n = 37) with at least two of the followin…
Racial/Ethnic Disparities in Antiretroviral Treatment Among HIV-Infected Pregnant Medicaid Enrollees, 2005–2007
Objectives. We examined racial/ethnic differences in prenatal antiretroviral (ARV) treatment among 3259 HIV-infected pregnant Medicaid enrollees. Methods. We analyzed 2005–2007 Medicaid claims data from 14 southern states, comparing rates of not receiving ARVs and suboptimal versus optimal ARV therapy. Results. More than one third (37.3%) had zero claims for ARV drugs. Three quarters (73.4%) of 346 Hispanic women received no prenatal ARVs. After …
Trying to Make Things Right
Adherence to treatment recommendations for chronic diseases is notoriously low across all patient populations. But African American patients, who are more likely to live in low-income neighborhoods and to have multiple chronic conditions, are even less likely to follow medical recommendations. Yet we know little about their contextually embedded, adherence-related experiences. We interviewed individuals ( n = 37) with at least two of the followin…
Racial/Ethnic Disparities in Antiretroviral Treatment Among HIV-Infected Pregnant Medicaid Enrollees, 2005–2007
Objectives. We examined racial/ethnic differences in prenatal antiretroviral (ARV) treatment among 3259 HIV-infected pregnant Medicaid enrollees. Methods. We analyzed 2005–2007 Medicaid claims data from 14 southern states, comparing rates of not receiving ARVs and suboptimal versus optimal ARV therapy. Results. More than one third (37.3%) had zero claims for ARV drugs. Three quarters (73.4%) of 346 Hispanic women received no prenatal ARVs. After …
Racial/Ethnic Disparities in Antiretroviral Treatment Among HIV-Infected Pregnant Medicaid Enrollees, 2005–2007
Objectives. We examined racial/ethnic differences in prenatal antiretroviral (ARV) treatment among 3259 HIV-infected pregnant Medicaid enrollees. Methods. We analyzed 2005–2007 Medicaid claims data from 14 southern states, comparing rates of not receiving ARVs and suboptimal versus optimal ARV therapy. Results. More than one third (37.3%) had zero claims for ARV drugs. Three quarters (73.4%) of 346 Hispanic women received no prenatal ARVs. After …
Trying to Make Things Right
Adherence to treatment recommendations for chronic diseases is notoriously low across all patient populations. But African American patients, who are more likely to live in low-income neighborhoods and to have multiple chronic conditions, are even less likely to follow medical recommendations. Yet we know little about their contextually embedded, adherence-related experiences. We interviewed individuals ( n = 37) with at least two of the followin…
A scientometric analysis of fairness in health AI literature
Artificial intelligence (AI) and machine learning are central components of today’s medical environment. The fairness of AI, i.e. the ability of AI to be free from bias, has repeatedly come into question. This study investigates the diversity of members of academia whose scholarship poses questions about the fairness of AI. The articles that combine the topics of fairness, artificial intelligence, and medicine were selected from Pubmed, Google Sc…
Political science (2 works) · Psychology (2 works) · Artificial Intelligence (1 works) · Artificial Intelligence in Healthcare and Education (1 works) · Atmospheric chemistry and aerosols (1 works) · Atmospheric Ozone and Climate (1 works) · Atomic oxygen (1 works) · Chemical Reactions and Isotopes (1 works) · Chemistry (1 works) · Chronic disease (1 works)