Apurv Soni
Biographic Data
| ID | 5124487 |
|---|---|
| NAME | Apurv Soni |
| GIVEN NAMES | Apurv |
| FAMILY NAME | Soni |
| SIGNATURE | SONI A |
| AFFILIATIONS | University of Massachusetts Chan Medical School |
| ORCID | 0000-0001-5049-3657 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Artificial intelligence conversational agents in mental health
AI conversational agents were perceived as useful and beneficial for increasing access to care, but concerns about AI's empathy, capabilities, safety, and human involvement in mental healthcare were prevalent. Future implementation and integration of AI conversational agents should consider patient perspectives to enhance their acceptability and effectiveness
Opinion
The integration of artificial intelligence (AI) and machine learning (ML) into healthcare is growing, with tools often limited by data scarcity and biases; issues particularly pronounced in mental health research. Data augmentation, a method of artificially expanding datasets, holds promise for addressing these challenges by creating synthetic data, improving diversity, and reducing costs. This has shown success in medical imaging, yet mental hea…
Mind the Gap
Association of neighborhood-level sociodemographic factors with Direct-to-Consumer (DTC) distribution of Covid-19 rapid antigen tests in 5 US communities
Our findings suggest that online ordering is not associated with geospatial clustering based on sociodemographic characteristics. Observed temporal preferences for DTC ordering can guide public health messaging around DTC testing programs
Predictive algorithm to stratify newborns at-risk for child undernutrition in India
Background: India is at the epicentre of global child undernutrition. Strategies to identify at-risk populations are needed in the context of limited resources. Methods: Data from children under the age of five surveyed in the 2015-2016 National Family Health Survey were used. Child undernutrition was assessed using anthropometric measurements. Predictor variables were identified from the extant literature and included if they could be measured a…
No prominent works on this page.
Predictive algorithm to stratify newborns at-risk for child undernutrition in India
Background: India is at the epicentre of global child undernutrition. Strategies to identify at-risk populations are needed in the context of limited resources. Methods: Data from children under the age of five surveyed in the 2015-2016 National Family Health Survey were used. Child undernutrition was assessed using anthropometric measurements. Predictor variables were identified from the extant literature and included if they could be measured a…
Association of neighborhood-level sociodemographic factors with Direct-to-Consumer (DTC) distribution of Covid-19 rapid antigen tests in 5 US communities
Our findings suggest that online ordering is not associated with geospatial clustering based on sociodemographic characteristics. Observed temporal preferences for DTC ordering can guide public health messaging around DTC testing programs
Mind the Gap
Artificial intelligence conversational agents in mental health
AI conversational agents were perceived as useful and beneficial for increasing access to care, but concerns about AI's empathy, capabilities, safety, and human involvement in mental healthcare were prevalent. Future implementation and integration of AI conversational agents should consider patient perspectives to enhance their acceptability and effectiveness
Opinion
The integration of artificial intelligence (AI) and machine learning (ML) into healthcare is growing, with tools often limited by data scarcity and biases; issues particularly pronounced in mental health research. Data augmentation, a method of artificially expanding datasets, holds promise for addressing these challenges by creating synthetic data, improving diversity, and reducing costs. This has shown success in medical imaging, yet mental hea…
Medicine (3 works) · Psychology (3 works) · Demography (2 works) · Environmental health (2 works) · Geography (2 works) · Health Policy Implementation Science (2 works) · Mental health (2 works) · Mental Health Treatment and Access (2 works) · Population (2 works) · Psychiatry (2 works)