257 research outputs found
The role of artificial intelligence in maternal and child health: Progress, controversies, and future directions.
No full textThis debate paper examines the transformative potential of Artificial Intelligence (AI), specifically through Machine Learning (ML), in enhancing preventive measures in maternal and child health (MCH). With the proliferation of Big Data, ML has become crucial in handling complex, non-linear interactions among health determinants to not only predict but also prevent adverse outcomes. This paper underscores AI's applications in early interventions that could decrease the incidence of MCH issues. It reviews technological advancements while addressing ethical, practical, and data-related challenges in applying AI in preventive healthcare. Emphasis is placed on recent supervised, unsupervised, and reinforcement learning applications that significantly advance preventive care, particularly in low-resource settings. The manuscript discusses the development of AI models for early diagnosis, comprehensive risk assessments, and customized preventive interventions, while highlighting challenges like data diversity, privacy issues, and integrating multimodal health data
Ill-Protected Portraits: Mathew Brady and Photographic Copyright
No full textThis article focuses on Mathew Brady’s attempts to use copyright to protect his photographs. For a time, Brady received so much credit in the press that his name became synonymous with all photographs of the Civil War. This prominence in the photography trade and in the public imagination makes Brady’s use of copyright an ideal case for considering the relationship between photography and authorship. The research of this study cites relevant archival sources, including copyright registration practices, copyright notices on printed photographs, and the case of Brady & Gibson v. Bellew (1865) to demonstrate deliberate attempts by Brady to protect his work from infringement, secure economic compensation, and to link his name legally with images he believed would have enduring value. While copyright ultimately failed to protect Brady’s long-term financial interests, part of his attribution strategy established him as a photographic “author” and ensured his name would remain linked with his photographs
Quantifying exposure to wildfire smoke among school-aged children in California, 2006 – 2021
No full textQuantifying exposure to wildfire smoke among school-aged children in California, 2006 – 2021
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Modeling, Prediction, and Inference: Applications in Social and Infectious Disease Epidemiology
No full textEpidemiology is at an exciting stage. Methods and techniques from other areas, such as data science, combined with advances in classic fields, such as Bayesian statistics, provide a new set of tools to explore epidemiological questions. In this dissertation, with collaboration from my committee members, I applied some of these new tools to three distinct epidemiological problems. My work contributes to the literature by showing how the synthesis and arbitrage of ideas from other fields can be adapted to diverse epidemiological settings.
First, I combined innovations in Bayesian statistics with advances in computation and statistical programming languages to jointly model racial/ethnic disparities in premature mortality at a scale not previously possible. Specifically, I used the shared component model to decompose premature mortality risk in non-Hispanic black and white Americans in the contiguous US into race-specific and shared components. I found that the majority of geographic variation in black-specific premature mortality risk was not shared with the white population, despite half of the geographic variation in white risk being shared with the black population.
Second, I estimated rates of missingness in a new method of spatiotemporally dense data collection called digital phenotyping. This type of data collection uses smartphones and does not require active participation by the user, making it a potentially useful data collection mechanism for epidemiologists interested in individual-level behavior. I found rates of missingness to be non-trivial (16-18%), increasing only slowly over time (0.5-1% per week), and largely uncorrelated with phone type or common demographic characteristics.
Third, I borrowed techniques from data science to systematically evaluate the performance of different classes and parameterizations of models in predicting dengue in Thailand at the province-level. Specifically, I compared an array of autoregressive models with regularized linear models. We found that model predictive performance varies greatly by both area and forecasting horizon with no single model or class of model performing best in every area or across all time horizons.
In summary, as data science and other fields become embedded in epidemiology, there is a large potential for the use of new tools to answer traditional and new public health questions.digital epidemiology; health inequalities; digital phenotyping; spatial epidemiolog
Quantifying exposure to wildfire smoke among school-aged children in California, 2006 – 2021
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