Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Toward precision public health
13
Zitationen
1
Autoren
2019
Jahr
Abstract
OBJECTIVES: The information in this article is based on a presentation given at the American Institute of Dental Public Health's 2018 Colloquium on Precision Public Health (PPH). A brief introduction to precision medicine provides context for PPH. Precision medicine tailors treatment and prevention strategies for individual patients based on variability of genetic, lifestyle, and environmental factors. An overview of PPH with examples of pilot studies with different research approaches bridges a discussion on an initiative undertaken by the University of Florida to build infrastructure and expertise for PPH research. METHODS: PPH use better and more precise data to target disease prevention and control in the right population at the right time. To facilitate the identification of relevant open access data sources for PPH research a "one-stop" shop was created and tested. Case studies were conducted to validate the data portal's usefulness in identifying at-risk populations. RESULTS: Details of portal data types, research challenges, and university-wide integrated programs are included. Case studies indicated that providing a "one-stop shop" of relevant data sources is an effective tool to aid researchers in identifying at-risk populations. CONCLUSIONS: Research studies undertaken by University of Florida graduate students illustrate how PPH aligns with essential public health services including community engagement to reduce disparities in health care. Assurance of a competent workforce in PPH research approaches may be improved by training new researchers graduating from health science programs with knowledge of data and tools. The move toward PPH is in early stages and much work lies ahead.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.687 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.591 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.114 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.867 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.