![]() ![]() Thinking outside the clinical "box" and beyond the strict limits of individual factors, Rumi Chunara, associate professor of computer science and engineering at NYU Tandon and of biostatistics at the NYU GPH, found a new approach to incorporating the larger web of relevant data for predictive modeling for individual and community health outcomes. Researchers at the NYU Tandon School of Engineering and NYU School of Global Public Health (NYU GPH), in a new perspective, "Machine learning and algorithmic fairness in public and population health," in Nature Machine Intelligence, aim to activate the machine learning community to account for "macro" factors and their impact on health. While data on such "macro" factors is critical to tracking and predicting health outcomes for individuals and communities, analysts who apply machine-learning tools to health outcomes tend to rely on "micro" data constrained to purely clinical settings and driven by healthcare data and processes inside the hospital, leaving factors that could shed light on healthcare disparities in the dark. ![]()
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