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As machine learning and deep learning technologies advance thanks to advances in computation, algorithms and data availability, the possibilities of the technology continue to expand in medicine. While these AI-driven approaches have real potential, such systems demand large volumes of representative data, careful privacy and security scrutiny and thoughtful long-term strategic planning. In this Q&A, Kathryn Rough, associate director of the Center for Advanced Evidence Generation at IQVIA, discusses the impact of deep learning on healthcare delivery and recommends steps to take during the design, training, evaluation and deployment phases to increase the likelihood that these models will be safe, effective and ethical when trained on real-world health data. Rough also explores the role of epidemiologists in evaluating these technologies as part of multidisciplinary teams and provides advice for health…Lumen Bioscience cracks the code on spirulina as a biologics factory for c. diff, metabolic disease and more
Clostridioides difficile, commonly known as C. diff, is a significant health threat in the U.S. Recent estimates suggest that C. diff, a common bacteria, can cause infection in roughly 500,000 patients annually in the U.S., with around 30,000 of these cases resulting in death. “Actually, it’s more like 5 million when you think about it in terms of people at risk,” estimated Brian Finrow, founder and CEO of Lumen Bioscience, a biotech specializing in developing novel biologics. C. diff infection (CDI) is especially prevalent and dangerous in the immunocompromised elderly patients who are hospitalized or have been in nursing homes for long periods. “It’s a huge cost driver,” Finrow. Annual U.S. costs tied to CDI have eclipsed $6 billion per year. The infection is especially prevalent and dangerous in the elderly. Complicating matters further, recurrent CDI infections are common, affecting anywhere from 20% to 35% of patients with an initial infection.
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