A fully automated artificial intelligence (AI)-based multispectral absorbance imaging system effectively classified function and potency of induced pluripotent stem cell derived retinal pigment epithelial cells (iPSC-RPE) from patients with age-related macular degeneration (AMD). The finding from the system could be applied to assessing future cellular therapies, according to research presented at the 2018 ARVO annual meeting. The software, which uses convolutional neural network (CNN) deep learning algorithms, effectively evaluated release criterion for the iPSC-RPE cell-based therapy in a standard, reproducible, and cost-effective fashion. The AI-based analysis was as specific and sensitive as traditional molecular and physiological assays, without the need for human intervention. "Cells can be classified with high accuracy using nothing but absorbance images," wrote lead investigator Nathan Hotaling and colleagues from the National Institutes of Health in their poster.
Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text.
The anti-vaccination movement threatens public health by reducing the likelihood of disease eradication. With social media’s purported role in disseminating anti-vaccine information, it is imperative to understand the drivers of attitudes among participants involved in the vaccination debate on a communication channel critical to the movement: Twitter. Using four years of longitudinal data capturing vaccine discussions on Twitter, we identify users who persistently hold pro and anti attitudes, and those who newly adopt anti attitudes towards vaccination. After gathering each user’s entire Twitter timeline, totaling to over 3 million tweets, we explore differences in the individual narratives across the user cohorts. We find that those with long-term anti-vaccination attitudes manifest conspiratorial thinking, mistrust in government, and are resolute and in-group focused in language. New adoptees appear to be predisposed to form anti-vaccination attitudes via similar government distrust and general paranoia, but are more social and less certain than their long-term counterparts. We discuss how this apparent predisposition can interact with social media-fueled events to bring newcomers into the anti-vaccination movement. Given the strong base of conspiratorial thinking underlying anti-vaccination attitudes, we conclude by highlighting the need for alternatives to traditional methods of using authoritative sources such as the government when correcting misleading vaccination claims.
Analyzing queries made to Google, Bing, and other search engines can reveal the potentially dangerous consequences of mixing prescriptions before they are known to the Food and Drug Administration (FDA), according to a new study. Such data mining could even expose medical risks that slip through clinical trials undetected. Pharmaceuticals often have side effects that go unnoticed until they're already available to the public. This is especially true of side effects that emerge when two drugs interact, largely because drug trials try to pinpoint the effects of one drug at a time. Physicians have a few ways to hunt for these hidden risks, such as reports to FDA from doctors, nurses, and patients.
The task of decision-making under uncertainty is daunting, especially for problems which have significant complexity. Healthcare policy makers across the globe are facing problems under challenging constraints, with limited tools to help them make data driven decisions. In this work we frame the process of finding an optimal malaria policy as a stochastic multi-armed bandit problem, and implement three agent based strategies to explore the policy space. We apply a Gaussian Process regression to the findings of each agent, both for comparison and to account for stochastic results from simulating the spread of malaria in a fixed population. The generated policy spaces are compared with published results to give a direct reference with human expert decisions for the same simulated population. Our novel approach provides a powerful resource for policy makers, and a platform which can be readily extended to capture future more nuanced policy spaces.