Public Health


Science Says _13 Reasons Why_ May Be the Public Health Scare People Thought

WIRED

They've done things like use Twitter data to call attention to a spike in distracted driving incidents thanks to Pokemon Go players behind the wheel. And previous studies have found correlations between suicide search trends and actual suicide rates. So Ayers and his colleagues grabbed search queries from the US between March 31, 2017, the series' release date and April 18, a date the team selected as a cutoff because news of former NFL player Aaron Hernandez's prison suicide might have contaminated the results otherwise. They looked at all searches containing the word "suicide," except for those accompanied by the word "squad," for obvious reasons.


Why Is U.S. Maternal Mortality So High?

Slate

Maternal-fetal medicine specialists like us are tasked with caring for women with "high-risk" pregnancies, usually defined as pregnancies complicated by chronic or acute maternal illness, fetal concerns, or problems related to pregnancy itself (e.g. Our nuclear event--one of the worst things that can happen when we practice--is a mother dying. Maternal mortality in the United States is rare but, sadly, nowhere near rare enough: Data collected from 1990–2015 show that the number of maternal deaths per 100,000 births has increased from 16.9 1990 to 26.4 in 2015. Not only are more American mothers dying than in our peer countries, but we're one of the only developed countries where the death rate is increasing, not decreasing.


AI can play key role in good governance: Microsoft official

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Artificial intelligence or AI as it is called in cyber parlance, and believed to be the next big thing in information and technology, can play a key role in good governance, a senior Microsoft official has said. "We are seeing that governments are benefitting through Artificial Intelligence and are able to bring (governance) closer to people in their countries," Dave Forstrom, director of communications for the Artificial Intelligence (AI) group at Microsoft, told PTI. "In terms of helping create good governance we're seeing an approach industry--wide right now where it's focused on ethical design and those principles that will help to really govern that," he said on the sidelines of the Microsoft's annual developers conference Build 2017. The senior Microsoft official said AI could be of great usage in various fields, including public health, law and order, education and even city sanitation and cleanliness.


IBM Impact Grant to Shenzhen Center for Disease Control & Prevention

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IBM provided an Analytics Assessment & Insights Impact Grant to the Shenzhen CDC to help further their mission of infectious disease prevention and control, shouldering the monitoring, alarming and treatment of emergency public health events in Shenzhen for the 12 million citizenry by building a self adaptive online machine learning module that provides cognitive-based modeling for epidemic disease prediction and analysis on case number and trend. The output of this work has allowed the organization to develop prediction models to help forecast seasonal flu outbreaks and provide information to citizens on affected areas. CDC organizations across China are now considering the implementation of this solution to help track flu and other infectious diseases.


Machine Learning, IBM Watson Aid In Answering Health Benefits Questions

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"We are just starting to discover the countless ways we can apply cognitive computing to healthcare," said Ryan Pellet, senior vice president of consulting and services for Welltok. "We are excited to have addressed a costly and cumbersome issue with our proprietary technology and IBM Watson, and will continue to explore opportunities to simplify the consumer's experience and drive new, more effective ways to engage with and satisfy them."


Machine learning seen speeding diagnoses

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Researchers at Regenstrief Institute and Indiana University School of Informatics and Computing say they now can detect cancer cases using data from free-text pathology reports at least as well--and faster--than clinicians reviewing reports manually. The researchers used existing data algorithms and open source machine learning tools to create a breakthrough electronic approach that could significantly speed patient diagnoses and public health reporting. At Regenstrief/IU, machine learning identified patterns of language in pathology reports, enabling algorithms to create a rule that if certain factors or findings are found in the automated pathology review, then a patient is likely to have cancer. But Indiana could be a good test bed for the technology, as the state as had automated public health surveillance reporting--currently, 40 notifiable diseases to report to public health agencies, since 2000, Grannis contends.


Machine learning rivals human skills in cancer detection

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Two announcements yesterday (April 21) suggest that deep learning algorithms rival human skills in detecting cancer from ultrasound images and in identifying cancer in pathology reports. Samsung Medison, a global medical equipment company and an affiliate of Samsung Electronics, has just updated its RS80A ultrasound imaging system with a deep learning algorithm for breast-lesion analysis. Meanwhile, researchers from the Regenstrief Institute and Indiana University School of Informatics and Computing at Indiana University-Purdue University Indianapolis say they've found that open-source machine learning tools are as good as -- or better than -- humans in extracting crucial meaning from free-text (unstructured) pathology reports and detecting cancer cases. Everything -- physician practices, health care systems, health information exchanges, insurers, as well as public health departments -- are awash in oceans of data.


Machine learning can help detect presence of cancer, improve public health reporting

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To support public health reporting, the use of computers and machine learning can better help with access to unstructured clinical data--including in cancer case detection, according to a recent study. Often, the unstructured free text data made available by electronic health records is obtained by means that are "resource intensive, inherently complex and rely on structured clinical data and dictionary-based approaches," according to the authors of the study, published in the Journal of Biomedical Informatics. The researchers, from the Regenstrief Institute and Indiana University-Purdue University in Indianapolis, used about 7,000 pathology reports from the Indiana health information exchange to attempt to detect cancer cases using already available algorithms and open source machine learning tools. Stanford University researchers also found success in using analysis of free-text notes in electronic health records for surveillance of drug interactions in near real time, adding that the evolution of better tools in natural language processing will help speed up the process.


This algorithm can tell if you're drunk tweeting

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The researchers collected more than 11,000 geotagged tweets from New York City and Monroe County, where Rochester is located, in the northern part of the state. The team also used the data to create heat maps that show drinking and tweeting hot-spots in New York City and Monroe County. "We see that NYC has a larger proportion of user-drinking-now tweets posted from home (within 100 meters from home) whereas in Monroe County a higher proportion of these tweets generated at driving distance (more than 1000 meters from home)," the authors write. "All these analyses will help us understand the merits of these methods for analyzing drinking behavior, via social media, at a large-scale with very little cost, which can lead to new ways of reducing alcohol consumption, a global public health concern," they write.