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.
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.
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.
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.
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.