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Spartan Daily, February 1, 2018
Volume 150, Issue 4https://scholarworks.sjsu.edu/spartan_daily_2018/1003/thumbnail.jp
Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance
We present a machine learning-based methodology capable of providing
real-time ("nowcast") and forecast estimates of influenza activity in the US by
leveraging data from multiple data sources including: Google searches, Twitter
microblogs, nearly real-time hospital visit records, and data from a
participatory surveillance system. Our main contribution consists of combining
multiple influenza-like illnesses (ILI) activity estimates, generated
independently with each data source, into a single prediction of ILI utilizing
machine learning ensemble approaches. Our methodology exploits the information
in each data source and produces accurate weekly ILI predictions for up to four
weeks ahead of the release of CDC's ILI reports. We evaluate the predictive
ability of our ensemble approach during the 2013-2014 (retrospective) and
2014-2015 (live) flu seasons for each of the four weekly time horizons. Our
ensemble approach demonstrates several advantages: (1) our ensemble method's
predictions outperform every prediction using each data source independently,
(2) our methodology can produce predictions one week ahead of GFT's real-time
estimates with comparable accuracy, and (3) our two and three week forecast
estimates have comparable accuracy to real-time predictions using an
autoregressive model. Moreover, our results show that considerable insight is
gained from incorporating disparate data streams, in the form of social media
and crowd sourced data, into influenza predictions in all time horizon
On the Ground Validation of Online Diagnosis with Twitter and Medical Records
Social media has been considered as a data source for tracking disease.
However, most analyses are based on models that prioritize strong correlation
with population-level disease rates over determining whether or not specific
individual users are actually sick. Taking a different approach, we develop a
novel system for social-media based disease detection at the individual level
using a sample of professionally diagnosed individuals. Specifically, we
develop a system for making an accurate influenza diagnosis based on an
individual's publicly available Twitter data. We find that about half (17/35 =
48.57%) of the users in our sample that were sick explicitly discuss their
disease on Twitter. By developing a meta classifier that combines text
analysis, anomaly detection, and social network analysis, we are able to
diagnose an individual with greater than 99% accuracy even if she does not
discuss her health.Comment: Presented at of WWW2014. WWW'14 Companion, April 7-11, 2014, Seoul,
Kore
On the Ground Validation of Online Diagnosis with Twitter and Medical Records
Social media has been considered as a data source for tracking disease.
However, most analyses are based on models that prioritize strong correlation
with population-level disease rates over determining whether or not specific
individual users are actually sick. Taking a different approach, we develop a
novel system for social-media based disease detection at the individual level
using a sample of professionally diagnosed individuals. Specifically, we
develop a system for making an accurate influenza diagnosis based on an
individual's publicly available Twitter data. We find that about half (17/35 =
48.57%) of the users in our sample that were sick explicitly discuss their
disease on Twitter. By developing a meta classifier that combines text
analysis, anomaly detection, and social network analysis, we are able to
diagnose an individual with greater than 99% accuracy even if she does not
discuss her health.Comment: Presented at of WWW2014. WWW'14 Companion, April 7-11, 2014, Seoul,
Kore
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