  'Health utilities' measure patient preferences for perfect health compared to
specific unhealthy states, such as asthma, a fractured hip, or colon cancer.
When integrated over time, these estimations are called quality adjusted life
years (QALYs). Until now, characterizing health utilities (HUs) required
detailed patient interviews or written surveys. While reliable and specific,
this data remained costly due to efforts to locate, enlist and coordinate
participants. Thus the scope, context and temporality of diseases examined has
remained limited.
  Now that more than a billion people use social media, we propose a novel
strategy: use natural language processing to analyze public online
conversations for signals of the severity of medical conditions and correlate
these to known HUs using machine learning. In this work, we filter a dataset
that originally contained 2 billion tweets for relevant content on 60 diseases.
Using this data, our algorithm successfully distinguished mild from severe
diseases, which had previously been categorized only by traditional techniques.
This represents progress towards two related applications: first, predicting
HUs where such information is nonexistent; and second, (where rich HU data
already exists) estimating temporal or geographic patterns of disease severity
through data mining.
