Q-Q similarity network based on common risk factors.

<p>The network consists of the 18 questions connected to each other with a criterion of q ≤ 0.01. Questions (circles) are connected by gray lines. The width of the edge reflects the significance of the q value. This network was visualized by Cytoscape 3.2.0.</p

Top Ranked Risk Factors for Each Category and Directions.

<p>Top Ranked Risk Factors for Each Category and Directions.</p

Network of risk factors (selected more than once in risk model profile) to each question.

<p>The network consists of the 38 risk factors, which have been selected more than once in risk model profile (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156076#pone.0156076.t001" target="_blank">Table 1</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156076#pone.0156076.s004" target="_blank">S3 Table</a>) and 18 questions (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156076#pone.0156076.s002" target="_blank">S1 Table</a>) with the LASSO algorithm. Risk factors (blue rectangle) and questions (orange circles) are connected by green lines (negative association) or red lines (positive association). The node size reflects the amount of associated risk factors. The edge width and the color grade reflect strength of the odds ratio from the risk model. This network was visualized using Cytoscape 3.2.0.</p

Hieratical clustering on binary responses for 18 questions.

<p>The cluster consists of similarity of 18 questions (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156076#sec006" target="_blank">Methods</a>).</p

Sample output.

<p>STORMSeq provides basic visualization for summary statistics, such as (A) genome-wide SNP density and (B) size distribution of short indels.</p

Automated Detection of Off-Label Drug Use

<div><p>Off-label drug use, defined as use of a drug in a manner that deviates from its approved use defined by the drug's FDA label, is problematic because such uses have not been evaluated for safety and efficacy. Studies estimate that 21% of prescriptions are off-label, and only 27% of those have evidence of safety and efficacy. We describe a data-mining approach for systematically identifying off-label usages using features derived from free text clinical notes and features extracted from two databases on known usage (Medi-Span and DrugBank). We trained a highly accurate predictive model that detects novel off-label uses among 1,602 unique drugs and 1,472 unique indications. We validated 403 predicted uses across independent data sources. Finally, we prioritize well-supported novel usages for further investigation on the basis of drug safety and cost.</p></div

Training and testing a classifier to recognize used-to-treat relationships.

<p>We created a gold standard of positive and negative examples of known drug usage. Positive examples were taken from Medi-Span. We created negative examples by randomly selecting positive examples and then randomly choosing a drug and indication with roughly the same frequency of mentions in STRIDE as the real usage. These were then checked against Medi-Span to filter out inadvertently generated known usages. The gold standard dataset contained 4 negative examples for each positive case. For each drug-indication pair in the gold standard, we calculated features summarizing the pattern of mentions of the drugs and indications in 9.5 million clinical notes from STRIDE. We used Medi-Span and Drugbank to calculate features summarizing domain knowledge about drugs and their usages. 80% of the gold standard was used to train an SVM classifier, and the resulting model was tested on the remaining 20%.</p

Approximate costs for STORMSeq.

<p>Note that these costs are approximate and may depend on a number of factors related to the input files.</p

Overview of the STORMSeq system.

<p>The user uploads short reads to Amazon S3 and starts a webserver on Amazon EC2, which controls the mapping and variant calling pipeline. Progress can be monitored on the webserver and results are uploaded to persistent storage on Amazon S3.</p

Predicted off-label usages binned by risk and cost and ranked by support in FAERS.

<p>We ranked predicted, novel off-label usages on the basis of risk and cost, as represented by our risk and cost indices for each drug. FAERS Support for each drug-indication pair is the number of distinct case reports in FAERS in which the drug was explicitly listed as being used to treat the indication. The risk index is a quantitative score that represents the expected disutility of adverse events related to the use of the drug in question, normalized to the range [0, 1] so that drugs that have a higher risk of causing serious adverse events have higher values. The cost index is based on the mean unit cost of the drug in question in Medi-Span, normalized to the range [0, 1] with more expensive drugs having a higher value.</p