We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers. A total of 7149 records (after removing duplicates) were retrieved and screened, and 86 were determined to fit the review criteria. These papers contained information about 71 different clinical NLP systems, which were then analyzed. The NLP systems address a wide variety of important clinical and research tasks. Certain tasks are well addressed by the existing systems, while others remain as open challenges that only a small number of systems attempt, such as extraction of temporal information or normalization of concepts to standard terminologies. This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP.

Salmonella Newport causes more than an estimated 100,000 infections annually in the United States. In 2002, tomatoes grown and packed on the eastern shore of Virginia contaminated with a pan-susceptible S. Newport strain caused illness in 510 patients in 26 states. In July-November 2005, the same strain caused illness in at least 72 patients in 16 states. We conducted a case-control study during the 2005 outbreak, enrolling 29 cases and 140 matched neighbourhood controls. Infection was associated with eating tomatoes (matched odds ratio 9.7, 95% confidence interval 3.3-34.9). Tomatoes were traced back to the eastern shore of Virginia, where the outbreak strain was isolated from pond water used to irrigate tomato fields. Two multistate outbreaks caused by one rare strain, and identification of that strain in irrigation ponds 2 years apart, suggest persistent contamination of tomato fields. Further efforts are needed to prevent produce contamination on farms and throughout the food supply chain.

The Kdp system of Escherichia coli, a transport ATPase with high affinity for potassium, is expressed when turgor pressure is low. Expression requires KdpD, a 99-kDa membrane protein, and KdpE, a 25-kDa soluble cytoplasmic protein. (16,41,49). We suggest that phosphoKdpE is a positive effector of Kdp expression and that low turgor pressure causes KdpD to phosphorylate KdpE. MATERUILS AND METHODSDNA sequencing. The 1.7-kb EcoRI fragment of kdpD, cut from plasmid pWE1103 (35), and the 3-kb EcoRI-HindIII fragment carrying kdpE and a part of kdpD, cut from plasmid pDE14 (35), were cloned in both directions in M13 (M13uml8 and M13uml9; International Biotechnologies, Inc.). The resulting phages, M13-JD2, M13-JD3, M13-JD24, and M13-JD34 (Fig. 1) (19). There were three differences between our initial results and his. The GC at positions 5245 and 5246 (Fig. 2) was erroneously recorded as CG in our work; review of the gels shows that GC is correct. The sequence on one strand at positions 7213 and 7214 differed from that of the complementary strand, but the gel with the least band compression suggested that GC as found by Igarashi was correct.

IntroductionThe rapid expansion of the Internet and computing power in recent years has opened up the possibility of using social media for pharmacovigilance. While this general concept has been proposed by many, central questions remain as to whether social media can provide earlier warnings for rare and serious events than traditional signal detection from spontaneous report data.ObjectiveOur objective was to examine whether specific product–adverse event pairs were reported via social media before being reported to the US FDA Adverse Event Reporting System (FAERS).MethodsA retrospective analysis of public Facebook and Twitter data was conducted for 10 recent FDA postmarketing safety signals at the drug–event pair level with six negative controls. Social media data corresponding to two years prior to signal detection of each product–event pair were compiled. Automated classifiers were used to identify each ‘post with resemblance to an adverse event’ (Proto-AE), among English language posts. A custom dictionary was used to translate Internet vernacular into Medical Dictionary for Regulatory Activities (MedDRA®) Preferred Terms. Drug safety physicians conducted a manual review to determine causality using World Health Organization-Uppsala Monitoring Centre (WHO-UMC) assessment criteria. Cases were also compared with those reported in FAERS.FindingsA total of 935,246 posts were harvested from Facebook and Twitter, from March 2009 through October 2014. The automated classifier identified 98,252 Proto-AEs. Of these, 13 posts were selected for causality assessment of product–event pairs. Clinical assessment revealed that posts had sufficient information to warrant further investigation for two possible product–event associations: dronedarone–vasculitis and Banana Boat Sunscreen--skin burns. No product–event associations were found among the negative controls. In one of the positive cases, the first report occurred in social media prior to signal detection from FAERS, whereas the other case occurred first in FAERS.ConclusionsAn efficient semi-automated approach to social media monitoring may provide earlier insights into certain adverse events. More work is needed to elaborate additional uses for social media data in pharmacovigilance and to determine how they can be applied by regulatory agencies.Electronic supplementary materialThe online version of this article (doi:10.1007/s40264-016-0491-0) contains supplementary material, which is available to authorized users.