Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation

Objective: Alcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma patients may be used for phenotyping tasks with natural language processing (NLP) and supervised machine learning. The objective of this study is to train and validate an NLP classifier for identifying patients with alcohol misuse. Materials and Methods: An observational cohort of 1422 adult patients admitted to a trauma center between April 2013 and November 2016. Linguistic processing of clinical notes was performed using the clinical Text Analysis and Knowledge Extraction System. The primary analysis was the binary classification of alcohol misuse. The Alcohol Use Disorders Identification Test served as the reference standard. Results: The data corpus comprised 91 045 electronic health record notes and 16 091 features. In the final machine learning classifier, 16 features were selected from the first 24 hours of notes for identifying alcohol misuse. The classifier's performance in the validation cohort had an area under the receiver-operating characteristic curve of 0.78 (95% confidence interval [CI], 0.72 to 0.85). Sensitivity and specificity were at 56.0% (95% CI, 44.1% to 68.0%) and 88.9% (95% CI, 84.4% to 92.8%). The Hosmer-Lemeshow goodness-of-fit test demonstrates the classifier fits the data well (P.17). A simpler rule-based keyword approach had a decrease in sensitivity when compared with the NLP classifier from 56.0% to 18.2%. Conclusions: The NLP classifier has adequate predictive validity for identifying alcohol misuse in trauma centers. External validation is needed before its application to augment screening

The influence of a season of extreme wet weather events on exposure of the World Heritage Area Great Barrier Reef to pesticides

The 2010–2011 wet season was one of extreme weather for the State of Queensland, Australia. Major rivers adjacent to the Great Barrier Reef (GBR) were discharging at rates 1.5 to >3 times higher than their long term median. Exposure to photosystem II herbicides has been routinely monitored over a period of up to 5 years at 12 inshore GBR sites. The influence of this wet season on exposure to photosystem II herbicides was examined in the context of this long-term monitoring record and during flood plume events in specific regions. Median exposures expressed as diuron equivalent concentration were an average factor of 2.3 times higher but mostly not significantly different (p < 0.05) to the median for the longterm monitoring record. The herbicides metolachlor and tebuthiuron were frequently detected in flood plume waters at concentrations that reached or exceeded relevant water quality guidelines (by up to 4.5 times)