Validation of a natural language processing algorithm using national reporting data to improve identification of anesthesia-related ADVerse evENTs: The "ADVENTURE" study.

BACKGROUND: Reporting and analysis of adverse events (AE) is associated with improved health system learning, quality outcomes, and patient safety. Manual text analysis is time-consuming, costly, and prone to human errors. We aimed to demonstrate the feasibility of novel machine learning and natural language processing (NLP) approaches for early predictions of adverse events and provide input to direct quality improvement and patient safety initiatives. METHODS: We used machine learning to analyze 9559 continuously reported AE by clinicians and healthcare systems to the French National Health accreditor (HAS) between January 1, 2009, and December 31, 2020 . We validated the labeling of 135,000 unique de-identified AE reports and determined the associations between different system's root causes and patient consequences. The model was validated by independent expert anesthesiologists. RESULTS: The machine learning (ML) and Artificial Intelligence (AI) model trained on 9559 AE datasets accurately categorized 8800 (88%) of reported AE. The three most frequent AE types were "difficult orotracheal intubation" (16.9% of AE reports), "medication error" (10.5%), and "post-induction hypotension" (6.9%). The accuracy of the AI model reached 70.9% sensitivity, 96.6% specificity for "difficult intubation", 43.2% sensitivity, and 98.9% specificity for "medication error." CONCLUSIONS: This unsupervised ML method provides an accurate, automated, AI-supported search algorithm that ranks and helps to understand complex risk patterns and has greater speed, precision, and clarity when compared to manual human data extraction. Machine learning and Natural language processing (NLP) models can effectively be used to process natural language AE reports and augment expert clinician input. This model can support clinical applications and methodological standards and used to better inform and enhance decision-making for improved risk management and patient safety. TRIAL REGISTRATION: The study was approved by the ethics committee of the French Society of Anesthesiology (IRB 00010254-2020-20) and the CNIL (CNIL: 118 58 95) and the study was registered with ClinicalTrials.gov (NCT: NCT05185479).

Alimentos orgánicos disminuyen la incidencia de cáncer / Organic foods reduce the incidence of cancer

En todo el mundo, el número de casos nuevos de cáncer se estimó en 2012 en más de 14 millones,1,2 y el cáncer sigue siendo una de las principales causas de mortalidad en Francia. Entre los factores de riesgo ambientales para el cáncer, existen preocupaciones sobre la exposición a diferentes clases de pesticidas, en particular a través de la exposición ocupacional.3 Una revisión reciente4 concluyó que el papel de los pesticidas para el riesgo de cáncer no podía ponerse en duda dado el creciente cuerpo de evidencia que vincula el desarrollo del cáncer a la exposición a plaguicidas. Si bien las respuestas a dosis de tales moléculas o los posibles efectos de coctel no se conocen bien, se ha sugerido un aumento de los efectos tóxicos, incluso a bajas concentraciones de mezclas de pesticidas.5

Worldwide, the number of new cases of cancer was estimated in 2012 at more than 14 million, 1,2 and cancer remains one of the leading causes of death in France. Among the environmental risk factors for cancer, there are concerns about exposure to different kinds of pesticides, particularly through occupational exposure.3 A recent review4 concluded that the role of pesticides in cancer risk could not be put in place. doubt given the growing body of evidence linking the development of cancer to exposure to pesticides. While responses to doses of such molecules or possible cocktail effects are not well known, an increase in toxic effects has been suggested, even at low concentrations of pesticide mixtures.5