Hye-Sook Park, Seokmin Kang, Sungyeun Kim
A longitudinal study of the effect of individual and socio-cultural factors on students' creativity
Frontiers in psychology
reporting analysis adverse events ae associated improved healthcare learning quality outcomes patient safety manual text analysis timeconsuming costly prone human errors aimed demonstrate feasibility machine learning natural language processing nlp approaches early predictions adverse events provide input direct quality improvement patient safety initiatives used machine learning analyze 9559 continuously reported ae clinicians healthcare systems french national health accreditor january 1 2009 december 31 2020 total 135000 unique deidentified ae reports validated labeling determined associations different root causes patient consequences model validated independent expert anesthesiologists machine learning artificial intelligence ai model trained 9559 ae datasets accurately categorized 8800 88 reported ae three frequent ae types difficult orotracheal intubation 169 ae reports medication error 105 postinduction hypotension 69 accuracy ai model reached 709 sensitivity 966 specificity difficult intubation 432 sensitivity 989 specificity medication error unsupervised method provides accurate automated aisupported search algorithm ranks helps understand complex patterns risky patient situations greater speed precision clarity compared manual human data extraction machine learning ml natural language processing models effectively used process natural language ae reports augment expert clinician input model support clinical applications methodological standards implementations used better inform enhance decisionmaking improved risk management patient safety study approved ethics committee french society anesthesiology irb 00010254202020 cnil cnil 118 58 95 study registered clinicaltrialsgov nct nct05185479