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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 2 of 2 Research Studies DisplayedLiu L, Ni Y, Zhang N
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
The objectives of this study were: 1) to develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children's risk of day-of-surgery cancellation. The study demonstrated the capacity of machine learning models for predicting pediatric patients at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The author’s approach offers the promise of targeted interventions to significantly decrease both healthcare costs and families' negative experiences.
AHRQ-funded; HS024983.
Citation: Liu L, Ni Y, Zhang N .
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
Int J Med Inform 2019 Sep;129:234-41. doi: 10.1016/j.ijmedinf.2019.06.007..
Keywords: Children/Adolescents, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery
Lindell RB, Nishisaki A, Weiss SL
Comparison of methods for identification of pediatric severe sepsis and septic shock in the Virtual Pediatric Systems Database.
This study compared the use of Virtual Pediatric Systems with traditional use of International Classification of Diseases, 9th edition (ICD) to identify children with severe sepsis or septic shock in PICU settings. Two different systems were compared “Martin” and “Angus”. Both showed good agreement, but ICD9 identified a smaller more accurate cohort of children. Additional analysis of discrepancies between the reference standard the two virtual systems showed that prospective screening missed 66 patients who were diagnosed with severe sepsis or severe shock. Once they were included in the standard cohort, agreement improved with a positive predictive value of 70%.
AHRQ-funded; HS024511; HS022464.
Citation: Lindell RB, Nishisaki A, Weiss SL .
Comparison of methods for identification of pediatric severe sepsis and septic shock in the Virtual Pediatric Systems Database.
Crit Care Med 2019 Feb;47(2):e129-e35. doi: 10.1097/ccm.0000000000003541..
Keywords: Children/Adolescents, Intensive Care Unit (ICU), Data, Sepsis