Predicting adverse drug events in older inpatients: a machine learning study

[11] Arbogast KL(2014)Characterisations of adverse events detected in a university hospital: a 4-year study using the Global Trigger Tool method BMJ Open 4 27-35

[12] Behrens MC(2006)Epidemiology of medication-related adverse events in nursing homes Am J Geriatr Pharmacother 4 1174-1183

[13] Yadesa TM(2014)Prevalence and severity of patient harm in a sample of UK-hospitalised children detected by the Paediatric Trigger Tool BMJ Open 4 945-1930

[14] Kitutu FE(2014)Characteristics associated with the occurrence of adverse events: a retrospective medical record review using the Global Trigger Tool in a fully digitalized tertiary teaching hospital in Korea J Eval Clin Pract 20 1920-97

[15] Tamukong R(2019)Development of a trigger tool for the detection of adverse drug events in Chinese geriatric inpatients using the Delphi method Int J Clin Pharm 41 91-181

[16] Adedapo ADA(2018)Machine learning in acute ischemic stroke neuroimaging Front Neurol 9 175-576

[17] Adedeji WA(2015)Machine learning in medicine Circulation 132 569-618

[18] Adedapo IA(2019)Adverse events identified by the global trigger tool at a university hospital: a retrospective medical record review J Evid Based Med 12 611-440

[19] Morimoto T(2007)Interrater agreement with a standard scheme for classifying medication errors Am J Health Syst Pharm 64 434-1634

[20] Sakuma M(2018)Role of big data and machine learning in diagnostic decision support in radiology J Am Coll Radiol 15 3555-153

[11] Arbogast KL(2014)Characterisations of adverse events detected in a university hospital: a 4-year study using the Global Trigger Tool method BMJ Open 4 27-35

[12] Behrens MC(2006)Epidemiology of medication-related adverse events in nursing homes Am J Geriatr Pharmacother 4 1174-1183

[13] Yadesa TM(2014)Prevalence and severity of patient harm in a sample of UK-hospitalised children detected by the Paediatric Trigger Tool BMJ Open 4 945-1930

[14] Kitutu FE(2014)Characteristics associated with the occurrence of adverse events: a retrospective medical record review using the Global Trigger Tool in a fully digitalized tertiary teaching hospital in Korea J Eval Clin Pract 20 1920-97

[15] Tamukong R(2019)Development of a trigger tool for the detection of adverse drug events in Chinese geriatric inpatients using the Delphi method Int J Clin Pharm 41 91-181

[16] Adedapo ADA(2018)Machine learning in acute ischemic stroke neuroimaging Front Neurol 9 175-576

[17] Adedeji WA(2015)Machine learning in medicine Circulation 132 569-618

[18] Adedapo IA(2019)Adverse events identified by the global trigger tool at a university hospital: a retrospective medical record review J Evid Based Med 12 611-440

[19] Morimoto T(2007)Interrater agreement with a standard scheme for classifying medication errors Am J Health Syst Pharm 64 434-1634

[20] Sakuma M(2018)Role of big data and machine learning in diagnostic decision support in radiology J Am Coll Radiol 15 3555-153