about
Development and validation of a risk model for predicting adverse drug reactions in older people during hospital stay: Brighton Adverse Drug Reactions Risk (BADRI) modelDevelopment and validation of algorithms for heart failure patient care: a Delphi studyThe use of potentially inappropriate medications and changes in quality of life among older nursing home residentsEvaluation of potentially inappropriate medications among older residents of Malaysian nursing homes.Clinical and economic impact of non-adherence to antidepressants in major depressive disorder: A systematic review.Predicting the Risk of Adverse Drug Reactions in Older Inpatients: External Validation of the GerontoNet ADR Risk Score Using the CRIME Cohort.Determinants of functional status among survivors of severe sepsis and septic shock: One-year follow-upInter-rater reliability of the assessment of adverse drug reactions in the hospitalised elderly.Comparison of nurses and general caregivers' knowledge, attitude, and practice on medication administration process and their distress level in long-term care facilities across Penang, Kuala Lumpur, and Selangor of Malaysia.Development and validation of a score to assess risk of adverse drug reactions among in-hospital patients 65 years or older: the GerontoNet ADR risk score.Adverse drug reactions in elderly: challenges in identification and improving preventative strategiesHospitalisation of multiethnic older patients with AECOPD: exploration of the occurrence of anxiety, depression and factors associated with short-term hospital readmissionImpact of adherence to key performance indicators on mortality among patients managed for ischemic strokeWhat do healthcare providers know about nutrition support? A survey of the knowledge, attitudes, and practice of pharmacists and doctors toward nutrition support in Malaysia
P50
Q28544629-4C328C4D-AE01-4689-AC70-9A537CCA6FB0Q35555345-660A0B99-634B-40D8-B80E-733539348921Q37524836-84B7F1F6-5825-4F54-958D-1F7EDCE397D6Q38012613-774FBAB3-5A60-4B4C-929D-19568571E479Q38691320-23C483B3-5E0C-43A0-9AB3-1FA13CE4DD52Q39079336-1CA66695-878A-4495-B3D3-19F7C18925F8Q41130157-1BCDB4C7-9B48-42C1-AC90-CCD2D57E2D25Q44445646-3E91C7FD-67A5-4043-B62C-1884FB2F9A7BQ47880877-1E39DFF7-97D1-4629-9083-C1606E648F3BQ51761086-618963A3-AE46-4721-981B-BBC0AE645152Q83779563-56905CC0-019D-46E6-8C3F-CCBB8513F8FBQ87071276-F45866EE-B957-42C0-BB99-4C8E58F3E4A1Q91640421-034AA055-C192-4C24-BCFA-40CA868BBE3CQ95560692-5D9D0EFD-F28E-46D1-960A-66ED11219EF2
P50
description
investigador
@es
researcher
@en
name
Balamurugan Tangiisuran
@en
type
label
Balamurugan Tangiisuran
@en
prefLabel
Balamurugan Tangiisuran
@en
P31
P496
0000-0002-4354-3342