Abstract：
:Only few applications are currently dealing with personalized adverse drug reactions (ADRs) prediction in case of polypharmacy. The study aimed to develop a patient-tailored ADR web application, considering characteristics from 734 drugs and relevant patient related factors. The application was designed in Python using a scoring and ranking system based on frequency and severity, computed for each ADR and expressed through an online platform. A neural networks algorithm was used for predicting the severity of ADRs. The application inputs are: age, gender, drugs, relevant pathologies. The outputs are: an overall severity profile (hospitalization and mortality risk), a stratified risk on specific ADR groups and a sorted list of the most important ADRs depending on frequency and severity. The Severity prediction model validation resulted in 79.7-85.1% Area Under the Receiver Operating Characteristic Curve Score, which lies in the good cut-off of 75-90%. The program offers a complex view regarding the ADR profile of a given patient and could be used by the physician and clinical pharmacist during patient safety monitoring, for a coherent therapy choice or medication adjustment, due to the good therapy coverage and the inclusion of relevant patient comorbidities.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Valeanu A,Damian C,Marineci CD,Negres Sdoi
10.1038/s41598-020-66611-8subject
Has Abstractpub_date
2020-06-12 00:00:00pages
9552issue
1issn
2045-2322pii
10.1038/s41598-020-66611-8journal_volume
10pub_type
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