Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian Networks
Managing patients with multimorbidity often results in polypharmacy: the prescription of multiple drugs. However, the long-term effects of specific combinations of drugs and diseases are typically unknown. In particular, drugs prescribed for one condition may result in adverse effects for the other. To investigate which types of drugs may affect the further progression of multimorbidity, we query models of diseases and prescriptions that are learned from primary care data. State-of-the-art tractable Bayesian network representations, on which such complex queries can be computed efficiently, are employed for these large medical networks. Our results confirm that prescriptions may lead to unintended negative consequences in further development of multimorbidity in cardiovascular diseases. Moreover, a drug treatment for one disease group may affect diseases of another group.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Identification of Adverse Drug Reaction Mentions in Tweets -- SMM4H Shared Task 2019
Analyzing social media posts can offer insights into a wide range of topics that are commonly discussed online, providing valuable information for studying various health-related phenomena reported online. The outcome of…
PharmacovigilanceExtracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017
Adverse drug reactions (ADRs) are unwanted or harmful effects experienced after the administration of a certain drug or a combination of drugs, presenting a challenge for drug development and drug administration. In this…
Detect adverse drug reactions for drug Alendronate
Adverse drug reaction (ADR) is widely concerned for public health issue. In this study we propose an original approach to detect the ADRs using feature matrix and feature selection. The experiments are carried out on the…
feature selectionAnnotation of Adverse Drug Reactions in Patients' Weblogs
Adverse drug reactions are a severe problem that significantly degrade quality of life, or even threaten the life of patients. Patient-generated texts available on the web have been gaining attention as a promising sourc…
ArticlesTransformer Models for Drug Adverse Effects Detection from Tweets
In this paper we present the drug adverse effects detection system developed during our participation in the Social Media Mining for Health Applications Shared Task 2020. We experimented with transfer learning approach f…
regressionTransfer Learning