Artificial Intelligence in Pharmacovigilance: Scoping Points to Consider

Hauben, Manfred; Hartford, Craig G.

doi:10.1016/j.clinthera.2020.12.014

Cited by 15 publications

(9 citation statements)

References 18 publications

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“…Beyond ICSR processing, other PV processes, including AI literature review and social listening, were frequently reported, as in the pilot or planning stages [ 10 ]. Additional signal detection and evaluation uses include the ability to connect and synthesize evidence from multiple data sources across R&D from multiple data sources and synthesize evidence from molecule to patients [ 17 ]. The continued evolution of sponsor risk perceptions and regulator acceptance of validation approaches will allow greater future benefits from ML and other intelligent automation across PV domains.…”

Section: Discussionmentioning

confidence: 99%

Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance

et al. 2022

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TransCelerate reports on the results of 2019, 2020, and 2021 member company (MC) surveys on the use of intelligent automation in pharmacovigilance processes. MCs increased the number and extent of implementation of intelligent automation solutions throughout Individual Case Safety Report (ICSR) processing, especially with rule-based automations such as robotic process automation, lookups, and workflows, moving from planning to piloting to implementation over the 3 survey years. Companies remain highly interested in other technologies such as machine learning (ML) and artificial intelligence, which can deliver a human-like interpretation of data and decision making rather than just automating tasks. Intelligent automation solutions are usually used in combination with more than one technology being used simultaneously for the same ICSR process step. Challenges to implementing intelligent automation solutions include finding/having appropriate training data for ML models and the need for harmonized regulatory guidance.

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Section: Discussionmentioning

confidence: 99%

Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance

et al. 2022

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“…Machine learning, deep learning, natural language processing (NLP) [ 14 ], and other AI technologies have been adopted to improve PV systems [ 15 , 16 ]. These technologies have been used to automatically high-throughput process or analyze PV-related information [ 17 ], such as the detection and extraction of adverse events from an unstructured text by NLP [ 18 – 20 ] and detection of potential PV signals in large databases using unsupervised Bayesian methods [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources

Liang

Sun

et al. 2022

Drug Saf

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With the rapid development of artificial intelligence (AI) technologies, and the large amount of pharmacovigilance-related data stored in an electronic manner, data-driven automatic methods need to be urgently applied to all aspects of pharmacovigilance to assist healthcare professionals. However, the quantity and quality of data directly affect the performance of AI, and there are particular challenges to implementing AI in limited-resource settings. Analyzing challenges and solutions for AI-based pharmacovigilance in resource-limited settings can improve pharmacovigilance frameworks and capabilities in these settings. In this review, we summarize the challenges into four categories: establishing a database for an AI-based pharmacovigilance system, lack of human resources, weak AI technology and insufficient government support. This study also discusses possible solutions and future perspectives on AI-based pharmacovigilance in resource-limited settings.

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“…Interest in Artificial Intelligence (AI)-assisted pharmacovigilance has grown in recent years ( 27 ). Within the field of AI, machine learning (ML) is a data-driven computational methodology increasingly applied for predictions of the post-marketing side-effects of drugs ( 28 ).…”

Section: Introductionmentioning

confidence: 99%

Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients

Zhu

Hu²,

Xiao³

et al. 2022

Front. Endocrinol.

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Background and aimAvailable evidence suggests elevated serum prolactin (PRL) levels in olanzapine (OLZ)-treated patients with schizophrenia. However, machine learning (ML)-based comprehensive evaluations of the influence of pathophysiological and pharmacological factors on PRL levels in OLZ-treated patients are rare. We aimed to forecast the PRL level in OLZ-treated patients and mine pharmacovigilance information on PRL-related adverse events by integrating ML and electronic health record (EHR) data.MethodsData were extracted from an EHR system to construct an ML dataset in 672×384 matrix format after preprocessing, which was subsequently randomly divided into a derivation cohort for model development and a validation cohort for model validation (8:2). The eXtreme gradient boosting (XGBoost) algorithm was used to build the ML models, the importance of the features and predictive behaviors of which were illustrated by SHapley Additive exPlanations (SHAP)-based analyses. The sequential forward feature selection approach was used to generate the optimal feature subset. The co-administered drugs that might have influenced PRL levels during OLZ treatment as identified by SHAP analyses were then compared with evidence from disproportionality analyses by using OpenVigil FDA.ResultsThe 15 features that made the greatest contributions, as ranked by the mean (|SHAP value|), were identified as the optimal feature subset. The features were gender_male, co-administration of risperidone, age, co-administration of aripiprazole, concentration of aripiprazole, concentration of OLZ, progesterone, co-administration of sulpiride, creatine kinase, serum sodium, serum phosphorus, testosterone, platelet distribution width, α-L-fucosidase, and lipoprotein (a). The XGBoost model after feature selection delivered good performance on the validation cohort with a mean absolute error of 0.046, mean squared error of 0.0036, root-mean-squared error of 0.060, and mean relative error of 11%. Risperidone and aripiprazole exhibited the strongest associations with hyperprolactinemia and decreased blood PRL according to the disproportionality analyses, and both were identified as co-administered drugs that influenced PRL levels during OLZ treatment by SHAP analyses.ConclusionsMultiple pathophysiological and pharmacological confounders influence PRL levels associated with effective treatment and PRL-related side-effects in OLZ-treated patients. Our study highlights the feasibility of integration of ML and EHR data to facilitate the detection of PRL levels and pharmacovigilance signals in OLZ-treated patients.

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Artificial Intelligence in Pharmacovigilance: Scoping Points to Consider

Cited by 15 publications

References 18 publications

Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance

Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance

Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources

Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients

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