The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature

Salas, Maribel; Petracek, Jan; Yalamanchili, Padmaja; Aimer, Omar; Kasthuril, Dinesh; Dhingra, Swati; Junaid, Toluwalope; Bostic, Tina

doi:10.1007/s40290-022-00441-z

Cited by 22 publications

(18 citation statements)

References 71 publications

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“…For example, e-Health data analysis facilitates the delivery of health care services in developing countries [8,9]. Although the massive amount of healthcare data provides valuable opportunities to improve patient health experience, it also prompts various sources of uncertainties (e.g., missing data, spelling errors, inefficient updates, anomalous patterns), which may lead to abusive activities and access restriction [17][18][19][20]. The discrepancy and variety of the sources of data used in healthcare create additional challenges associated with formatting and protection issues.…”

Section: A Social Network and Healthcarementioning

confidence: 99%

“…In this context, individual units can refer to actors, organizations, group of individuals or events, depending on the context of study. This empowered SNA to emerge in several fields related to healthcare and health sciences such as neuroscience [13], epidemiology [14], social-support [15], cancerology [16], pharmacovigilance [17], rehabilitation [18], among others. The link between healthcare and SNA can be viewed from different standpoints [19].…”

Section: A Social Network and Healthcarementioning

confidence: 99%

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Community Detection Algorithms in Healthcare Applications: A Systematic Review

et al. 2023

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Over the past few years, the number and volume of data sources in healthcare databases has grown exponentially. Analyzing these voluminous medical data is both opportunity and challenge for knowledge discovery in health informatics. In the last decade, social network analysis techniques and community detection algorithms are being used more and more in scientific fields, including healthcare and medicine. While community detection algorithms have been widely used for social network analysis, a comprehensive review of its applications for healthcare in a way to benefit both health practitioners and the health informatics community is still overwhelmingly missing. This paper contributes to fill in this gap and provide a comprehensive and up-to-date literature research. Especially, categorizations of existing community detection algorithms are presented and discussed. Moreover, most applications of social network analysis and community detection algorithms in healthcare are reviewed and categorized. Finally, publicly available healthcare datasets, key challenges, and knowledge gaps in the field are studied and reviewed.

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Section: A Social Network and Healthcarementioning

confidence: 99%

Section: A Social Network and Healthcarementioning

confidence: 99%

Community Detection Algorithms in Healthcare Applications: A Systematic Review

et al. 2023

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“…In implementing organizational change management, we conduct activities that are required to change the way of working of all operators in the working process. Process automation means developing improvement in the IT sector that supports the current system to be more efficient and increase productivity (Salas et al, 2022).…”

Section: Process Implementationmentioning

confidence: 99%

“…By using AI, the unstructured data can be recognized and identified and input the case report form automatically to the as is the company PV application. Case intake is the process in pharmacovigilance that takes the most workload (50% of daily work hours) and most budget (40 % to 80% of the departmental budget) (Salas et al, 2022).…”

Section: Process Implementationmentioning

confidence: 99%

Processing of Individual Case Safety Reports in The Pharmacovigiland Department of Indonesian Vaccine Companies

Irham

Basri

2023

JWS

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Introduction: This study aimed to determine and analyze the processing design of individual case safety reports in Indonesia's pharmacovigilance department of vaccine companies. Method: This study combines qualitative and quantitative studies that propose implementing the new artificial intelligence system for the PV Department. Result: The results obtained from this study are problem-solving solutions by increasing the number of staff, using artificial intelligence (AI) technology in AEFI case processing to avoid errors and manual input, and approaching external stakeholders to be able to report safety data efficiently and more structured. Conclusion: With the addition of human resources to process the AEFI reporting data by one person, performance value will be increased to 165%. If the process utilizes AI technology, case processing performance increases to 219%. Conclusion: disruption of the performance of the pharmacovigilance department in Indonesian biopharmaceutical companies can be caused by the pandemic disrupting the way KIPI cases are reported, so the solution is the addition of human resources, changes in processes and SOPs, as well as approaches to external stakeholders to streamline case processing.

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“…When pharmaceutical companies implement advanced PV *Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states [6].…”

Section: Advantages Of Automation In Pharmacovigilancementioning

confidence: 99%

Automation in pharmacovigilance: artificial intelligence and machine learning for patient safety

Divya¹,

Sushmitha²,

Ch³

et al. 2022

J Inn App Pharma Sci

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Automation promises to be a game- change for pharmacovigilance decreasing the cost of case reporting and improving data quality to truly add value, including signal detection in drug safety. Pharmacovigilance analytic and benefit – risk assessment.Technology advances are playing a major role in pharmaceutical PV strategy updates. For example more companies are looking towards cloud- based solutions, mobile applications, robotic automation, artificial intelligence and big data analytics as a vital part of clinical safety and regulatory operations in the pharmaceutical industry. Applying innovative technology automation tools and processes to PV strategies is now a critical requirement for managing the safety of pharmaceutical products. The role of artificial intelligence and machine learning in pharmacovigilance can enhance the productivity in identification, detection, management and reporting of ADRs. The main objective ofArtificial intelligence is meant to challenges to implementing intelligent automatic solutions include finding / having appropriate training data for machine learning models and the need for harmonised regulatory guidance. AI can analyse and interpret data at lightning speed, never gets tried or sick and can simply work by 24/7. Thousands of adverse effects are processed every month by ICSR in PV that incudes native automation and standalone technologies like AI and ML that reduce the manual effort.

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The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature

Cited by 22 publications

References 71 publications

Community Detection Algorithms in Healthcare Applications: A Systematic Review

Community Detection Algorithms in Healthcare Applications: A Systematic Review

Processing of Individual Case Safety Reports in The Pharmacovigiland Department of Indonesian Vaccine Companies

Automation in pharmacovigilance: artificial intelligence and machine learning for patient safety

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