Big data, medicines safety and pharmacovigilance

Since the 1990s, the concept of big data has emerged as more relevant, diverse and larger data sets, responsible for the introduction of new drug developments, improved clinical practices and healthcare fnancing in the healthcare industry [1]. For big data analysis, one can handle a large pool of digital medical records or administrative data including drug safety reports, drug prescriptions as well as hospital discharge datasets [2]. Many rare adverse efects remain undetected due to a limited number of sampled individuals in a clinical trial; hence, it is necessary to monitor the drugs even after their release into the market. In this context, “pharmacovigilance” helps to collect, analyze, and disseminate adverse drug reaction reports collected during the postmarketing phase [3, 4]. Data mining from drug safety report databases and medical literature is a time-consuming task; however, with the digital revolution, the researchers are exploring if the potential of big data could be used to study and monitor drug safety. In many developed countries, drug safety surveillance based on databases through automation is becoming increasingly common [2]. Tis involves the usage of electronic methods to systematically analyze the large volume of information. Tis could be further helpful to detect data patterns to identify new adverse drug reactions, which are otherwise not available through normal screening [2]. Tis commentary discusses big data, artifcial intelligence and the use of social media. It also elaborates, how “big data” feeds into evaluating the safety of new and orphan medicines (Fig. 1). Artifcial intelligence and pharmacovigilance To better understand the use of artifcial intelligence in pharmacovigilance, it may be useful to defne this in terms of methods, tasks and data sets [5]. Machine learning is part of artifcial intelligence that deals with the ability of machines to learn without having human input. Due to improved computational techniques and the availability of larger datasets, there is an increasing trend in machine learning adoption in healthcare [6]. For an automated signal generation in pharmacovigilance, both supervised and unsupervised machine learning approaches are used. Te unsupervised machine learning approach employs the identifcation of drug safety signals as well as explores the pattern of drug utilization. While in supervised machine learning, the computer is provided with a set of instructions to produce an algorithm based on the desired output [7]. It could be explained by considering the identifcation of an ADR from free text [8]. Tis is done by creating an identifcation pattern extracting information from the medical records and then applying the algorithms to the full electronic medication records. Te process is called natural language processing (NLP). It can be applied to identify drug interactions from clinical notes and to fnd the association between drugs and potential ADRs

Since the 1990s, the concept of big data has emerged as more relevant, diverse and larger data sets, responsible for the introduction of new drug developments, improved clinical practices and healthcare fnancing in the healthcare industry [1]. For big data analysis, one can handle a large pool of digital medical records or administrative data including drug safety reports, drug prescriptions as well as hospital discharge datasets [2]. Many rare adverse efects remain undetected due to a limited number of sampled individuals in a clinical trial; hence, it is necessary to monitor the drugs even after their release into the market. In this context, “pharmacovigilance” helps to collect, analyze, and disseminate adverse drug reaction reports collected during the postmarketing phase [3, 4]. Data mining from drug safety report databases and medical literature is a time-consuming task; however, with the digital revolution, the researchers are exploring if the potential of big data could be used to study and monitor drug safety. In many developed countries, drug safety surveillance based on databases through automation is becoming increasingly common [2]. Tis involves the usage of electronic methods to systematically analyze the large volume of information. Tis could be further helpful to detect data patterns to identify new adverse drug reactions, which are otherwise not available through normal screening [2]. Tis commentary discusses big data, artifcial intelligence and the use of social media. It also elaborates, how “big data” feeds into evaluating the safety of new and orphan medicines (Fig. 1). Artifcial intelligence and pharmacovigilance To better understand the use of artifcial intelligence in pharmacovigilance, it may be useful to defne this in terms of methods, tasks and data sets [5]. Machine learning is part of artifcial intelligence that deals with the ability of machines to learn without having human input. Due to improved computational techniques and the availability of larger datasets, there is an increasing trend in machine learning adoption in healthcare [6]. For an automated signal generation in pharmacovigilance, both supervised and unsupervised machine learning approaches are used. Te unsupervised machine learning approach employs the identifcation of drug safety signals as well as explores the pattern of drug utilization. While in supervised machine learning, the computer is provided with a set of instructions to produce an algorithm based on the desired output [7]. It could be explained by considering the identifcation of an ADR from free text [8]. Tis is done by creating an identifcation pattern extracting information from the medical records and then applying the algorithms to the full electronic medication records. Te process is called natural language processing (NLP). It can be applied to identify drug interactions from clinical notes and to fnd the association between drugs and potential ADRs