ISSN- 2394-5125 VOL 7, ISSUE 19, 2020 A REVIEW ON POSSIBILITY OF USING SOCIAL MEDIA DATA FOR PHARMACOVIGILANCE

Dr. Suman Yadav1, Dr. Md. Aftab Alam2, Dr. Ranjana Patnaik3

1Research Scholar, Department of Clinical Research, School of Biosciences and Biomedical Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India.

2 Department of Pharmacy, School of Medical & Allied Sciences, Galgotias University, Greater Noida, Uttar Pradesh, India.

3Department of Clinical Research, School of Biosciences and Biomedical Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

Received: 14 March 2020 Revised and Accepted: 8 July 2020

ABSTRACT: Adverse Drug Reactions (ADRs) are the harmful reactions caused by medication intake/application. Pharmacovigilance is the collection of practices that are correlated with the medicines' identification, evaluation, awareness, and avoidance of adverse effects. Social media frameworks are useful in gaining information to recognize the developments in public health sector. It provides a considerable amount of information to detect ADRs.There are some issues on getting, detecting and investigating information from social media to prepare it in a user readable format.This paper presents the extensive survey on various methods to extract the information for Pharmacovigilance purpose from social media. The main methods discussed here include: Text mining, Text classification and Machine Learning or Artificial Intelligence.

I. INTRODUCTION

A. Adverse Drug Reactions (ADRs)

The destructive effects that arise due to the intake aor application of medicines are called as Adverse Drug Reactions (ADRs). Pharmacovigilance is the practices related to the identification of adverse effects caused due to drugs and further evaluation, awareness and prevention and safe guard public health. . It is the set of activities which is associated with the identification, evaluation, comprehension and prevention of destructive effects caused by or to be caused by medicines. By reason of the several confines of medical trials, it is not probable to completely evaluate the significances of the usage of a specific medicine before it is brought out. Contrary responses produced by medicines, succeeding their release into the advertise, is a main communal fitness issue. Thus, post-marketing investigation of medicines is of dominant significance for medicine producers, national bodies like the U.S. Food and Drug Administration (FDA), and global establishments like the World Health Organization (WHO) [1].

Recently, user-supplied information on social media has been considered as a meaningful resource for ADR maintenance. Research with the aid of social media information has developed using various mechanisms and resources. The developments related to ADR maintenance from social media have been noticed in drug mining and corresponding effects [1]

B. Sources of Social Media Platforms

Social media provide a considerable amount of information to detect ADRs. One of these social media is Twitter, which is a good source of data for broad-spectrum issues, particularly ADR-related discussions and posts. Currently, Twitter has the record of daily 342,000,000 active and 1350000 registered users[1]. It has been revealed that most patients shared their health status data positively on various medical, public web pages or open forums such as the "Ask a Patient" website (5), Twitter, etc., provided a powerful tool for monitoring ADR. However, from social media the extraction of useful information is difficult due to its writing style and language used to transfer this type of information [2].

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ISSN- 2394-5125 VOL 7, ISSUE 19, 2020 II. RELEVANCE OF PUBLIC HEALTH AND SOCIAL MEDIA

Latest history has perceived progresses in the technology accessible to the common community for communication commitments, and a momentous supsurge in the usage of the internet, specifically social media networks. Usage of these systems has turned out to be an important part of contemporary life, so it appears serviceable to reconsider their usage for information-gathering commitments. Patient meeting medium and health care systems may also be potentially utilized. Social media boards have ascertained to be beneficial in attaining data, so as to recognize styles in communal fitness. This medium offers a dais for operators to converse opinions and thoughts as correlated to their fitness, together with the usage of the medical products and their side effects[3].

Consumers sharing encounters about ADRs and knowledge of drug on social media could be intriguing for the use of information. Additionally, a clear exchange of information about the advantage and danger of medications is a key requirement for ensuring safety. [4].

III. APPLICATIONS OF SOCIAL MEDIA IN PHARMACOVIGILANCE

Enhanced reachability and computing power have opened the avenue for using social media for pharmacovigilance. The social media can provide a platform for easy and open communication between the patients/consumers and healthcare providers regard to the utilization of medicinal products, subsequently building open trust. For example, a private or public group can be created including all parties to share the communication. The social networking platforms which can be used for Pharmacovigilance can range from routine social networking websites like Facebook, Twitter to websites specially tailored to healthcare, wellness programs and support linkages such as “Patients Like Me, Daily Strength, and Med Help” [4].

IV. CHALLENGES OF SOCIAL MEDIA IN PHARMACOVIGILANCE

Numerous encounters are there for retrieving, recognizing, and evaluating data from social media, and attaining it in a user readable format. Results to these experiments are being discovered and verified, and include examining and changing operator posts to a serviceable arrangement to excerpt medicine and response evidence. One of the most fascinating encounters is the moral allegations related with retrieving, exploiting and performing upon data from social media boards. On the other hand, communal data can be retrieved by anybody. The usage of the data would be fall deprived of confab or clarification as to what for and how they are being utilised. Hence, even though there may be aquarrel for accord, it is not informed accord. This could be challenging from a controlling view point in association to accord and data defence rules[5].

V. EXISTING REVIEW ON ROLE OF SOCIAL MEDIA IN PHARMACOVIGILANCE

There are plenty of reviews made related to social media and its consequence on Pharmacovigilance. In this section, the existing literature review done on the role played by social media in Pharmacovigilance have been presented.

Abeed Sarker et al[1] premeditated the concept of methods for the identification of ADRs. Educations that crossed our insertion standards were those that tried to draw out the evidence sent by operators on any social media dais that was openly accessible. They characterised the trainings based on diverse physiognomies like prime ADR discovery method, dimension of body, data source(s), accessibility, and assessment norms. Still there is very restricted quantity of glossed data openly obtainable, and, as designated by the favourable outcomes got by current directed learning methods, there is a sturdy requisite to make such statistics accessible to the investigation communal.

Richard Sloane et al [5] have focussed on the approaches related to social media analysis and pharmacovigilance analysis. It involves various text resources like biomedical literature, medical descriptions. They have conducted a detailed review on implementing the technical, industrial and legal aspects of social media application for pharmacovigilance to address the challenges and future prospects.

Andrea C. Tricco et al [6] have proposed that, from an initial progressive outlook, the usage of social media is being examined for safety of drugs . In this structure, data obtained from social media has the capability to add- on the data to the databases of the drug regulators, find out Adverse Events with low frequency, and recognize the adverse events s former than authorised warnings or supervisory variations. On the other hand, the utilization, validation and, and data execution data extraction from social digital media for PV are yet to be

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ISSN- 2394-5125 VOL 7, ISSUE 19, 2020 fully studied. Additional assessment is needed to reinforce and regulate the methods as well as to guarantee that valid results are obtained which can be useful for pharmacovigilance purpose.

Andrea C Tricco et al [7] have adverse drug events related with medicines are reported less frequently in post marketing drug investigation system. A methodical appraisal of printed data from 37 studies universally (including Canada) discovered the under-reporting proportion of ADE to be 94% in unprompted reporting systems. This expectation appraisal goals to evaluate the usefulness of social media and crowd-sourced data to perceive and observe ADE associated to pharmaceuticals, biologics, medical devices, and natural fitness goods.

Dimitra Pappa et al [8] have incorporated the plotting and codification of the present knowledge in the arena by sketching assessments of diverse methods, kinds of social media data and of pertinent origin presently utilised in the arena, and by increasing novel categorizations of social media data sources and templates for their usage in pharmacovigilance, besides the documentation of significant encounters and the removal of novel visions regarding possibility for hands-onuses and upcoming investigation guidelines in the region of pharmacovigilance.

VI. METHODS FOR DATA EXTRACTION FOR PHARMACOVIGILANCE FROM SOCIAL MEDIA

This section presents the extensive survey on various methods to extract the information related to Pharmacovigilance from social media. The main methods discussed here include: Text mining, Text classification and Machine Learning or Artificial Intelligence. a. Text Mining

Investigation of an enormous amount of descriptive messages needs text mining methods. Data mining is one of the archetypal text mining errands aimed at generating an organized presentation of the data available in language of human transcript and arranging it more manageable way for machine handling. A preliminary phase of handling is Named Entity Recognition (NER). It includes classifying predefined groups into transcript examples. Initial research in NER schemes was targeted primarily at people mining and place names from editorial posts. Rapidly, NER was well-thought-out in understanding genetic factor and generates genetic factor.

Shaika Chowdhury et al [9] have suggested a multi-task neural network agenda that studies numerous responsibilities related with ADR observing with diverse stages of managements together. In addition to being able to properly categorize ADR posts and precisely extract ADR references from online posts, the suggested agenda can also understand the explanations for which the medicine is being taken from the provided social media post, called 'indications. In their agenda, a coverage-based mechanism of thoughtfulness is accepted to help the exemplary appropriately classify 'phrasal' ADRs and Signs that are observant of numerous verses in a post. Their agenda is appropriate in circumstances where minimal equivalent data for various pharmacovigilance errands are needed. They evaluated the proposed agenda on practical Twitter datasets, where the suggested exemplary outdoes the advanced replacements for each specific task reliably.

Xiaoyi Chen et al [10] have developed two diverse methods to examine data automatically mined from social media. These two methods were , Signal detection and topic analysis . They deliver balancing viewpoints to comprehend the influence of a medicine on patients. Signal detection permits to recognize exact data associated with probable novel side effects. Topic replicas, conversely, offer an investigative method permitting to realize more qualitative data about the challenge associated to medicine usage. Topic replicas permit to recognize data non specifically examined in the principal place and to determine unanticipated problems that can be more intensely examined later. In their study, they recognized patients that have psychiatric ailments besides Attention deficit hyperactivity disorder posts were consequence of methylphenidate and cocaine were compared by the patient, were also detected. Conflicting with these posts, numerous patients or their families articulated qualms about the hazards of methylphenidate and the probable dependence to the medicine.

AzadehNikfarjam et al [11] have suggested ADRMine, which is a machine learning-based order tagger. It is used for involuntary mining of Adverse Drug Reaction references from the text posted by the operator in social digital media. They discovered the efficacy of numerous cataloguing features in preparing the CRF exemplary. Also discovered that implanting groups and framework were the causal features. In addition, they used a huge capacity of unlabeled operator posts for unverified learning of the implanting groups, which allowed resemblance modelling amid the tokens, and provided a substantial upsurge to the remembrance.

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ISSN- 2394-5125 VOL 7, ISSUE 19, 2020 Christopher et al [12] have proposed a method to recognize early ADR symptoms from social media data. They have collected traces of various drugs and extracted their adverse effects. They also proposed to use tensor decomposition to handle sparseness and missing data in social digital media.

Author & Year Method Name of Data set Advantages Name the technique Text A multi- Real- It achieves ShaikaChowdhury Mining task neural world higher et al.,2018. network Twitter phrasal ADR framework word coverage

Topic Twenty It present Xiaoyi Chen et modeling one balancing al.,2018 based on million viewpoints the messages to Correlated comprehend Topic the influence Model of a medicine on patients.

ADRMine, Annotated It is possible AzadehNikfarjam a machine training to extract et al.,2014 learning- complex based medical concept concepts, extraction with system relatively that uses high conditional performance. random fields (CRFs). Tensor MedHelp It gives Christopher et Nased better al.,2015 methods performance than matrix- based techniques

Table 1 Comparison of Text mining methods b. Text Classification

Prevailing therapeutic social media text mining approaches characteristically utilise regularized therapeutic jargon. The next sort of jargon (i.e., operator fitness jargon) is frequently bandoned as it is not official. On the other hand, the second sort of terms inhabits a main part of therapeutic social media transcripts. Thus, this non- canonical transcript need to be deliberated when completely excavating therapeutic social media transcript data. Intrinsically, a transcript cataloguing procedure that assimilates customer fitness jargon and efficiently progresses the enactment of therapeutic social media transcript cataloguing.

Alexander Kotov et al [13] have deliberated social media as a completebase of individual-level familiarities to be utilised by patients for fitness self-education or by sources to notifyexperimentalpreparation and as an approximately unrestricted base of collective data for large-scale populace studies. The key benefits of social media based methods to communal fitness investigation are that they do not need to openly employ members

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ISSN- 2394-5125 VOL 7, ISSUE 19, 2020 and are able to offer huge capacities of data in nearby present at almost no price. Their main drawbacks are model prejudice and dependability of the data.

Kan Liu et al [14] Suggested a medical social media text classification protocol (MSMTC). This protocol would assimilate customer health jargon. Cataloguing of transcript from social digital media on medical topics is divided into two parts: customer fitness jargon mining and transcript cataloguing. Initially, transcript physiognomies centered on the double channel building being utilized for training, and consequently , customer fitness jargon is extracted through a confrontational network.After that, transcript cataloguing is employed depending on the mined customer fitness jargon and double channel deduction technique. This paper revenues datasets that include as an example patient portrayals from social media.

Nikhil Pattisapu et al [15] have suggested numerous methods to conclude the therapeutic façade related with a social media post. They modelled this as an overseen multi-label transcript cataloguing issue. The key encounter is to recognize the concealed signs in a post that are symptomatic of a specific façade. They initially suggested a huge group of physically contrived structures for this task. Additionally, they suggested manifold neural network based designs to excerpt valuable structures from these posts by means of pre-trained expression embeddings. Their trials on thousands of blogs and tweets convey that the suggested method results for blogs and tweets correspondingly in 7% and 5% F-measure improvement over physical feature engineering based method.

Method Name of the Data set Advantages Author Name technique & Year Text A medical Patient The KAN Classification social media description proposed LIU et text taken from method al.,2019 classification social outperforms (MSMTC) media single algorithm channel methods or baseline models.

The authors Blogs and It gives Nikhil proposed a Tweets higher Pattisapu multiple performance et neural than al.,2017 network existing based approaches architecture

Table 2 Comparison of Text classification methods

c. Using Artificial Intelligence or Machine Learning

Automation was in use in other subdivisions as early as the 1950s, such as banking and financial undertakings (e.g. automated check handling) and assimilated Artificial Intelligence for the last decade (e.g. automated insurance guarantee).No suppliers are currently bidding for an inclusive AE case handling resolution despite that comprehensive history. The extremely composite nature of Adverse Events case handling regarding meaningfully added verdict points and settlements within a highly delimited and controlled atmosphere associated with handling of case workflows in other businesses is a significant differentiator to other businesses. Furthermore, most source forms are just semi-structured or are completely amorphous. At the maximum level, AE case handling includes four chief events, comprising aperture, assessment, continuation, and dissemination.

Zahra Rezaei et al [2] have focused on Twitter data. However, they added some data from other public databases for scientific comparisons. The obtained results highlighted that the combination of "Ask a patient," and Twitter datasets significantly improved the accuracy of classification. Furthermore, pooling ADR training data for "Ask a patient", and Twitter datasets showed a slight improvement in classification. These results suggest that normalized datasets in terms of type and structure of sentences are able to be merged as a training

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ISSN- 2394-5125 VOL 7, ISSUE 19, 2020 dataset. "Ask a patient", and Twitter datasets represent different characteristics. The former present valuable information related to the cause of side effects which leads to a better orientation of user comments, the latter does not have this feature, which mainly ends up with more general points of view over a specific drug. In order to measure the compatibility of text, several features have been considered, including the indication of topics, ADRs, and concepts.

Ilseyar Alimova et al [16] have concentrated on documentation of contrary drug responses from operator appraisals and express this issue as a dualistic cataloguing task. To determine this problem, a machine learning based classifier was developed with a group of structures. Their feature-rich classifier makes major improvements over traditional approaches and the Convolutionary Neural Networks ( CNN) on a large dataset.

Juergen Schmider et al [17] have signified a substantial chance to disturb the sturdiest rate driver for a corporation’s whole pharmacovigilance budget. A trial was conducted to check the possibility of using simulated mechanization of the intellect and robotic method to systematize handling of case event reports. 03 competitive sellers' resolutions was used as trial model The result identified that with the use of AI-based technologies to sustain forms of mining from the contrary source of events and determine the validity of cases is possible. . In addition, the trial identified the feasibility of using database arenas for protection as a substitute for laborious and costly direct explanation of source types. In conclusion, assessment and counting technique utilised in the trial was able to distinguish seller competences and recognize the finest applicant to transfer into the detection stage.

Author & Year Method Name Name of the Data set Advantages technique Machine NLP Technique Ask a Patient and These data sets Zahra Rezaei et Learning Twitter give better results al.,2019 ADR drugscom.com, Their feature-rich IlseyarAlimova et classification amazon health- classifier achieves al.,2018 technique related significant improvements on a benchmark dataset over baseline approaches and CNN.

Pilot Paradigm PVNet It is used to JuergenSchmider benchmark data identify the best et al.,2018 candidate to move into the discovery phase.

Table 3 Comparison of Machine learning methods

VII. CONCLUSION

In this survey, a review on the possibility of using social digital media information for Pharmacovigilance activities was analyzed with the following sub sections. In section 2, Existing Review on role of social media in Pharmacovigilance was described and in section 3, Methods of Pharmacovigilance from Social Media was studied and under this section, methods such as Text mining, Text classification and using Artificial Intelligence were analyzed. The pending task is to perform statistical analyzes on pairs of mined drug-ADRs to identify potentially harmful drugs. Hence, future research on pharmacovigilance focus on this aspect.

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ISSN- 2394-5125 VOL 7, ISSUE 19, 2020 VIII. REFERENCES

[1] AbeedSarker , Rachel Ginn , AzadehNikfarjam ,Karen O’Connor , Karen Smith ,SwethaJayaraman , TejaswiUpadhaya , Graciela Gonzalez, "Utilizing social media data for pharmacovigilance: A review",Elsevier,Journal of Biomedical Informatics 54 (2015),pp:202-212,2015. [2] Rezaei, Z., Ebrahimpour-Komleh, H., Eslami, B., Chavoshinejad, R., & Totonchi, M. (2020). Adverse Drug Reaction Detection in Social Media by Deep Learning Methods. Cell Journal (Yakhteh), 22(3). [3] Azam, R. (2018). Accessing social media information for pharmacovigilance: what are the ethical implications?. [4] SHRESTHA, S., PALAIAN, S., SHRESTHA, B., SANTOSH, K., & KHANAL, S. (2019). The Potential Role of Social Media in Pharmacovigilance in Nepal: Glimpse from a Resource-limited Setting. Journal of Clinical & Diagnostic Research, 13(3). [5] Sloane, R., Osanlou, O., Lewis, D., Bollegala, D., Maskell, S., & Pirmohamed, M. (2015). Social media and pharmacovigilance: a review of the opportunities and challenges. British journal of clinical pharmacology, 80(4), 910-920. [6] Tricco, A. C., Zarin, W., Lillie, E., Jeblee, S., Warren, R., Khan, P. A., ... & Straus, S. E. (2018). Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review. BMC medical informatics and decision making, 18(1), 38. [7] Tricco, A. C., Zarin, W., Lillie, E., Pham, B., & Straus, S. E. (2017). Utility of social media and crowd- sourced data for pharmacovigilance: a scoping review protocol. BMJ open, 7(1), e013474. [8] DimitraPappa and Lampros K. Stergioulas, "Harnessing socialmedia data for pharmacovigilance: a review of current state of the art, challenges and future directions",International Journal of Data Science and Analytics (2019) 8:113–135,2019. [9] Chowdhury, S., Zhang, C., & Yu, P. S. (2018, April). Multi-task pharmacovigilance mining from social media posts. In Proceedings of the 2018 World Wide Web Conference (pp. 117-126). [10] Chen, X., Faviez, C., Schuck, S., Lillo-Le-Louët, A., Texier, N., Dahamna, B., ... & Karapetiantz, P. (2018). Mining patients' narratives in social media for pharmacovigilance: adverse effects and misuse of methylphenidate. Frontiers in pharmacology, 9, 541. [11] Nikfarjam, A., Sarker, A., O’connor, K., Ginn, R., & Gonzalez, G. (2015). Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association, 22(3), 671-681. [12] Yang, C. C., & Yang, H. (2015, November). Exploiting social media with tensor decomposition for pharmacovigilance. In 2015 IEEE International Conference on Data Mining Workshop (ICDMW) (pp. 188-195). IEEE. [13] Kotov, A. (2015). Social Media Analytics for Healthcare. Healthcare data analytics, 1, 309-340. [14] Liu, K., & Chen, L. (2019). Medical Social Media Text Classification Integrating Consumer Health Terminology. IEEE Access, 7, 78185-78193. [15] Pattisapu, N., Gupta, M., Kumaraguru, P., & Varma, V. (2017, July). Medical persona classification in social media. In 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 377-384). IEEE. [16] Alimova, I., & Tutubalina, E. (2017, July). Automated detection of adverse drug reactions from social media posts with machine learning. In International Conference on Analysis of Images, Social Networks and Texts (pp. 3-15). Springer, Cham. [17] Schmider, J., Kumar, K., LaForest, C., Swankoski, B., Naim, K., & Caubel, P. M. (2019). Innovation in pharmacovigilance: use of artificial intelligence in adverse event case processing. Clinical pharmacology & therapeutics, 105(4), 954-961.

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