SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media

Liu, Jing; Zhao, Shengchuan; Wang, Gang

doi:10.1016/j.artmed.2017.10.003

Cited by 26 publications

(20 citation statements)

References 40 publications

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“…This is essential due to the fact that in many real-world scenarios involving automatic categorization of content, like ours, and because labeled data are both expensive and timeconsuming to gather as well as scarce (e.g., needs expert involvement and manual labor), although unlabeled data are comparatively huge and easier to gather. To build robust classification models that can generalize across datasets, settings, and mental health campaigns, we use semi-supervised learning [35][36][37] in this second phase, that is able to leverage both labeled and unlabeled data in unison, thereby is to cover a better diversity of training examples [38].…”

Section: Semi-supervised Classifier (C)mentioning

confidence: 99%

A computational study of mental health awareness campaigns on social media

Saha

Torous

Ernala

et al. 2019

Translational Behavioral Medicine

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Section: Semi-supervised Classifier (C)mentioning

confidence: 99%

A computational study of mental health awareness campaigns on social media

Saha

Torous

Ernala

et al. 2019

Translational Behavioral Medicine

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“…Signal detection (Sarntivijai et al, 2012; Tao et al, 2012; Cheng et al, 2013; Boyce et al, 2014; Cheng and Zhao, 2014; Courtot et al, 2014; Iyer et al, 2014; Wang et al, 2014; Cai et al, 2015, 2017; Dupuch and Grabar, 2015; Liu and Chen, 2015; Koutkias and Jaulent, 2016; Liu et al, 2016, 2018; Knowledge Base Workgroup of the Observational Health Data Sciences and Informatics (OHDSI) Collaborative, 2017; Voss et al, 2017)…”

Section: Resultsmentioning

confidence: 99%

“…NLP (e.g., Named Entity Recognition) (Segura-Bedmar et al, 2010, 2011; He et al, 2013; Kang et al, 2014; Shang et al, 2014; Jiang et al, 2015; Sarker and Gonzalez, 2015; Eshleman and Singh, 2016; Liu et al, 2016, 2018; Zhang et al, 2016)…”

Section: Resultsmentioning

confidence: 99%

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

et al. 2019

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Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.

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“…Supervised learning has been tested in PV, largely in ICSR processing, where a human-annotated answer file ("ground truth") is used to teach the machine learning algorithm(s) [28,29]. Unsupervised learning and reinforcement learning methods, where there is no "ground truth", may have utility in signal management, as they would avoid introduction of bias in identifying potential signals ICSR [29,30], aggregate, signal management, risk management [31], QMS Arguments and use case for blockchain Blockchain technology has been widely adopted in financial systems and is used for tracking, tracing, auditing, and monitoring transactions. We are aware of potential applicability in healthcare and biomedical research [34].…”

Section: Cognitive Computingmentioning

confidence: 99%

“…Supervised learning has been tested in PV, largely in ICSR processing, where a human–annotated answer file (“ground truth”) is used to teach the machine learning algorithm(s) [ 28 , 29 ]. Unsupervised learning and reinforcement learning methods, where there is no “ground truth”, may have utility in signal management, as they would avoid introduction of bias in identifying potential signals ICSR [ 29 , 30 ], aggregate, signal management, risk management [ 31 ], QMS Neural network A computer system modeled on the neuronal structure of the mammalian brain. Neural networks are typically organized in layers made up of a number of thousands of interconnected nodes.…”

Section: Current Uses Of Information Technology In Pharmacovigilance:mentioning

confidence: 99%

Utilizing Advanced Technologies to Augment Pharmacovigilance Systems: Challenges and Opportunities

Lewis

McCallum

2019

Ther Innov Regul Sci

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There are significant challenges and opportunities in deploying and utilizing advanced information technology (IT) within pharmacovigilance (PV) systems and across the pharmaceutical industry. Various aspects of PV will benefit from automation (e.g., by improving standardization or increasing data quality). Several themes are developed, highlighting the challenges faced, exploring solutions, and assessing the potential for further research. Automation of the workflow for processing of individual case safety reports (ICSRs) is adopted as a use case. This involves a logical progression through a series of steps that when linked together comprise the complete work process required for the effective management of ICSRs. We recognize that the rapid development of new technologies will invariably outpace the regulations applicable to PV systems. Nevertheless, we believe that such systems may be improved by intelligent automation. It is incumbent on the owners of these systems to explore opportunities presented by new technologies with regulators in order to evaluate the applicability, design, deployment, performance, validation and maintenance of advanced technologies to ensure that the PV system continues to be fit for purpose. Proposed approaches to the validation of automated PV systems are presented. A series of definitions and a critical appraisal of important considerations are provided in the form of use cases. We summarize progress made and opportunities for the development of automation of future systems. The overall goal of automation is to provide high quality safety data in the correct format, in context, more quickly, and with less manual effort. This will improve the evidence available for scientific assessment and helps to inform and expedite decisions about the minimization of risks associated with medicines.

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SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media

Cited by 26 publications

References 40 publications

A computational study of mental health awareness campaigns on social media

A computational study of mental health awareness campaigns on social media

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Utilizing Advanced Technologies to Augment Pharmacovigilance Systems: Challenges and Opportunities

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