Methods for Leveraging Social Media Data to Quantify and Improve Pharmacovigilance Efforts

Methods for Leveraging Social Media Data to Quantify and Improve Pharmacovigilance Efforts PDF Author: Adam Allen Joseph Lavertu
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
Adverse drug reactions impact the health of 100,000s of individuals annually in the United States with associated costs in the hundreds of billions. Pharmacovigilance seeks to improve drug safety and limit patient potential for adverse drug events. Social media has been identified as a promising potential source of information for pharmacovigilance. The adoption of social media data as a component of pharmacovigilance efforts has been hindered by the massive and noisy nature of the data. I present work seeking to (1) identify social media data discussions of drug and drug-related terms, (2) use word embeddings derived from social media data to create quantitative severity scores for adverse drug reactions, and (3) create digital cohorts of social media users and monitor them for changes in prevalence of drug discussion rates in the general US potential, specifically with regards opioids. I present novel methods that enabled these efforts, as well as the results and findings of these efforts. I believe leveraging social media data to enhance pharmacovigilance will benefit U.S. patient populations and further empower public health and regulatory efforts.

Methods for Leveraging Social Media Data to Quantify and Improve Pharmacovigilance Efforts

Methods for Leveraging Social Media Data to Quantify and Improve Pharmacovigilance Efforts PDF Author: Adam Allen Joseph Lavertu
Publisher:
ISBN:
Category :
Languages : en
Pages :

Get Book Here

Book Description
Adverse drug reactions impact the health of 100,000s of individuals annually in the United States with associated costs in the hundreds of billions. Pharmacovigilance seeks to improve drug safety and limit patient potential for adverse drug events. Social media has been identified as a promising potential source of information for pharmacovigilance. The adoption of social media data as a component of pharmacovigilance efforts has been hindered by the massive and noisy nature of the data. I present work seeking to (1) identify social media data discussions of drug and drug-related terms, (2) use word embeddings derived from social media data to create quantitative severity scores for adverse drug reactions, and (3) create digital cohorts of social media users and monitor them for changes in prevalence of drug discussion rates in the general US potential, specifically with regards opioids. I present novel methods that enabled these efforts, as well as the results and findings of these efforts. I believe leveraging social media data to enhance pharmacovigilance will benefit U.S. patient populations and further empower public health and regulatory efforts.

Social Monitoring for Public Health

Social Monitoring for Public Health PDF Author: Michael J. Paul
Publisher: Morgan & Claypool Publishers
ISBN: 1681736101
Category : Computers
Languages : en
Pages : 188

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Book Description
Public health thrives on high-quality evidence, yet acquiring meaningful data on a population remains a central challenge of public health research and practice. Social monitoring, the analysis of social media and other user-generated web data, has brought advances in the way we leverage population data to understand health. Social media offers advantages over traditional data sources, including real-time data availability, ease of access, and reduced cost. Social media allows us to ask, and answer, questions we never thought possible. This book presents an overview of the progress on uses of social monitoring to study public health over the past decade. We explain available data sources, common methods, and survey research on social monitoring in a wide range of public health areas. Our examples come from topics such as disease surveillance, behavioral medicine, and mental health, among others. We explore the limitations and concerns of these methods. Our survey of this exciting new field of data-driven research lays out future research directions.

Practical Aspects of Signal Detection in Pharmacovigilance

Practical Aspects of Signal Detection in Pharmacovigilance PDF Author: Council for International Organizations of Medical Sciences (CIOMS)
Publisher: Cioms
ISBN: 9789290360827
Category : Drug monitoring
Languages : en
Pages : 0

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Book Description
In recent years public expectations for rapid identification and prompt management of emerging drug safety issues have grown swiftly. Over a similar timeframe, the move from paper-based adverse event reporting systems to electronic capture and rapid transmission of data has resulted in the accrual of substantial datasets capable of complex analysis and querying by industry, regulators and other public health organizations. These two drivers have created a fertile environment for pharmacovigilance scientists, information technologists and statistical experts, working together, to deliver novel approaches to detect signals from these extensive and quickly growing datasets, and to manage them appropriately. In following this exciting story, this report looks at the practical consequences of these developments for pharmacovigilance practitioners. The report provides a comprehensive resource for those considering how to strengthen their pharmacovigilance systems and practices, and to give practical advice. But the report does not specify instant solutions. These will inevitably be situation specific and require careful consideration taking into account local needs. However, the CIOMS Working Group VIII is convinced that the combination of methods and a clear policy on the management of signals will strengthen current systems. Finally, in looking ahead, the report anticipates a number of ongoing developments, including techniques with wider applicability to other data forms than individual case reports. The ultimate test for pharmacovigilance systems is the demonstration of public health benefit and it is this test which signal detection methodologies need to meet if the expectations of all stakeholders are to be fulfilled.

Healthcare Data Analytics

Healthcare Data Analytics PDF Author: Chandan K. Reddy
Publisher: CRC Press
ISBN: 148223212X
Category : Business & Economics
Languages : en
Pages : 756

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Book Description
At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available

Smart Health

Smart Health PDF Author: Hsinchun Chen
Publisher: Springer
ISBN: 9783030036485
Category : Medical
Languages : en
Pages : 360

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Book Description
This book constitutes the thoroughly refereed post-conference proceedings of the International Conference for Smart Health, ICSH 2018, held in Wuhan, China, in July 2018. The 14 full papers and 21 short papers presented were carefully reviewed and selected from 49 submissions. They focus on studies on the principles, approaches, models, frameworks, new applications, and effects of using novel information technology to address healthcare problems and improve social welfare. The selected papers are organized into the following topics: smart hospital; online health community; mobile health; medical big data and healthcare machine learning; chronic disease management; and health informatics.

Registries for Evaluating Patient Outcomes

Registries for Evaluating Patient Outcomes PDF Author: Agency for Healthcare Research and Quality/AHRQ
Publisher: Government Printing Office
ISBN: 1587634333
Category : Medical
Languages : en
Pages : 385

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Book Description
This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.

Translational Biomedical Informatics

Translational Biomedical Informatics PDF Author: Bairong Shen
Publisher: Springer
ISBN: 9811015031
Category : Science
Languages : en
Pages : 331

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Book Description
This book introduces readers to essential methods and applications in translational biomedical informatics, which include biomedical big data, cloud computing and algorithms for understanding omics data, imaging data, electronic health records and public health data. The storage, retrieval, mining and knowledge discovery of biomedical big data will be among the key challenges for future translational research. The paradigm for precision medicine and healthcare needs to integratively analyze not only the data at the same level – e.g. different omics data at the molecular level – but also data from different levels – the molecular, cellular, tissue, clinical and public health level. This book discusses the following major aspects: the structure of cross-level data; clinical patient information and its shareability; and standardization and privacy. It offers a valuable guide for all biologists, biomedical informaticians and clinicians with an interest in Precision Medicine Informatics.

The Digitization of Healthcare

The Digitization of Healthcare PDF Author: Loick Menvielle
Publisher: Springer
ISBN: 1349951730
Category : Medical
Languages : en
Pages : 476

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Book Description
Combining conceptual, pragmatic and operational approaches, this edited collection addresses the demand for knowledge and understanding of IT in the healthcare sector. With new technology outbreaks, our vision of healthcare has been drastically changed, switching from a ‘traditional’ path to a digitalized one. Providing an overview of the role of IT in the healthcare sector, The Digitization of Healthcare illustrates the potential benefits and challenges for all those involved in delivering care to the patient. The incursion of IT has disrupted the value chain and changed business models for companies working in the health sector, and also raised ethical issues and new paradigms about delivering care. This book illustrates the rise of patient empowerment through the development of patient communities such as PatientLikeMe, and medical collaborate platforms such as DockCheck, thus providing a necessary tool to patients, caregivers and academics alike.

Big Data Preprocessing

Big Data Preprocessing PDF Author: Julián Luengo
Publisher: Springer Nature
ISBN: 3030391051
Category : Computers
Languages : en
Pages : 193

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Book Description
This book offers a comprehensible overview of Big Data Preprocessing, which includes a formal description of each problem. It also focuses on the most relevant proposed solutions. This book illustrates actual implementations of algorithms that helps the reader deal with these problems. This book stresses the gap that exists between big, raw data and the requirements of quality data that businesses are demanding. This is called Smart Data, and to achieve Smart Data the preprocessing is a key step, where the imperfections, integration tasks and other processes are carried out to eliminate superfluous information. The authors present the concept of Smart Data through data preprocessing in Big Data scenarios and connect it with the emerging paradigms of IoT and edge computing, where the end points generate Smart Data without completely relying on the cloud. Finally, this book provides some novel areas of study that are gathering a deeper attention on the Big Data preprocessing. Specifically, it considers the relation with Deep Learning (as of a technique that also relies in large volumes of data), the difficulty of finding the appropriate selection and concatenation of preprocessing techniques applied and some other open problems. Practitioners and data scientists who work in this field, and want to introduce themselves to preprocessing in large data volume scenarios will want to purchase this book. Researchers that work in this field, who want to know which algorithms are currently implemented to help their investigations, may also be interested in this book.

Artificial Intelligence in Healthcare

Artificial Intelligence in Healthcare PDF Author: Adam Bohr
Publisher: Academic Press
ISBN: 0128184396
Category : Computers
Languages : en
Pages : 385

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Book Description
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data