scholarly journals The 2011–2020 Trends of Data-Driven Approaches in Medical Informatics for Active Pharmacovigilance

2021 ◽  
Vol 11 (5) ◽  
pp. 2249
Author(s):  
Hyunah Shin ◽  
Jaehun Cha ◽  
Chungchun Lee ◽  
Hyejin Song ◽  
Hyuntae Jeong ◽  
...  
Keyword(s):  
Medical Informatics ◽  
Drug Safety ◽  
Evaluation System ◽  
Scientific Discipline ◽  
Data Driven ◽  
Drug Reactions ◽  
The Past ◽  
Assessment And Monitoring ◽  
Medical Big Data ◽  
Data Analysis Methods

Pharmacovigilance, the scientific discipline pertaining to drug safety, has been studied extensively and is progressing continuously. In this field, medical informatics techniques and interpretation play important roles, and appropriate approaches are required. In this study, we investigated and analyzed the trends of pharmacovigilance systems, especially the data collection, detection, assessment, and monitoring processes. We used PubMed to collect papers on pharmacovigilance published over the past 10 years, and analyzed a total of 40 significant papers to determine the characteristics of the databases and data analysis methods used to identify drug safety indicators. Through systematic reviews, we identified the difficulty of standardizing data and terminology and establishing an adverse drug reactions (ADR) evaluation system in pharmacovigilance, and their corresponding implications. We found that appropriate methods and guidelines for active pharmacovigilance using medical big data are still required and should continue to be developed.

METHODOLOGIES FOR ASSESSING DRUG SAFETY, EFFECTIVENESS AND ADVERSE DRUG REACTIONS

2018 ◽  
Vol 20 (3) ◽  
pp. 112-117
Author(s):  
O.I. Slyusar ◽  
◽  
A.B. Kachalov ◽  
M.V. Ryzhkova ◽  
I.В. Slyusar ◽  
...  
Keyword(s):  
Adverse Drug Reactions ◽  
Drug Safety ◽  
Drug Reactions ◽  
Safety Effectiveness

Methods in Year 50: Preserving the Past and Preparing for the Future

2011 ◽  
Vol 50 (01) ◽  
pp. 1-6 ◽  
Author(s):  
D. Aronsky ◽  
T. Y. Leong ◽  
A. T. McCray ◽  
R. Haux
Keyword(s):  
Medical Informatics ◽  
Strategic Decisions ◽  
Major Focus ◽  
Editorial Policies ◽  
The Past ◽  
Processing Data ◽  
Online Access ◽  
Aims And Scope ◽  
Online Methods ◽  
Sixth Decade

Summary Background: Founded in 1962 and, therefore, the oldest international journal in medical informatics, Methods of Information in Medicine will publish its 50th volume in 2011. At the start of the journal’s sixth decade, a discussion on the journal’s profile seems appropriate. Objectives: To report on the new opportunities for online access to Methods publications as well as on the recent strategic decisions regarding the journal‘s aims and editorial policies. Methods: Describing and analyzing the journal’s aims and scope. Reflecting on recent publications and on the journal’s development during the last decade. Results: From 2011 forward all articles of Methods from 1962 until the present can be accessed online. Methods of Information in Medicine stresses the basic methodology and scientific fundamentals of processing data, information and knowledge in medicine and health care. Although the journal‘s major focus is on publications in medical informatics, it has never been restricted to publications only in this discipline. For example, articles in medical biometry, in or close to biomedical engineering, and, later, articles in bioinformatics continue to be a part of this journal. Conclusions: There is a continuous and, as it seems, ever growing overlap in the research methodology and application areas of the mentioned disciplines. As there is a continuing and even growing need for such a publication forum, Methods of Information in Medicine will keep its broad scope. As an organizational consequence, the journal’s number of associate editors has increased accordingly.

LBE for Generalized Hydrodynamics

2018 ◽  
Author(s):  
Sauro Succi
Keyword(s):  
Potential Energy ◽  
Electric Fields ◽  
Chemical Reactions ◽  
Soft Matter ◽  
Fluid Motion ◽  
Life Sciences ◽  
Scientific Discipline ◽  
The Past ◽  
Generalized Hydrodynamics ◽  
Internal Forces

This chapter presents the main techniques to incorporate the effects of external and/or internal forces within the LB formalism. This is a very important task, for it permits us to access a wide body of generalized hydrodynamic applications whereby fluid motion couples to a variety of additional physical aspects, such as gravitational and electric fields, potential energy interactions, chemical reactions and many others. It should be emphasized that while hosting a broader and richer phenomenology than “plain” hydrodynamics, generalized hydrodynamics still fits the hydrodynamic picture of weak departure from suitably generalized local equilibria. This class is all but an academic curiosity; for instance, it is central to the fast-growing science of Soft Matter, a scientific discipline which has received an impressive boost in the past decades, under the drive of micro- and nanotechnological developments and major strides in biology and life sciences at large.

scholarly journals Data-driven control of complex networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Giacomo Baggio ◽  
Danielle S. Bassett ◽  
Fabio Pasqualetti
Keyword(s):  
Complex Networks ◽  
Network Dynamics ◽  
Noisy Data ◽  
Data Driven ◽  
Control Algorithms ◽  
Optimal Controls ◽  
Network Systems ◽  
The Past ◽  
Efficient Control ◽  
Finite Set

AbstractOur ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.

Data-driven modeling decadal-to-centennial ENSO variability and its response to external forcing

2021 ◽  
Author(s):  
Aleksei Seleznev ◽  
Dmitry Mukhin ◽  
Andrey Gavrilov ◽  
Alexander Feigin
Keyword(s):  
Co2 Concentration ◽  
Data Driven ◽  
Time Interval ◽  
Solar Forcing ◽  
Russian Science ◽  
Dynamical Mechanism ◽  
Enso Variability ◽  
The Past ◽  
Sst Variability ◽  
Data Driven Modeling

<p>We investigate the decadal-to-centennial ENSO variability based on nonlinear data-driven stochastic modeling. We construct data-driven model of yearly Niño-3.4 indices reconstructed from paleoclimate proxies based on three different sea-surface temperature (SST) databases at the time interval from 1150 to 1995 [1]. The data-driven model is forced by the solar activity and CO2 concentration signals. We find the persistent antiphasing relationship between the solar forcing and Niño-3.4 SST on the bicentennial time scale. The dynamical mechanism of such a response is discussed.</p><p>The work was supported by the Russian Science Foundation (Grant No. 20-62-46056)</p><p>1. Emile-Geay, J., Cobb, K. M., Mann, M. E., & Wittenberg, A. T. (2013). Estimating Central Equatorial Pacific SST Variability over the Past Millennium. Part II: Reconstructions and Implications, Journal of Climate, 26(7), 2329-2352.</p>

The vigorous development of data driven astronomy education and public outreach (DAEPO)

2019 ◽  
Vol 15 (S367) ◽  
pp. 199-209
Author(s):  
Shanshan Li ◽  
Chenzhou Cui ◽  
Cuilan Qiao ◽  
Dongwei Fan ◽  
Changhua Li ◽  
...  
Keyword(s):  
Best Practices ◽  
Rapid Evolution ◽  
Internet Technology ◽  
Data Driven ◽  
Public Outreach ◽  
Continuous Change ◽  
Astronomy Education ◽  
The Past ◽  
Education And Public Outreach ◽  
Science Educators

AbstractAstronomy education and public outreach (EPO) is one of the important part of the future development of astronomy. During the past few years, as the rapid evolution of Internet and the continuous change of policy, the breeding environment of science EPO keep improving and the number of related projects show a booming trend. EPO is no longer just a matter of to teachers and science educators but also attracted the attention of professional astronomers. Among all activates of astronomy EPO, the data driven astronomy education and public outreach (abbreviated as DAEPO) is special and important. It benefits from the development of Big Data and Internet technology and is full of flexibility and diversity. We will present the history, definition, best practices and prospective development of DAEPO for better understanding this active field.

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

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