scholarly journals Correction to: Prediction Modeling Methodology

2019 ◽  
pp. C1-C1
Author(s):  
Frank J. W. M. Dankers ◽  
Alberto Traverso ◽  
Leonard Wee ◽  
Sander M. J. van Kuijk
Keyword(s):  
Clinical Data ◽  
Data Science ◽  
Prediction Modeling ◽  
Link Type ◽  
Modeling Methodology

Correction to: Chapter 8 in: P. Kubben et al. (eds.), Fundamentals of Clinical Data Science,10.1007/978-3-319-99713-1_8

scholarly journals Evaluation Considerations for Secondary Uses of Clinical Data: Principles for an Evidence-based Approach to Policy and Implementation of Secondary Analysis

2017 ◽  
Vol 26 (01) ◽  
pp. 59-67 ◽  
Author(s):  
P. J. Scott ◽  
M. Rigby ◽  
E. Ammenwerth ◽  
J. McNair ◽  
A. Georgiou ◽  
...  
Keyword(s):  
Clinical Data ◽  
Health Informatics ◽  
Best Practice ◽  
Data Science ◽  
Clinical Performance ◽  
Secondary Analysis ◽  
Health Data ◽  
Evidence Based ◽  
Great Expectations ◽  
Scientific Context

Summary Objectives: To set the scientific context and then suggest principles for an evidence-based approach to secondary uses of clinical data, covering both evaluation of the secondary uses of data and evaluation of health systems and services based upon secondary uses of data. Method: Working Group review of selected literature and policy approaches. Results: We present important considerations in the evaluation of secondary uses of clinical data from the angles of governance and trust, theory, semantics, and policy. We make the case for a multi-level and multi-factorial approach to the evaluation of secondary uses of clinical data and describe a methodological framework for best practice. We emphasise the importance of evaluating the governance of secondary uses of health data in maintaining trust, which is essential for such uses. We also offer examples of the re-use of routine health data to demonstrate how it can support evaluation of clinical performance and optimize health IT system design. Conclusions: Great expectations are resting upon “Big Data” and innovative analytics. However, to build and maintain public trust, improve data reliability, and assure the validity of analytic inferences, there must be independent and transparent evaluation. A mature and evidence-based approach needs not merely data science, but must be guided by the broader concerns of applied health informatics.

scholarly journals Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science

PLoS ONE ◽  
2016 ◽  
Vol 11 (8) ◽  
pp. e0161135 ◽  
Author(s):  
Julio Montes-Torres ◽  
José Luis Subirats ◽  
Nuria Ribelles ◽  
Daniel Urda ◽  
Leonardo Franco ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Clinical Data ◽  
Data Science ◽  
Predictive Modelling ◽  
Analysis Tool

A Simple and Replicable Framework for the Implementation of Clinical Data Science

2020 ◽  
pp. 137-161
Author(s):  
Juan Luis Cruz ◽  
Mariano Provencio ◽  
Ernestina Menasalvas
Keyword(s):  
Clinical Data ◽  
Data Science

scholarly journals Critical Care, Critical Data

2019 ◽  
Vol 10 ◽  
pp. 117959721985656 ◽  
Author(s):  
Christopher V Cosgriff ◽  
Leo Anthony Celi ◽  
David J Stone
Keyword(s):  
Artificial Intelligence ◽  
Critical Care ◽  
Clinical Data ◽  
Continuous Monitoring ◽  
Data Science ◽  
Clinical Decision ◽  
Critical Care Medicine ◽  
Data Driven ◽  
Critical Data

As big data, machine learning, and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine.

scholarly journals Evaluation Considerations for Secondary Uses of Clinical Data: Principles for an Evidence-based Approach to Policy and Implementation of Secondary Analysis

2017 ◽  
Vol 26 (01) ◽  
pp. 59-67 ◽  
Author(s):  
P. J. Scott ◽  
M. Rigby ◽  
E. Ammenwerth ◽  
J. McNair ◽  
A. Georgiou ◽  
...  
Keyword(s):  
Clinical Data ◽  
Health Informatics ◽  
Best Practice ◽  
Data Science ◽  
Clinical Performance ◽  
Secondary Analysis ◽  
Health Data ◽  
Evidence Based ◽  
Great Expectations ◽  
Scientific Context

Summary Objectives: To set the scientific context and then suggest principles for an evidence-based approach to secondary uses of clinical data, covering both evaluation of the secondary uses of data and evaluation of health systems and services based upon secondary uses of data. Method: Working Group review of selected literature and policy approaches. Results: We present important considerations in the evaluation of secondary uses of clinical data from the angles of governance and trust, theory, semantics, and policy. We make the case for a multi-level and multi-factorial approach to the evaluation of secondary uses of clinical data and describe a methodological framework for best practice. We emphasise the importance of evaluating the governance of secondary uses of health data in maintaining trust, which is essential for such uses. We also offer examples of the re-use of routine health data to demonstrate how it can support evaluation of clinical performance and optimize health IT system design. Conclusions: Great expectations are resting upon “Big Data” and innovative analytics. However, to build and maintain public trust, improve data reliability, and assure the validity of analytic inferences, there must be independent and transparent evaluation. A mature and evidence-based approach needs not merely data science, but must be guided by the broader concerns of applied health informatics.

scholarly journals Data Sources

2018 ◽  
pp. 3-9
Author(s):  
Pieter Kubben
Keyword(s):  
Clinical Data ◽  
Data Science ◽  
Data Sources ◽  
Many Sources

AbstractThere are many sources that relevant data for clinical data science can originate from. The brief overview in this chapter highlights the most frequent sources, but is definitely not exhaustive. The goal of this chapter is to provide an introduction to the most common data sources and to familiarize the reader with basic terminology in this context, in order to more easily understand discussions in next chapters and in literature in general.

scholarly journals OverCOVID: an integrative web portal for SARS-CoV-2 bioinformatics resources

2021 ◽  
Vol 18 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Md. Asif Ahsan ◽  
Yongjing Liu ◽  
Cong Feng ◽  
Ralf Hofestädt ◽  
Ming Chen
Keyword(s):  
Clinical Research ◽  
Human Health ◽  
Clinical Data ◽  
The Novel ◽  
Web Portal ◽  
Vast Amount ◽  
Link Type ◽  
Other Information ◽  
Novel Coronavirus ◽  
The Web

Abstract Outbreaks of COVID-19 caused by the novel coronavirus SARS-CoV-2 is still a threat to global human health. In order to understand the biology of SARS-CoV-2 and developing drug against COVID-19, a vast amount of genomic, proteomic, interatomic, and clinical data is being generated, and the bioinformatics researchers produced databases, webservers and tools to gather those publicly available data and provide an opportunity of analyzing such data. However, these bioinformatics resources are scattered and researchers need to find them from different resources discretely. To facilitate researchers in finding the resources in one frame, we have developed an integrated web portal called OverCOVID (http://bis.zju.edu.cn/overcovid/). The publicly available webservers, databases and tools associated with SARS-CoV-2 have been incorporated in the resource page. In addition, a network view of the resources is provided to display the scope of the research. Other information like SARS-CoV-2 strains is visualized and various layers of interaction resources is listed in distinct pages of the web portal. As an integrative web portal, the OverCOVID will help the scientist to search the resources and accelerate the clinical research of SARS-CoV-2.

Large-Scale Analysis of Genetic and Clinical Patient Data

2018 ◽  
Vol 1 (1) ◽  
pp. 263-274 ◽  
Author(s):  
Marylyn D. Ritchie
Keyword(s):  
Clinical Data ◽  
Large Scale ◽  
Data Science ◽  
Genomic Analysis ◽  
Genomic Data ◽  
Data Sets ◽  
Biomedical Data ◽  
Data Types ◽  
Phenotypic Data ◽  
Clinical Patient

Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.

scholarly journals mSphere of Influence: Predicting Immune Responses and Susceptibility to Influenza Virus—May the Data Be with You

mSphere ◽  
2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Irene Ramos
Keyword(s):  
Influenza Virus ◽  
Immune Responses ◽  
Ab Initio ◽  
Data Science ◽  
Viral Infections ◽  
Science Methods ◽  
Molecular Signatures ◽  
Host Interactions ◽  
Link Type ◽  
Human Immune

ABSTRACT Irene Ramos works in the field of immunology to viral infections. In this mSphere of Influence article, she reflects on how “Global analyses of human immune variation reveal baseline predictors of postvaccination responses” by Tsang et al. (Cell 157:499–513, 2014, https://doi.org/10.1016/j.cell.2014.03.031) and “A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection” by Fourati et al. (Nat Commun 9:4418, 2018, https://doi.org/10.1038/s41467-018-06735-8) made an impact on her by highlighting the importance of data science methods to understand virus-host interactions.

scholarly journals Prediction Modeling Methodology

2018 ◽  
pp. 101-120 ◽  
Author(s):  
Frank J. W. M. Dankers ◽  
Alberto Traverso ◽  
Leonard Wee ◽  
Sander M. J. van Kuijk
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Machine Learning Techniques ◽  
Prediction Modeling ◽  
Modeling Methodology ◽  
Learning Techniques ◽  
Regression Techniques

AbstractIn the previous chapter, you have learned how to prepare your data before you start the process of generating a predictive model. In this chapter, you will learn how to make a predictive model using very common regression techniques and how to evaluate the performance of a model. In the next chapter we will then look at more advanced machine learning techniques that have become increasingly popular in recent years.

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