Catálogo de publicaciones - libros

There 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.

basic knowledge of major sources of clinical data.

in the previous chapter, you have learned what the major sources of clinical data are. In this chapter, we will dive into the main characteristics of presented data sources. In particular,

you will learn the major differences between data sources presented in previous chapters; how clinical data can be classified according to its scale. You will get familiar with the concept of ‘big’ clinical data; you will learn which are the major concerns limiting ‘big’ data exchange.

Clinical data interoperability requires shared specifications of meaning. This is the rationale for clinical data standards. Up until now, the adoption of such standards has been varied, although they are increasingly advocated in an area where proprietary specifications prevail, and semantic resources are geared to specific purposes and limited by boundaries of languages and jurisdictions. This chapter highlights the need of data standards in the context of the difficult and heterogeneous field of clinical data and the way how they are addressed by terminologies, ontologies and information models. It provides an overview of existing standards and discusses quality and implementation issues. Emphasis is also put on the eStandards methodology, which investigates needs for health data standards, supports the creation of standardised artefacts and defines actions for the implementation of standards.

Research data stewardship refers to the long-term and sustainable care for research data, from study design to data collection, analysis, storage, and sharing. It involves all activities that are required to ensure that digital research data is findable, accessible, interoperable, and reusable (FAIR) in the long term, including data management, archiving, and reuse by third parties. This chapter provides an overview of the aspects of FAIR data stewardship that you should consider when you are involved in clinical research.

This chapter introduces the rational and regulatory mechanism underlying the EU data protection framework with specific focus on the EU’s General Data Protection Regulation (GDPR). It outlines the applicability of the research exemption included in the GDPR and discusses further or secondary use of personal data for research purposes.

This is the first chapter of five that cover an introduction to developing and validating models for predicting outcomes for the individual patient. Such prediction models can be used for predicting the occurrence or recurrence of an event, or of the most likely value on a continuous outcome. We will mainly focus on the prediction of binary outcomes, such as the occurrence of a complication, recurrence of disease, the presence of metastases, remission, survival, etc. This chapter deals with the selection of an appropriate study design for a study on prediction, and on methods to manipulate the data before the statistical modelling can begin.

Clinical data is often collected and processed as time series: a sequence of data indexed by successive time points. Such time series can be from sources that are sampled over short time intervals to represent continuous biophysical wave-(one word waveforms) forms such as the voltage measurements representing the electrocardiogram, to measurements that are sampled daily, weekly, yearly, etc. such as patient weight, blood triglyceride levels, etc. When analyzing clinical data or designing biomedical systems for measurements, interventions, or diagnostic aids, it is important to represent the information contained within such time series in a more compact or meaningful form (e.g., noise filtering), amenable to interpretation by a human or computer. This process is known as feature extraction. This chapter will discuss some fundamental techniques for extracting features from time series representing general forms of clinical data.

In 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.

knowledge of the major steps and procedures of developing a clinical prediction model.

in the last chapters, you have learned how to develop and validate a clinical prediction model. You have been learning logistic regression as main algorithm to build the model. However, several different more complex algorithms can be used to build a clinical prediction model. In this chapter, the main machine learning based algorithms will be presented to you.

you will be presented with the definitions of: machine learning, supervised and unsupervised learning. The major algorithms for the last two categories will be introduced.