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Data Mining in Clinical Medicine

Overview of attention for book
Cover of 'Data Mining in Clinical Medicine'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Actigraphy pattern analysis for outpatient monitoring.
  3. Altmetric Badge
    Chapter 2 Definition of Loss Functions for Learning from Imbalanced Data to Minimize Evaluation Metrics
  4. Altmetric Badge
    Chapter 3 Audit Method Suited for DSS in Clinical Environment.
  5. Altmetric Badge
    Chapter 4 Incremental logistic regression for customizing automatic diagnostic models.
  6. Altmetric Badge
    Chapter 5 Using Process Mining for Automatic Support of Clinical Pathways Design
  7. Altmetric Badge
    Chapter 6 Analyzing complex patients' temporal histories: new frontiers in temporal data mining.
  8. Altmetric Badge
    Chapter 7 The Snow System: A Decentralized Medical Data Processing System
  9. Altmetric Badge
    Chapter 8 Data Mining for Pulsing the Emotion on the Web
  10. Altmetric Badge
    Chapter 9 Introduction on Health Recommender Systems
  11. Altmetric Badge
    Chapter 10 Cloud Computing for Context-Aware Enhanced m-Health Services
  12. Altmetric Badge
    Chapter 11 Analysis of Speech-Based Measures for Detecting and Monitoring Alzheimer’s Disease
  13. Altmetric Badge
    Chapter 12 Applying Data Mining for the Analysis of Breast Cancer Data
  14. Altmetric Badge
    Chapter 13 Mining Data When Technology Is Applied to Support Patients and Professional on the Control of Chronic Diseases: The Experience of the METABO Platform for Diabetes Management
  15. Altmetric Badge
    Chapter 14 Data Analysis in Cardiac Arrhythmias
  16. Altmetric Badge
    Chapter 15 Knowledge-Based Personal Health System to Empower Outpatients of Diabetes Mellitus by Means of P4 Medicine
  17. Altmetric Badge
    Chapter 16 Serious Games for Elderly Continuous Monitoring
Attention for Chapter 6: Analyzing complex patients' temporal histories: new frontiers in temporal data mining.
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  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

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Chapter title
Analyzing complex patients' temporal histories: new frontiers in temporal data mining.
Chapter number 6
Book title
Data Mining in Clinical Medicine
Published in
Methods in molecular biology, November 2014
DOI 10.1007/978-1-4939-1985-7_6
Pubmed ID
25417081
Book ISBNs
978-1-4939-1984-0, 978-1-4939-1985-7
Authors

Sacchi L, Dagliati A, Bellazzi R, Lucia Sacchi, Arianna Dagliati, Riccardo Bellazzi

Editors

Carlos Fernández-Llatas, Juan Miguel García-Gómez

Abstract

In recent years, data coming from hospital information systems (HIS) and local healthcare organizations have started to be intensively used for research purposes. This rising amount of available data allows reconstructing the compete histories of the patients, which have a strong temporal component. This chapter introduces the major challenges faced by temporal data mining researchers in an era when huge quantities of complex clinical temporal data are becoming available. The analysis is focused on the peculiar features of this kind of data and describes the methodological and technological aspects that allow managing such complex framework. The chapter shows how heterogeneous data can be processed to derive a homogeneous representation. Starting from this representation, it illustrates different techniques for jointly analyze such kind of data. Finally, the technological strategies that allow creating a common data warehouse to gather data coming from different sources and with different formats are presented.

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 21%
Student > Bachelor 6 21%
Other 3 11%
Student > Master 3 11%
Researcher 2 7%
Other 5 18%
Unknown 3 11%
Readers by discipline Count As %
Medicine and Dentistry 8 29%
Computer Science 7 25%
Engineering 4 14%
Arts and Humanities 2 7%
Biochemistry, Genetics and Molecular Biology 1 4%
Other 2 7%
Unknown 4 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 December 2014.
All research outputs
#14,792,181
of 22,774,233 outputs
Outputs from Methods in molecular biology
#4,677
of 13,091 outputs
Outputs of similar age
#145,265
of 262,691 outputs
Outputs of similar age from Methods in molecular biology
#53
of 155 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,091 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 262,691 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 155 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.

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