Evidence map›Paper›PMID 22146916›Full record

ArticleMethods of information in medicine2011

Data analysis and data mining: current issues in biomedical informatics.

R Bellazzi, M Diomidous, I N Sarkar, K Takabayashi, A Ziegler, A T McCray

Abstract read
In one paragraph

Article in Methods of information in medicine, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

  1. Machine learning in physical activity, sedentary, and sleep behavior research.Journal of activity, sedentary and sleep behaviors · 2024
    Review
  2. Article
  3. Review
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  5. Correlates of physical activity behavior in adults: a data mining approach.The international journal of behavioral nutrition and physical activity · 2020
    Article
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  16. Review
  17. In silico methods for drug repurposing and pharmacology.Wiley interdisciplinary reviews. Systems biology and medicine · 2016
    Review
  18. Article
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

R BellazziUniversity of Pavia, Dipartimento di Informatica e Sistemistica, Via Ferrata 1, 27100 Pavia (PV), Italy. riccardo.bellazzi@unipv.it
M Diomidous
I N Sarkar
K Takabayashi
A Ziegler
A T McCray

Funding

Enhancing Organism Based Disease Knowledge Via Name Based Taxonomic IntelligenceR01LM009725 · NLM · MARINE BIOLOGICAL LABORATORY · PI SARKAR, INDRA N · 2008 to 2010
$1.1M
NLM NIH HHS R01 LM009725NLM NIH HHS R01LM009725
6 · The paper itself

Abstract

backgroundMedicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research.

objectivesTo reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics.

methodsOn the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, which reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field.

resultsThe paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology.

conclusionsBiomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers.

Indexed as

Data Interpretation, StatisticalData MiningCongresses as TopicMedical InformaticsModels, StatisticalMolecular Epidemiology

Identifiers

PMID22146916
PMCPMC3233983

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.