Evidence map›Paper›PMID 32130138›Full record

ArticleJournal of medical Internet research2020

Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper.

Guy Fagherazzi

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
–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

31 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Multi-scale digital twins for personalized medicine.Frontiers in digital health · 2026
    Review
  3. Article
  4. Article
  5. Article
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  7. Article
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  9. Article
  10. Review
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  12. Article
  13. Data-driven meal events detection using blood glucose response patterns.BMC medical informatics and decision making · 2023
    Article
  14. Article
  15. Article
  16. Article
  17. Intelligent Digital Twins for Personalized Migraine Care.Journal of personalized medicine · 2023
    Review
  18. Review
  19. Article
  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

1 author.

Guy FagherazziLuxembourg Institute of Health, Department of Population Health, Digital Epidemiology Hub, Strassen, Luxembourg.ORCID 0000-0001-5033-5966

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This viewpoint describes the urgent need for more large-scale, deep digital phenotyping to advance toward precision health. It describes why and how to combine real-world digital data with clinical data and omics features to identify someone's digital twin, and how to finally enter the era of patient-centered care and modify the way we view disease management and prevention.

Indexed as

HumansPatient-Centered CarePhenotypePrecision Medicinebig datadata lakedeep digital phenotypingdigital cohortdigital epidemiologydigital healthdigital orthodoxydigital phenotypingdigitosomeomicspersonalized medicineprecision healthprecision medicineprecision prevention

Identifiers

PMID32130138
PMCPMC7078624

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.