Evidence map›Paper›PMID 29188348›Full record

ReviewDer Nervenarzt2018

[Big data approaches in psychiatry: examples in depression research].

D Bzdok, T M Karrer, U Habel, F Schneider

Abstract readReview
PubMed Publisher
In one paragraph

Review in Der Nervenarzt, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

4 authors.

D BzdokKlinik für Psychiatrie, Psychotherapie und Psychosomatik, Uniklinik RWTH Aachen, Pauwelstraße 30, 52074, Aachen, Deutschland. dbzdok@ukaachen.de.
T M KarrerKlinik für Psychiatrie, Psychotherapie und Psychosomatik, Uniklinik RWTH Aachen, Pauwelstraße 30, 52074, Aachen, Deutschland.
U HabelKlinik für Psychiatrie, Psychotherapie und Psychosomatik, Uniklinik RWTH Aachen, Pauwelstraße 30, 52074, Aachen, Deutschland.
F SchneiderKlinik für Psychiatrie, Psychotherapie und Psychosomatik, Uniklinik RWTH Aachen, Pauwelstraße 30, 52074, Aachen, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe exploration and therapy of depression is aggravated by heterogeneous etiological mechanisms and various comorbidities. With the growing trend towards big data in psychiatry, research and therapy can increasingly target the individual patient. This novel objective requires special methods of analysis.

objectiveThe possibilities and challenges of the application of big data approaches in depression are examined in closer detail. MATERIAL AND

methodsExamples are given to illustrate the possibilities of big data approaches in depression research. Modern machine learning methods are compared to traditional statistical methods in terms of their potential in applications to depression.

resultsBig data approaches are particularly suited to the analysis of detailed observational data, the prediction of single data points or several clinical variables and the identification of endophenotypes. A current challenge lies in the transfer of results into the clinical treatment of patients with depression.

conclusionBig data approaches enable biological subtypes in depression to be identified and predictions in individual patients to be made. They have enormous potential for prevention, early diagnosis, treatment choice and prognosis of depression as well as for treatment development.

Indexed as

Big DataDepressionPsychiatryResearchHumansBiological subtypesEndophenotypesMachine learningPersonalized medicinePrognosis

Identifiers

PMID29188348

What OpenQuestion holds

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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.