Evidence map›Paper›PMID 32215364›Full record

ArticleGeneral psychiatry2020

Advanced machine learning methods in psychiatry: an introduction.

Tsung-Chin Wu, Zhirou Zhou, Hongyue Wang, Bokai Wang, Tuo Lin, Changyong Feng, Xin M Tu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in General psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

7 authors.

Tsung-Chin WuDepartment of Mathematics, University of California San Diego, La Jolla, California, USA.ORCID 0000-0002-5224-6307
Zhirou ZhouDepartment of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA.
Hongyue WangDepartments of Biostatistics and Computational Biology and Anesthesiology, University of Rochester, Rochester, New York, USA.
Bokai WangDepartment of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA.ORCID 0000-0002-2998-693X
Tuo LinClinical and Translational Research Institute, University of California San Diego, San Diego, California, USA.
Changyong FengDepartment of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA.
Xin M TuFamily Medicine and Public Health, University of California San Diego, La Jolla, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mental health questions can be tackled through machine learning (ML) techniques. Apart from the two ML methods we introduced in our previous paper, we discuss two more advanced ML approaches in this paper: support vector machines and artificial neural networks. To illustrate how these ML methods have been employed in mental health, recent research applications in psychiatry were reported.

Indexed as

mental health

Identifiers

PMID32215364
PMCPMC7076259

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.