This paper is in the following e-collection/theme issue:

Depression and Mood Disorders; Suicide Prevention (1273) Clinical Information and Decision Making (1315) New Methods (902) Methods and Feasibility Studies (367) Decision Support for Health Professionals (1129) Electronic Health Records (961) Diagnostic Tools in Mental Health (220) Machine Learning (1373) Special Issue on Computing and Mental Health (2018) (33)

Published on in Vol 5, No 2 (2018): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10144, first published .
Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study

Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study

Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study

Authors of this article:

Marcos DelPozo-Banos1 Author Orcid Image ;   Ann John1 Author Orcid Image ;   Nicolai Petkov2 Author Orcid Image ;   Damon Mark Berridge1 Author Orcid Image ;   Kate Southern3 Author Orcid Image ;   Keith LLoyd1 Author Orcid Image ;   Caroline Jones4 Author Orcid Image ;   Sarah Spencer5 Author Orcid Image ;   Carlos Manuel Travieso6 Author Orcid Image
Article Authors Cited by (31) Tweetations (64) Metrics

Marcos DelPozo-Banos   1 , PhD ;   Ann John   1 , Prof, FFPH ;   Nicolai Petkov   2 , Prof Dr ;   Damon Mark Berridge   1 , Prof, PhD ;   Kate Southern   3 , MPharm, PgCert ;   Keith LLoyd   1 , Prof, FRCPsych ;   Caroline Jones   4 , LLB, PhD ;   Sarah Spencer   5 , FRCEM, Dr ;   Carlos Manuel Travieso   6 , Prof, PhD

1 Swansea University, Swansea University Medical School, Swansea, United Kingdom

2 Division of Intelligent Systems, Department of Computer Science, Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen, Groningen, Netherlands

3 Cardiff Adult Self Injury Project, Cardiff, United Kingdom

4 Hillary Rodham Clinton School of Law, Swansea University, Swansea, United Kingdom

5 Princess of Wales Hospital, Bridgend, ABMU Health Board, Swansea, United Kingdom

6 Signals and Communications Department, IDeTIC, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

Corresponding Author:

  • Marcos DelPozo-Banos, PhD
  • Swansea University
  • Swansea University Medical School
  • Institute of Life Science 2, 3rd floor
  • Swansa University Singleton Park campus
  • Swansea, SA2 8PP
  • United Kingdom
  • Phone: 44 1792604094
  • Email: M.DelPozoBanos@swansea.ac.uk