A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults
Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, t...
Uloženo v:
| Vydáno v: | Sensors (Basel, Switzerland) Ročník 22; číslo 13; s. 4790 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Basel
MDPI AG
24.06.2022
MDPI |
| Témata: | |
| ISSN: | 1424-8220, 1424-8220 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, the Leave-One-Subject-Out technique was used to train classification trees with a maximum of five splits. The results show an accuracy of 84.78%, a sensitivity of 64.64% and a specificity of 85.53%. In addition, positive and negative predictive values were also obtained, with results of 14.48% and 98.47%, respectively. These results are in line with those reported in previous work using a multilevel mixed-effect regression analysis. The combination of apps and AI techniques is a powerful way to improve the tools to accompany and support the care and treatment of patients with this type of behaviour. These studies also guide the improvement of apps on the user side, simplifying and collecting more meaningful data, and on the therapist side, progressing in pathology treatments. Traditional therapy involves observing and reconstructing what had happened before episodes once they have occurred. This new generation of tools will make it possible to monitor the pathology more closely and to act preventively. |
|---|---|
| AbstractList | Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, the Leave-One-Subject-Out technique was used to train classification trees with a maximum of five splits. The results show an accuracy of 84.78%, a sensitivity of 64.64% and a specificity of 85.53%. In addition, positive and negative predictive values were also obtained, with results of 14.48% and 98.47%, respectively. These results are in line with those reported in previous work using a multilevel mixed-effect regression analysis. The combination of apps and AI techniques is a powerful way to improve the tools to accompany and support the care and treatment of patients with this type of behaviour. These studies also guide the improvement of apps on the user side, simplifying and collecting more meaningful data, and on the therapist side, progressing in pathology treatments. Traditional therapy involves observing and reconstructing what had happened before episodes once they have occurred. This new generation of tools will make it possible to monitor the pathology more closely and to act preventively. Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, the Leave-One-Subject-Out technique was used to train classification trees with a maximum of five splits. The results show an accuracy of 84.78%, a sensitivity of 64.64% and a specificity of 85.53%. In addition, positive and negative predictive values were also obtained, with results of 14.48% and 98.47%, respectively. These results are in line with those reported in previous work using a multilevel mixed-effect regression analysis. The combination of apps and AI techniques is a powerful way to improve the tools to accompany and support the care and treatment of patients with this type of behaviour. These studies also guide the improvement of apps on the user side, simplifying and collecting more meaningful data, and on the therapist side, progressing in pathology treatments. Traditional therapy involves observing and reconstructing what had happened before episodes once they have occurred. This new generation of tools will make it possible to monitor the pathology more closely and to act preventively.Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, the Leave-One-Subject-Out technique was used to train classification trees with a maximum of five splits. The results show an accuracy of 84.78%, a sensitivity of 64.64% and a specificity of 85.53%. In addition, positive and negative predictive values were also obtained, with results of 14.48% and 98.47%, respectively. These results are in line with those reported in previous work using a multilevel mixed-effect regression analysis. The combination of apps and AI techniques is a powerful way to improve the tools to accompany and support the care and treatment of patients with this type of behaviour. These studies also guide the improvement of apps on the user side, simplifying and collecting more meaningful data, and on the therapist side, progressing in pathology treatments. Traditional therapy involves observing and reconstructing what had happened before episodes once they have occurred. This new generation of tools will make it possible to monitor the pathology more closely and to act preventively. |
| Author | Marti-Puig, Pere Capra, Chiara Solé-Casals, Jordi Vega, Daniel Llunas, Laia |
| AuthorAffiliation | 2 beHIT, Carrer de Mata 1, 08004 Barcelona, Spain; laia.llunas@behit.cat 1 Data and Signal Processing Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain; pere.marti@uvic.cat (P.M.-P.); chiara.capra@uvic.cat (C.C.) 4 Department of Psychiatry and Forensic Medicine, Institute of Neurosciences, Universitat Autònoma de Barcelona (UAB), 08193 Cerdanyola del Vallés, Barcelona, Spain 3 Psychiatry and Mental Health Department, Hospital Universitari d’Igualada, Consorci Sanitari de l’Anoia & Fundació Sanitària d’Igualada, 08700 Igualada, Barcelona, Spain; daniel.vega@uab.cat |
| AuthorAffiliation_xml | – name: 1 Data and Signal Processing Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain; pere.marti@uvic.cat (P.M.-P.); chiara.capra@uvic.cat (C.C.) – name: 3 Psychiatry and Mental Health Department, Hospital Universitari d’Igualada, Consorci Sanitari de l’Anoia & Fundació Sanitària d’Igualada, 08700 Igualada, Barcelona, Spain; daniel.vega@uab.cat – name: 2 beHIT, Carrer de Mata 1, 08004 Barcelona, Spain; laia.llunas@behit.cat – name: 4 Department of Psychiatry and Forensic Medicine, Institute of Neurosciences, Universitat Autònoma de Barcelona (UAB), 08193 Cerdanyola del Vallés, Barcelona, Spain |
| Author_xml | – sequence: 1 givenname: Pere orcidid: 0000-0001-6582-4551 surname: Marti-Puig fullname: Marti-Puig, Pere – sequence: 2 givenname: Chiara surname: Capra fullname: Capra, Chiara – sequence: 3 givenname: Daniel orcidid: 0000-0002-5621-8987 surname: Vega fullname: Vega, Daniel – sequence: 4 givenname: Laia surname: Llunas fullname: Llunas, Laia – sequence: 5 givenname: Jordi orcidid: 0000-0002-6534-1979 surname: Solé-Casals fullname: Solé-Casals, Jordi |
| BookMark | eNptkU1r3DAQhkVJaD7aQ_-BoZf24EZflqVLYQlpu7BpC5scehKyNN5o0UpbyS7k39fOhtCEnmZ49czDoDlDRzFFQOgdwZ8YU_iiUEoYbxV-hU4Jp7yWlOKjf_oTdFbKFmPKGJOv0QlrJJZUilO0XlTXxt75CNUKTI4-bqrFfp_TFFZ9ytXPDM7bYc6_p1ivR2-9M6FaQ-jrZdyO-b7ysfqVxnnSjWEob9Bxb0KBt4_1HN1-ubq5_FavfnxdXi5WteVcDLUCInsMLSUOCHVCEsNFZ6mkvO95C41shbMYt5K1jELTEcybjjUN7oSjQNk5Wh68Lpmt3me_M_leJ-P1Q5DyRps8eBtAK2kUc5SpvmFc8k5JjknXt9I0ykjVTK7PB9d-7HbgLMQhm_BM-vwl-ju9SX-0okJxIifBh0dBTr9HKIPe-WIhBBMhjUVTIduWCPWAvn-BbtOY4_RVMyWIwljN1McDZXMqJUP_tAzBej67fjr7xF68YK0fzODTvKsP_5n4C40zrIM |
| CitedBy_id | crossref_primary_10_1007_s12144_025_07436_4 crossref_primary_10_1016_j_jad_2023_10_110 crossref_primary_10_1016_j_chbah_2023_100008 crossref_primary_10_1186_s12889_025_23610_6 crossref_primary_10_1038_s41598_023_36172_7 crossref_primary_10_1109_JBHI_2025_3558170 crossref_primary_10_2196_63192 crossref_primary_10_1049_bme2_12110 crossref_primary_10_1016_j_ijmedinf_2023_105164 crossref_primary_10_1016_j_jad_2025_120110 crossref_primary_10_56083_RCV5N8_068 crossref_primary_10_1055_a_1915_2589 crossref_primary_10_1186_s12889_025_24354_z |
| Cites_doi | 10.1111/sltb.12237 10.1177/1073191114565878 10.1055/s-0038-1633857 10.1002/jclp.22037 10.1016/j.brat.2008.10.011 10.3390/ijerph16224581 10.1007/s12652-019-01355-6 10.1016/j.jad.2017.11.073 10.1016/j.neuroimage.2009.10.092 10.1016/j.cpr.2006.08.002 10.1001/jamapsychiatry.2019.2664 10.1016/j.beth.2006.08.003 10.1073/pnas.1716686115 10.2196/10275 10.1038/npp.2013.328 10.1037/per0000104 10.1016/j.nicl.2018.10.011 10.1109/JBHI.2016.2601123 10.1109/TIFS.2021.3076932 10.1016/j.psychres.2018.02.051 10.1007/s12671-016-0492-1 10.1023/A:1012779403943 10.1007/s11126-020-09875-7 10.1038/tp.2015.22 10.1111/sltb.12186 10.1037/per0000205 10.1007/978-3-030-86993-9_50 10.1016/j.psychres.2020.112761 10.1162/tacl_a_00111 10.1016/j.jsat.2019.01.020 10.1016/j.brat.2005.03.005 10.1002/widm.8 10.1371/journal.pone.0185123 10.1037/cou0000382 10.1037/abn0000141 10.1017/S0033291715001804 10.1016/j.beth.2011.01.002 10.1146/annurev.clinpsy.3.022806.091415 10.1002/brb3.1384 |
| ContentType | Journal Article |
| Copyright | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
| Copyright_xml | – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
| DBID | AAYXX CITATION 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.3390/s22134790 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | CrossRef Publicly Available Content Database MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_98a93d239f53484b98401bf78a59a895 PMC9269418 10_3390_s22134790 |
| GrantInformation_xml | – fundername: Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) of the Catalan Government grantid: 2020-DI-068 – fundername: Spanish government grant grantid: PSI2016–79980-P |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M 3V. 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c446t-9e18f0e721de12d681a46bc2824ff47e5876dc00783732e5b1045b3550b6d2e23 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 13 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000824185500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1424-8220 |
| IngestDate | Tue Oct 14 19:04:11 EDT 2025 Tue Nov 04 02:02:26 EST 2025 Fri Sep 05 06:02:24 EDT 2025 Tue Oct 07 07:09:03 EDT 2025 Sat Nov 29 07:18:47 EST 2025 Tue Nov 18 20:52:04 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 13 |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c446t-9e18f0e721de12d681a46bc2824ff47e5876dc00783732e5b1045b3550b6d2e23 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 These authors contributed equally to this work. |
| ORCID | 0000-0001-6582-4551 0000-0002-5621-8987 0000-0002-6534-1979 |
| OpenAccessLink | https://www.proquest.com/docview/2686190098?pq-origsite=%requestingapplication% |
| PMID | 35808286 |
| PQID | 2686190098 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_98a93d239f53484b98401bf78a59a895 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9269418 proquest_miscellaneous_2687716918 proquest_journals_2686190098 crossref_primary_10_3390_s22134790 crossref_citationtrail_10_3390_s22134790 |
| PublicationCentury | 2000 |
| PublicationDate | 20220624 |
| PublicationDateYYYYMMDD | 2022-06-24 |
| PublicationDate_xml | – month: 6 year: 2022 text: 20220624 day: 24 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Frick (ref_23) 2015; 5 Bremer (ref_27) 2018; 20 Gratz (ref_3) 2015; 22 ref_33 Loh (ref_38) 2011; 1 Goldberg (ref_20) 2020; 67 Schmidt (ref_16) 2021; 92 Esterman (ref_36) 2010; 50 Nicolai (ref_41) 2016; 46 Yuan (ref_40) 2021; 16 Klonsky (ref_8) 2007; 27 Snir (ref_14) 2015; 6 Ball (ref_21) 2014; 39 Reggente (ref_26) 2018; 115 Chapman (ref_1) 2006; 44 Seuchter (ref_29) 2004; 43 Fitzpatrick (ref_43) 2020; 284 ref_39 Shiffman (ref_11) 2008; 4 ref_37 Ewbank (ref_32) 2020; 77 Andrewes (ref_12) 2017; 8 Schultz (ref_24) 2018; 11 (ref_44) 2021; 33 Sintes (ref_5) 2018; 46 Kiekens (ref_6) 2016; 46 Muehlenkamp (ref_18) 2009; 47 Schmitgen (ref_22) 2019; 9 Symons (ref_28) 2019; 99 Tsanas (ref_35) 2022; Volume 4 Beierle (ref_34) 2020; 11 Gratz (ref_2) 2001; 23 Fresco (ref_45) 2007; 38 Bi (ref_19) 2019; 188 Ribeiro (ref_7) 2016; 46 Althoff (ref_31) 2016; 4 Armey (ref_17) 2011; 42 Taylor (ref_9) 2018; 227 Victor (ref_10) 2014; 70 Elices (ref_42) 2016; 7 Turner (ref_15) 2016; 125 ref_4 Tolmeijer (ref_25) 2018; 20 Hoogendoorn (ref_30) 2016; 21 Carballo (ref_13) 2018; 263 |
| References_xml | – volume: 46 start-page: 563 year: 2016 ident: ref_6 article-title: Lifetime and 12-month nonsuicidal self-injury and academic performance in college freshmen publication-title: Suicide Life-Threat. Behav. doi: 10.1111/sltb.12237 – volume: 22 start-page: 527 year: 2015 ident: ref_3 article-title: Diagnosis and characterization of DSM-5 nonsuicidal self-injury disorder using the clinician-administered nonsuicidal self-injury disorder index publication-title: Assessment doi: 10.1177/1073191114565878 – volume: 43 start-page: 184 year: 2004 ident: ref_29 article-title: Methods for predictor analysis of repeated measurements: Application to psychiatric data publication-title: Methods Inf. Med. doi: 10.1055/s-0038-1633857 – volume: 70 start-page: 364 year: 2014 ident: ref_10 article-title: Daily emotion in non-suicidal self-injury publication-title: J. Clin. Psychol. doi: 10.1002/jclp.22037 – volume: 47 start-page: 83 year: 2009 ident: ref_18 article-title: Emotional states preceding and following acts of non-suicidal self-injury in bulimia nervosa patients publication-title: Behav. Res. Ther. doi: 10.1016/j.brat.2008.10.011 – ident: ref_4 doi: 10.3390/ijerph16224581 – volume: 11 start-page: 2277 year: 2020 ident: ref_34 article-title: What data are smartphone users willing to share with researchers? publication-title: J. Ambient. Intell. Humaniz. Comput. doi: 10.1007/s12652-019-01355-6 – volume: 227 start-page: 759 year: 2018 ident: ref_9 article-title: A meta-analysis of the prevalence of different functions of non-suicidal self-injury publication-title: J. Affect. Disord. doi: 10.1016/j.jad.2017.11.073 – volume: 46 start-page: 146 year: 2018 ident: ref_5 article-title: Review and update on non-suicidal self-injury: Who, how and why? publication-title: Actas Esp Psiquiatr – volume: 50 start-page: 572 year: 2010 ident: ref_36 article-title: Avoiding non-independence in fMRI data analysis: Leave one subject out publication-title: Neuroimage doi: 10.1016/j.neuroimage.2009.10.092 – volume: 27 start-page: 226 year: 2007 ident: ref_8 article-title: The functions of deliberate self-injury: A review of the evidence publication-title: Clin. Psychol. Rev. doi: 10.1016/j.cpr.2006.08.002 – ident: ref_39 – volume: 77 start-page: 35 year: 2020 ident: ref_32 article-title: Quantifying the association between psychotherapy content and clinical outcomes using deep learning publication-title: JAMA Psychiatry doi: 10.1001/jamapsychiatry.2019.2664 – volume: 38 start-page: 234 year: 2007 ident: ref_45 article-title: Initial psychometric properties of the experiences questionnaire: Validation of a self-report measure of decentering publication-title: Behav. Ther. doi: 10.1016/j.beth.2006.08.003 – volume: 115 start-page: 2222 year: 2018 ident: ref_26 article-title: Multivariate resting-state functional connectivity predicts response to cognitive behavioral therapy in obsessive–compulsive disorder publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.1716686115 – volume: 20 start-page: e10275 year: 2018 ident: ref_27 article-title: Predicting therapy success and costs for personalized treatment recommendations using baseline characteristics: Data-driven analysis publication-title: J. Med. Internet Res. doi: 10.2196/10275 – volume: 39 start-page: 1254 year: 2014 ident: ref_21 article-title: Single-subject anxiety treatment outcome prediction using functional neuroimaging publication-title: Neuropsychopharmacology doi: 10.1038/npp.2013.328 – volume: 6 start-page: 267 year: 2015 ident: ref_14 article-title: Explicit and inferred motives for nonsuicidal self-injurious acts and urges in borderline and avoidant personality disorders publication-title: Personal. Disord. Theory Res. Treat. doi: 10.1037/per0000104 – volume: 20 start-page: 1053 year: 2018 ident: ref_25 article-title: Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis publication-title: Neuroimage Clin. doi: 10.1016/j.nicl.2018.10.011 – volume: 21 start-page: 1449 year: 2016 ident: ref_30 article-title: Predicting social anxiety treatment outcome based on therapeutic email conversations publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2016.2601123 – volume: 16 start-page: 3154 year: 2021 ident: ref_40 article-title: Gini-Impurity index analysis publication-title: IEEE Trans. Inf. Forensics Secur. doi: 10.1109/TIFS.2021.3076932 – volume: 263 start-page: 212 year: 2018 ident: ref_13 article-title: Use of ecological momentary assessment (EMA) in non-suicidal self-injury (NSSI): A systematic review publication-title: Psychiatry Res. doi: 10.1016/j.psychres.2018.02.051 – volume: 7 start-page: 584 year: 2016 ident: ref_42 article-title: Impact of mindfulness training on borderline personality disorder: A randomized trial publication-title: Mindfulness doi: 10.1007/s12671-016-0492-1 – volume: 11 start-page: 7 year: 2018 ident: ref_24 article-title: Improving therapy outcome prediction in major depression using multimodal functional neuroimaging: A pilot study publication-title: Pers. Med. Psychiatry – volume: 23 start-page: 253 year: 2001 ident: ref_2 article-title: Measurement of deliberate self-harm: Preliminary data on the Deliberate Self-Harm Inventory publication-title: J. Psychopathol. Behav. Assess. doi: 10.1023/A:1012779403943 – volume: 92 start-page: 1035 year: 2021 ident: ref_16 article-title: Predicting non-suicidal self-injury in young adults with and without borderline personality disorder: A multilevel approach combining ecological momentary assessment and self-report measures publication-title: Psychiatr. Q. doi: 10.1007/s11126-020-09875-7 – volume: 5 start-page: e530 year: 2015 ident: ref_23 article-title: Predicting long-term outcome of Internet-delivered cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning publication-title: Transl. Psychiatry doi: 10.1038/tp.2015.22 – volume: 46 start-page: 223 year: 2016 ident: ref_41 article-title: Identifying risk for self-harm: Rumination and negative affectivity in the prospective prediction of nonsuicidal self-injury publication-title: Suicide Life-Threat. Behav. doi: 10.1111/sltb.12186 – volume: 8 start-page: 357 year: 2017 ident: ref_12 article-title: Ecological momentary assessment of nonsuicidal self-injury in youth with borderline personality disorder publication-title: Personal. Disord. Theory Res. Treat. doi: 10.1037/per0000205 – ident: ref_37 doi: 10.1007/978-3-030-86993-9_50 – volume: 33 start-page: 407 year: 2021 ident: ref_44 article-title: Mindfulness in borderline personality disorder: Decentering mediates the effectiveness publication-title: Psicothema – volume: 284 start-page: 112761 year: 2020 ident: ref_43 article-title: Investigating the role of the intensity and duration of self-injury thoughts in self-injury with ecological momentary assessment publication-title: Psychiatry Res. doi: 10.1016/j.psychres.2020.112761 – volume: 4 start-page: 463 year: 2016 ident: ref_31 article-title: Large-scale analysis of counseling conversations: An application of natural language processing to mental health publication-title: Trans. Assoc. Comput. Linguist. doi: 10.1162/tacl_a_00111 – volume: 99 start-page: 156 year: 2019 ident: ref_28 article-title: Machine learning vs addiction therapists: A pilot study predicting alcohol dependence treatment outcome from patient data in behavior therapy with adjunctive medication publication-title: J. Subst. Abus. Treat. doi: 10.1016/j.jsat.2019.01.020 – volume: 44 start-page: 371 year: 2006 ident: ref_1 article-title: Solving the puzzle of deliberate self-harm: The experiential avoidance model publication-title: Behav. Res. Ther. doi: 10.1016/j.brat.2005.03.005 – volume: 1 start-page: 14 year: 2011 ident: ref_38 article-title: Classification and regression trees publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.8 – ident: ref_33 doi: 10.1371/journal.pone.0185123 – volume: 67 start-page: 438 year: 2020 ident: ref_20 article-title: Machine learning and natural language processing in psychotherapy research: Alliance as example use case publication-title: J. Couns. Psychol. doi: 10.1037/cou0000382 – volume: 125 start-page: 588 year: 2016 ident: ref_15 article-title: The role of interpersonal conflict and perceived social support in nonsuicidal self-injury in daily life publication-title: J. Abnorm. Psychol. doi: 10.1037/abn0000141 – volume: 46 start-page: 225 year: 2016 ident: ref_7 article-title: Self-injurious thoughts and behaviors as risk factors for future suicide ideation, attempts, and death: A meta-analysis of longitudinal studies publication-title: Psychol. Med. doi: 10.1017/S0033291715001804 – volume: 42 start-page: 579 year: 2011 ident: ref_17 article-title: Changes in ecological momentary assessment reported affect associated with episodes of nonsuicidal self-injury publication-title: Behav. Ther. doi: 10.1016/j.beth.2011.01.002 – volume: 4 start-page: 1 year: 2008 ident: ref_11 article-title: Ecological momentary assessment publication-title: Annu. Rev. Clin. Psychol. doi: 10.1146/annurev.clinpsy.3.022806.091415 – volume: 9 start-page: e01384 year: 2019 ident: ref_22 article-title: Individualized treatment response prediction of dialectical behavior therapy for borderline personality disorder using multimodal magnetic resonance imaging publication-title: Brain Behav. doi: 10.1002/brb3.1384 – volume: 188 start-page: 2222 year: 2019 ident: ref_19 article-title: What is machine learning? A primer for the epidemiologist publication-title: Am. J. Epidemiol. – volume: Volume 4 start-page: 278 year: 2022 ident: ref_35 article-title: Preliminary Results on the Use of Classification Trees to Predict Non-suicidal Self-injury with Data Collected through a Mobile App publication-title: Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022 |
| SSID | ssj0023338 |
| Score | 2.4662778 |
| Snippet | Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database... |
| SourceID | doaj pubmedcentral proquest crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database |
| StartPage | 4790 |
| SubjectTerms | Accuracy Anxiety disorders app EMA Emotions Machine learning Magnetic resonance imaging Mental disorders Mental health NSSI Patients Psychotherapy Questionnaires Self destructive behavior Social anxiety Statistical methods Suicides & suicide attempts Support vector machines Young adults |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrZ1LS8QwEMeDiAc9iE9cXSWKBy_BNo82OVZR9OCysAreStokWpGu7MPP7yTtLlsQvHhthpDONJ35k-QXhC4dJF1m0pRwzxnikeNEWyuI5orqGPIfF2W4bCIdDOTrqxquXPXl94Q1eODGcddKauiMMuUE45IXChRJXLhUaqG0VIFeClXPQky1UouB8mo4QgxE_fWU0nBkMupknwDp71SW3X2RK4nmfgdttxUizpqR7aI1W--hrRVu4D4aZfgpbIK0uOWjvuGshYNjqELxcOLXX_yOZjwY12Q0r8rKQKcj--nIY_0BjsRVjcNUx5lHcEwP0Mv93fPtA2lvRyAlSLgZUTaWLrKg4IyNqUlkrHlSlCChuHM8tQL-c6b0JQBLGbWiAOElCigvoiIx1FJ2iNbrcW2PEDYxZDLOSu5MxI2LtBGmoNZQn0GFcD10tfBaXrbocH-DxWcOEsI7OF86uIculqZfDS_jN6Mb7_qlgUdchwcQ-LwNfP5X4Huovwhc3s67aU4TCYrQQ1J76HzZDDPGL4Po2o7nwSYNjCCwSTsB7wyo21JX74G9rcLJX3n8H29wgjapP0wRJYTyPlqfTeb2FG2U37NqOjkLH_QPBcb5-A priority: 102 providerName: Directory of Open Access Journals |
| Title | A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults |
| URI | https://www.proquest.com/docview/2686190098 https://www.proquest.com/docview/2687716918 https://pubmed.ncbi.nlm.nih.gov/PMC9269418 https://doaj.org/article/98a93d239f53484b98401bf78a59a895 |
| Volume | 22 |
| WOSCitedRecordID | wos000824185500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Nj9MwEB2xXQ5w4BtRWCqDOHCxNnHs2jmhLuqKPbSKKEjlFCWxvRu0Spam5chvZ-y6YSMhLlxysEeJlRl75tnjNwDvLDrdREtJueMZ4pHltDBG0IKnrIjR_3FR-WITcrlU63WahQ23LqRVHtZEv1DrtnJ75KdsqjDWd_SXH25-UFc1yp2uhhIaR3DsmMr4CI7P5svscw-5EkRgez6hBMH9aceYvzoZDbyQJ-sfRJjD_MhbDuf84f8O9RE8CKEmme1t4zHcMc0TuH-LgPAprGZk4bMpDQlEq5dkFljGCYazJNu4gxyXGk2WbUNXu7qqNb50Za4tvWi-o0ZI3RC_ZpCZ4_LonsHX8_mXj59oKLNAK8SCW5qaWNnIIBTUJmZ6quKCT8sKsRi3lksjcMHUlYslEpkwI0pEcKLEOCUqp5oZljyHUdM25gUQHaNL5EnFrY64tlGhhS6Z0cy5YiHsGN4ffnteBQ5yVwrjOkcs4jSU9xoaw9te9GZPvPE3oTOnu17AcWX7hnZzmYepl6eqQHNkSWpFwhUvU8S0cWmlKkRaqFSM4eSgxjxM4C7_o8MxvOm7ceq585SiMe3Oy0hPNoQycmAxgwENe5r6ypN4p_4KsXr574-_gnvM3beIppTxExhtNzvzGu5WP7d1t5nAkVxL_1STYPkTv6mAz8WvObZlF4vs22_bqhBR |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB5VWyTgwBuxUMAgkLhYTRx7Yx8QWh5VV-2uVtoilVNIYrtNVSVlswviT_EbGXuT0EiIWw9ck1Fe_jwzX-z5BuCVxaAb6Tim3OkM8cBymhojaMoVS0OMf1zkvtlEPJvJ42M134JfbS2M21bZ-kTvqHWVu3_ku2wkMdd38pfvLr5R1zXKra62LTQ2sDgwP38gZavfTj7i-L5mbO_T0Yd92nQVoDlSnxVVJpQ2MMh8tAmZHskw5aMsR-rBreWxEegfdO5CZxRHzIgMCYvIMCwH2Ugz44QO0OVvcwS7HMD2fDKdf-koXoSMb6NfFEUq2K0Z86WaQS_q-eYAvYy2vx_zUoDbu_2_fZo7cKtJpcl4g_27sGXKe3DzksDifViMydTvFjWkEZI9IeNGRZ1guk7mS7dQ5bZ-k1lV0sW6yAuNF12Yc0sn5RkijhQl8T6RjJ1WSf0APl_JWz2EQVmV5hEQHWLI51HOrQ64tkGqhc6Y0cylGkLYIbxphznJG4111-rjPEGu5RCRdIgYwsvO9GIjLPI3o_cOK52B0wL3B6rlSdK4lkTJFKcbi5QVEZc8U8jZw8zGMhUqlUoMYaeFTdI4qDr5g5khvOhOo2tx60Vpaaq1t4m9mBLaxD2E9h6of6YsTr1IufIl0vLxv2_-HK7vH00Pk8PJ7OAJ3GCutiQYUcZ3YLBars1TuJZ_XxX18lkz0wh8vWoE_waTsmUw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9QwEB1VLUL0wDfqQgsGgcTF2sSxk_iA0JayYlW6irQglVNIYrsEVUnZ7IL4a_w6xt4kNBLi1gPXZJQvP8_Mi8dvAJ4bDLqBiiLKrc4Q9wynmdaCZlyyzMf4x0Xhmk1E83l8eiqTLfjV7YWxZZWdT3SOWtWF_Uc-ZmGMub6VvxybtiwiOZq-vvhGbQcpu9LatdPYQORY__yB9K15NTvCsX7B2PTthzfvaNthgBZIg1ZUaj82nkYWpLTPVBj7GQ_zAmkIN4ZHWqCvUIUNo0EUMC1yJC8ixxDt5aFi2ooeoPvfwZSc4xzbSWYnyaee7gXI_jZaRkEgvXHDmNu26Q0ioGsUMMhuh7WZl4Ld9Nb__Jluw802xSaTzZy4A1u6ugu7l4QX78FiQk5cFakmrcDsGZm06uoE03iSLO0Cli0JJ_O6oot1WZQKL7rQ54bOqq-IRFJWxPlKMrEaJs19-Hglb_UAtqu60ntAlI-pAA8KbpTHlfEyJVTOtGI2BRHCjOBlN-Rp0Wqv2xYg5ylyMIuOtEfHCJ71phcbwZG_GR1a3PQGViPcHaiXZ2nrclIZZzgNWSCNCHjMc4lc3s9NFGdCZrEUI9jvIJS2jqtJ_-BnBE_70-hy7DpSVul67WwiJ7KENtEArYMHGp6pyi9OvFy6rdPxw3_f_AlcR9im72fz40dwg9ktJ15IGd-H7dVyrQ_gWvF9VTbLx-2kI_D5qgH8G5pubfA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Machine+Learning+Approach+for+Predicting+Non-Suicidal+Self-Injury+in+Young+Adults&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Marti-Puig%2C+Pere&rft.au=Capra%2C+Chiara&rft.au=Vega%2C+Daniel&rft.au=Llunas%2C+Laia&rft.date=2022-06-24&rft.pub=MDPI&rft.eissn=1424-8220&rft.volume=22&rft.issue=13&rft_id=info:doi/10.3390%2Fs22134790&rft_id=info%3Apmid%2F35808286&rft.externalDocID=PMC9269418 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |