Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations
BackgroundRecently, artificial intelligence technologies and machine learning methods have offered attractive prospects to design and manage crisis response processes, especially in suicide crisis management. In other domains, most algorithms are based on big data to help dia...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2021-09-01
|
| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2021/9/e24560 |
| _version_ | 1797735704554373120 |
|---|---|
| author | Stephane Mouchabac Philippe Leray Vladimir Adrien Fanny Gollier-Briant Olivier Bonnot |
| author_facet | Stephane Mouchabac Philippe Leray Vladimir Adrien Fanny Gollier-Briant Olivier Bonnot |
| author_sort | Stephane Mouchabac |
| collection | DOAJ |
| description |
BackgroundRecently, artificial intelligence technologies and machine learning methods have offered attractive prospects to design and manage crisis response processes, especially in suicide crisis management. In other domains, most algorithms are based on big data to help diagnose and suggest rational treatment options in medicine. But data in psychiatry are related to behavior and clinical evaluation. They are more heterogeneous, less objective, and incomplete compared to other fields of medicine. Consequently, the use of psychiatric clinical data may lead to less accurate and sometimes impossible-to-build algorithms and provide inefficient digital tools. In this case, the Bayesian network (BN) might be helpful and accurate when constructed from expert knowledge. Medical Companion is a government-funded smartphone application based on repeated questions posed to the subject and algorithm-matched advice to prevent relapse of suicide attempts within several months.
ObjectiveOur paper aims to present our development of a BN algorithm as a medical device in accordance with the American Psychiatric Association digital healthcare guidelines and to provide results from a preclinical phase.
MethodsThe experts are psychiatrists working in university hospitals who are experienced and trained in managing suicidal crises. As recommended when building a BN, we divided the process into 2 tasks. Task 1 is structure determination, representing the qualitative part of the BN. The factors were chosen for their known and demonstrated link with suicidal risk in the literature (clinical, behavioral, and psychometrics) and therapeutic accuracy (advice). Task 2 is parameter elicitation, with the conditional probabilities corresponding to the quantitative part. The 4-step simulation (use case) process allowed us to ensure that the advice was adapted to the clinical states of patients and the context.
ResultsFor task 1, in this formative part, we defined clinical questions related to the mental state of the patients, and we proposed specific factors related to the questions. Subsequently, we suggested specific advice related to the patient’s state. We obtained a structure for the BN with a graphical representation of causal relations between variables. For task 2, several runs of simulations confirmed the a priori model of experts regarding mental state, refining the precision of our model. Moreover, we noticed that the advice had the same distribution as the previous state and was clinically relevant. After 2 rounds of simulation, the experts found the exact match.
ConclusionsBN is an efficient methodology to build an algorithm for a digital assistant dedicated to suicidal crisis management. Digital psychiatry is an emerging field, but it needs validation and testing before being used with patients. Similar to psychotropics, any medical device requires a phase II (preclinical) trial. With this method, we propose another step to respond to the American Psychiatric Association guidelines.
Trial RegistrationClinicalTrials.gov NCT03975881; https://clinicaltrials.gov/ct2/show/NCT03975881 |
| first_indexed | 2024-03-12T13:02:42Z |
| format | Article |
| id | doaj.art-ec0f0526c9f2426e964997b868061050 |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T13:02:42Z |
| publishDate | 2021-09-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-ec0f0526c9f2426e964997b8680610502023-08-28T19:10:11ZengJMIR PublicationsJournal of Medical Internet Research1438-88712021-09-01239e2456010.2196/24560Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient SimulationsStephane Mouchabachttps://orcid.org/0000-0002-4226-7956Philippe Lerayhttps://orcid.org/0000-0002-0207-9280Vladimir Adrienhttps://orcid.org/0000-0001-8035-8843Fanny Gollier-Brianthttps://orcid.org/0000-0003-0350-9825Olivier Bonnothttps://orcid.org/0000-0003-3776-3645 BackgroundRecently, artificial intelligence technologies and machine learning methods have offered attractive prospects to design and manage crisis response processes, especially in suicide crisis management. In other domains, most algorithms are based on big data to help diagnose and suggest rational treatment options in medicine. But data in psychiatry are related to behavior and clinical evaluation. They are more heterogeneous, less objective, and incomplete compared to other fields of medicine. Consequently, the use of psychiatric clinical data may lead to less accurate and sometimes impossible-to-build algorithms and provide inefficient digital tools. In this case, the Bayesian network (BN) might be helpful and accurate when constructed from expert knowledge. Medical Companion is a government-funded smartphone application based on repeated questions posed to the subject and algorithm-matched advice to prevent relapse of suicide attempts within several months. ObjectiveOur paper aims to present our development of a BN algorithm as a medical device in accordance with the American Psychiatric Association digital healthcare guidelines and to provide results from a preclinical phase. MethodsThe experts are psychiatrists working in university hospitals who are experienced and trained in managing suicidal crises. As recommended when building a BN, we divided the process into 2 tasks. Task 1 is structure determination, representing the qualitative part of the BN. The factors were chosen for their known and demonstrated link with suicidal risk in the literature (clinical, behavioral, and psychometrics) and therapeutic accuracy (advice). Task 2 is parameter elicitation, with the conditional probabilities corresponding to the quantitative part. The 4-step simulation (use case) process allowed us to ensure that the advice was adapted to the clinical states of patients and the context. ResultsFor task 1, in this formative part, we defined clinical questions related to the mental state of the patients, and we proposed specific factors related to the questions. Subsequently, we suggested specific advice related to the patient’s state. We obtained a structure for the BN with a graphical representation of causal relations between variables. For task 2, several runs of simulations confirmed the a priori model of experts regarding mental state, refining the precision of our model. Moreover, we noticed that the advice had the same distribution as the previous state and was clinically relevant. After 2 rounds of simulation, the experts found the exact match. ConclusionsBN is an efficient methodology to build an algorithm for a digital assistant dedicated to suicidal crisis management. Digital psychiatry is an emerging field, but it needs validation and testing before being used with patients. Similar to psychotropics, any medical device requires a phase II (preclinical) trial. With this method, we propose another step to respond to the American Psychiatric Association guidelines. Trial RegistrationClinicalTrials.gov NCT03975881; https://clinicaltrials.gov/ct2/show/NCT03975881https://www.jmir.org/2021/9/e24560 |
| spellingShingle | Stephane Mouchabac Philippe Leray Vladimir Adrien Fanny Gollier-Briant Olivier Bonnot Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations Journal of Medical Internet Research |
| title | Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations |
| title_full | Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations |
| title_fullStr | Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations |
| title_full_unstemmed | Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations |
| title_short | Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations |
| title_sort | prevention of suicidal relapses in adolescents with a smartphone application bayesian network analysis of a preclinical trial using in silico patient simulations |
| url | https://www.jmir.org/2021/9/e24560 |
| work_keys_str_mv | AT stephanemouchabac preventionofsuicidalrelapsesinadolescentswithasmartphoneapplicationbayesiannetworkanalysisofapreclinicaltrialusinginsilicopatientsimulations AT philippeleray preventionofsuicidalrelapsesinadolescentswithasmartphoneapplicationbayesiannetworkanalysisofapreclinicaltrialusinginsilicopatientsimulations AT vladimiradrien preventionofsuicidalrelapsesinadolescentswithasmartphoneapplicationbayesiannetworkanalysisofapreclinicaltrialusinginsilicopatientsimulations AT fannygollierbriant preventionofsuicidalrelapsesinadolescentswithasmartphoneapplicationbayesiannetworkanalysisofapreclinicaltrialusinginsilicopatientsimulations AT olivierbonnot preventionofsuicidalrelapsesinadolescentswithasmartphoneapplicationbayesiannetworkanalysisofapreclinicaltrialusinginsilicopatientsimulations |