Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations
1
Service de psychiatrie adulte [CHU Saint-Antoine]
2
ICM - Institut du Cerveau et de la Moëlle
Epinière = Brain and Spine Institute
3
LS2N - Laboratoire des Sciences du Numérique de Nantes
4
CHU Nantes - Centre hospitalier universitaire de Nantes
5
LPPL - Laboratoire de Psychologie des Pays de la Loire
lundi 11 octobre 2021
ETUDE RECHERCHE Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations
Abstract : Background:
Recently, 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.
Objective: Our 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.
Methods: The 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.
Results: For 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.
Conclusions: BN 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.
