A Machine Learning-based Prediction Of Treatment Outcomes Following Digital Cognitive Behavioral Therapy For Suicidal Ideation Using Individual Participant Data From Randomized Controlled Trials - Info and Reading Options
By Jael Behrendt, Rebekka Büscher, Lasse Sander, Yannik Terhorst and Johannes Massell
“A Machine Learning-based Prediction Of Treatment Outcomes Following Digital Cognitive Behavioral Therapy For Suicidal Ideation Using Individual Participant Data From Randomized Controlled Trials” Metadata:
- Title: ➤ A Machine Learning-based Prediction Of Treatment Outcomes Following Digital Cognitive Behavioral Therapy For Suicidal Ideation Using Individual Participant Data From Randomized Controlled Trials
- Authors: Jael BehrendtRebekka BüscherLasse SanderYannik TerhorstJohannes Massell
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- Internet Archive ID: osf-registrations-jesk2-v1
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"A Machine Learning-based Prediction Of Treatment Outcomes Following Digital Cognitive Behavioral Therapy For Suicidal Ideation Using Individual Participant Data From Randomized Controlled Trials" Description:
The Internet Archive:
Suicidal ideation (SI) poses a significant burden on individuals and society, necessitating effective interventions to address this growing crisis. While face-to-face cognitive behavioral therapy (CBT) has demonstrated its effectiveness in reducing SI, access to treatment remains limited due to various barriers. Digital interventions, particularly digital cognitive-behavioral therapy (iCBT), offer a promising solution by providing greater accessibility and flexibility. However, accurate prediction of treatment outcomes following iCBT remains challenging, and machine learning (ML) algorithms have been recommended to enhance prediction accuracy. We aim to leverage supervised ML models trained on pooled individual participant data (IPD) from multiple previous iCBT studies / RCTs, to predict treatment outcome (operationalized as reliable change in SI). We will employ various supervised ML algorithms, such as support vector machines, random forest or extreme gradient boosted regression trees. By employing a personalized and data-driven approach, this research seeks to assess the accuracy of outcome prediction following iCBT.
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- Added Date: 2024-01-17 09:01:44
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