ReviewInternet interventions2018
Predictive modeling in e-mental health: A common language framework.
Review in Internet interventions, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 40 citations in OpenAlex.
- Capitalizing on natural language processing (NLP) to automate the evaluation of coach implementation fidelity in guided digital cognitive-behavioral therapy (GdCBT).Psychological medicine · 2025Trial
- Finding the Best Match - a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions.Journal of healthcare informatics research · 2023Article
- Data mining techniques in psychotherapy: applications for studying therapeutic alliance.Scientific reports · 2023Article
- Synthesis of Affective Expressions and Artificial Intelligence to Discover Mental Distress in Online Community.International journal of mental health and addiction · 2022Article
- Natural Language Processing Methods and Bipolar Disorder: Scoping Review.JMIR mental health · 2022Article
- iHealth: The ethics of artificial intelligence and big data in mental healthcare.Internet interventions · 2022Article
- Personalised depression forecasting using mobile sensor data and ecological momentary assessment.Frontiers in digital health · 2022Article
- Protecting Mental Health Data Privacy in India: The Case of Data Linkage With Aadhaar.Global health, science and practice · 2021Article
- A learning algorithm for predicting mental health symptoms and substance use.Journal of psychiatric research · 2021Article
- Daily Self-Monitoring of Symptoms and Skills Learning in Patients With Borderline Personality Disorder Through a Mobile Phone App: Protocol for a Pragmatic Randomized Controlled Trial.JMIR research protocols · 2020Article
- A Framework for Applying Natural Language Processing in Digital Health Interventions.Journal of medical Internet research · 2020Article
- Using the Personalized Advantage Index for Individual Treatment Allocation to Blended Treatment or Treatment as Usual for Depression in Secondary Care.Journal of clinical medicine · 2020Article
- Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent developments in mobile technology, sensor devices, and artificial intelligence have created new opportunities for mental health care research. Enabled by large datasets collected in e-mental health research and practice, clinical researchers and members of the data mining community increasingly join forces to build predictive models for health monitoring, treatment selection, and treatment personalization. This paper aims to bridge the historical and conceptual gaps between the distant research domains involved in this new collaborative research by providing a conceptual model of common research goals. We first provide a brief overview of the data mining field and methods used for predictive modeling. Next, we propose to characterize predictive modeling research in mental health care on three dimensions: 1) time, relative to treatment (i.e., from screening to post-treatment relapse monitoring), 2) types of available data (e.g., questionnaire data, ecological momentary assessments, smartphone sensor data), and 3) type of clinical decision (i.e., whether data are used for screening purposes, treatment selection or treatment personalization). Building on these three dimensions, we introduce a framework that identifies four model types that can be used to classify existing and future research and applications. To illustrate this, we use the framework to classify and discuss published predictive modeling mental health research. Finally, in the discussion, we reflect on the next steps that are required to drive forward this promising new interdisciplinary field.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.