ArticlePreventing chronic disease2015
Development of a Clinical Forecasting Model to Predict Comorbid Depression Among Diabetes Patients and an Application in Depression Screening Policy Making.
Article in Preventing chronic disease, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
- Machine learning-based risk predictive models for depression in patients with diabetes: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Comparative Effectiveness of a Technology-Facilitated Depression Care Management Model in Safety-Net Primary Care Patients With Type 2 Diabetes: 6-Month Outcomes of a Large Clinical Trial.Journal of medical Internet research · 2018Trial
- Development of a Practical Nomogram for Depression Risk Stratification in Older Adults With Hypertension and Diabetes: Retrospective Analysis of Data From the China Health and Retirement Longitudinal Study.JMIR medical informatics · 2026Article
- A review of risk factors and intervention measures for comorbidity of diabetes and mental disorders.Frontiers in psychiatry · 2026Review
- Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in Questionnaire Responses: Machine Learning Model Development and Validation.JMIR formative research · 2024Article
- Antiplasmodial peptaibols act through membrane directed mechanisms.Cell chemical biology · 2024Article
- Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Article
- Predicting the presence of depressive symptoms in the HIV-HCV co-infected population in Canada using supervised machine learning.BMC medical research methodology · 2022Article
- Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts.BMC medical informatics and decision making · 2022Article
- Machine Learning Methods for Predicting Postpartum Depression: Scoping Review.JMIR mental health · 2021Article
- Bidirectional Representation Learning From Transformers Using Multimodal Electronic Health Record Data to Predict Depression.IEEE journal of biomedical and health informatics · 2021Article
- Learning From Clinical Consensus Diagnosis in India to Facilitate Automatic Classification of Dementia: Machine Learning Study.JMIR mental health · 2021Article
- HCET: Hierarchical Clinical Embedding With Topic Modeling on Electronic Health Records for Predicting Future Depression.IEEE journal of biomedical and health informatics · 2021Article
- Comparison of Regression and Machine Learning Methods in Depression Forecasting Among Home-Based Elderly Chinese: A Community Based Study.Frontiers in psychiatry · 2021Article
- A Machine Learning Approach to Predicting New-onset Depression in a Military Population.Psychiatric research and clinical practice · 2021Article
- Prediction of evening fatigue severity in outpatients receiving chemotherapy: less may be more.Fatigue : biomedicine, health & behavior · 2021Article
- Machine Learning Models for the Prediction of Postpartum Depression: Application and Comparison Based on a Cohort Study.JMIR medical informatics · 2020Article
- Text Messaging as a Screening Tool for Depression and Related Conditions in Underserved, Predominantly Minority Safety Net Primary Care Patients: Validity Study.Journal of medical Internet research · 2020Article
- Development and internal validation of a depression severity prediction model for tinnitus patients based on questionnaire responses and socio-demographics.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
introductionDepression is a common but often undiagnosed comorbid condition of people with diabetes. Mass screening can detect undiagnosed depression but may require significant resources and time. The objectives of this study were 1) to develop a clinical forecasting model that predicts comorbid depression among patients with diabetes and 2) to evaluate a model-based screening policy that saves resources and time by screening only patients considered as depressed by the clinical forecasting model.
methodsWe trained and validated 4 machine learning models by using data from 2 safety-net clinical trials; we chose the one with the best overall predictive ability as the ultimate model. We compared model-based policy with alternative policies, including mass screening and partial screening, on the basis of depression history or diabetes severity.
resultsLogistic regression had the best overall predictive ability of the 4 models evaluated and was chosen as the ultimate forecasting model. Compared with mass screening, the model-based policy can save approximately 50% to 60% of provider resources and time but will miss identifying about 30% of patients with depression. Partial-screening policy based on depression history alone found only a low rate of depression. Two other heuristic-based partial screening policies identified depression at rates similar to those of the model-based policy but cost more in resources and time.
conclusionThe depression prediction model developed in this study has compelling predictive ability. By adopting the model-based depression screening policy, health care providers can use their resources and time better and increase their efficiency in managing their patients with depression.
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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.