[HTML][HTML] Data mining algorithm predicts a range of adverse outcomes in major depression
Background Course of illness in major depression (MD) is highly varied, which might lead to
both under-and overtreatment if clinicians adhere to a'one-size-fits-all'approach. Novel …
both under-and overtreatment if clinicians adhere to a'one-size-fits-all'approach. Novel …
[HTML][HTML] Predicting 3-year persistent or recurrent major depressive episode using machine learning techniques
AR Fialho, BB Montezano, PL Ballester… - Psychiatry research …, 2022 - Elsevier
Background The identification of predictors of recurrence and persistence of depressive
episodes in major depressive disorder (MDD) can be important to inform clinicians and …
episodes in major depressive disorder (MDD) can be important to inform clinicians and …
Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports
Heterogeneity of major depressive disorder (MDD) illness course complicates clinical
decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically …
decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically …
[PDF][PDF] Developing depression symptoms prediction models to improve depression care outcomes: preliminary results
Patients with diabetes are twice as likely to experience clinically significant depressive
symptoms as the general population. Using a rich dataset yielded from a recent, large-scale …
symptoms as the general population. Using a rich dataset yielded from a recent, large-scale …
[HTML][HTML] Development and validation of prediction algorithms for major depressive episode in the general population
Background To develop and validate sex specific prediction algorithms for 4-year risk of
major depressive episode (MDE) using data from a population-based longitudinal cohort …
major depressive episode (MDE) using data from a population-based longitudinal cohort …
Using machine learning to forecast symptom changes among subclinical depression patients receiving stepped care or usual care
BT Scodari, S Chacko, R Matsumura… - Journal of Affective …, 2023 - Elsevier
Background Subclinical depression (SD) is a mental health disorder characterized by minor
depressive symptoms. Most SD patients are treated in the primary practice, but many …
depressive symptoms. Most SD patients are treated in the primary practice, but many …
External validation of the international risk prediction algorithm for major depressive episode in the US general population: the PredictD-US study
Background Multivariable risk prediction algorithms are useful for making clinical decisions
and for health planning. While prediction algorithms for new onset of major depression in the …
and for health planning. While prediction algorithms for new onset of major depression in the …
Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder
Backgrounds. Clinicians need guidance to address the heterogeneity of treatment
responses of patients with major depressive disorder (MDD). While prediction schemes …
responses of patients with major depressive disorder (MDD). While prediction schemes …
Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis
Background Multiple treatments are effective for major depressive disorder (MDD), but the
outcomes of each treatment vary broadly among individuals. Accurate prediction of …
outcomes of each treatment vary broadly among individuals. Accurate prediction of …
Applying machine-learning techniques to build self-reported depression prediction models
BACKGROUND Data Mining While data science is a set of principles and methods to guide
extraction and discovery of knowledge from data, 11, 12 data mining is a method to discover …
extraction and discovery of knowledge from data, 11, 12 data mining is a method to discover …