Data mining algorithm predicts a range of adverse outcomes in major depression

•The course of major depression is highly varied.•Genetic and environmental risk factors influence the course of depression.•Data mining techniques can be used to predict the course of depression.•Replication in new samples is crucial to test these prediction models.•Prediction models may assist cli...

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Veröffentlicht in:Journal of affective disorders Jg. 276; S. 945 - 953
Hauptverfasser: van Loo, Hanna M., Bigdeli, Tim B., Milaneschi, Yuri, Aggen, Steven H., Kendler, Kenneth S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 01.11.2020
Schlagworte:
Algorithms
Anxiety Disorders - diagnosis
Anxiety Disorders - epidemiology
Course of illness
Data Mining
Data mining, prediction
Depression
Depressive Disorder, Major - diagnosis
Humans
Major depression
Psychiatric/Mental Health
Recurrence
Replication
Recurrence
Replication
Data mining, prediction
Major depression
Course of illness
ISSN:0165-0327, 1573-2517, 1573-2517
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Zusammenfassung:•The course of major depression is highly varied.•Genetic and environmental risk factors influence the course of depression.•Data mining techniques can be used to predict the course of depression.•Replication in new samples is crucial to test these prediction models.•Prediction models may assist clinicians in treatment decisions. 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 opportunities in data mining could lead to prediction models that can assist clinicians in treatment decisions tailored to the individual patient. This study assesses the performance of a previously developed data mining algorithm to predict future episodes of MD based on clinical information in new data. We applied a prediction model utilizing baseline clinical characteristics in subjects who reported lifetime MD to two independent test samples (total n = 4226). We assessed the model's performance to predict future episodes of MD, anxiety disorders, and disability during follow-up (1–9 years after baseline). In addition, we compared its prediction performance with well-known risk factors for a severe course of illness. Our model consistently predicted future episodes of MD in both test samples (AUC 0.68–0.73, modest prediction). Equally accurately, it predicted episodes of generalized anxiety disorder, panic disorder and disability (AUC 0.65–0.78). Our model predicted these outcomes more accurately than risk factors for a severe course of illness such as family history of MD and lifetime traumas. Prediction accuracy might be different for specific subgroups, such as hospitalized patients or patients with a different cultural background. Our prediction model consistently predicted a range of adverse outcomes in MD across two independent test samples derived from studies in different subpopulations, countries, using different measurement procedures. This replication study holds promise for application in clinical practice.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0165-0327
1573-2517
1573-2517
DOI:10.1016/j.jad.2020.07.098