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Toward personalizing treatment for depression: predicting diagnosis and severity

OBJECTIVE: Depression is a prevalent disorder difficult to diagnose and treat. In particular, depressed patients exhibit largely unpredictable responses to treatment. Toward the goal of personalizing treatment for depression, we develop and evaluate computational models that use electronic health re...

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Autores principales: Huang, Sandy H, LePendu, Paea, Iyer, Srinivasan V, Tai-Seale, Ming, Carrell, David, Shah, Nigam H
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4215055/
https://www.ncbi.nlm.nih.gov/pubmed/24988898
http://dx.doi.org/10.1136/amiajnl-2014-002733
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author Huang, Sandy H
LePendu, Paea
Iyer, Srinivasan V
Tai-Seale, Ming
Carrell, David
Shah, Nigam H
author_facet Huang, Sandy H
LePendu, Paea
Iyer, Srinivasan V
Tai-Seale, Ming
Carrell, David
Shah, Nigam H
author_sort Huang, Sandy H
collection PubMed
description OBJECTIVE: Depression is a prevalent disorder difficult to diagnose and treat. In particular, depressed patients exhibit largely unpredictable responses to treatment. Toward the goal of personalizing treatment for depression, we develop and evaluate computational models that use electronic health record (EHR) data for predicting the diagnosis and severity of depression, and response to treatment. MATERIALS AND METHODS: We develop regression-based models for predicting depression, its severity, and response to treatment from EHR data, using structured diagnosis and medication codes as well as free-text clinical reports. We used two datasets: 35 000 patients (5000 depressed) from the Palo Alto Medical Foundation and 5651 patients treated for depression from the Group Health Research Institute. RESULTS: Our models are able to predict a future diagnosis of depression up to 12 months in advance (area under the receiver operating characteristic curve (AUC) 0.70–0.80). We can differentiate patients with severe baseline depression from those with minimal or mild baseline depression (AUC 0.72). Baseline depression severity was the strongest predictor of treatment response for medication and psychotherapy. CONCLUSIONS: It is possible to use EHR data to predict a diagnosis of depression up to 12 months in advance and to differentiate between extreme baseline levels of depression. The models use commonly available data on diagnosis, medication, and clinical progress notes, making them easily portable. The ability to automatically determine severity can facilitate assembly of large patient cohorts with similar severity from multiple sites, which may enable elucidation of the moderators of treatment response in the future.
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spelling pubmed-42150552014-11-03 Toward personalizing treatment for depression: predicting diagnosis and severity Huang, Sandy H LePendu, Paea Iyer, Srinivasan V Tai-Seale, Ming Carrell, David Shah, Nigam H J Am Med Inform Assoc Research and Applications OBJECTIVE: Depression is a prevalent disorder difficult to diagnose and treat. In particular, depressed patients exhibit largely unpredictable responses to treatment. Toward the goal of personalizing treatment for depression, we develop and evaluate computational models that use electronic health record (EHR) data for predicting the diagnosis and severity of depression, and response to treatment. MATERIALS AND METHODS: We develop regression-based models for predicting depression, its severity, and response to treatment from EHR data, using structured diagnosis and medication codes as well as free-text clinical reports. We used two datasets: 35 000 patients (5000 depressed) from the Palo Alto Medical Foundation and 5651 patients treated for depression from the Group Health Research Institute. RESULTS: Our models are able to predict a future diagnosis of depression up to 12 months in advance (area under the receiver operating characteristic curve (AUC) 0.70–0.80). We can differentiate patients with severe baseline depression from those with minimal or mild baseline depression (AUC 0.72). Baseline depression severity was the strongest predictor of treatment response for medication and psychotherapy. CONCLUSIONS: It is possible to use EHR data to predict a diagnosis of depression up to 12 months in advance and to differentiate between extreme baseline levels of depression. The models use commonly available data on diagnosis, medication, and clinical progress notes, making them easily portable. The ability to automatically determine severity can facilitate assembly of large patient cohorts with similar severity from multiple sites, which may enable elucidation of the moderators of treatment response in the future. BMJ Publishing Group 2014-11 2014-07-02 /pmc/articles/PMC4215055/ /pubmed/24988898 http://dx.doi.org/10.1136/amiajnl-2014-002733 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 3.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/3.0/
spellingShingle Research and Applications
Huang, Sandy H
LePendu, Paea
Iyer, Srinivasan V
Tai-Seale, Ming
Carrell, David
Shah, Nigam H
Toward personalizing treatment for depression: predicting diagnosis and severity
title Toward personalizing treatment for depression: predicting diagnosis and severity
title_full Toward personalizing treatment for depression: predicting diagnosis and severity
title_fullStr Toward personalizing treatment for depression: predicting diagnosis and severity
title_full_unstemmed Toward personalizing treatment for depression: predicting diagnosis and severity
title_short Toward personalizing treatment for depression: predicting diagnosis and severity
title_sort toward personalizing treatment for depression: predicting diagnosis and severity
topic Research and Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4215055/
https://www.ncbi.nlm.nih.gov/pubmed/24988898
http://dx.doi.org/10.1136/amiajnl-2014-002733
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