An Interactive Paradigm for the End-User Development of Chatbots for Data Exploration

Piro, Ludovica; Desolda, Giuseppe; Matera, Maristella; Lanzilotti, Rosa; Mosca, Sara; Pucci, Emanuele

doi:10.1007/978-3-030-85610-6_11

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…The fact that some of these associations were nonmonotonic is consistent with naturalistic evidence that moderate, compared to extremely low or high, levels of emotional reactivity to stress predict low future depression severity (Santee & Starr, 2021) and that baseline self-reported resilience is sometimes significant in predicting depression treatment response only in interaction with other predictors (Choi et al, 2021; Min, Lee, Lee, Lee, & Chae, 2012). These specifications might reflect the greater importance of protective/resilience factors in the subset of patients whose depressive episodes are triggered by stressful life experiences, which could be the subject of future investigation (Chromik, 2021).…”

Section: Discussionmentioning

confidence: 99%

Development of a model to predict antidepressant treatment response for depression among Veterans

et al. 2022

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Background Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). Methods A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. Results In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. Conclusions Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

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