Implementing a college mental health program – an overview of the first twelve months

Downs, Nancy; Alderman, Tracy; Bhakta, Savita; Greenwood, Tiffany A.

doi:10.1080/07448481.2018.1458032

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…Overall, 14 adult human subjects (mean age 21.6 ± 2.8 years, 10 females) took part in this study. All participants were referred to the study from the University of California San Diego College Mental Health Program 37 . For study inclusion, participants must be experiencing moderate depression symptoms, which we assessed using the Patient Health Questionnaire, PHQ-9 scale (score > 9; participant score range 10-17) 38 .…”

Section: Participantsmentioning

confidence: 99%

Personalized machine learning of depressed mood using wearables

et al. 2021

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Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and precision therapeutics, new personalized treatment approaches are warranted for depression. Here, we use a combination of longitudinal ecological momentary assessments of depression, neurocognitive sampling synchronized with electroencephalography, and lifestyle data from wearables to generate individualized predictions of depressed mood over a 1-month time period. This study, thus, develops a systematic pipeline for N-of-1 personalized modeling of depression using multiple modalities of data. In the models, we integrate seven types of supervised machine learning (ML) approaches for each individual, including ensemble learning and regression-based methods. All models were verified using fourfold nested cross-validation. The best-fit as benchmarked by the lowest mean absolute percentage error, was obtained by a different type of ML model for each individual, demonstrating that there is no one-size-fits-all strategy. The voting regressor, which is a composite strategy across ML models, was best performing on-average across subjects. However, the individually selected best-fit models still showed significantly less error than the voting regressor performance across subjects. For each individual’s best-fit personalized model, we further extracted top-feature predictors using Shapley statistics. Shapley values revealed distinct feature determinants of depression over time for each person ranging from co-morbid anxiety, to physical exercise, diet, momentary stress and breathing performance, sleep times, and neurocognition. In future, these personalized features can serve as targets for a personalized ML-guided, multimodal treatment strategy for depression.

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Section: Participantsmentioning

confidence: 99%

Personalized machine learning of depressed mood using wearables

et al. 2021

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“…Suicide generally is caused by the convergence of multiple risk factorsthe most common being untreated or inadequately managed mental health conditions. 20 At this public university, the schools of medicine and pharmacy, employ a variety of strategies and resources to enhance student well-being such as improved access to psychiatric care 21 , drop-in counseling services and wellness symposia along with annual screenings. At the School of Pharmacy, quality of life surveys are also administered each year for all students, and a faculty guide has been developed for students in distress as a resource.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Suicidal Ideation and Depressive Symptoms Between Medical and Pharmacy Students

Lee

Huang

Tal

et al. 2023

American Journal of Pharmaceutical Education

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“…Human participants were recruited to the study from the University of California San Diego College Mental Health Program [ 51 ]. The study included participants experiencing moderate depression symptoms assessed by using the Patient Health Questionnaire (PHQ-9) scale [ 52 ].…”

Section: Methodsmentioning

confidence: 99%

Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data

Chatterjee,

Mishra,

Sundram

et al. 2023

Sensors

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Digital health applications using Artificial Intelligence (AI) are a promising opportunity to address the widening gap between available resources and mental health needs globally. Increasingly, passively acquired data from wearables are augmented with carefully selected active data from depressed individuals to develop Machine Learning (ML) models of depression based on mood scores. However, most ML models are black box in nature, and hence the outputs are not explainable. Depression is also multimodal, and the reasons for depression may vary significantly between individuals. Explainable and personalised models will thus be beneficial to clinicians to determine the main features that lead to a decline in the mood state of a depressed individual, thus enabling suitable personalised therapy. This is currently lacking. Therefore, this study presents a methodology for developing personalised and accurate Deep Learning (DL)-based predictive mood models for depression, along with novel methods for identifying the key facets that lead to the exacerbation of depressive symptoms. We illustrate our approach by using an existing multimodal dataset containing longitudinal Ecological Momentary Assessments of depression, lifestyle data from wearables and neurocognitive assessments for 14 mild to moderately depressed participants over one month. We develop classification- and regression-based DL models to predict participants’ current mood scores—a discrete score given to a participant based on the severity of their depressive symptoms. The models are trained inside eight different evolutionary-algorithm-based optimisation schemes that optimise the model parameters for a maximum predictive performance. A five-fold cross-validation scheme is used to verify the DL model’s predictive performance against 10 classical ML-based models, with a model error as low as 6% for some participants. We use the best model from the optimisation process to extract indicators, using SHAP, ALE and Anchors from explainable AI literature to explain why certain predictions are made and how they affect mood. These feature insights can assist health professionals in incorporating personalised interventions into a depressed individual’s treatment regimen.

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Implementing a college mental health program – an overview of the first twelve months

Abstract: Students with complex psychiatric/medical co-morbidities received cost effective, convenient and integrative treatment. Over time, we hope to intervene earlier and decrease ER/inpatient visits.

Cited by 9 publications

References 19 publications

Personalized machine learning of depressed mood using wearables

Personalized machine learning of depressed mood using wearables

Comparison of Suicidal Ideation and Depressive Symptoms Between Medical and Pharmacy Students

Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data

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