From 3cef0674775b83f49d0fe8deaaaca819c01dd713 Mon Sep 17 00:00:00 2001 From: Andreas Grivas Date: Wed, 10 Apr 2024 14:02:12 +0100 Subject: [PATCH] Add Filippo's paper --- _publications/corponi2024tp.md | 28 ++++++++++++++++++ .../corponi2024tp/pipeline-spotlight.png | Bin 0 -> 158667 bytes images/papers/corponi2024tp/pipeline.png | Bin 0 -> 56356 bytes 3 files changed, 28 insertions(+) create mode 100644 _publications/corponi2024tp.md create mode 100644 images/papers/corponi2024tp/pipeline-spotlight.png create mode 100644 images/papers/corponi2024tp/pipeline.png diff --git a/_publications/corponi2024tp.md b/_publications/corponi2024tp.md new file mode 100644 index 0000000..507a48f --- /dev/null +++ b/_publications/corponi2024tp.md @@ -0,0 +1,28 @@ +--- +collection: publications +ref: "corponi2024tp" +permalink: "publications/corponi2024tp" +title: "Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number" +date: 2024-10-04 00:00 +tags: wearables time_series mental_health +image: "/images/papers/corponi2024tp/pipeline.png" +spotlight: "/images/papers/corponi2024tp/pipeline-spotlight.png" +authors: "Filippo Corponi, Bryan M. Li, Gerard Anmella, Ariadna Mas, Isabella Pacchiarotti, Marc Valentí, Iria Grande, Antoni Benabarre, Marina Garriga, Eduard Vieta, Stephen M. Lawrie, Heather C. Whalley, Diego Hidalgo-Mazzei, Antonio Vergari" +paperurl: https://www.nature.com/articles/s41398-024-02876-1 +pdf: "https://www.nature.com/articles/s41398-024-02876-1" +venue: "Nature Translational Psychiatry" +code: "https://github.com/april-tools/wear-your-scales" +excerpt: "A novel task in remote sensing for mood disorders, better aligned with the real-world clinical practice, beyond a reductioninst acute illness yes-no binary classification: old and new machine learning challenges" +abstract: "Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician’soffice. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen’s κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of distribution samples." +supplemental: "https://www.nature.com/articles/s41398-024-02876-1#Sec21" +bibtex: "@article{corponi2024automated, + title={Automated mood disorder symptoms monitoring from multivariate time-series sensory data: Getting the full picture beyond a single number}, + author={Corponi, Filippo and Li, Bryan M and Anmella, Gerard and Mas, Ariadna and Pacchiarotti, Isabella and Valentí, Marc and Grande, Iria and Benabarre, Antoni and Garriga, Marina and Vieta, Eduard and others}, + journal={Translational Psychiatry}, + volume={14}, + number={1}, + pages={161}, + year={2024}, + publisher={Nature Publishing Group UK London} +}" +--- diff --git a/images/papers/corponi2024tp/pipeline-spotlight.png b/images/papers/corponi2024tp/pipeline-spotlight.png new file mode 100644 index 0000000000000000000000000000000000000000..78d83b6adc9c6d77acf8685972539cb7e771c86f GIT binary patch literal 158667 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