update

content/publication/corponi-antidepressants-2020/index.md

@@ -8,7 +8,7 @@ date: '2020-01-01'
 publishDate: '2023-11-10T22:13:28.829683Z'
 publication_types:
 - '6'
-featured: true
+featured: false
 publication: '*NeuroPsychopharmacotherapy*'
 url_pdf: "https://link.springer.com/referenceworkentry/10.1007/978-3-030-62059-2_29"
 ---

content/publication/corponi-automated-2023/cite.bib

@@ -1,8 +1,22 @@
-@article{corponi_automated_2023,
- author = {Corponi, Filippo and Li, Bryan M and Anmella, Gerard and Mas, Ariadna and Sanabra, Miriam and Vieta, Eduard and Group, INTREPIBD and Lawrie, Stephen M and Whalley, Heather C and Hidalgo-Mazzei, Diego and others},
- date = {2023},
- journaltitle = {medRxiv},
- note = {Publisher: Cold Spring Harbor Laboratory Press},
- pages = {2023--03},
- title = {Automated mood disorder symptoms monitoring from multivariate time-series sensory data: Getting the full picture beyond a single number}
+
+@article{corponi_automated_2024,
+	title = {Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number},
+	volume = {14},
+	rights = {2024 The Author(s)},
+	issn = {2158-3188},
+	url = {https://www.nature.com/articles/s41398-024-02876-1},
+	doi = {10.1038/s41398-024-02876-1},
+	shorttitle = {Automated mood disorder symptoms monitoring from multivariate time-series sensory data},
+	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’s office. 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.},
+	pages = {1--9},
+	number = {1},
+	journaltitle = {Translational Psychiatry},
+	shortjournal = {Transl Psychiatry},
+	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 Lawrie, Stephen M. and Whalley, Heather C. and Hidalgo-Mazzei, Diego and Vergari, Antonio},
+	urldate = {2024-06-11},
+	date = {2024-03-26},
+	langid = {english},
+	note = {Publisher: Nature Publishing Group},
+	keywords = {Biomarkers, Diagnostic markers},
+	file = {Full Text PDF:/Users/filippo/Zotero/storage/7AJMEZ5I/Corponi et al. - 2024 - Automated mood disorder symptoms monitoring from m.pdf:application/pdf},
 }

content/publication/corponi-automated-2023/index.md

@@ -6,18 +6,21 @@ authors:
 - Bryan M Li
 - Gerard Anmella
 - Ariadna Mas
-- Miriam Sanabra
+- Isabella Pacchiarotti
+- Marc Valenti
+- Iria Grande
+- Antonio Benabarre
+- Marina Garrica
 - Eduard Vieta
-- INTREPIBD Group
 - Stephen M Lawrie
 - Heather C Whalley
 - Diego Hidalgo-Mazzei
 - Antonio Vergari
-date: '2023-01-01'
-publishDate: '2023-11-10T22:13:28.838395Z'
+date: '2024-03-26'
+publishDate: '2024-26-03T22:13:28.838395Z'
 publication_types:
 - '3'
 featured: true
-publication: '*MedRxiv*'
-url_pdf: "https://www.medrxiv.org/content/10.1101/2023.03.25.23287744v1"
+publication: 'Translational psychiatry'
+url_pdf: "https://www.nature.com/articles/s41398-024-02876-1"
 ---

content/publication/corponi-mario-2023/cite.bib

@@ -1,8 +1,16 @@
-@article{corponi_automated_2023,
- author = {Corponi, Filippo and Li, Bryan M and Anmella, Gerard and Mas, Ariadna and Sanabra, Miriam and Vieta, Eduard and Group, INTREPIBD and Lawrie, Stephen M and Whalley, Heather C and Hidalgo-Mazzei, Diego and others},
- date = {2023},
- journaltitle = {medRxiv},
- note = {Publisher: Cold Spring Harbor Laboratory Press},
- pages = {2023--03},
- title = {Automated mood disorder symptoms monitoring from multivariate time-series sensory data: Getting the full picture beyond a single number}
+
+@misc{corponi_wearable_2023,
+	title = {Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning},
+	url = {http://arxiv.org/abs/2311.04215},
+	doi = {10.48550/arXiv.2311.04215},
+	abstract = {Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders ({MDs}), a major determinant of worldwide disease burden. However, collecting and annotating wearable data is very resource-intensive. Studies of this kind can thus typically afford to recruit only a couple dozens of patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to {MDs} detection. In this paper, we overcome this data bottleneck and advance the detection of {MDs} acute episode vs stable state from wearables data on the back of recent advances in self-supervised learning ({SSL}). This leverages unlabelled data to learn representations during pre-training, subsequently exploited for a supervised task. First, we collected open-access datasets recording with an Empatica E4 spanning different, unrelated to {MD} monitoring, personal sensing tasks -- from emotion recognition in Super Mario players to stress detection in undergraduates -- and devised a pre-processing pipeline performing on-/off-body detection, sleep-wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduce E4SelfLearning, the largest to date open access collection, and its pre-processing pipeline. Second, we show that {SSL} confidently outperforms fully-supervised pipelines using either our novel E4-tailored Transformer architecture (E4mer) or classical baseline {XGBoost}: 81.23\% against 75.35\% (E4mer) and 72.02\% ({XGBoost}) correctly classified recording segments from 64 (half acute, half stable) patients. Lastly, we illustrate that {SSL} performance is strongly associated with the specific surrogate task employed for pre-training as well as with unlabelled data availability.},
+	number = {{arXiv}:2311.04215},
+	publisher = {{arXiv}},
+	author = {Corponi, Filippo and Li, Bryan M. and Anmella, Gerard and Valenzuela-Pascual, Clàudia and Mas, Ariadna and Pacchiarotti, Isabella and Valentí, Marc and Grande, Iria and Benabarre, Antonio and Garriga, Marina and Vieta, Eduard and Young, Allan H. and Lawrie, Stephen M. and Whalley, Heather C. and Hidalgo-Mazzei, Diego and Vergari, Antonio},
+	urldate = {2024-06-11},
+	date = {2023-11-07},
+	eprinttype = {arxiv},
+	eprint = {2311.04215 [cs, eess]},
+	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing},
+	file = {arXiv Fulltext PDF:/Users/filippo/Zotero/storage/UJ8V7XPV/Corponi et al. - 2023 - Wearable data from subjects playing Super Mario, s.pdf:application/pdf;arXiv.org Snapshot:/Users/filippo/Zotero/storage/2AGGEQX4/2311.html:text/html},
 }

content/publication/corponi-mario-2023/index.md

@@ -1,5 +1,5 @@
 ---
-title: 'Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning'
+title: 'Wearable data from students, teachers or subjects with alcohol use disorder help detect acute mood episodes via self-supervised learning'
 authors:
 - admin
 - Bryan M Li
@@ -17,11 +17,11 @@ authors:
 - Heather C Whalley
 - Diego Hidalgo-Mazzei
 - Antonio Vergari
-date: '2023-11-07'
+date: '2024-05-26'
 publishDate: '2023-11-07T22:13:28.838395Z'
 publication_types:
 - '3'
 featured: true
-publication: '*arXiv*'
+publication: 'JMIR mHealth and uHealth'
 url_pdf: "https://arxiv.org/abs/2311.04215"
 ---

content/publication/li-inferring-2022/index.md

@@ -8,7 +8,7 @@ authors:
 - Miriam Sanabra
 - Diego Hidalgo-Mazzei
 - Antonio Vergari
-date: '2022-01-01'
+date: '2022-12-08'
 featured: true
 publishDate: '2023-11-10T22:13:28.825821Z'
 publication_types: