Explainable artificial intelligence for mental health through transparency and interpretability for understandability

DW Joyce, A Kormilitzin, KA Smith, A Cipriani - npj Digital Medicine, 2023 - nature.com
The literature on artificial intelligence (AI) or machine learning (ML) in mental health and
psychiatry lacks consensus on what “explainability” means. In the more general XAI …

Robust and replicable functional brain signatures of 22q11. 2 deletion syndrome and associated psychosis: a deep neural network-based multi-cohort study

K Supekar, C de Los Angeles, S Ryali, L Kushan… - Molecular …, 2024 - nature.com
A major genetic risk factor for psychosis is 22q11. 2 deletion (22q11. 2DS). However, robust
and replicable functional brain signatures of 22q11. 2DS and 22q11. 2DS-associated …

Explainable artificial intelligence for mental health through transparency and interpretability for understandability

KA Smith, A Cipriani - 2023 - oxfordhealth-nhs.archive …
The literature on artificial intelligence (AI) or machine learning (ML) in mental health and
psychiatry lacks consensus on what “explainability” means. In the more general XAI …

[图书][B] A Biologist's Guide to Artificial Intelligence: Building the foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences

A Hamadani, NA Ganai, H Henna, J Bashir - 2024 - books.google.com
A Biologist's Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence
and Machine Learning for Achieving Advancements in Life Sciences provides an overview …

[PDF][PDF] Autism spectrum disorder: time to notice the individuals more than the group

G Hwang - Biological Psychiatry, 2022 - Elsevier
Autism spectrum disorder (ASD) is notoriously heterogeneous in its clinical presentation.
Moreover, its definition has been revised many times since its first discovery, leading to …

Classification of Recurrent Major Depressive Disorder Using a Residual Denoising Autoencoder Framework: Insights from Large-Scale Multisite fMRI Data

P Dai, Y Shi, D Lu, Y Zhou, J Luo, Z He, Z Chen… - Computer Methods and …, 2024 - Elsevier
Background and objective: Recurrent major depressive disorder (rMDD) has a high
recurrence rate, and symptoms often worsen with each episode. Classifying rMDD using …

Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volume

W Dai, Z Zhang, L Tian, S Yu, S Wang, Z Dong… - arXiv preprint arXiv …, 2022 - arxiv.org
In neuroimaging analysis, fMRI can well assess the function changes for brain diseases with
no obvious structural lesions. To date, most deep-learning-based fMRI studies have …

End-to-end explainable ai: Derived theory-of-mind fingerprints to distinguish between autistic and typically developing and social symptom severity

K Bhavna, R Banerjee, D Roy - bioRxiv, 2023 - biorxiv.org
Abstract Theory-of-Mind (ToM) is an evolving ability that significantly impacts human
learning and cognition. Early development of ToM ability allow one to comprehend other …

[HTML][HTML] Generalizable neuromarker for autism spectrum disorder across imaging sites and developmental stages: A multi-site study

T Itahashi, A Yamashita, Y Takahara, N Yahata… - bioRxiv, 2023 - ncbi.nlm.nih.gov
Autism spectrum disorder (ASD) is a lifelong condition, and its underlying biological
mechanisms remain elusive. The complexity of various factors, including inter-site and …

Dynamic functional connectivity analysis in individuals with Autism Spectrum Disorder

PKC Prasad, K Dadi… - 2023 International Joint …, 2023 - ieeexplore.ieee.org
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that predominantly
occurs in children. Previous brain research in ASD has mainly studied biomarkers based on …