Schizophrenia

Increasing value in the Veterans Affairs Healthcare System (VA) with precision health: a continuing landmark collaboration with the Department of Energy.

OBJECTIVE: Phase II of MVP-CHAMPION, a federal collaboration between the Veterans Affairs Healthcare System (VA) and the Department of Energy (DoE), leveraged large-scale clinical, geo-spatial, and genetic data with state-of-the-art artificial intelligence (AI), and high-performance computing (HPC) to improve value in healthcare. MATERIALS AND METHODS: Eight clinical priority projects for which AI...

Using large language models to identify geriatric assessment domains in patients with advanced cancer: A feasibility study.

INTRODUCTION: In older adults with cancer, geriatric assessment (GA) can improve care quality. In-person assessment may not be feasible for all patients, and relevant information already exists in electronic health record (EHRs). However, chart review is time-consuming. Recently, large language models (LLMs) have demonstrated potential for automated abstraction and summarization tasks. The purpose...

A frontotemporal dementia-like phenotype in schizophrenia: links to striatal dopamine and iron accumulation.

Some schizophrenia patients share characteristics with behavioral variant frontotemporal dementia (bvFTD) including gray matter volume (GMV) similarit...

Causal ordinal connections based characterization of weighted effective brain network for schizophrenia detection.

The human brain is responsible for a wide range of a person's behavioral and cognitive capabilities. The functionality of the brain is affected by var...

EEG-based schizophrenia detection using handcrafted biomarkers and a TOA-optimized hybrid multi-branch CNN-Transformer framework.

Schizophrenia is a chronic psychiatric disorder for which electroencephalography (EEG) offers a low-cost, non-invasive window into abnormal neural dyn...

A Bibliometric Examination of EEGLAB Publications in Scopus and WoS Indexed Sources: A 20-Year Study of Asia-Pacific and Arabian Countries.

IntroductionEEGLAB is a widely used software for analyzing electroencephalography (EEG) datasets, with over 20 years of global use. This bibliometric ...

Omission and hallucination prevalence of clinical guidelines in diagnostic large language model outputs.

OBJECTIVE: Meaningful assessments of how large language models (LLMs) incorporate clinical guidelines require large-scale testing over many queries. H...

Exploring schizophrenia-associated image-derived digital phenotypes in dual-energy X-ray densitometer scans of non-dominant distal forearm: An explainable machine-learning framework.

Schizophrenia remains diagnosed primarily through clinical assessment, which motivates the researchers to search for quantifiable digital phenotypes. ...

Neuroimaging-Based Subgroups in Schizophrenia: A Critical Appraisal of Clustering Studies.

Efforts to define biologically grounded subtypes of schizophrenia have increasingly leveraged neuroimaging data and clustering algorithms. Such approa...

Factors influencing disinformation avoidance behavior among generative artificial intelligence users: A cognition-affect-conation framework.

This study aimed to uncover the mechanisms driving disinformation avoidance behavior among generative artificial intelligence users to reduce negative...

Using Large Language Models to Identify Patient-Oncologist Communication Domains: A Feasibility Study.

BACKGROUND: The American Society of Clinical Oncology (ASCO) convened a multidisciplinary panel in 2017, resulting in patient-oncologist communication...

Physical mechanisms governing generalization and hallucination in deep learning for imaging through scattering media.

Deep learning has revolutionized computational imaging, yet its real-world deployment remains constrained by two critical challenges: poor generalizat...

A multimodal fNIRS-based machine learning model for symptom assessment and treatment response prediction in schizophrenia.

OBJECTIVE: Predicting early symptom severity and treatment response in schizophrenia is crucial for selecting optimal therapeutic strategies. This stu...

A comparative evaluation of EEG-based deep learning models for schizophrenia detection with cross-dataset validation and explainable AI.

OBJECTIVES: Schizophrenia is a neuropsychiatric disorder that affects emotional, behavioral, and brain functions that can be tracked using electroence...

On-device artificial intelligence agent based on language models for electrochemical water desalination.

Electrochemical water treatment is essential for tackling global water scarcity but remains difficult to optimize due to limited expertise and computi...

Performance evaluation of five large language models for assisting in the interpretation of urinalysis reports for kidney diseases: a real-world study.

OBJECTIVES: Large language models (LLMs) show promise for interpreting laboratory reports, yet real-world validation remains limited. This study evalu...

Non-European migrants with schizophrenia spectrum disorders in Swiss forensic and general psychiatric care facilities - A comparative study using machine learning.

Personal history of migration poses an important risk factor for schizophrenia spectrum disorders (SSD), which are also associated with a higher rate ...

Scenario-based evaluation of large language models for reference accuracy in dermatology: literature retrieval on latent tuberculosis in psoriasis patients on anti-IL-17/23 therapy.

BACKGROUND: Large language models (LLMs) could accelerate clinical literature searches, but their reliability is compromised by "hallucinations" gener...

Are routinely collected clinical and sociodemographic characteristics associated with social functioning and activities of daily living in schizophrenia? A machine learning approach descriptive of a schizophrenia cohort.

Patients with schizophrenia often experience substantial impairments in social functioning and activities of daily living (ADLs). Previous studies hav...

Multidisciplinary blinded randomized expert evaluation of large language models for clinical diagnosis and management.

BACKGROUND: Direct clinical uses of large language models (LLMs) remain controversial, partly because of the lack of methodological rigor in assessing...