Machine learning (ML) is increasingly prevalent in mental health care, with contemporary initiatives leveraging these technologies, sometimes in combination with wearable devices, toward novel interventions. This paper investigates the development of...
Previous models of depression outcomes have been limited by symptom heterogeneity within populations. This study conducted a retrospective analysis using latent growth mixture models to identify heterogeneous trajectories within a clinical population...
PURPOSE: Traditional vocal fold pathology recognition typically requires expertise of laryngologists and advanced instruments, primarily through direct visualization. This study aims to augment this conventional paradigm by introducing a parallel dia...
OBJECTIVE: The capacity of machine-learning algorithms to predict medication adherence was assessed using data from AiCure, a computer vision-assisted smartphone application, which records the medication ingestion event.
Neoadjuvant chemotherapy assessment is imperative for prognostication and clinical management of locally advanced gastric cancer. We propose an incremental supervised contrastive learning model (iSCLM), an interpretable artificial intelligence framew...
Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and treatment effects. Schizophrenia is associated with impaired functional connectivity but characteriz...
International journal of molecular sciences
Dec 4, 2024
Breast cancer is a global concern as a leading cause of death for women. Early and precise diagnosis can be vital in handling the disease efficiently. Breast cancer subtyping based on estrogen receptor (ER) status is crucial for determining prognosis...
BMC medical informatics and decision making
Dec 4, 2024
BACKGROUND: The digitisation of healthcare records has generated vast amounts of unstructured data, presenting opportunities for improvements in disease diagnosis when clinical coding falls short, such as in the recording of patient symptoms. This st...
OBJECTIVE: We aimed to develop an AI-based model that uses a portable electronic oral endoscope to capture intraoral images of patients for the detection of oral cancer.
BACKGROUND: Preterm birth (PTB) is a significant cause of neonatal mortality and long-term health issues. Accurate prediction and timely prevention of PTB are essential for reducing associated child mortality and morbidity. Traditional predictive met...
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