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Examining arterial pulsation to identify and risk-stratify heart failure subjects with deep neural network.

Physical and engineering sciences in medicine
Hemodynamic parameters derived from pulse wave analysis have been shown to predict long-term outcomes in patients with heart failure (HF). Here we aimed to develop a deep-learning based algorithm that incorporates pressure waveforms for the identific...

Development of a Multimodal Machine Learning-Based Prognostication Model for Traumatic Brain Injury Using Clinical Data and Computed Tomography Scans: A CENTER-TBI and CINTER-TBI Study.

Journal of neurotrauma
Computed tomography (CT) is an important imaging modality for guiding prognostication in patients with traumatic brain injury (TBI). However, because of the specialized expertise necessary, timely and dependable TBI prognostication based on CT imagin...

Machine Learning to Allocate Palliative Care Consultations During Cancer Treatment.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: For patients with advanced cancer, early consultations with palliative care (PC) specialists reduce costs, improve quality of life, and prolong survival. However, capacity limitations prevent all patients from receiving PC shortly after diag...

Improved Glycemic Control through Robot-Assisted Remote Interview for Outpatients with Type 2 Diabetes: A Pilot Study.

Medicina (Kaunas, Lithuania)
: Our research group developed a robot-assisted diabetes self-management monitoring system to support Certified Diabetes Care and Education Specialists (CDCESs) in tracking the health status of patients with type 2 diabetes (T2D). This study aimed to...

Machine learning prediction models for in-hospital postoperative functional outcome after moderate-to-severe traumatic brain injury.

European journal of trauma and emergency surgery : official publication of the European Trauma Society
AIM: This study aims to utilize machine learning (ML) and logistic regression (LR) models to predict surgical outcomes among patients with traumatic brain injury (TBI) based on admission examination, assisting in making optimal surgical treatment dec...

Immersive participatory design of assistive robots to support older adults.

Ergonomics
Assistive robots have the potential to support independence, enhance safety, and lower healthcare costs for older adults, as well as alleviate the demands of their care partners. However, ensuring that these robots will effectively and reliably addre...

Potential of radiomics analysis and machine learning for predicting brain metastasis in newly diagnosed lung cancer patients.

Clinical radiology
AIM: To explore the potential of utilising radiomics analysis and machine-learning models that incorporate intratumoural and peritumoural regions of interest (ROIs) for predicting brain metastasis (BM) in newly diagnosed lung cancer patients.

Malnutrition risk assessment using a machine learning-based screening tool: A multicentre retrospective cohort.

Journal of human nutrition and dietetics : the official journal of the British Dietetic Association
BACKGROUND: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST-Plus) implemented in re...

Meta-lasso: new insight on infection prediction after minimally invasive surgery.

Medical & biological engineering & computing
Surgical site infection (SSI) after minimally invasive lung cancer surgery constitutes an important factor influencing the direct and indirect economic implications, patient prognosis, and the 5-year survival rate for early-stage lung cancer patients...