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An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery.
BMC anesthesiology
Dec 19, 2024
AIM: The study aimed to develop a predictive model with machine learning (ML) algorithm, to predict and manage the need for red blood cell (RBC) transfusion during hip fracture surgery.
Evaluation of prediction errors in nine intraocular lens calculation formulas using an explainable machine learning model.
BMC ophthalmology
Dec 19, 2024
BACKGROUND: The purpose of the study was to evaluate the relationship between prediction errors (PEs) and ocular biometric variables in cataract surgery using nine intraocular lens (IOL) formulas with an explainable machine learning model.
Artificial intelligence for better goals of care documentation.
BMJ supportive & palliative care
Dec 19, 2024
OBJECTIVES: Lower rates of goals of care (GOC) conversations have been observed in non-white hospitalised patients, which may contribute to racial disparities in end-of-life care. We aimed to assess how a targeted initiative to increase GOC documenta...
A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer.
Abdominal radiology (New York)
Dec 18, 2024
OBJECTIVE: To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images.
Optimizing the management of immune-related adverse events and survival in patients with thoracic cancer receiving immunotherapy through artificial intelligence (electronic patient-reported outcomes): The IMPATHI study.
Bulletin du cancer
Dec 18, 2024
INTRODUCTION: The lung cancer continues to be the primary cause of cancer-related deaths, despite significant advancements in treatment through the introduction of immunological checkpoint inhibitors (ICI). These inhibitors, initially used as monothe...
Clinical characteristics and prediction model of re-positive nucleic acid tests among Omicron infections by machine learning: a real-world study of 35,488 cases.
BMC infectious diseases
Dec 18, 2024
BACKGROUND: During the Omicron BA.2 variant outbreak in Shanghai, China, from April to May 2022, PCR nucleic acid test re-positivity (TR) occurred frequently, yet the risk factors and predictive models for TR remain unclear. This study aims to identi...
A potential predictive model based on machine learning and CPD parameters in elderly patients with aplastic anemia and myelodysplastic neoplasms.
BMC medical informatics and decision making
Dec 18, 2024
BACKGROUND: Aplastic anemia (AA) and myelodysplastic neoplasms (MDS) have similar peripheral blood manifestations and are clinically characterized by reduced hematological triad. It is challenging to distinguish and diagnose these two diseases. Hence...
MHAGuideNet: a 3D pre-trained guidance model for Alzheimer's Disease diagnosis using 2D multi-planar sMRI images.
BMC medical imaging
Dec 18, 2024
BACKGROUND: Alzheimer's Disease is a neurodegenerative condition leading to irreversible and progressive brain damage, with possible features such as structural atrophy. Effective precision diagnosis is crucial for slowing disease progression and red...
Deep learning algorithm enables automated Cobb angle measurements with high accuracy.
Skeletal radiology
Dec 17, 2024
OBJECTIVE: To determine the accuracy of automatic Cobb angle measurements by deep learning (DL) on full spine radiographs.
Use of Hearing Aids Embedded with Inertial Sensors and Artificial Intelligence to Identify Patients at Risk for Falling.
Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
Dec 17, 2024
OBJECTIVE: To compare fall risk scores of hearing aids embedded with inertial measurement units (IMU-HAs) and powered by artificial intelligence (AI) algorithms with scores by trained observers.
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