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Machine learning-based prediction of duodenal stump leakage following laparoscopic gastrectomy for gastric cancer.
Surgery
Dec 27, 2024
BACKGROUND: Duodenal stump leakage is one of the most critical complications following gastrectomy surgery, with a high mortality rate. The present study aimed to establish a predictive model based on machine learning for forecasting the occurrence o...
Analyzing the Effect of Surgical and Corneal Parameters on the Postoperative Refractive Outcomes of Smile in Myopic Eyes Based on Machine Learning.
American journal of ophthalmology
Dec 27, 2024
PURPOSE: To analyze the influence of individual parameters on the postoperative refractive outcomes of small incision lenticule extraction (SMILE) in myopic eyes using machine learning.
Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics.
Schizophrenia research
Dec 27, 2024
Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from...
Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Study.
Medicina (Kaunas, Lithuania)
Dec 27, 2024
: Venous thromboembolism (VTE) can be the first manifestation of an underlying cancer. This study aimed to develop a predictive model to assess the risk of occult cancer between 30 days and 24 months after a venous thrombotic event using machine lear...
A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system.
PloS one
Dec 27, 2024
BACKGROUND: Dyspnoea is one of the emergency department's (ED) most common and deadly chief complaints, but frequently misdiagnosed and mistreated. We aimed to design a diagnostic decision support which classifies dyspnoeic ED visits into acute heart...
Predicting lack of clinical improvement following varicose vein ablation using machine learning.
Journal of vascular surgery. Venous and lymphatic disorders
Dec 26, 2024
OBJECTIVE: Varicose vein ablation is generally indicated in patients with active/healed venous ulcers. However, patient selection for intervention in individuals without venous ulcers is less clear. Tools that predict lack of clinical improvement (LC...
Deep Learning Model for the Differential Diagnosis of Nasal Polyps and Inverted Papilloma by CT Images: A Multicenter Study.
Academic radiology
Dec 26, 2024
RATIONALE AND OBJECTIVES: Nasal polyps (NP) and inverted papilloma (IP) are benign tumors within the nasal cavity, each necessitating distinct treatment approaches. Herein, we investigate the utility of a deep learning (DL) model for distinguishing b...
Predicting the likelihood of readmission in patients with ischemic stroke: An explainable machine learning approach using common data model data.
International journal of medical informatics
Dec 26, 2024
BACKGROUND: Ischemic stroke affects 15 million people worldwide, causing five million deaths annually. Despite declining mortality rates, stroke incidence and readmission risks remain high, highlighting the need for preventing readmission to improve ...
Prognostic Impact of Tumor Cell Nuclear Size Assessed by Artificial Intelligence in Esophageal Squamous Cell Carcinoma.
Laboratory investigation; a journal of technical methods and pathology
Dec 26, 2024
Tumor cell nuclear size (NS) indicates malignant potential in breast cancer; however, its clinical significance in esophageal squamous cell carcinoma (ESCC) is unknown. Artificial intelligence (AI) can quantitatively evaluate histopathological findin...
Artificial intelligence virtual assistants in primary eye care practice.
Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)
Dec 26, 2024
PURPOSE: To propose a novel artificial intelligence (AI)-based virtual assistant trained on tabular clinical data that can provide decision-making support in primary eye care practice and optometry education programmes.
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