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Hospitals

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Predicting SARS-CoV-2 infection duration at hospital admission:a deep learning solution.

Medical & biological engineering & computing
COVID-19 cases are increasing around the globe with almost 5 million of deaths. We propose here a deep learning model capable of predicting the duration of the infection by means of information available at hospital admission. A total of 222 patients...

Explanation of machine learning models using shapley additive explanation and application for real data in hospital.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: When using machine learning techniques in decision-making processes, the interpretability of the models is important. In the present paper, we adopted the Shapley additive explanation (SHAP), which is based on fair profit al...

Artificial Intelligence Assistive Technology in Hospital Professional Nursing Technology.

Journal of healthcare engineering
Global aging is becoming more and more serious, and the nursing problems of the elderly will become very serious in the future. The article designs a control system with ATmega128 as the main controller based on the function of the multifunctional nu...

A Deep Learning-Based Text Classification of Adverse Nursing Events.

Journal of healthcare engineering
Adverse nursing events occur suddenly, unpredictably, or unexpectedly during course of clinical diagnosis and treatment processes in the hospitals. These events adversely affect the patient's diagnosis and treatment results and even increase the pati...

Development and validation of a sensitive and high throughput UPLC-MS/MS method for determination of paraquat and diquat in human plasma and urine: application to poisoning cases at emergency departments of hospitals.

Forensic toxicology
PURPOSE: Paraquat and diquat are well-known toxic herbicides, at least responsible for hundreds of fatal poisoning events worldwide. However, the determination of diquat and paraquat in plasma and urine is very challenging because of their high polar...

A clinical specific BERT developed using a huge Japanese clinical text corpus.

PloS one
Generalized language models that are pre-trained with a large corpus have achieved great performance on natural language tasks. While many pre-trained transformers for English are published, few models are available for Japanese text, especially in c...

Generalisability and performance of an OCT-based deep learning classifier for community-based and hospital-based detection of gonioscopic angle closure.

The British journal of ophthalmology
PURPOSE: To assess the generalisability and performance of a deep learning classifier for automated detection of gonioscopic angle closure in anterior segment optical coherence tomography (AS-OCT) images.

Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score.

eLife
An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated using a machine-learning model. In total, 2782 patients were enrolled between March 2020 and Decembe...

Predicting microvascular invasion in hepatocellular carcinoma: a deep learning model validated across hospitals.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: The accuracy of estimating microvascular invasion (MVI) preoperatively in hepatocellular carcinoma (HCC) by clinical observers is low. Most recent studies constructed MVI predictive models utilizing radiological and/or radiomics features ...