AIMC Topic: Hospitals

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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 ...

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring.

Applied clinical informatics
A new development in the practice of medicine is Artificial Intelligence-based predictive analytics that forewarn clinicians of future deterioration of their patients. This proactive opportunity, though, is different from the reactive stance that cli...

Using machine learning to predict severe hypoglycaemia in hospital.

Diabetes, obesity & metabolism
AIM: To predict the risk of hypoglycaemia using machine-learning techniques in hospitalized patients.

Evaluation of external contamination on the vial surfaces of some hazardous drugs that commonly used in Chinese hospitals and comparison between environmental contamination generated during robotic compounding by IV: Dispensing robot vs. manual compounding in biological safety cabinet.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
OBJECTIVES: The aims of the study were to evaluate the external contamination of hazardous drug vials used in Chinese hospitals and to compare environmental contamination generated by a robotic intelligent dispensing system (WEINAS) and a manual comp...

How do machine learning algorithms perform in predicting hospital choices? evidence from changing environments.

Journal of health economics
Researchers have found that machine learning methods are typically better at prediction than econometric models when the choice environment is stable. We study hospital demand models, and evaluate the relative performance of machine learning algorith...