Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease.

Background: The National Early Warning Score-2 (NEWS-2) is used to detect patient deterioration in UK hospitals but fails to take account of the detailed granularity or temporal trends in clinical observations. We used data-driven methods to develop dynamic early warning scores (DEWS) to address these deficiencies, and tested their accuracy in patients with respiratory disease for predicting (1) death or intensive care unit admission, occurring within 24 h (D/ICU), and (2) clinically significant deterioration requiring urgent intervention, occurring within 4 h (CSD).

Methods: Clinical observations data were extracted from electronic records for 31,590 respiratory in-patient episodes from April 2015 to December 2020 at a large acute NHS Trust.

LMISA: A lightweight multi-modality image segmentation network via domain adaptation using gradient magnitude and shape constraint.

In medical image segmentation, supervised machine learning models trained using one image modality (e.g. computed tomography (CT)) are often prone to failure when applied to another image modality (e.

Lessons learned from the COVID-19 pandemic about sample access for research in the UK.

Objective: Annotated clinical samples taken from patients are a foundation of translational medical research and give mechanistic insight into drug trials. Prior research by the Tissue Directory and Coordination Centre (TDCC) indicated that researchers, particularly those in industry, face many barriers in accessing patient samples. The arrival of the COVID-19 pandemic to the UK produced an immediate and extreme shockwave, which impacted on the ability to undertake all crucial translational research.

Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis.

Background: Lymphangioleiomyomatosis (LAM) is a rare multisystem disease with variable clinical manifestations and differing rates of progression that make management decisions and giving prognostic advice difficult. We used machine learning to identify clusters of associated features which could be used to stratify patients and predict outcomes in individuals.

Patients And Methods: Using unsupervised machine learning we generated patient clusters using data from 173 women with LAM from the UK and 186 replication subjects from the US National Heart, Lung, and Blood Institute (NHLBI) LAM registry.