Clinical Sensitivity and Specificity of the PROSTest in an American Cohort.

Introduction: Prostate cancer (PCa) is the most commonly diagnosed cancer in men in the United States, following skin cancer, with an incidence rate of 112.7 per 100,000 men per year. The need for a reliable, non-invasive diagnostic tool for early PCa detection (screening, biochemical residual disease) remains unmet due to the limitations of PSA testing, which often leads to overdiagnosis and overtreatment.

Early detection of heart failure using in-patient longitudinal electronic health records.

Heart Failure (HF) is common, with worldwide prevalence of 1%-3% and a lifetime risk of 20% for individuals 40 years or older. Despite its considerable health economic burden, techniques for early detection of HF in the general population are sparse. In this work we tested the hypothesis that a simple Transformer neural network, trained on comprehensive collection of secondary care data across the general population, can be used to prospectively (three-year predictive window) identify patients at an increased risk of first hospitalisation due to HF (HHF).

Phase transitions in random circuit sampling.

Undesired coupling to the surrounding environment destroys long-range correlations in quantum processors and hinders coherent evolution in the nominally available computational space. This noise is an outstanding challenge when leveraging the computation power of near-term quantum processors. It has been shown that benchmarking random circuit sampling with cross-entropy benchmarking can provide an estimate of the effective size of the Hilbert space coherently available.

Evaluation of a multigenomic liquid biopsy (PROSTest) for prostate cancer detection and follow-up in a Caribbean population.

Introduction: The PROSTest is a novel machine learning-based liquid biopsy assay that functions as a diagnostic and prognostic tool in prostate cancer (PCa). The algorithm outcome (scored 0-100) has a cutoff of >50 to indicate PCa. In this study, we evaluated the screening utility of the test in comparison with the commonly used PSA test.

Accurate prediction of all-cause mortality in patients with metabolic dysfunction-associated steatotic liver disease using electronic health records.

Introduction And Objectives: Despite the huge clinical burden of MASLD, validated tools for early risk stratification are lacking, and heterogeneous disease expression and a highly variable rate of progression to clinical outcomes result in prognostic uncertainty. We aimed to investigate longitudinal electronic health record-based outcome prediction in MASLD using a state-of-the-art machine learning model.

Patients And Methods: n = 940 patients with histologically-defined MASLD were used to develop a deep-learning model for all-cause mortality prediction.