All Outputs (2)

Machine learning models based on routinely sampled blood tests can predict the presence of malignancy amongst patients with suspected musculoskeletal malignancy (2023)
Journal Article
Bentick, K., Runevic, J., Akula, S., Kyriacou, T., Cool, P., & Andras, P. (2023). Machine learning models based on routinely sampled blood tests can predict the presence of malignancy amongst patients with suspected musculoskeletal malignancy. Methods, 220, 55-60. https://doi.org/10.1016/j.ymeth.2023.10.012

This study explores the possibility of using routinely taken blood tests in the diagnosis and triage of patients with suspected musculoskeletal malignancy. A retrospective study was performed on results of patients who had presented for assessment to... Read More about Machine learning models based on routinely sampled blood tests can predict the presence of malignancy amongst patients with suspected musculoskeletal malignancy.

Determining cardiovascular risk in patients with unattributed chest pain in UK primary care: an electronic health record study (2023)
Journal Article
Jordan, K. P., Rathod-Mistry, T., van der Windt, D. A., Bailey, J., Chen, Y., Clarson, L., …Mamas, M. A. (2023). Determining cardiovascular risk in patients with unattributed chest pain in UK primary care: an electronic health record study. European Journal of Preventive Cardiology, 30(11), 1151-1161. https://doi.org/10.1093/eurjpc/zwad055

BACKGROUND: Most adults presenting in primary care with chest pain symptoms will not receive a diagnosis ("unattributed" chest pain) but are at increased risk of cardiovascular events. AIM: To assess within patients with unattributed chest pain, risk... Read More about Determining cardiovascular risk in patients with unattributed chest pain in UK primary care: an electronic health record study.