Friday 2 October · 8 papers, 6 in the digest and 2 also worth a look
PubMed23 Sept 2026
Lindholm A, Okonkwo C, Varga M and 1 other
Why it’s here In Cardiology and Endocrinology & Metabolism, which you follow · Field fit: 96 of 100
medRxiv22 Sept 2026
Nakamura H, Delacroix P
Why it’s here In Cardiovascular Medicine and Endocrinology & Metabolism, which you follow · Field fit: 91 of 100
arXiv22 Sept 2026
Iyer S, Novotny J, Haddad A and 2 others
Why it’s here In Machine Learning, which you follow · Field fit: 88 of 100
PubMed21 Sept 2026
Whitfield R
Why it’s here In Endocrinology & Metabolism, which you follow · Field fit: 84 of 100
PubMed21 Sept 2026
Mensah K, Iyer S
Why it’s here In Cardiology, which you follow · Field fit: 81 of 100
medRxiv20 Sept 2026
Bergström L, Chen W, Dlamini N
Why it’s here In Cardiovascular Medicine, which you follow · Field fit: 80 of 100
arXiv20 Sept 2026
Tanaka R, Silva T, Achebe K
Why it’s here In Machine Learning, which you follow · Field fit: 79 of 100
From the abstract Labelled echocardiography data are scarce. Pretraining on 1.2 million unlabelled clips before fine-tuning on 5,000 labelled ones improved view classification over training from scratch.
arXiv19 Sept 2026
Fischer L, Osei B, Kowalski J and 2 others
Why it’s here In Machine Learning, which you follow · Field fit: 76 of 100
From the abstract We benchmark six federated learning methods for 30-day readmission prediction across eleven hospitals and find that simple federated averaging performs within two points of centralised training.