首页|Data on Neuroendocrine Cancer Reported by Ash Kieran Clift and Colleagues (Ident ifying patients with undiagnosed small intestinal neuroendocrine tumours in prim ary care using statistical and machine learning: model development and validatio n ...)
Data on Neuroendocrine Cancer Reported by Ash Kieran Clift and Colleagues (Ident ifying patients with undiagnosed small intestinal neuroendocrine tumours in prim ary care using statistical and machine learning: model development and validatio n ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Neuroendocr ine Cancer is the subject of a report. According to news originating from London , United Kingdom, by NewsRx correspondents, research stated, "Neuroendocrine tum ours (NETs) are increasing in incidence, often diagnosed at advanced stages, and individuals may experience years of diagnostic delay, particularly when arising from the small intestine (SI). Clinical prediction models could present novel o pportunities for case finding in primary care." Our news journalists obtained a quote from the research, "An open cohort of adul ts (18+ years) contributing data to the Optimum Patient Care Research Database b etween 1st Jan 2000 and 30th March 2023 was identified. This database collects d e-identified data from general practices in the UK. Model development approaches comprised logistic regression, penalised regression, and XGBoost. Performance ( discrimination and calibration) was assessed using internal-external cross-valid ation. Decision analysis curves compared clinical utility. Of 11.7 million indiv iduals, 382 had recorded SI NET diagnoses (0.003 %). The XGBoost mod el had the highest AUC (0.869, 95% confidence interval [CI]: 0.841-0.898) but was mildly miscalibrated (slope 1.165, 95% CI: 1.088-1.243; calibration-in-the-large 0.010, 95% CI: -0.164 to 0.185). Clinical utility was similar across all models. Multivaria ble prediction models may have clinical utility in identifying individuals with undiagnosed SI NETs using information in their primary care records."
LondonUnited KingdomEuropeCancerCyborgsEmerging TechnologiesHealth and MedicineMachine LearningNeuroendo crine CancerNeuroendocrine TumorsOncology
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