BACKGROUND AND OBJECTIVE: To examine whether the use of natural language processing (NLP) technology is effective in assisting rapid title and abstract screening when updating a systematic review.
INTRODUCTION: Acute kidney injury (AKI) is common and is associated with negative long-term outcomes. Given the heterogeneity of the syndrome, the ability to predict outcomes of AKI may be beneficial towards effectively using resources and personalis...
BACKGROUND: Despite existing research on text mining and machine learning for title and abstract screening, the role of machine learning within systematic literature reviews (SLRs) for health technology assessment (HTA) remains unclear given lack of ...
Active learning for systematic review screening promises to reduce the human effort required to identify relevant documents for a systematic review. Machines and humans work together, with humans providing training data, and the machine optimising th...
BACKGROUND: We evaluated the benefits and risks of using the Abstrackr machine learning (ML) tool to semi-automate title-abstract screening and explored whether Abstrackr's predictions varied by review or study-level characteristics.
To summarize the current evidence on robot-assisted radical cystectomy (RARC) with intracorporeal urinary diversion (ICUD) and compare perioperative outcomes and postoperative complications of patients undergoing RARC with extracorporeal urinary div...
INTRODUCTION: Studies addressing the development and/or validation of diagnostic and prognostic prediction models are abundant in most clinical domains. Systematic reviews have shown that the methodological and reporting quality of prediction model s...
OBJECTIVES: This study developed, calibrated, and evaluated a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews.