The American journal of emergency medicine
Mar 10, 2020
BACKGROUND: Low-acuity outpatients constitute the majority of emergency department (ED) patients, and these patients often experience an unpredictable length of stay (LOS). Effective LOS prediction might improve the quality of ED care and reduce ED c...
International journal of environmental research and public health
Jan 24, 2020
Numerous societal trends are compelling a transition from inpatient to outpatient venues of care for medical rehabilitation. While there are advantages to outpatient rehabilitation (e.g., lower cost, more relevant to home and community function), the...
Clinical & experimental ophthalmology
Nov 14, 2019
IMPORTANCE: Triaging of outpatient referrals to ophthalmology services is required for the maintenance of patient care and appropriate resource allocation. Machine learning (ML), in particular natural language processing, may be able to assist with t...
OBJECTIVE: To develop and validate a novel, machine learning-derived model to predict the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM).
OBJECTIVES: To compare the early (≤30 days) postoperative mortality and morbidity in patients who underwent robot-assisted radical prostatectomy (RARP) and were discharged the same surgery day to a propensity score matched patient population of RARP ...
Traditional methods have long been used for clinical demand forecasting. Machine learning methods represent the next evolution in forecasting, but model choice and optimization remain challenging for achieving optimal results. To determine the best m...
INTRODUCTION: Readmission from inpatient rehabilitation facilities to acute care hospitals is a serious problem. This study aims to develop a predictive model based on machine learning algorithms to identify patients at high risk of readmission.
Journal of evaluation in clinical practice
Aug 22, 2018
RATIONALE, AIMS AND OBJECTIVES: Implementation of robotic systems in outpatient hospital pharmacies is uncommon. Other than cost, 1 of the barriers to widespread adoption is the lack of definitive evidence that this technology actually reduces dispen...
De-identification of clinical notes is a special case of named entity recognition. Supervised machine-learning (ML) algorithms have achieved promising results for this task. However, ML-based de-identification systems often require annotating a large...
Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resource...
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