Studies in health technology and informatics
31118336
Adoption of electronic medical records in hospitals generates a large amount of data. Health care professionals can easily lose their sight on the important insights of the patients' clinical and medical history. Although machine learning algorithms ...
Studies in health technology and informatics
31118320
BACKGROUND: In a database of electronic health records, the amount of available information varies widely between patients. In a real-time prediction scenario, a machine learning model may receive limited information for some patients.
Currently, many critical care indices are not captured automatically at a granular level, rather are repetitively assessed by overburdened nurses. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial i...
Studies in health technology and informatics
31438234
With the vast increase of digital healthcare data, there is an opportunity to mine the data for understanding inherent health patterns. Although machine-learning techniques demonstrated their applications in healthcare to answer several questions, th...
The international journal of medical robotics + computer assisted surgery : MRCAS
32073227
BACKGROUND: The aim of this study was to compare the incidence of early postoperative delirium in the postanesthesia care unit (PACU) between robot-assisted radical prostatectomy (RARP) in the extreme Trendelenburg position and open retropubic radica...
Studies in health technology and informatics
32578554
Delirium is an acute mental disturbance that particularly occurs during hospital stay. Current clinical assessment instruments include the Delirium Observation Screening Scale (DOSS) or the Confusion Assessment Method (CAM). The aim of this work is t...
Journal of the American Medical Informatics Association : JAMIA
32968811
OBJECTIVE: Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest-based algorithm to iden...
BACKGROUND: Our objective was to assess the performance of machine learning methods to predict post-operative delirium using a prospective clinical cohort.