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Delirium

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An Improvised Classification Model for Predicting Delirium.

Studies in health technology and informatics
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...

Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning.

Scientific reports
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...

Is Regular Re-Training of a Predictive Delirium Model Necessary After Deployment in Routine Care?

Studies in health technology and informatics
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 ...

Information Adapted Machine Learning Models for Prediction in Clinical Workflow.

Studies in health technology and informatics
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.

Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

Physiological measurement
OBJECTIVE: Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distingu...

Evaluating the Impact of Incorrect Diabetes Coding on the Performance of Multivariable Prediction Models.

Studies in health technology and informatics
The use of electronic health records for risk prediction models requires a sufficient quality of input data to ensure patient safety. The aim of our study was to evaluate the influence of incorrect administrative diabetes coding on the performance of...

On the Representation of Machine Learning Results for Delirium Prediction in a Hospital Information System in Routine Care.

Studies in health technology and informatics
Digitalisation of health care for the purpose of medical documentation lead to huge amounts of data, hence having an opportunity to derive knowledge and associations of different attributes recorded. Many health care events can be prevented when iden...

Decreased Incidence of Postoperative Delirium in Robot-assisted Thoracoscopic Esophagectomy Compared With Open Transthoracic Esophagectomy.

Surgical laparoscopy, endoscopy & percutaneous techniques
BACKGROUND: Postoperative delirium (POD) is one of messy complications related with increased mortality and hospital costs. Patients undergoing esophagectomy are more in danger of delirium than other kinds of surgeries. We investigated the impact of ...