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Inpatients

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In-Hospital Cancer Mortality Prediction by Multimodal Learning of Non-English Clinical Texts.

Studies in health technology and informatics
Predicting important outcomes in patients with complex medical conditions using multimodal electronic medical records remains challenge. We trained a machine learning model to predict the inpatient prognosis of cancer patients using EMR data with Jap...

Cost supervision mining from EMR based on artificial intelligence technology.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: To effectively monitor medical insurance funds in the era of big data, the study tries to construct an inpatient cost rationality judgement model by designing a virtuous cycle of inpatient cost supervision information system and exploring...

Development and usability evaluation of a bedside robot system for inpatients.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Many inpatients become anxious or frightened about scheduled treatment processes, and medical staff do not have sufficient time to provide emotional support. The recent advancement of information and communications technology (ICT) and th...

Machine learning for initial insulin estimation in hospitalized patients.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The study sought to determine whether machine learning can predict initial inpatient total daily dose (TDD) of insulin from electronic health records more accurately than existing guideline-based dosing recommendations.

The Purpose of Bedside Robots: Exploring the Needs of Inpatients and Healthcare Professionals.

Computers, informatics, nursing : CIN
Robotic systems are used to support inpatients and healthcare professionals and to improve the efficiency and quality of nursing. There is a lack of scientific literature on how applied robotic systems can be used to support inpatients. This study us...

Predicting Inpatient Length of Stay After Brain Tumor Surgery: Developing Machine Learning Ensembles to Improve Predictive Performance.

Neurosurgery
BACKGROUND: Current outcomes prediction tools are largely based on and limited by regression methods. Utilization of machine learning (ML) methods that can handle multiple diverse inputs could strengthen predictive abilities and improve patient outco...

An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Regular nutrient intake monitoring in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition (DRM). Although several methods to estimate nutrient intake have been developed, there is still a clear demand for ...

Borderline Personality Features in Inpatients with Bipolar Disorder: Impact on Course and Machine Learning Model Use to Predict Rapid Readmission.

Journal of psychiatric practice
BACKGROUND: Earlier research indicated that nearly 20% of patients diagnosed with either bipolar disorder (BD) or borderline personality disorder (BPD) also met criteria for the other diagnosis. Yet limited data are available concerning the potential...