AIMC Topic: Patient Discharge
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Hierarchical label-wise attention transformer model for explainable ICD coding.
Journal of biomedical informatics
Aug 20, 2022
International Classification of Diseases (ICD) coding plays an important role in systematically classifying morbidity and mortality data. In this study, we propose a hierarchical label-wise attention Transformer model (HiLAT) for the explainable pred...
Classifying Characteristics of Opioid Use Disorder From Hospital Discharge Summaries Using Natural Language Processing.
Frontiers in public health
May 9, 2022
BACKGROUND: Opioid use disorder (OUD) is underdiagnosed in health system settings, limiting research on OUD using electronic health records (EHRs). Medical encounter notes can enrich structured EHR data with documented signs and symptoms of OUD and s...
Optimizing discharge after major surgery using an artificial intelligence-based decision support tool (DESIRE): An external validation study.
Surgery
May 4, 2022
BACKGROUND: In the DESIRE study (Discharge aftEr Surgery usIng aRtificial intElligence), we have previously developed and validated a machine learning concept in 1,677 gastrointestinal and oncology surgery patients that can predict safe hospital disc...
Preliminary Outcomes After Same Day Discharge Protocol for Robot-Assisted Partial Nephrectomy: A Single Centre Experience.
Urology
Apr 8, 2022
OBJECTIVE: To assess the feasibility and safety of same-day discharge (SDD) surgery after robot-assisted partial nephrectomy (RAPN) in patients with small renal tumors.
Effect of Different Nursing Interventions on Discharged Patients with Cardiac Valve Replacement Evaluated by Deep Learning Algorithm-Based MRI Information.
Contrast media & molecular imaging
Mar 21, 2022
This study was aimed to explore the application of cardiac magnetic resonance imaging (MRI) image segmentation model based on U-Net in the diagnosis of a valvular heart disease. The effect of continuous nursing on the survival of discharged patients ...
Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example.
BMC medical informatics and decision making
Jan 13, 2022
BACKGROUND: There are often many missing values in medical data, which directly affect the accuracy of clinical decision making. Discharge assessment is an important part of clinical decision making. Taking the discharge assessment of patients with s...
Evaluation of shape factor impact on discharge coefficient of side orifices using boost simulation model with extreme learning machine data-driven.
Network (Bristol, England)
Jan 9, 2022
In this paper, for the first time, the impact of the shape factor on the discharge coefficient of side orifices is evaluated using the novel Extreme Learning Machine (ELM) model. In addition, the Monte Carlo simulations (MCs) are applied to assess th...
Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.
PloS one
Nov 23, 2021
BACKGROUND: Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and eval...
Research on Rehabilitation Effect Prediction for Patients with SCI Based on Machine Learning.
World neurosurgery
Nov 15, 2021
OBJECTIVE: Because of the complex condition of patients with spinal cord injury (SCI), it is difficult to accurately calculate the activity of daily living (ADL) score of discharged patients. In view of the above problem, this research proposes a pre...
Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments.
Scientific reports
Nov 2, 2021
Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance could help reduce the number of missed critical illnesses and could reduce avoidable utilization of em...
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