AIMC Topic: Inpatients
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Higher blood biochemistry-based biological age developed by advanced deep learning techniques is associated with frailty in geriatric rehabilitation inpatients: RESORT.
Experimental gerontology
Apr 13, 2024
BACKGROUND: Accelerated biological ageing is a major underlying mechanism of frailty development. This study aimed to investigate if the biological age measured by a blood biochemistry-based ageing clock is associated with frailty in geriatric rehabi...
Generative Artificial Intelligence to Transform Inpatient Discharge Summaries to Patient-Friendly Language and Format.
JAMA network open
Mar 4, 2024
IMPORTANCE: By law, patients have immediate access to discharge notes in their medical records. Technical language and abbreviations make notes difficult to read and understand for a typical patient. Large language models (LLMs [eg, GPT-4]) have the ...
Predicting extremely low body weight from 12-lead electrocardiograms using a deep neural network.
Scientific reports
Feb 26, 2024
Previous studies have successfully predicted overweight status by applying deep learning to 12-lead electrocardiogram (ECG); however, models for predicting underweight status remain unexplored. Here, we assessed the feasibility of deep learning in pr...
Enhancing Pressure Injury Surveillance Using Natural Language Processing.
Journal of patient safety
Dec 26, 2023
OBJECTIVE: This study assessed the feasibility of nursing handoff notes to identify underreported hospital-acquired pressure injury (HAPI) events.
Predicting treatment response using machine learning: A registered report.
The British journal of clinical psychology
Dec 18, 2023
OBJECTIVE: Previous research on psychotherapy treatment response has mainly focused on outpatients or clinical trial data which may have low ecological validity regarding naturalistic inpatient samples. To reduce treatment failures by proactively scr...
Explainable hierarchical clustering for patient subtyping and risk prediction.
Experimental biology and medicine (Maywood, N.J.)
Dec 15, 2023
We present a pipeline in which machine learning techniques are used to automatically identify and evaluate subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. Patient clusters are determined using routinely c...
Machine Learning Web Application for Predicting Functional Outcomes in Patients With Traumatic Spinal Cord Injury Following Inpatient Rehabilitation.
Journal of neurotrauma
Nov 28, 2023
Accurately predicting functional outcomes in patients with spinal cord injury (SCI) helps clinicians set realistic functional recovery goals and improve the home environment after discharge. The present study aimed to develop and validate machine lea...
An unsupervised learning approach to identify immunoglobulin utilization patterns using electronic health records.
Transfusion
Oct 20, 2023
BACKGROUND: Managing Canada's immunoglobulin (Ig) product resource allocation is challenging due to increasing demand, high expenditure, and global shortages. Detection of groups with high utilization rates can help with resource planning for Ig prod...
A deep learning approach for inpatient length of stay and mortality prediction.
Journal of biomedical informatics
Oct 17, 2023
PURPOSE: Accurate prediction of the Length of Stay (LoS) and mortality in the Intensive Care Unit (ICU) is crucial for effective hospital management, and it can assist clinicians for real-time demand capacity (RTDC) administration, thereby improving ...
Identifying inpatient mortality in MarketScan claims data using machine learning.
Pharmacoepidemiology and drug safety
Jul 5, 2023
PURPOSE: Inpatient mortality is an important variable in epidemiology studies using claims data. In 2016, MarketScan data began obscuring specific hospital discharge status types for patient privacy, including inpatient deaths, by setting the values ...
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