AIMC Topic: Inpatients

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Predicting treatment response using machine learning: A registered report.

The British journal of clinical psychology
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.)
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
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
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
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
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 ...

Outcomes of robot-assisted versus laparoscopic surgery for colorectal cancer in morbidly obese patients: A propensity score-matched analysis of the US Nationwide Inpatient Sample.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Morbid obesity is associated with poorer postoperative outcomes in colorectal cancer (CRC) patients. We aimed to evaluate short-term outcomes after robotic versus conventional laparoscopic CRC resection in morbidly obese patients.

Artificial intelligence and the potential for perioperative delabeling of penicillin allergies for neurosurgery inpatients.

British journal of neurosurgery
PURPOSE OF THE ARTICLE: Patients with penicillin allergy labels are more likely to have postoperative wound infections. When penicillin allergy labels are interrogated, a significant number of individuals do not have penicillin allergies and may be d...

Use of Robot-Assisted Gait Training in Pediatric Patients with Cerebral Palsy in an Inpatient Setting-A Randomized Controlled Trial.

Sensors (Basel, Switzerland)
Robot-assisted gait training (RAGT) provides a task-based support of walking using exoskeletons. Evidence shows moderate, but positive effects in the therapy of patients with cerebral palsy (CP). This study investigates the impact of RAGT on walking ...

Neurosurgery inpatient outcome prediction for discharge planning with deep learning and transfer learning.

British journal of neurosurgery
INTRODUCTION: Deep learning may be able to assist with the prediction of neurosurgical inpatient outcomes. The aims of this study were to investigate deep learning and transfer learning in the prediction of several inpatient outcomes including timing...