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Bias Assessment and Correction in Machine Learning Algorithms: A Use-Case in a Natural Language Processing Algorithm to Identify Hospitalized Patients with Unhealthy Alcohol Use.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Unhealthy alcohol use represents a major economic burden and cause of morbidity and mortality in the United States. Implementation of interventions for unhealthy alcohol use depends on the availability and accuracy of screening tools. Our group previ...

Analysis of Characteristic Factors of Nursing Safety Incidents in ENT Surgery by Deep Learning-Based Medical Data Association Rules Method.

Computational and mathematical methods in medicine
It is of great significance to explore the characteristic factors of postoperative nursing safety events in patients with otolaryngology surgery and to understand the characteristics of postoperative nursing safety events in otolaryngology surgery pa...

Prediction of Bronchopneumonia Inpatients' Total Hospitalization Expenses Based on BP Neural Network and Support Vector Machine Models.

Computational and mathematical methods in medicine
OBJECTIVE: BP neural network (BPNN) model and support vector machine (SVM) model were used to predict the total hospitalization expenses of patients with bronchopneumonia.

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality.

Academic radiology
RATIONALE AND OBJECTIVES: The burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on computed tomography. The purpose of this study was to evaluate the ability of a deep convolutional neural ...

A deep learning based multimodal interaction system for bed ridden and immobile hospital admitted patients: design, development and evaluation.

BMC health services research
BACKGROUND: Hospital cabins are a part and parcel of the healthcare system. Most patients admitted in hospital cabins reside in bedridden and immobile conditions. Though different kinds of systems exist to aid such patients, most of them focus on spe...

Mortality Prediction Analysis among COVID-19 Inpatients Using Clinical Variables and Deep Learning Chest Radiography Imaging Features.

Tomography (Ann Arbor, Mich.)
The emergence of the COVID-19 pandemic over a relatively brief interval illustrates the need for rapid data-driven approaches to facilitate clinical decision making. We examined a machine learning process to predict inpatient mortality among COVID-19...

Socially assistive robots on the market : Experiences from inpatient care and potentials for care at home.

Zeitschrift fur Gerontologie und Geriatrie
BACKGROUND: The article addresses commercial socially assistive robots (SAR). There is evidence of the impact of SAR on older persons in institutional settings. Family care at home has not yet been the subject of systematic research; however, especia...

Inpatient Fall Prediction Models: A Scoping Review.

Gerontology
INTRODUCTION: The digitization of hospital systems, including integrated electronic medical records, has provided opportunities to improve the prediction performance of inpatient fall risk models and their application to computerized clinical decisio...

Minimally Invasive Surgery in the United States, 2022: Understanding Its Value Using New Datasets.

The Journal of surgical research
INTRODUCTION: While minimally invasive surgery (MIS) has transformed the treatment landscape of surgical care, its utilization is not well understood. The newly released Nationwide Ambulatory Surgery Sample allows for more accurate estimates of MIS v...

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...