AIMC Topic: Health Services

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Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management.

Frontiers in public health Nov 17, 2021
The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in o...
Machine Learning Health Services Algorithms Reproducibility of Results Support Vector Machine
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Characterising the nationwide burden and predictors of unkept outpatient appointments in the National Health Service in England: A cohort study using a machine learning approach.

PLoS medicine Oct 12, 2021
BACKGROUND: Unkept outpatient hospital appointments cost the National Health Service £1 billion each year. Given the associated costs and morbidity of unkept appointments, this is an issue requiring urgent attention. We aimed to determine rates of un...
Humans Machine Learning Delivery of Health Care Health Services Appointments and Schedules England Outpatients Likelihood Functions Cohort Studies Models, Theoretical
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Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network.

Journal of healthcare engineering Sep 29, 2021
In recent decades, heart disease threatens people's health seriously because of its prevalence and high risk of death. Therefore, predicting heart disease through some simple physical indicators obtained from the regular physical examination at an ea...
Algorithms Humans Health Services Neural Networks, Computer Heart Diseases Physical Examination
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Machine learning versus regression modelling in predicting individual healthcare costs from a representative sample of the nationwide claims database in France.

The European journal of health economics : HEPAC : health economics in prevention and care Aug 9, 2021
BACKGROUND: Innovative provider payment methods that avoid adverse selection and reward performance require accurate prediction of healthcare costs based on individual risk adjustment. Our objective was to compare the performances of a simple neural ...
Health Services Linear Models Health Care Costs Machine Learning Databases, Factual Humans
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What Do Patients Care About? Mining Fine-grained Patient Concerns from Online Physician Reviews Through Computer-Assisted Multi-level Qualitative Analysis.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Online physician review (OPR) websites have been increasingly used by healthcare consumers to make informed decisions in selecting healthcare providers. However, consumer-generated online reviews are often unstructured and contain plural topics with ...
Health Services Data Mining Patient Satisfaction Quality of Health Care Physicians Natural Language Processing Internet Humans Computers Delivery of Health Care
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Generalized linear mixed-model (GLMM) trees: A flexible decision-tree method for multilevel and longitudinal data.

Psychotherapy research : journal of the Society for Psychotherapy Research Jun 30, 2020
Decision-tree methods are machine-learning methods which provide results that are relatively easy to interpret and apply by human decision makers. The resulting decision trees show how baseline patient characteristics can be combined to predict trea...
Humans Machine Learning Treatment Outcome Health Services Linear Models Female Male Child
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What are the main patient safety concerns of healthcare stakeholders: a mixed-method study of Web-based text.

International journal of medical informatics May 4, 2020
OBJECTIVES: Various healthcare stakeholders define quality of care in different ways. Public policy could advocate all these concerns. This study was conducted to identify the main themes on patient safety of stakeholders expressed before and after t...
Government Internet Health Services Quality Improvement Humans Research Design Patient Safety Longitudinal Studies Natural Language Processing Republic of Korea Hospitals
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Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore.

BMJ open Jan 6, 2020
OBJECTIVE: We aim to characterise persistent high utilisers (PHUs) of healthcare services, and correspondingly, transient high utilisers (THUs) and non-high utilisers (non-HUs) for comparison, to facilitate stratifying HUs for targeted intervention. ...
Health Services Health Care Costs Humans Patient Acceptance of Health Care Male Machine Learning Aged Middle Aged Adult Retrospective Studies Singapore Female
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A deep learning model for pediatric patient risk stratification.

The American journal of managed care Oct 1, 2019
OBJECTIVES: Current models for patient risk prediction rely on practitioner expertise and domain knowledge. This study presents a deep learning model-a type of machine learning that does not require human inputs-to analyze complex clinical and financ...
Neural Networks, Computer Prospective Studies Reproducibility of Results Risk Assessment Deep Learning Risk Factors Health Resources Humans Accountable Care Organizations Age Factors Health Services Sex Factors Child Socioeconomic Factors Residence Characteristics
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Estimating causal effects for survival (time-to-event) outcomes by combining classification tree analysis and propensity score weighting.

Journal of evaluation in clinical practice Dec 12, 2017
RATIONALE, AIMS AND OBJECTIVES: A common approach to assessing treatment effects in nonrandomized studies with time-to-event outcomes is to estimate propensity scores and compute weights using logistic regression, test for covariate balance, and then...
Propensity Score Monte Carlo Method Patient Readmission Survival Analysis Proportional Hazards Models Middle Aged Comorbidity Decision Trees Socioeconomic Factors Time Factors Humans Health Services Female Age Factors Heart Failure Pulmonary Disease, Chronic Obstructive Machine Learning Models, Statistical Sex Factors Male
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