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Cost Minimization Using an Artificial Neural Network Sleep Apnea Prediction Tool for Sleep Studies
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1001/archpedi.156.7.651openalex-snapshot
Type 2 Diabetes Mellitus in Children
inferredcites →https://doi.org/10.1001/jama.2013.276185openalex-snapshot
Does This Patient Have Obstructive Sleep Apnea?
inferredcites →https://doi.org/10.1002/sim.2850openalex-snapshot
Comparison of the likelihood ratios of two binary diagnostic tests in paired designs
inferredcites →https://doi.org/10.1007/s11325-000-0011-3openalex-snapshot
Symptom-Based Prevalence of Sleep Disorders in an Adult Primary Care Population
inferredcites →https://doi.org/10.1007/s11325-010-0384-xopenalex-snapshot
A prediction model based on an artificial intelligence system for moderate to severe obstructive sleep apnea
inferredcites →https://doi.org/10.1007/s12016-012-8349-4openalex-snapshot
Presence of Anti-proteinase 3 Antineutrophil Cytoplasmic Antibodies (Anti-PR3 ANCA) as Serologic Markers in Inflammatory Bowel Disease
inferredcites →https://doi.org/10.1016/j.ccc.2011.10.002openalex-snapshot
Health Economic Methods: Cost-Minimization, Cost-Effectiveness, Cost-Utility, and Cost-Benefit Evaluations
inferredcites →https://doi.org/10.1016/j.sleep.2009.08.015openalex-snapshot
Initial use of portable monitoring versus polysomnography to confirm obstructive sleep apnea in symptomatic patients: An economic decision model
inferredcites →https://doi.org/10.1016/s0895-4356(96)00002-9openalex-snapshot
Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes
inferredcites →https://doi.org/10.1038/scientificamerican0992-144openalex-snapshot
How Neural Networks Learn from Experience
inferredcites →https://doi.org/10.1046/j.1525-1497.2002.10750.xopenalex-snapshot
Simplifying likelihood ratios
inferredcites →https://doi.org/10.1055/s-2000-11530openalex-snapshot
Symptom-Based Prevalence of Sleep Disorders in an Adult Primary Care Population
inferredcites →https://doi.org/10.1093/aje/kws342openalex-snapshot
Increased Prevalence of Sleep-Disordered Breathing in Adults
inferredcites →https://doi.org/10.1093/bioinformatics/btr574openalex-snapshot
BDTcomparator: a program for comparing binary classifiers
inferredcites →https://doi.org/10.1093/bja/aes022openalex-snapshot
High STOP-Bang score indicates a high probability of obstructive sleep apnoea
inferredcites →https://doi.org/10.1093/sleep/22.1.105openalex-snapshot
Validity of Neural Network in Sleep Apnea
inferredcites →https://doi.org/10.1093/sleep/23.7.929openalex-snapshot
The Use of Clinical Prediction Formulas in the Evaluation of Obstructive Sleep Apnea
inferredcites →https://doi.org/10.1093/sleep/29.3.375openalex-snapshot
Practice Parameters for the Use of Continuous and Bilevel Positive Airway Pressure Devices to Treat Adult Patients With Sleep-Related Breathing Disorders
inferredcites →https://doi.org/10.1109/72.143374openalex-snapshot
Learning with limited numerical precision using the cascade-correlation algorithm
inferredcites →https://doi.org/10.1111/j.1540-8159.2012.03476.xopenalex-snapshot
Electrocardiographic Diagnosis of Biventricular Pacing in Patients with Nonapical Right Ventricular Leads
inferredcites →https://doi.org/10.1136/bmj.307.6906.726openalex-snapshot
Costs and cost-minimisation analysis.
inferredcites →https://doi.org/10.1136/bmj.329.7458.168openalex-snapshot
Diagnostic tests 4: likelihood ratios
inferredcites →https://doi.org/10.1136/thx.2010.152272openalex-snapshot
Effectiveness of home respiratory polygraphy for the diagnosis of sleep apnoea and hypopnoea syndrome
inferredcites →https://doi.org/10.1164/ajrccm.150.5.7952553openalex-snapshot
Likelihood Ratios for a Sleep Apnea Clinical Prediction Rule
inferredcites →https://doi.org/10.1164/rccm.201011-1770ocopenalex-snapshot
Noninferiority of Functional Outcome in Ambulatory Management of Obstructive Sleep Apnea
inferredcites →https://doi.org/10.1164/rccm.201103-0428ocopenalex-snapshot
Therapeutic Decision-making for Sleep Apnea and Hypopnea Syndrome Using Home Respiratory Polygraphy: A Large Multicentric Study
inferredcites →https://doi.org/10.1184/r1/6610403openalex-snapshot
The Cascade-Correlation learning architecture
inferredcites →https://doi.org/10.1191/1740774506cn147oaopenalex-snapshot
Comparing the predictive values of diagnostic tests: sample size and analysis for paired study designs
inferredcites →https://doi.org/10.1214/ss/1177013815openalex-snapshot
Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy
inferredcites →https://doi.org/10.1378/chest.11-1945openalex-snapshot
Prevalence of Diagnosed Sleep Apnea Among Patients With Type 2 Diabetes in Primary Care
inferredcites →https://doi.org/10.1378/chest.116.2.409openalex-snapshot
Neural Network Prediction of Obstructive Sleep Apnea From Clinical Criteria
inferredcites →https://doi.org/10.1513/annalsats.201305-134ocopenalex-snapshot
STOP-BANG Questionnaire Performance in a Veterans Affairs Unattended Sleep Study Program
inferredcites →https://doi.org/10.2307/4615733openalex-snapshot
A Simple Sequentially Rejective Multiple Test Procedure
inferredcites →https://doi.org/10.52202/079017openalex-snapshot
Advances in Neural Information Processing Systems 37
inferredcites →https://doi.org/10.5664/jcsm.1308openalex-snapshot
Identification of Patients with Sleep Disordered Breathing: Comparing the Four-Variable Screening Tool, STOP, STOP-Bang, and Epworth Sleepiness Scales
inferredcites →https://doi.org/10.5664/jcsm.1450openalex-snapshot
Sleep Medicine: Strategies for Change
inferredcites →https://doi.org/10.5664/jcsm.2570openalex-snapshot
A Warning Shot Across the Bow: The Changing Face of Sleep Medicine
inferredcites →https://doi.org/10.5664/jcsm.26292openalex-snapshot
A Quantitative Assessment of Sleep Laboratory Activity in the United States
inferredcites →https://doi.org/10.5664/jcsm.27032openalex-snapshot
Clinical Guidelines for the Use of Unattended Portable Monitors in the Diagnosis of Obstructive Sleep Apnea in Adult Patients
inferredcites →https://doi.org/10.5665/sleep.1870openalex-snapshot
A Multisite Randomized Trial of Portable Sleep Studies and Positive Airway Pressure Autotitration Versus Laboratory-Based Polysomnography for the Diagnosis and Treatment of Obstructive Sleep Apnea: The HomePAP Study
inferredcites →https://doi.org/10.7326/0003-4819-115-11-843openalex-snapshot
Use of an Artificial Neural Network for the Diagnosis of Myocardial Infarction
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4676969openalex-extracted
Development and Validation of Novel Machine Learning-Derived Obstructive Sleep Apnea Prediction Tools Using Multiple Obesity Indicators
inferred← citeshttps://doi.org/10.1007/978-3-030-92087-6_50openalex-snapshot
Health Economics, Economic Evaluation and Artificial Intelligence Technology
inferred← citeshttps://doi.org/10.1016/b978-0-443-15728-8.00021-5openalex-snapshot
Transforming primary care through digital health technology
inferred← citeshttps://doi.org/10.1016/j.apmr.2020.03.020openalex-snapshot
Cost-Benefit Analysis From the Payor’s Perspective for Screening and Diagnosing Obstructive Sleep Apnea During Inpatient Rehabilitation for Moderate to Severe TBI
inferred← citeshttps://doi.org/10.1016/j.bspc.2021.102928openalex-snapshot
Obstructive sleep apnea event prediction using recurrence plots and convolutional neural networks (RP-CNNs) from polysomnographic signals
inferred← citeshttps://doi.org/10.1016/j.ccc.2015.03.008openalex-snapshot
Perioperative Issues and Sleep-Disordered Breathing
inferred← citeshttps://doi.org/10.1016/j.chest.2021.10.023openalex-snapshot
Diagnostic Performance of Machine Learning-Derived OSA Prediction Tools in Large Clinical and Community-Based Samples
inferred← citeshttps://doi.org/10.1016/j.healthpol.2026.105715openalex-snapshot
A systematic review of economic evidence of artificial intelligence in healthcare
inferred← citeshttps://doi.org/10.1016/j.isci.2025.111841openalex-snapshot
Accessible moderate-to-severe obstructive sleep apnea screening tool using multidimensional obesity indicators as compact representations
inferred← citeshttps://doi.org/10.1088/1361-6579/aa6ec6openalex-snapshot
Sleep apnea: a review of diagnostic sensors, algorithms, and therapies
inferred← citeshttps://doi.org/10.1093/jamia/ocaa102openalex-snapshot
Economic evaluations of big data analytics for clinical decision-making: a scoping review
inferred← citeshttps://doi.org/10.1097/htr.0000000000000951openalex-snapshot
Cost-Effectiveness of Sleep Apnea Diagnosis and Treatment in Hospitalized Persons With Moderate to Severe Traumatic Brain Injury
inferred← citeshttps://doi.org/10.1111/resp.13063openalex-snapshot
P4 medicine approach to obstructive sleep apnoea
inferred← citeshttps://doi.org/10.1142/s0219635215500144openalex-snapshot
Withstanding the obstructive sleep apnea syndrome at the expense of arousal instability, altered cerebral autoregulation and neurocognitive decline
inferred← citeshttps://doi.org/10.1177/1055665618778400openalex-snapshot
A Single Lab Test to Aid Pierre Robin Sequence Severity Diagnosis
inferred← citeshttps://doi.org/10.1186/s40463-022-00566-wopenalex-snapshot
Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea
inferred← citeshttps://doi.org/10.15308/sinteza-2016-112-117openalex-snapshot
The Use of Artificial Neural Networks in Clinical Medicine
inferred← citeshttps://doi.org/10.22201/fesi.20071523e.2024.2.808openalex-snapshot
Análisis de somnolencia y hábitos de salud en pacientes apnea del sueño mediante Red Neuronal Artificial
inferred← citeshttps://doi.org/10.3390/healthcare13020181openalex-snapshot
Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review
inferred← citeshttps://doi.org/10.3414/me16-01-0084openalex-snapshot
Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomno graphy Resources?
inferred← citeshttps://doi.org/10.35376/10324/39242openalex-snapshot
Evaluación de la oximetría nocturna portátil como método simplificado de ayuda al diagnóstico del síndrome de apnea-hipopnea del sueño en pacientes con enfermedad pulmonar obstructiva crónica
inferred← citeshttps://doi.org/10.5435/jaaos-22-11-683openalex-snapshot
Obesity, Orthopaedics, and Outcomes
inferred← citeshttps://doi.org/10.5435/jaaos-d-15-00098openalex-snapshot
Obesity, Orthopaedics, and Outcomes
inferred← citeshttps://doi.org/10.5664/jcsm.10532openalex-snapshot
Application of artificial intelligence in the diagnosis of sleep apnea
inferred← citeshttps://doi.org/10.5664/jcsm.5596openalex-snapshot
Decision Modeling in Sleep Apnea: The Critical Roles of Pretest Probability, Cost of Untreated Obstructive Sleep Apnea, and Time Horizon
inferred← citeshttps://doi.org/10.5664/jcsm.7182openalex-snapshot
A Novel Artificial Neural Network Based Sleep-Disordered Breathing Screening Tool
inferred← citeshttps://doi.org/10.5664/jcsm.7804openalex-snapshot
Tracheal Sound Analysis Using a Deep Neural Network to Detect Sleep Apnea
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