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Zhang Y, Shi Y, Su Y, Cao Z, Li C, Xie Y, Niu X, Yuan Y, Ma L, Zhu S, Zhou Y, Wang Z, Hei X, Shi Z, Ren X, Liu H. Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals. J Sleep Res 2024:e14285. [PMID: 39021352 DOI: 10.1111/jsr.14285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 05/12/2024] [Accepted: 07/02/2024] [Indexed: 07/20/2024]
Abstract
Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep-learning model that uses single-lead electrocardiogram signals for obstructive sleep apnea detection and severity assessment. The detection model consisted of signal preprocessing, feature extraction, time-frequency domain information fusion, and classification segments. A total of 375 patients who underwent polysomnography were included. The single-lead electrocardiogram signals obtained by polysomnography were used to train, validate and test the model. Moreover, the proposed model performance on a public dataset was compared with the findings of previous studies. In the test set, the accuracy of per-segment and per-recording detection were 82.55% and 85.33%, respectively. The accuracy values for mild, moderate and severe obstructive sleep apnea were 69.33%, 74.67% and 85.33%, respectively. In the public dataset, the accuracy of per-segment detection was 91.66%. A Bland-Altman plot revealed the consistency of true apnea-hypopnea index and predicted apnea-hypopnea index. We confirmed the feasibility of single-lead electrocardiogram signals and deep-learning model for obstructive sleep apnea detection and severity evaluation in both hospital and public datasets. The detection performance is high for patients with obstructive sleep apnea, especially those with severe obstructive sleep apnea.
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Affiliation(s)
- Yitong Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yewen Shi
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yonglong Su
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Zine Cao
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Chengjian Li
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Yushan Xie
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiaoxin Niu
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yuqi Yuan
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Lina Ma
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Simin Zhu
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yanuo Zhou
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Zitong Wang
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - XinHong Hei
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Zhenghao Shi
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Xiaoyong Ren
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Haiqin Liu
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Zhang Y, Kim M, Prerau M, Mobley D, Rueschman M, Sparks K, Tully M, Purcell S, Redline S. The National Sleep Research Resource: making data findable, accessible, interoperable, reusable and promoting sleep science. Sleep 2024; 47:zsae088. [PMID: 38688470 PMCID: PMC11236948 DOI: 10.1093/sleep/zsae088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/15/2024] [Indexed: 05/02/2024] Open
Abstract
This paper presents a comprehensive overview of the National Sleep Research Resource (NSRR), a National Heart Lung and Blood Institute-supported repository developed to share data from clinical studies focused on the evaluation of sleep disorders. The NSRR addresses challenges presented by the heterogeneity of sleep-related data, leveraging innovative strategies to optimize the quality and accessibility of available datasets. It provides authorized users with secure centralized access to a large quantity of sleep-related data including polysomnography, actigraphy, demographics, patient-reported outcomes, and other data. In developing the NSRR, we have implemented data processing protocols that ensure de-identification and compliance with FAIR (Findable, Accessible, Interoperable, Reusable) principles. Heterogeneity stemming from intrinsic variation in the collection, annotation, definition, and interpretation of data has proven to be one of the primary obstacles to efficient sharing of datasets. Approaches employed by the NSRR to address this heterogeneity include (1) development of standardized sleep terminologies utilizing a compositional coding scheme, (2) specification of comprehensive metadata, (3) harmonization of commonly used variables, and (3) computational tools developed to standardize signal processing. We have also leveraged external resources to engineer a domain-specific approach to data harmonization. We describe the scope of data within the NSRR, its role in promoting sleep and circadian research through data sharing, and harmonization of large datasets and analytical tools. Finally, we identify opportunities for approaches for the field of sleep medicine to further support data standardization and sharing.
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Affiliation(s)
- Ying Zhang
- Division of Sleep Medicine and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Matthew Kim
- Division of Endocrinology, Diabetes and Hypertension, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael Prerau
- Division of Sleep Medicine and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Daniel Mobley
- Division of Sleep Medicine and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael Rueschman
- Division of Sleep Medicine and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Kathryn Sparks
- Division of Sleep Medicine and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Meg Tully
- Division of Sleep Medicine and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Shaun Purcell
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Susan Redline
- Division of Sleep Medicine and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
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Bandyopadhyay A, Oks M, Sun H, Prasad B, Rusk S, Jefferson F, Malkani RG, Haghayegh S, Sachdeva R, Hwang D, Agustsson J, Mignot E, Summers M, Fabbri D, Deak M, Anastasi M, Sampson A, Van Hout S, Seixas A. Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary. J Clin Sleep Med 2024; 20:1183-1191. [PMID: 38533757 PMCID: PMC11217619 DOI: 10.5664/jcsm.11132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 03/20/2024] [Indexed: 03/28/2024]
Abstract
Over the past few years, artificial intelligence (AI) has emerged as a powerful tool used to efficiently automate several tasks across multiple domains. Sleep medicine is perfectly positioned to leverage this tool due to the wealth of physiological signals obtained through sleep studies or sleep tracking devices and abundance of accessible clinical data through electronic medical records. However, caution must be applied when utilizing AI, due to intrinsic challenges associated with novel technology. The Artificial Intelligence in Sleep Medicine Committee of the American Academy of Sleep Medicine reviews advancements in AI within the sleep medicine field. In this article, the Artificial Intelligence in Sleep Medicine committee members provide a commentary on the scope of AI technology in sleep medicine. The commentary identifies 3 pivotal areas in sleep medicine that can benefit from AI technologies: clinical care, lifestyle management, and population health management. This article provides a detailed analysis of the strengths, weaknesses, opportunities, and threats associated with using AI-enabled technologies in each pivotal area. Finally, the article broadly reviews barriers and challenges associated with using AI-enabled technologies and offers possible solutions. CITATION Bandyopadhyay A, Oks M, Sun H, et al. Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary. J Clin Sleep Med. 2024;20(7):1183-1191.
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Affiliation(s)
- Anuja Bandyopadhyay
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Margarita Oks
- Department of Medicine, Northwell Health System, New York, New York
| | - Haoqi Sun
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Bharati Prasad
- Department of Medicine, University of Illinois, Chicago, Illinois
| | - Sam Rusk
- EnsoData Research, EnsoData, Madison, Wisconsin
| | - Felicia Jefferson
- Department of Biochemistry and Molecular Biology, University of Nevada, Reno, Nevada
| | - Roneil Gopal Malkani
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Neurology Service, Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois
| | - Shahab Haghayegh
- Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Ramesh Sachdeva
- Children’s Hospital of Michigan and Central Michigan University College of Medicine, Detroit, Michigan
| | - Dennis Hwang
- Kaiser Permanente Southern California, Los Angeles, California
| | | | - Emmanuel Mignot
- Stanford University, School of Medicine, Stanford, California
| | - Michael Summers
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Nebraska Medical Center, Omaha, Nebraska
| | | | | | | | | | | | - Azizi Seixas
- Department of Informatics and Health Data Science, University of Miami Miller School of Medicine, Miami, Florida
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Gaeta AM, Quijada-López M, Barbé F, Vaca R, Pujol M, Minguez O, Sánchez-de-la-Torre M, Muñoz-Barrutia A, Piñol-Ripoll G. Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach. Front Aging Neurosci 2024; 16:1369545. [PMID: 38988328 PMCID: PMC11233742 DOI: 10.3389/fnagi.2024.1369545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 06/04/2024] [Indexed: 07/12/2024] Open
Abstract
Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers. Methods Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers. Results On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aβ42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aβ42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors. Conclusions Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions.
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Affiliation(s)
- Anna Michela Gaeta
- Servicio de Neumología, Hospital Universitario Severo Ochoa, Leganés, Spain
| | - María Quijada-López
- Departamento de Bioingeniería, Universidad Carlos III de Madrid, Leganés, Spain
| | - Ferran Barbé
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, Institut de Recerca Biomedica de Lleida (IRBLleida), Lleida, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain
| | - Rafaela Vaca
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, Institut de Recerca Biomedica de Lleida (IRBLleida), Lleida, Spain
| | - Montse Pujol
- Unitat Trastorns Cognitius, Clinical Neuroscience Research, Institut de Recerca Biomedica de Lleida (IRBLleida), Hospital Universitari Santa Maria, Lleida, Spain
| | - Olga Minguez
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, Institut de Recerca Biomedica de Lleida (IRBLleida), Lleida, Spain
| | - Manuel Sánchez-de-la-Torre
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain
- Group of Precision Medicine in Chronic Diseases, Hospital Nacional de Parapléjicos, IDISCAM, Department of Nursing, Physiotherapy and Occupational Therapy, Faculty of Physiotherapy and Nursing, University of Castilla-La Mancha, Toledo, Spain
| | - Arrate Muñoz-Barrutia
- Departamento de Bioingeniería, Universidad Carlos III de Madrid, Leganés, Spain
- Departamento de Bioingegneria, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Gerard Piñol-Ripoll
- Unitat Trastorns Cognitius, Clinical Neuroscience Research, Institut de Recerca Biomedica de Lleida (IRBLleida), Hospital Universitari Santa Maria, Lleida, Spain
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BaHammam AS. Artificial Intelligence in Sleep Medicine: The Dawn of a New Era. Nat Sci Sleep 2024; 16:445-450. [PMID: 38711863 PMCID: PMC11070441 DOI: 10.2147/nss.s474510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 04/25/2024] [Indexed: 05/08/2024] Open
Affiliation(s)
- Ahmed Salem BaHammam
- Department of Medicine, University Sleep Disorders Center and Pulmonary Service, King Saud University, Riyadh, Saudi Arabia
- King Saud University Medical City, Riyadh, Saudi Arabia
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Vennard H, Buchan E, Davies P, Gibson N, Lowe D, Langley R. Paediatric sleep diagnostics in the 21st century: the era of "sleep-omics"? Eur Respir Rev 2024; 33:240041. [PMID: 38925792 PMCID: PMC11216690 DOI: 10.1183/16000617.0041-2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/16/2024] [Indexed: 06/28/2024] Open
Abstract
Paediatric sleep diagnostics is performed using complex multichannel tests in specialised centres, limiting access and availability and resulting in delayed diagnosis and management. Such investigations are often challenging due to patient size (prematurity), tolerability, and compliance with "gold standard" equipment. Children with sensory/behavioural issues, at increased risk of sleep disordered breathing (SDB), often find standard diagnostic equipment difficult.SDB can have implications for a child both in terms of physical health and neurocognitive development. Potential sequelae of untreated SDB includes failure to thrive, cardiopulmonary disease, impaired learning and behavioural issues. Prompt and accurate diagnosis of SDB is important to facilitate early intervention and improve outcomes.The current gold-standard diagnostic test for SDB is polysomnography (PSG), which is expensive, requiring the interpretation of a highly specialised physiologist. PSG is not feasible in low-income countries or outwith specialist sleep centres. During the coronavirus disease 2019 pandemic, efforts were made to improve remote monitoring and diagnostics in paediatric sleep medicine, resulting in a paradigm shift in SDB technology with a focus on automated diagnosis harnessing artificial intelligence (AI). AI enables interrogation of large datasets, setting the scene for an era of "sleep-omics", characterising the endotypic and phenotypic bedrock of SDB by drawing on genetic, lifestyle and demographic information. The National Institute for Health and Care Excellence recently announced a programme for the development of automated home-testing devices for SDB. Scorer-independent scalable diagnostic approaches for paediatric SDB have potential to improve diagnostic accuracy, accessibility and patient tolerability; reduce health inequalities; and yield downstream economic and environmental benefits.
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Affiliation(s)
- Hannah Vennard
- College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
| | - Elise Buchan
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
| | - Philip Davies
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
| | - Neil Gibson
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
| | - David Lowe
- College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Ross Langley
- College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
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Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering (Basel) 2024; 11:337. [PMID: 38671759 PMCID: PMC11047988 DOI: 10.3390/bioengineering11040337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.
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Affiliation(s)
| | - Mohamad Forouzanfar
- Département de Génie des Systèmes, École de Technologie Supérieure (ÉTS), Université du Québec, Montréal, QC H3C 1K3, Canada
- Centre de Recherche de L’institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC H3W 1W5, Canada
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8
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Le TQ, Huynh P, Tomaselli L. Navigating the night: evaluating and accessing wearable sleep trackers for clinical use. Sleep 2024; 47:zsad319. [PMID: 38097380 PMCID: PMC10925943 DOI: 10.1093/sleep/zsad319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024] Open
Affiliation(s)
- Trung Q Le
- Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL, USA
- Department of Medical Engineering, University of South Florida, Tampa, FL, USA
- James A. Haley Veterans’ Hospital, Tampa VA Healthcare System, Tampa, FL, USA and
| | - Phat Huynh
- Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL, USA
| | - Lennon Tomaselli
- Department of Health Sciences, University of South Florida, Tampa, FL, USA
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Alattar M, Govind A, Mainali S. Artificial Intelligence Models for the Automation of Standard Diagnostics in Sleep Medicine-A Systematic Review. Bioengineering (Basel) 2024; 11:206. [PMID: 38534480 DOI: 10.3390/bioengineering11030206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/31/2024] [Accepted: 02/09/2024] [Indexed: 03/28/2024] Open
Abstract
Sleep disorders, prevalent in the general population, present significant health challenges. The current diagnostic approach, based on a manual analysis of overnight polysomnograms (PSGs), is costly and time-consuming. Artificial intelligence has emerged as a promising tool in this context, offering a more accessible and personalized approach to diagnosis, particularly beneficial for under-served populations. This is a systematic review of AI-based models for sleep disorder diagnostics that were trained, validated, and tested on diverse clinical datasets. An extensive search of PubMed and IEEE databases yielded 2114 articles, but only 18 met our stringent selection criteria, underscoring the scarcity of thoroughly validated AI models in sleep medicine. The findings emphasize the necessity of a rigorous validation of AI models on multimodal clinical data, a step crucial for their integration into clinical practice. This would be in line with the American Academy of Sleep Medicine's support of AI research.
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Affiliation(s)
- Maha Alattar
- Division of Adult Neurology, Sleep Medicine, Vascular Neurology, Department of Neurology, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Alok Govind
- Department of Neurology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India
| | - Shraddha Mainali
- Division of Vascular Neurology and Neurocritical Care, Department of Neurology, Virginia Commonwealth University, Richmond, VA 23284, USA
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Verma RK, Dhillon G, Grewal H, Prasad V, Munjal RS, Sharma P, Buddhavarapu V, Devadoss R, Kashyap R, Surani S. Artificial intelligence in sleep medicine: Present and future. World J Clin Cases 2023; 11:8106-8110. [PMID: 38130791 PMCID: PMC10731177 DOI: 10.12998/wjcc.v11.i34.8106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/03/2023] [Accepted: 11/24/2023] [Indexed: 12/06/2023] Open
Abstract
Artificial intelligence (AI) has impacted many areas of healthcare. AI in healthcare uses machine learning, deep learning, and natural language processing to analyze copious amounts of healthcare data and yield valuable outcomes. In the sleep medicine field, a large amount of physiological data is gathered compared to other branches of medicine. This field is primed for innovations with the help of AI. A good quality of sleep is crucial for optimal health. About one billion people are estimated to have obstructive sleep apnea worldwide, but it is difficult to diagnose and treat all the people with limited resources. Sleep apnea is one of the major contributors to poor health. Most of the sleep apnea patients remain undiagnosed. Those diagnosed with sleep apnea have difficulty getting it optimally treated due to several factors, and AI can help in this situation. AI can also help in the diagnosis and management of other sleep disorders such as insomnia, hypersomnia, parasomnia, narcolepsy, shift work sleep disorders, periodic leg movement disorders, etc. In this manuscript, we aim to address three critical issues about the use of AI in sleep medicine: (1) How can AI help in diagnosing and treating sleep disorders? (2) How can AI fill the gap in the care of sleep disorders? and (3) What are the ethical and legal considerations of using AI in sleep medicine?
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Affiliation(s)
- Ram Kishun Verma
- Department of Sleep Medicine, Parkview Health System, Fort Wayne, IN 46845, United States
| | - Gagandeep Dhillon
- Department of Medicine, UM Baltimore Washington Medical Center, Glen Burnie, MD 21061, United States
| | - Harpreet Grewal
- Department of Radiology, Ascension Sacred Heart Hospital, Pensacola, FL 32504, United States
| | - Vinita Prasad
- Department of Psychiatry, Parkview Health System, Fort Wayne, IN 46845, United States
| | - Ripudaman Singh Munjal
- Department of Medicine, Kaiser Permanente Medical Center, Modesto, CA 95356, United States
| | - Pranjal Sharma
- Department of Medicine, Banner Health, Phoenix, AZ 85006, United States
| | - Venkata Buddhavarapu
- Department of Medicine, Norteast Ohio Medical University, Rootstown, OH 44272, United States
| | - Ramprakash Devadoss
- Department of Cardiology, Carle Methodist Medical Center, Peroria, IL 61637, United States
| | - Rahul Kashyap
- Department of Research, Wellspan Health, York, PA 17403, United States
| | - Salim Surani
- Department of Medicine & Pharmacology, Texas A&M University, College Station, TX 77843, United States
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11
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Liao X, Yao C, Zhang J, Liu LZ. Recent advancement in integrating artificial intelligence and information technology with real-world data for clinical decision-making in China: A scoping review. J Evid Based Med 2023; 16:534-546. [PMID: 37772921 DOI: 10.1111/jebm.12549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 08/31/2023] [Indexed: 09/30/2023]
Abstract
OBJECTIVE Striking innovations and advancements have been achieved with the use of artificial intelligence and healthcare information technology being integrated into clinical real-world data. The current scoping review aimed to provide an overview of the current status of artificial intelligence-/information technology-based clinical decision support tools in China. METHODS PubMed/MEDLINE, Embase, China National Knowledge Internet, and Wanfang data were searched for both English and Chinese literature. The gray literature search was conducted for commercially available tools. Original studies that focused on clinical decision support tools driven by artificial intelligence or information technology in China and were published between 2010 and February 2022 were included. Information extracted from each article was further synthesized by themes based on three types of clinical decision-making. RESULTS A total of 37 peer-reviewed publications and 13 commercially available tools were included in the final analysis. Among them, 32.0% were developed for disease diagnosis, 54.0% for risk prediction and classification, and 14.0% for disease management. Chronic diseases were the most popular therapeutic areas of exploration, with particular emphasis on cardiovascular and cerebrovascular diseases. Single-center electronic medical records were the mainstream data sources leveraged to inform clinical decision-making, with internal validation being predominately used for model evaluation. CONCLUSIONS To effectively promote the extensive use of real-world data and drive a paradigm shift in clinical decision-making in China, multidisciplinary collaboration of key stakeholders is urgently needed.
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Affiliation(s)
- Xiwen Liao
- Peking University Clinical Research Institute, Peking University First Hospital, Beijing, China
| | - Chen Yao
- Peking University Clinical Research Institute, Peking University First Hospital, Beijing, China
- Hainan Institute of Real World Data, Qionghai, Hainan, China
| | - Jun Zhang
- Center for Observational and Real-world Evidence (CORE), MSD R&D (China) Co., Ltd., Beijing, China
| | - Larry Z Liu
- Center for Observational and Real-world Evidence (CORE), Merck & Co Inc, Rahway, Rahway, New Jersey, USA
- Department of Population Health Sciences, Weill Cornell Medical College, New York City, New York, USA
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12
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Shajari S, Kuruvinashetti K, Komeili A, Sundararaj U. The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:9498. [PMID: 38067871 PMCID: PMC10708748 DOI: 10.3390/s23239498] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 11/20/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023]
Abstract
Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.
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Affiliation(s)
- Shaghayegh Shajari
- Center for Applied Polymers and Nanotechnology (CAPNA), Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB T2N1 N4, Canada;
- Center for Bio-Integrated Electronics (CBIE), Querrey Simpson Institute for Bioelectronics (QSIB), Northwestern University, Evanston, IL 60208, USA
| | - Kirankumar Kuruvinashetti
- Intelligent Human and Animal Assistive Devices, Department of Biomedical Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.K.); (A.K.)
- Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Amin Komeili
- Intelligent Human and Animal Assistive Devices, Department of Biomedical Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.K.); (A.K.)
- Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Uttandaraman Sundararaj
- Center for Applied Polymers and Nanotechnology (CAPNA), Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB T2N1 N4, Canada;
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13
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Smith CM, Vendrame M. Perspective: A resident's role in promoting safe machine-learning tools in sleep medicine. J Clin Sleep Med 2023; 19:1985-1987. [PMID: 37477148 PMCID: PMC10620660 DOI: 10.5664/jcsm.10724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/02/2023] [Accepted: 07/05/2023] [Indexed: 07/22/2023]
Abstract
Residents and fellows can play a helpful role in promoting safe and effective machine-learning tools in sleep medicine. Here we highlight the importance of establishing ground truths, considering key variables, and prioritizing transparency and accountability in the development of machine-learning tools within the field of artificial intelligence. Through understanding, communication, and collaboration, in-training physicians have a meaningful opportunity to help progress the field toward safe machine-learning tools in sleep medicine. CITATION Smith CM, Vendrame M. Perspective: a resident's role in promoting safe machine-learning tools in sleep medicine. J Clin Sleep Med. 2023;19(11):1985-1987.
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Affiliation(s)
- Colin M. Smith
- Lehigh Valley Fleming Neuroscience Institute, Lehigh Valley Health Network, Allentown, Pennsylvania
| | - Martina Vendrame
- Lehigh Valley Fleming Neuroscience Institute, Lehigh Valley Health Network, Allentown, Pennsylvania
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14
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Shi Y, Zhang Y, Cao Z, Ma L, Yuan Y, Niu X, Su Y, Xie Y, Chen X, Xing L, Hei X, Liu H, Wu S, Li W, Ren X. Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults. BMC Med Inform Decis Mak 2023; 23:230. [PMID: 37858225 PMCID: PMC10585776 DOI: 10.1186/s12911-023-02331-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 10/10/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND Obstructive sleep apnea (OSA) is a globally prevalent disease with a complex diagnostic method. Severe OSA is associated with multi-system dysfunction. We aimed to develop an interpretable machine learning (ML) model for predicting the risk of severe OSA and analyzing the risk factors based on clinical characteristics and questionnaires. METHODS This was a retrospective study comprising 1656 subjects who presented and underwent polysomnography (PSG) between 2018 and 2021. A total of 23 variables were included, and after univariate analysis, 15 variables were selected for further preprocessing. Six types of classification models were used to evaluate the ability to predict severe OSA, namely logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and multilayer perceptron (MLP). All models used the area under the receiver operating characteristic curve (AUC) was calculated as the performance metric. We also drew SHapley Additive exPlanations (SHAP) plots to interpret predictive results and to analyze the relative importance of risk factors. An online calculator was developed to estimate the risk of severe OSA in individuals. RESULTS Among the enrolled subjects, 61.47% (1018/1656) were diagnosed with severe OSA. Multivariate LR analysis showed that 10 of 23 variables were independent risk factors for severe OSA. The GBM model showed the best performance (AUC = 0.857, accuracy = 0.766, sensitivity = 0.798, specificity = 0.734). An online calculator was developed to estimate the risk of severe OSA based on the GBM model. Finally, waist circumference, neck circumference, the Epworth Sleepiness Scale, age, and the Berlin questionnaire were revealed by the SHAP plot as the top five critical variables contributing to the diagnosis of severe OSA. Additionally, two typical cases were analyzed to interpret the contribution of each variable to the outcome prediction in a single patient. CONCLUSIONS We established six risk prediction models for severe OSA using ML algorithms. Among them, the GBM model performed best. The model facilitates individualized assessment and further clinical strategies for patients with suspected severe OSA. This will help to identify patients with severe OSA as early as possible and ensure their timely treatment. TRIAL REGISTRATION Retrospectively registered.
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Affiliation(s)
- Yewen Shi
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Yitong Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Zine Cao
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Lina Ma
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Yuqi Yuan
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Xiaoxin Niu
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Yonglong Su
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Yushan Xie
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Xi Chen
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Liang Xing
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Xinhong Hei
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaan'xi Province, China
| | - Haiqin Liu
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Shinan Wu
- School of Medicine, Eye Institute of Xiamen University, Xiamen University, Xiamen, Fujian Province, China.
| | - Wenle Li
- Molecular Imaging and Translational Medicine Research Center, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, Fujian Province, China.
| | - Xiaoyong Ren
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China.
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15
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Araujo M, Mehra R. The Influence of Physiologic Burdens Related to Obstructive Sleep Apnea on Cardiovascular Outcomes. Am J Respir Crit Care Med 2023; 208:752-754. [PMID: 37610456 PMCID: PMC10563193 DOI: 10.1164/rccm.202307-1243ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 08/23/2023] [Indexed: 08/24/2023] Open
Affiliation(s)
- Matheus Araujo
- Neurological Institute Cleveland Clinic Foundation Cleveland, Ohio
- Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland, Ohio
| | - Reena Mehra
- Neurological Institute Respiratory Institute
- Heart and Vascular Institute Cleveland Clinic Foundation Cleveland, Ohio
- Department of Cardiovascular and Metabolic Sciences Cleveland Clinic Lerner Research Institute Cleveland, Ohio
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16
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Bandyopadhyay A, Bae C, Cheng H, Chiang A, Deak M, Seixas A, Singh J. Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine. J Clin Sleep Med 2023; 19:1823-1833. [PMID: 37394867 PMCID: PMC10545999 DOI: 10.5664/jcsm.10702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/19/2023] [Accepted: 06/22/2023] [Indexed: 07/04/2023]
Abstract
Since the publication of its 2020 position statement on artificial intelligence (AI) in sleep medicine by the American Academy of Sleep Medicine, there has been a tremendous expansion of AI-related software and hardware options for sleep clinicians. To help clinicians understand the current state of AI and sleep medicine, and to further enable these solutions to be adopted into clinical practice, a discussion panel was conducted on June 7, 2022, at the Associated Professional Sleep Societies Sleep Conference in Charlotte, North Carolina. The article is a summary of key discussion points from this session, including aspects of considerations for the clinician in evaluating AI-enabled solutions including but not limited to what steps might be taken both by the Food and Drug Administration and clinicians to protect patients, logistical issues, technical challenges, billing and compliance considerations, education and training considerations, and other unique challenges specific to AI-enabled solutions. Our summary of this session is meant to support clinicians in efforts to assist in the clinical care of patients with sleep disorders utilizing AI-enabled solutions. CITATION Bandyopadhyay A, Bae C, Cheng H, et al. Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine. J Clin Sleep Med. 2023;19(10):1823-1833.
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Affiliation(s)
- Anuja Bandyopadhyay
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Charles Bae
- Division of Sleep Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hao Cheng
- Department of Pulmonary and Sleep Medicine, Miami VA Healthcare System, Miami, Florida
| | - Ambrose Chiang
- Louis Stokes Cleveland VA Medical Center, Case Western Reserve University, Cleveland, Ohio
| | | | - Azizi Seixas
- Department of Informatics and Health Data Science, University of Miami Miller School of Medicine, Coral Gables, Florida
| | - Jaspal Singh
- Atrium Health Department of Medicine, Wake Forest School of Medicine, Charlotte, North Carolina
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17
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Kryger MH. Artificial intelligence in sleep and art. Sleep Health 2023; 9:387-388. [PMID: 37460378 DOI: 10.1016/j.sleh.2023.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
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18
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Black JE, Kueper JK, Williamson TS. An introduction to machine learning for classification and prediction. Fam Pract 2023; 40:200-204. [PMID: 36181463 DOI: 10.1093/fampra/cmac104] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Classification and prediction tasks are common in health research. With the increasing availability of vast health data repositories (e.g. electronic medical record databases) and advances in computing power, traditional statistical approaches are being augmented or replaced with machine learning (ML) approaches to classify and predict health outcomes. ML describes the automated process of identifying ("learning") patterns in data to perform tasks. Developing an ML model includes selecting between many ML models (e.g. decision trees, support vector machines, neural networks); model specifications such as hyperparameter tuning; and evaluation of model performance. This process is conducted repeatedly to find the model and corresponding specifications that optimize some measure of model performance. ML models can make more accurate classifications and predictions than their statistical counterparts and confer greater flexibility when modelling unstructured data or interactions between covariates; however, many ML models require larger sample sizes to achieve good classification or predictive performance and have been criticized as "black box" for their poor transparency and interpretability. ML holds potential in family medicine for risk profiling of patients' disease risk and clinical decision support to present additional information at times of uncertainty or high demand. In the future, ML approaches are positioned to become commonplace in family medicine. As such, it is important to understand the objectives that can be addressed using ML approaches and the associated techniques and limitations. This article provides a brief introduction into the use of ML approaches for classification and prediction tasks in family medicine.
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Affiliation(s)
- Jason E Black
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Jacqueline K Kueper
- Department of Epidemiology and Biostatistics, Western University Schulich School of Medicine & Dentistry, London, ON, Canada.,Department of Computer Science, Western University Faculty of Science, London, ON, Canada
| | - Tyler S Williamson
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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19
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Zhuang L, Dai M, Zhou Y, Sun L. Intelligent automatic sleep staging model based on CNN and LSTM. Front Public Health 2022; 10:946833. [PMID: 35968483 PMCID: PMC9364961 DOI: 10.3389/fpubh.2022.946833] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 06/22/2022] [Indexed: 01/10/2023] Open
Abstract
Since electroencephalogram (EEG) is a significant basis to treat and diagnose somnipathy, sleep electroencephalogram automatic staging methods play important role in the treatment and diagnosis of sleep disorders. Due to the characteristics of weak signals, EEG needs accurate and efficient algorithms to extract feature information before applying it in the sleep stages. Conventional feature extraction methods have low efficiency and are difficult to meet the time validity of fast staging. In addition, it can easily lead to the omission of key features owing to insufficient a priori knowledge. Deep learning networks, such as convolutional neural networks (CNNs), have powerful processing capabilities in data analysis and data mining. In this study, a deep learning network is introduced into the study of the sleep stage. In this study, the feature fusion method is presented, and long-term and short-term memory (LSTM) is selected as the classification network to improve the accuracy of sleep stage recognition. First, based on EEG and deep learning network, an automatic sleep phase method based on a multi-channel EGG is proposed. Second, CNN-LSTM is used to monitor EEG and EOG samples during sleep. In addition, without any signal preprocessing or feature extraction, data expansion (DA) can be realized for unbalanced data, and special data and non-general data can be deleted. Finally, the MIT-BIH dataset is used to train and evaluate the proposed model. The experimental results show that the EEG-based sleep phase method proposed in this paper provides an effective method for the diagnosis and treatment of sleep disorders, and hence has a practical application value.
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Affiliation(s)
- Lan Zhuang
- Staff Hospital, Central South University, Changsha, China
| | - Minhui Dai
- Teaching and Research Section of Clinical Nursing, Xiangya Hospital of Central South University, Changsha, China
- Department of Ophthalmology, Xiangya Hospital, Central South University, Changsha, China
| | - Yi Zhou
- Department of Ophthalmology, Xiangya Hospital, Central South University, Changsha, China
| | - Lingyu Sun
- Teaching and Research Section of Clinical Nursing, Xiangya Hospital of Central South University, Changsha, China
- Department of Ophthalmology, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Lingyu Sun
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