[1] BESEDOVSKY L, LANGE T, HAACK M. The sleep-immune crosstalk in health and disease[J]. Physiol Rev, 2019, 99(3): 1325-1380.
[2] 赵李璞. 回顾中国睡眠医学发展史探索睡眠障碍的机体损害——访北京大学人民医院韩芳教授[J]. 中华医学信息导报, 2021, 36(8): 17.
[3] WICKWIRE E M, SHAYA F T, SCHARF S M. Health economics of insomnia treatments: the return on investment for a good night's sleep[J]. Sleep Med Rev, 2016, 30:72-82.
[4] 黄鑫,李苏宁, 尹军祥, 等. 我国睡眠障碍防控研究现状及建议[J]. 四川大学学报(医学版), 2023, 54(2): 226-230.
[5] REDLINE S, AZARBARZIN A, PEKER Y. Obstructive sleep apnoea heterogeneity and cardiovascular disease[J]. Nat Rev Cardiol, 2023, 20(8): 560-573.
[6] PEARSON O, UGLIK-MARUCHA N, MISKOWIAK K W, et al. The relationship between sleep disturbance and cognitive impairment in mood disorders: a systematic review[J]. J Affect Disord, 2023, 327:207-216.
[7] LOH H W, OOI C P, DHOK S G, et al. Automated detection of cyclic alternating pattern and classification of sleep stages using deep neural network[J]. Applied Intelligence, 2021, 52(3): 2903-2917.
[8] HAUG C J, DRAZEN J M. Artificial intelligence and machine learning in clinical medicine, 2023[J].N Engl J Med, 2023, 388(13): 1201-1208.
[9] TING D S W, PENG L, VARADARAJAN A V, et al. Deep learning in ophthalmology: the technical and clinical considerations[J]. Prog Retin Eye Res, 2019, 72:100759.
[10] YU K H, BEAM A L, KOHANE I S. Artificial intelligence in healthcare[J]. Nat Biomed Eng, 2018, 2(10): 719-731.
[11] JIANG F, JIANG Y, ZHI H, et al. Artificial intelligence in healthcare: past, present and future[J]. Stroke Vasc Neurol, 2017, 2(4): 230-243.
[12] BI W L, HOSNY A, SCHABATH M B, et al. Artificial intelligence in cancer imaging: clinical challenges and applications [J]. CA Cancer J Clin, 2019, 69(2): 127-157.
[13] MANNINI A, TROJANIELLO D, CEREATTI A, et al. A machine learning framework for gait classification using inertial sensors: application to elderly, post-stroke and Huntington's disease patients[J]. Sensors:Basel, 2016, 16(1): 134.
[14] TING D S W, PASQUALE L R, PENG L, et al. Artificial intelligence and deep learning in ophthalmology[J]. Br J Ophthalmol, 2019, 103(2): 167-175.
[15] CHRISTOPOULOU F, TRAN T T, SAHU S K, et al. Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods[J]. J Am Med Inform Assoc, 2020, 27(1): 39-46.
[16] NETTLETON D, MUÑIZ J. Processing and representation of meta-data for sleep apnea diagnosis with an artificial intelligence approach[J]. Int J Med Inform, 2001, 63(1/2): 77-89.
[17] SUN L M, CHIU H W, CHUANG C Y, et al. A prediction model based on an artificial intelligence system for moderate to severe obstructive sleep apnea[J]. Sleep Breath, 2011, 15(3): 317-323.
[18] KEENAN B T, KIRCHNER H L, VEATCH O J, et al. Multisite validation of a simple electronic health record algorithm for identifying diagnosed obstructive sleep apnea[J]. J Clin Sleep Med, 2020, 16(2): 175-183.
[19] MULLINS A E, KIM J W, WONG K K H, et al. Sleep EEG microstructure is associated with neurobehavioural impairment after extended wakefulness in obstructive sleep apnea[J]. Sleep Breath, 2021, 25(1): 347-354.
[20] URTNASAN E, PARK J U, JOO E Y, et al. Automated detection of obstructive sleep apnea events from a single-lead electrocardiogram using a convolutional neural network[J]. J Med Syst, 2018, 42(6): 104.
[21] CHERVIN R D, SHELGIKAR A V, BURNS J W. Respiratory cycle-related EEG changes: response to CPAP[J]. Sleep, 2012, 35(2): 203-209.
[22] SUN H, PAIXAO L, OLIVA J T, et al. Brain age from the electroencephalogram of sleep[J]. Neurobiol Aging, 2019, 74:112-120.
[23] ZAFFARONI A, KENT B, O'HARE E, et al. Assessment of sleep-disordered breathing using a non-contact bio-motion sensor[J]. J Sleep Res, 2013, 22(2): 231-236.
[24] ALSHAER H, HUMMEL R, MENDELSON M, et al. Objective relationship between sleep apnea and frequency of snoring assessed by machine learning[J]. J Clin Sleep Med, 2019, 15(3): 463-470.
[25] KANG S, KIM D K, LEE Y, et al. Non-contact diagnosis of obstructive sleep apnea using impulse-radio ultra-wideband radar[J]. Sci Rep, 2020, 10(1): 5261.
[26] GAO W D, XU Y B, LI S S, et al. Obstructive sleep apnea syndrome detection based on ballistocardiogram via machine learning approach[J]. Math Biosci Eng, 2019, 16(5): 5672-5686.
[27] DUARTE M, PEREIRA-RODRIGUES P, FERREIRA-SANTOS D. The role of novel digital clinical tools in the screening or diagnosis of obstructive sleep apnea: Systematic Review[J]. J Med Internet Res, 2023, 25:e47735.
[28] ALLOCCA G, MA S, MARTELLI D, et al. Validation of ‘Somnivore', a machine learning algorithm for automated scoring and analysis of polysomnography data[J]. Front Neurosci, 2019, 13: 207.
[29] BISWAL S, SUN H, GOPARAJU B, et al. Expert-level sleep scoring with deep neural networks[J]. J Am Med Inform Assoc, 2018, 25(12): 1643-1650.
[30] YOUNES M, OSTROWSKI M, SOIFERMAN M, et al. Odds ratio product of sleep EEG as a continuous measure of sleep state[J]. Sleep, 2015, 38(4): 641-654.
[31] HANIF U, SCHNEIDER L D, TRAP L, et al. Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology[J]. Physiol Meas, 2019, 40(2): 025008.
[32] DOPAZO J. Genomics and transcriptomics in drug discovery[J]. Drug Discov Today, 2014, 19(2): 126-132.
[33] BAZOUKIS G, BOLLEPALLI S C, CHUNG C T, et al. Application of artificial intelligence in the diagnosis of sleep apnea[J]. J Clin Sleep Med, 2023, 19(7): 1337-1363.
[34] KRITTANAWONG C, JOHNSON K W, ROSENSON R S, et al. Deep learning for cardiovascular medicine: a practical primer[J]. Eur Heart J, 2019, 40(25): 2058-2073.
[35] BRIGANTI G, LE MOINE O. Artificial intelligence in medicine: today and tomorrow[J]. Front Med:Lausanne, 2020, 7:27.
[36] CAO C, LIU F, TAN H, et al. Deep learning and its applications in biomedicine[J]. Genomics Proteomics Bioinformatics, 2018, 16(1): 17-32. |
