Computer Science ›› 2023, Vol. 50 ›› Issue (4): 1-15.doi: 10.11896/jsjkx.220600166
• Database & Big Data & Data Science • Previous Articles Next Articles
XUE Fenghao1, JIANG Haibo2, TANG Dan1
CLC Number:
[1]RONG G,MENDEZ A,BOU ASSI E,et al.Artificial Intelligence in Healthcare:Review and Prediction Case Studies[J].Engineering,2020,6(3):291-301. [2]HALEEM A,JAVAID M,KHAN I H.Current status and applications of Artificial Intelligence(AI)in medical field:An overview[J].Current Medicine Research and Practice,2019,9(6):231-237. [3]RUMSFELD J S,JOYNT K E,MADDOX T M.Big data analy-tics to improve cardiovascular care:promise and challenges[J].Nature Reviews Cardiology,2016,13(6):350-359. [4]LEE C H,YOON H J.Medical big data:promise and challenges[J].Kidney Research and Clinical Practice,2017,36(1):3-11. [5]LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444. [6]ESTEVA A,ROBICQUET A,RAMSUNDAR B,et al.A guide to deep learning in healthcare[J].Nature Medicine,2019,25(1):24-29. [7]YU K H,BEAM A L,KOHANE I S.Artificial intelligence in healthcare[J].Nature Biomedical Engineering,2018,2(10):719-731. [8]LITJENS G,KOOI T,BEJNORDI B E,et al.A survey on deep learning in medical image analysis[J/OL].Medical Image Analysis,2017,42:60-88.https://www.sciencedirect.com/science/article/abs/pii/S1361841517301135. [9]RAVI MANNE S K,SNEHA K.Classification of Skin cancerusing deep learning,Convolutional Neural Networks - Opportunities and vulnerabilities- A systematic Review[J].International Journal for Modern Trends in Science and Technology,2020,6(11):101-108. [10]MCKINNEY S M,SIENIEK M,GODBOLE V,et al.International evaluation of an AI system for breast cancer screening[J].Nature,2020,577(7788):89-94. [11]ESTEVA A,KUPREL B,NOVOA R A,et al.Dermatologist-level classification of skin cancer with deep neural networks[J].Nature,2017,542(7639):115-118. [12]SOENKSEN L R,KASSIS T,CONOVER S T,et al.Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images[J/OL].Science Translational Medicine,2021,13(581):eabb3652.https://www.science.org/doi/abs/10.1126/scitranslmed.abb3652 [13]ARDILA D,KIRALY A P,BHARADWAJ S,et al.End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography[J].Nature Medicine,2019,25(6):954-961. [14]XIAO C,CHOI E,SUN J.Opportunities and challenges in developing deep learning models using electronic health records data:a systematic review[J].Journal of the American Medical Informatics Association,2018,25(10):1419-1428. [15]AYALA SOLARES J R,DILETTA RAIMONDI F E,ZHU Y,et al.Deep learning for electronic health records:A comparative review of multiple d-eep neural architectures[J].Journal of Biomedical Informatics,2020,101:103337. [16]WU M,LUO J.Wearable technology applications in health care:a literature review[J/OL].https://www.himss.org/res-ources/wearable-technology-applications-healthcare-literature-re-view. [17]RAMANUJAM E,PERUMAL T,PADMAVATHI S.Human Activity Recognition With Smartphone and Wearable Sensors Using Deep Learning Techniques:A Review[J].IEEE Sensors Journal,2021,21(12):13029-13040. [18]TIAN J,SMITH G,GUO H,et al.Modular machine learning for Alzheimer’s disease classification from retinal vasculature[J].Scientific Reports,2021,11(1):238. [19]CHEUNG C Y,XU D,CHENG C Y,et al.A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre[J].Nature Biomedical Engineering,2021,5(6):498-508. [20]POPLIN R,VARADARAJAN A V,BLUMER K,et al.Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning[J].Nature Biomedical Engineering,2018,2(3):158-164. [21]KOROT E,PONTIKOS N,LIU X,et al.Predicting sex fromretinal fundus photographs using automated deep learning[J].Scientific Reports,2021,11(1):1-8. [22]WANG S,SHI J,YE Z,et al.Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning[J/OL].European Respiratory Journal,2019,53(3):1800986. [23]CALLAWAY E.It will change everything:DeepMind’s AImakes gigantic leap in solving protein structures[J].Nature,2020,588(7837):203-204. [24]TOWNSHEND R J L,EISMANN S,WATKINS A M,et al.Geometric deep learning of RNA structure[J].Science,2021,373(6558):1047-1051. [25]QIN Y,HUTTLIN E L,WINSNES C F,et al.A multi-scale map of cell structure fusing protein images and interactions[J].Nature,2021, 600(7889):536-542. [26]MCCULLOCH W S,PITTS W.A logical calculus of the ideas immanent in nervous activity[J].The Bulletin of Mathematical Biophysics,1943,5(4):115-133. [27]ROSENBLATT F.Perceptron Simulation Experiments[J].Proceedings of the IRE,1960,48(3):301-309. [28]LEDLEY R S,LUSTED L B.Reasoning Foundations of Medical Diagnosis[J].Science,1959,130(3366):9-21. [29]RUMELHART D E,HINTON G E,WILLIAMS R J.Learning representations by back-propagating errors[J].Nature,1986,323(6088):533-536. [30]MINSKY M,PAPERT S A.Perceptrons:An Introduction toComputational Geometry[M].Massachussetts:The MIT Press,1969. [31]RUMELHART D E,HINTON G E,WILLIAMS R J.Learning representations by back-propagating errors[J].Nature,1986,323(6088):533-536. [32]SMOLENSKY P.Information processing in dynamical sys-tems:Foundations of harmony theory[R/OL].https://apps.dtic.mil/sti/citations/ADA620727. [33]QIAN N,SEJNOWSKI T J.Predicting the secondary structure of globular proteins using neural network models[J].Journal of Molecular Biology,1988,202(4):865-884. [34]FUKUSHIMA K,MIYAKE S.Neocognitron:A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition[C]//Competition and Cooperation in Neural Nets.1982:267-285. [35]LECUN Y,BOSER B,DENKER J S,et al.Backpropagation applied to handwritten zip code recognition[J].Neural Computation,1989,1(4):541-551. [36]LO S B,LOU S A,LIN J S,et al.Artificial convolution neural network techniques and applications for lung nodule detection[J].IEEE Transactions on Medical Imaging,1995,14(4):711-718. [37]SAHINER B,HEANG-PING C,PETRICK N,et al.Classification of mass and normal breast tissue:a convolution neural network classifier with spatial domain and texture images[J].IEEE Transactions on Medical Imaging,1996,15(5):598-610. [38]HOCHREITER S,SCHMIDHUBER J.Long Short-Term Me-mory[J].Neural Computation,1997,9(8):1735-1780. [39]SCHUSTER M,PALIWAL K K.Bidirectional recurrent neural networks[J].IEEE transactions on Signal Processing,1997,45(11):2673-2681. [40]LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324. [41]HINTON G E,SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507. [42]HINTON G E,OSINDERO S,TEH Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,18(7):1527-1554. [43]RUSSAKOVSKY O,DENG J,SU H,et al.ImageNet LargeScale Visual Recognition Challenge[J].International Journal of Computer Vision,2015,115(3):211-252. [44]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolutional neural networks[J].Communications of the ACM,2017,60(6):84-90. [45]SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:1-9. [46]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778. [47]MA J,SHERIDAN R P,LIAW A,et al.Deep neural nets as a method for quantitative structure-activity relationships[J].Journal of Chemical Information and Modeling,2015,55(2):263-274. [48]CIREŞAN D C,GIUSTI A,GAMBARDELLA L M,et al.Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks[C]//Medical Image Computing and Computer-Assisted Intervention(MICCAI 2013).2013:411-418. [49]KINGMA D P,WELLING M.Auto-encoding variational bayes[J].arXiv:1312.6114,2013. [50]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial nets[C]//Advances in Neural Information Processing Systems.2014:2672-2680. [51]WU Z,PAN S,CHEN F,et al.A comprehensive survey ongraph neural networks[J].IEEE Transactions on Neural Networks and Learning Systems,2020,32(1):4-24. [52]ZHOU J,CUI G,ZHANG Z,et al.Graph neural networks:A review of methods and applications[J].arXiv:1812.08434,2018. [53]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[C]//Proceedings of the 31st International Confe-rence on Neural Information Processing Systems.2017:6000-6010. [54]DOSOVITSKIY A,BEYER L,KOLESNIKOV A,et al.Animage is worth 16x16 words:Transformers for image recognition at scale[J].arXiv:2010.11929,2020. [55]MATSOUKAS C,HASLUM J F,SÖDERBERG M,et al. Is it time to replace cnns with transformers for medical images?[J].arXiv:2108.09038,2021. [56]JUMPER J,EVANS R,PRITZEL A,et al.Highly accurate protein structure prediction with AlphaFold[J].Nature,2021,596(7873):583-589. [57]DAI L,WU L,LI H,et al.A deep learning system for detecting diabetic retinopathy across the disease spectrum[J].Nature Communications,2021,12(1):1-11. [58]STOKES J M,YANG K,SWANSON K,et al.A Deep Learning Approach to Antibiotic Discovery[J].Cell,2020,180(4):688-702. [59]ALQURAISHI M.End-to-End Differentiable Learning of Protein Structure[J].Cell Systems,2019,8(4):292-301. [60]ALQURAISHI M.ProteinNet:a standardized data set for machine learning of protein structure[J].BMC Bioinformatics,2019,20(1):1-10. [61]PURUSHOTHAM S,MENG C,CHE Z,et al.Benchmarkingdeep learning models on large healt-hcare datasets[J/OL].Journal of Biomedical Informatics,2018,83:112-134.https://www.sciencedirect.com/science/article/pii/S1532046418300716. [62]SAMBASIVAN N,KAPANIA S,HIGHFILL H,et al.“Everyone wants to do the model work,not the data work”:Data Cascades in High-Stakes AI[C]//Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems.2021:1-15. [63]LI J,ZHU G,HUA C,et al.A Systematic Collection of Medical Image Datasets for Deep Learning[J].arXiv:2106.12864,2021. [64]SHAMSHIRBAND S,FATHI M,DEHZANG-I A,et al.A review on deep learning approaches in healthcare systems:Taxonomies,challenges,and o-pen issues[J/OL].Journal of Biomedi-cal Informatics,2021,113:103627.https://www.sciencedirect.com/science/article/pii/S1532046420302550?via%3Dihub. [65]BURGOS N,BOTTANI S,FAOUZI J,et al.Deep learning for brain disorders:from data processing to disease treatment[J].Briefings in Bioinformatics,2021,22(2):1560-1576. [66]LIU M,ZHANG J,ADELI E,et al.Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheime’s Disease Diagnosis[J].IEEE Transactions on Biomedical Engineering,2019,66(5):1195-1206. [67]RUMALA D J,YUNIARNO E M,RACHMADI R F,et al.Bilinear MobileNets for Multi-class Brain Disease Classification Based on Magnetic Resonance Images[C]//2021 IEEE Region 10 Symposium(TENSYMP).2021:1-6. [68]KUCUR S S,HOLLO G,SZNITMAN R.A deep learn-ing approach to automatic detection of early glauco-ma from visual fields[J/OL].PloS one,2018,13(11):e0206081. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0206081. [69]VARUN G,LILY P,MARC C,et al.Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs[J].JAMA,2016,316(22):2402-2410. [70]SAHLSTEN J,JASKARI J,KIVINEN J,et al.Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading[J].Scientific Reports,2019,9(1):1-11. [71]TRAN B X,VU G T,HA G H,et al.Global Evolution of Research in Artificial Intelligence in Health and Medicine:A Bibliometric Study[J/OL].https://www.mdpi.com/2077-0383/8/3/360. [72]YANG H C,ISLAM M M,JACK LI Y C.Potentiality of deep learning application in healthcare[J/OL].https://www.scien-cedirect.com/science/article/abs/pii/S0169260718306862?via%3Dihub. [73]TOPOL E.Deep Medicine:How Artificial Intelligence Can Make Healthcare Human Again[M].Basic Books,2019. [74]HE J,BAXTER S L,XU J,et al.The practical implementation of artificial intelligence technologies in medicine[J].Nature Medicine,2019,25(1):30-36. [75]SZOLOVITS P.AI for the M.D[J].Science,2019,363(6434):1402-1402. [76]BENJAMENS S,DHUNNOO P,MESKO B.The state of artificial intelligence-based FDA-approved medical devices and algorithms:an online database[J].NPJ Digital Medicine,2020,3(1):1-8. [77]COPPOLA F,FAGGIONI L,REGGE D,et al.Artificial intelligence:radiologists’ expectations and opinions gleaned from a nationwide online survey[J].La radiologia medica,2021,126(1):63-71. [78]RICHARDSON J P,SMITH C,CURTIS S,et al.Patient apprehensions about the use of artificial intelligence in healthcare[J].NPJ Digital Medicine,2021,4(1):140. [79]SEETHARAM K,SHRESTHA S,SENGUPTA P P.Artificial Intelligence in Cardiovascular Medicine[J].Current Treatment Options in Cardiovascular Medicine,2019,21(5):25. [80]RANKA S,REDDY M,NOHERIA A.Artificial intelligence in cardiovascular medicine[J].Current Opinion in Cardiology,2021,36(1):26-35. [81]SIONTIS K C,NOSEWORTHY P A,ATTIA Z I,et al.Artificial intelligence-enhanced electrocardiography in cardiovascular disease management[J].Nature Reviews Cardiology,2021,18(7):465-478. [82]ZHANG X,GU K,MIAO S,et al.Automated detection of cardiovascular disease by electrocardiogram signal analysis:a deep learning system[J].Cardiovascular Diagnosis and Therapy,2020,10(2):227-235. [83]GAO J,ZHANG H,LU P,et al.An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset[J/OL].https://www.hindawi.com/journals/jhe/2019/6320651/. [84]ZHU H,CHENG C,YIN H,et al.Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning:a cohort study[J].The Lancet Digital Health,2020,2(7):e348-e357. [85]IVANOVIC M D,ATANASOSKI V,SHVILKIN A,et al.Deep Learning Approach for Highly Specific Atrial Fibrillation and Flutter Detection based on RR Intervals[C]//2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC).2019:1780-1783. [86]SEETHARAM K,BRITO D,FARJO P D,et al.The role of artificial intelligence in cardiovascular imaging:state of the art review[J/OL].Frontiers in Cardiovascular Medicine,2020,7:618849.https://www.frontier-sin.org/articles/10.3389/fcvm.2020.618849/full. [87]Al’AREF S J,ANCHOUCHE K,SINGH G,et al.Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging[J].European Heart Journal,2019,40(24):1975-1986. [88]XU B,KOCYIGIT D,GRIMM R,et al.Applications of artificial intelligence in multimodality cardiovascular imaging:A state-of-the-art review[J].Progress in Cardiovascular Disease,2020,63(3):367-376. [89]CHEN Z,XIAO C,QIU H,et al.Recent Advances of Artificial Intelligence in Cardiovascular Disease[J].J Biomed Nanotech-nol,2020,16(7):1065-1081. [90]MATHUR P,SRIVASTAVA S,XU X,et al.Artificial Intelligence,Machine Learning,and Cardio-vascular Disease[J/OL].Clinical Medicine Insights:Cardiology,2020,14:1-9.https://journals.sagepub.com/doi/full/10.1177/1179546820927404. [91]SUNG H,FERLAY J,SIEGEL R L,et al.Global Cancer Statistics 2020:GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J].CA Cancer J Clin,2021,71(3):209-249. [92]KOOI T,LITJENS G,VAN GINNEKEN B,et al.Large scaledeep learning for computer aided detection of mammographic lesions[J/OL].Medical Image Analysis,2017,35:303-312.https://www.sciencedirect.com/science/article/abs/pii/S1361841516301244#pr-eview-section-cited-by. [93]FREEMAN K,GEPPERT J,STINTON C,et al.Use of artificial intelligence for image analysis in breast cancer screening programmes:systematic review of test accuracy[J/OL].BMJ,2021,374(8304):n1872.https://www.bmj.com/content/374/bmj.n1872. [94]COUDRAY N,OCAMPO P S,SAKELLAROPOULOS T,et al.Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning[J].Nature Medicine,2018,24(10):1559-1567. [95]SANCHEZ-PERALTA L F,BOTE-CURIEL L,PICON A,et al.Deep learning to find colorectal polyps in colonoscopy:A systematic literature review[J/OL].Artificial Intelligence Medicine,2020,108:101923.https://www.sciencedirect.com/science/article/pii/S0933365719307493. [96]ZHOU D,TIAN F,TIAN X,et al.Diagnostic evaluation of adeep learning model for optical diagnosis of colorectal cancer[J].Nature Communications,2020,11(1):2961. [97]STRÖM P,KARTASALO K,OLSSON H,et al.Artificial intelligence for diagnosis and grading of prostate cancer in biopsies:a population-based,diagnostic study[J].The Lancet Oncology,2020,21(2):222-232. [98]SINGH D,FEBBO P G,ROSS K,et al.Gene expression correlates of clinical prostate cancer behavior[J].Cancer Cell,2002,1(2):203-209. [99]TIRUMALA S S,NARAYANAN A.Attribute Selection andClassification of Prostate Cancer Gene Expression Data Using Artificial Neural Networks[C]//knowledge discovery and data mining.2016:26-34. [100]TIRUMALA S S,NARAYANAN A.Classification and diag-nostic prediction of prostate cancer using gene expression and artificial neural networks[J].Neural Computing and Applications,2019,31(11):7539-7548. [101]NAGPAL K,FOOTE D,LIU Y,et al.Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer[J].NPJ Digital Medicine,2019,2(1):48. [102]NAGPAL K,FOOTE D,TAN F,et al.Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens[J].JAMA Oncology,2020,6(9):1372-1380. [103]JIN G,LV J,YANG M,et al.Genetic risk,incident gastric can-cer,and healthy lifestyle:a meta-analysis of genome-wide association studies and prospective cohort study[J].The Lancet Onco-logy,2020,21(10):1378-1386. [104]HU H,GONG L,DONG D,et al.Identifying early gastric cancer under magnifying narrow-band images with deep learning:a multicenter study[J].Gastrointestinal Endoscopy,2021,93(6):1333-1341. [105]CHEN M,ZHANG B,TOPATANA W,et al.Classification and mutation prediction based on histopathology H&E images in li-ver cancer using deep learning[J].npj Precision Oncology,2020,4(1):14. [106]MANHAS J,GUPTA R K,ROY P P.A Review on Automated Cancer Detection in Medical Images using Machine Learning and Deep Learning based Computational Techniques:Challenges and Opportunities[J].Archives of Computational Methods in Engineering,2022,29(5):2893-933. [107]BHINDER B,GILVARY C,MADHUKAR N S,et al.Artificial Intelligence in Cancer Research and Precision Medicine[J].Cancer Discovery,2021,11(4):900-915. [108]AZAM M A,KHAN K B,SALAHUDDIN S,et al.A review on multimodal medical image fusion:Compendious analysis of medical modalities,multimodal databases,fusion techniques and quality metrics[J/OL].Computers in Biology and Medicine,2022,144:105253.https://www.sciencedirect.com/science/article/abs/pii/S0010482522000452. [109]MOHAMMED M,MWAMBI H,MBOYA I B,et al.A stacking ensemble deep learning approach to cancer type classification based on TCGA data[J].Scientific Reports,2021,11(1):15626. [110]LU H,LIU Y,WANG J,et al.Detection of ovarian cancer using plasma cell-free DNA methylomes[J].Clinical Epigenetics,2022,14(1):74. [111]NASSIRI F,CHAKRAVARTHY A,FENG S,et al.Detectionand discrimination of intracranial tumors using plasma cell-free DNA methylomes[J].Nature Medicine,2020,26(7):1044-1047. [112]WANG Z,KEANE P A,CHIANG M,et al.Artificial intelligence and deep learning in ophthalmology[M]//Artificial Intelligence in Medicine.Cham:Springer International Publishing,2022:1519-1552. [113]THYLEFORS B,NÉGREL A D.The global impact of glaucoma[J].Bulletin ofthe World Health Organization,1994,72(3):323-326. [114]AURENHAMMER F.Voronoi diagrams-a survey of a fundamental geometric data structure[J].ACM Computing Surveys(CSUR),1991,23(3):345-405. [115]VORONOI G.Nouvelles applications des paramètres continus à la théorie des formes quadratiques.Deuxième mémoire.Recherches sur les parallélloèdres primitifs[J].Journal für die reine und angewandte Mathematik,1908,1908(134):198-287. [116]RYO A,HIROSHI M,KAZUNORI H,et al.Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images[J/OL].American journal of ophthalmology,2019,198:136-145.https://www.sciencedirect.com/science/article/a-bs/pii/S0002939418305890. [117]RAN A R,CHEUNG C Y,WANG X,et al.Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography:a retrospective training and validation deep-lear-ning analysis[J].The Lancet Digital Health,2019,1(4):e172-e182. [118]ZHANG K,LIU X,XU J,et al.Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images[J].Nature Biome-dical Engineering,2021,5(6):533-545. [119]SAFAYARI A,BOLHASANI H.Depression diagnosis by deep learning using EEG signals:A systematic review[J/OL].Medicine in Novel Technology and Devices,2021,12:100102.https://www.sciencedirect.com/science/article/pii/S259009352000461. [120]AGGARWAL R,SOUNDERAJAH V,MARTIN G,et al.Diagnostic accuracy of deep learning in medical imaging:a systematic review and meta-analysis[J].NPJ Digital Medicine,2021,4(1):65. [121]ZHOU S K,GREENSPAN H,DAVATZIKOS C,et al.A Re-view of Deep Learning in Medical Imaging:Imaging Traits,Technology Trends,Case Studies With Progress Highlights,and Future Promises[J].Proceedings of the IEEE,2021,109(5):820-838. [122]WULCZYN E,STEINER D F,MORAN M,et al.Interpretable survival prediction for colorectal cancer using deep learning[J].NPJ Digital Medicine,2021,4(1):71. [123]QIU J,SUN M,WANG Y,et al.Identification and validation of an individualized autophagy-clinical prognostic index in gastric cancer patients[J].Cancer Cell International,2020,20(1):1-11. [124]LIU Z,WANG Y,LIU X,et al.Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas[J/OL].Neuroimage Clinical,2018,19:271-278.https://www.sciencedirect.com/science/article/pii/S2213158218301335. [125]ALMOG Y A,RAI A,ZHANG P,et al.Deep Learning WithElectronic Health Records for Short-Term Fracture Risk Identification:Crystal Bone Algorithm Development and Validation[J/OL].Journal of Medical Internet Research,2020,22(10):e22550.https://www.jmir.org/2020/10/e22550/. [126]DONZÉ J,AUJESKY D,WILLIAMS D,et al.PotentiallyAvoidable 30-Day Hospital Readmissions in Medical Patients:Derivation and Validation of a Prediction Model[J].JAMA Internal Medicine,2013,173(8):632-638. [127]PHAM T,TRAN T,PHUNG D,et al.DeepCare:A Deep Dynamic Memory Model for Predictive Medicine[J].arXiv:1602.00357,2016. [128]NGUYEN P,TRAN T,WICKRAMASINGHE N,et al.Deepr:A Convolutional Net for Medical Records[J].IEEE Journal of Biomedical and Health Informatics,2017,21(1):22-30. [129]RAJKOMAR A,OREN E,CHEN K,et al.Scalable and accurate deep learning with electronic health records[J].NPJ Digital Medicine,2018,1(1):1-10. [130]TOMAEV N,HARRIS N,BAUR S,et al.Use of deep lear-ning to develop continuous-risk models for adverse event prediction from electronic health records[J].Nature Protocols,2021,16(6):2765-2787. [131]LUNDBERG S M,NAIR B,VAVILALA M S,et al.Explai-nable machine-learning predictions for the prevention of hypoxaemia during surgery[J].Nature Biomedical Engineering,2018,2(10):749-760. [132]KANG D Y,CHO K J,KWON O,et al.Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services[J].Scandinavian Journal of Trauma,Resuscitation and Emergency Medicine,2020,28(1):17. [133]TORRES-SOTO J,ASHLEY E A.Multi-task deep learning for cardiac rhythm detection in wearable devices[J].NPJ Digital Medicine,2020,3(1):1-8. [134]PORUMB M,STRANGES S,PESCAPÈ A,et al.PrecisionMedicine and Artificial Intelligence:A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG[J].Scientific Reports,2020,10(1):1-16. [135]NAGANUR V,SIVATHAMBOO S,CHEN Z,et al.Automated seizure detection with noninvasive wearable devices:A systematic review and meta-analysis[J].Epilepsia,2022,63(8):1930-1941. [136]WANG Y,ZHOU H,YANG Z,et al.An intelligent wearable device for human’s cervical vertebra posture monitoring[C]//2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC).2018:3280-3283. [137]MUHAMMAD G,ALSHEHRI F,KARRAY F,et al.A comprehensive survey on multimodal medical signals fusion for smart healthcare systems[J/OL].Information Fusion,2021,76:355-375.https://www.sciencedirect.com/science/article/abs/pii/S1566253521001330. [138]CHITI F,DOBSON C M.Protein misfolding,functional amy-loid,and human disease[J].Annual Review Biochemistry,2006,75(1):333-366. [139]DILL K A,MACCALLUM J L.The protein-folding problem,50 years on[J].Science,2012,338(6110):1042-1046. [140]PAUL D,SANAP G,SHENOY S,et al.Artificial intelligence in drug discovery and development[J].Drug Discovery Today,2021,26(1):80-93. [141]GUPTA R,SRIVASTAVA D,SAHU M,et al.Artificial intelligence to deep learning:machine intelligence approach for drug discovery[J].Molecular Diversity,2021,25(3):1315-1360. [142]ALQURAISHI M.AlphaFold at CASP13[J].Bioinformatics,2019,35(22):4862-4865. [143]BAEK M,DIMAIO F,ANISHCHENKO I,et al.Accurate prediction of protein structures and interactions using a three-track neural network[J].Science,2021,373(6557):871-876. [144]HUMPHREYS I R,PEI J,BAEK M,et al.Computed structures of core eukaryotic protein complexes[J/OL].Science,2021,374(6573):eabm4805.https://www.science.org/doi/full/10.1126/science.abm4805. [145]BENNETT W F D,HE S,BILODEAU C L,et al.Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning[J].Journal of Chemical Information and Modeling,2020,60(11):5375-5381. [146]ALLEY E C,KHIMULYA G,BISWAS S,et al.Unified rational protein engineering with sequence-based deep representation learning[J].Nature Methods,2019,16(12):1315-1322. [147]RIVES A,MEIER J,SERCU T,et al.Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences[J/OL].Proceedings of the National Academy of Sciences of the United States of America,2021,118(15):e2016239118.https://www.pnas.org/doi/abs/10.1073/pnas.2016239118. [148]ANAND N,HUANG P.Generative modeling for protein structures[C]//Advances in Neural Information Processing Systems.2018:7494-7505. [149]THYGESEN C B,STEENMANS C S,AL-SIBAHI A S,et al.Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model[C]//International Conference on Machine Learning.PMLR.2021:10258-10267. [150]CARRACEDO-REBOREDO P,LINARES-BLANCO J,RO-DRIGUEZ-FERNANDEZ N,et al.A review on machine lear-ning approaches and trends in drug discovery[J/OL].Computational Structural Biotechnology Journal ,2021,19:4538-4558.https://www.sciencedirect.com/science/article/pii/S2001037021003421. [151]CHEN H,ENGKVIST O,WANG Y,et al.The rise of deep learning in drug discovery[J].Drug Discovery Today,2018,23(6):1241-1250. [152]CAMACHO D M,COLLINS K M,POWERS R K,et al.Next-Generation Machine Learning for Biological Networks[J].Cell,2018,173(7):1581-1592. [153]MAMOSHINA P,VIEIRA A,PUTIN E,et al.Applications of Deep Learning in Biomedicine[J].Molecular Pharmaceutics,2016,13(5):1445-1454. [154]MAK K K,PICHIKA M R.Artificial intelligence in drug deve-lopment:present status and future prospects[J].Drug Discovery Today,2019,24(3):773-780. [155]SELLWOOD M A,AHMED M,SEGLER M H,et al.Artificial intelligence in drug discovery[J].Future Medicinal Chemistry,2018,10(17):2025-2028. [156]YUAN Y,PEI J,LAI L.LigBuilder V3:A Multi-Target de novo Drug Design Approach[J/OL].Frontiers in chemistry,2020,8:142.https://www.fronti-ersin.org/articles/10.3389/fchem.2020.00142/full. [157]BHATTACHARYYA R P,BANDYOPADHYAY N,MA P,et al.Simultaneous detection of genotype and phenotype enables rapid and accurate antibiotic susceptibility determination[J].Nature medicine,2019,25(12):1858-1864. [158]LAVECCHIA A.Deep learning in drug discovery:opportunities,challenges and future prospects[J].Drug Discovery Today,2019,24(10):2017-2032. [159]JIMÉNEZ-LUNA J,GRISONI F,WESKAMP N,et al.Artificial intelligence in drug discovery:recent advances and future perspectives[J].Expert Opinion on Drug Discovery,2021,16(9):949-959. [160]SCHNEIDER P,WALTERS W P,PLOWRIGHT A T,et al.Rethinking drug design in the artificial intelligence era[J].Nature Reviews Drug Discovery,2020,19(5):353-364. [161]JIMÉNEZ-LUNA J,GRISONI F,SCHNEIDER G.Drug disco-very with explainable artificial intelligence[J].Nature Machine Intelligence,2020,2(10):573-584. [162]WALTERS W P,BARZILAY R.Critical assessment of AI indrug discovery[J].Expert Opinion on Drug Discovery,2021,16(9):937-947. [163]DAKKA M A,NGUYEN T V,HALL J M M,et al.Automated detection of poor-quality data:case studies in healthcare[J].Scientific Reports,2021,11(1):1-10. [164]ABNAR S,DEHGHANI M,NEYSHABUR B,et al.Exploring the limits of large scale pretraining[J].arXiv:2110.02095,2021. [165]ZHUANG F,QI Z,DUAN K,et al.A comprehensive survey on transfer learning[J].Proceedings of the IEEE,2020,109(1):43-76. [166]WANG Y,YAO Q,KWOK J T,et al.Generalizing from a Few Examples:A Survey on Few-shot Learning[J].ACM Computing Surveys(CSUR),2020,53(3):1-34. [167]BROWN T B,MANN B,RYDER N,et al.Language models are few-shot learners[J].arXiv:2005.14165,2020. [168]ZHANG Y,TIČO P,LEONARDIS A,et al.A Survey on Neural Network Interpretability[J].IEEE Transactions on Emerging Topics in Computational Intelligence,2021,5(5):726-742. [169]FAN F L,XIONG J,LI M,et al.On Interpretability of Artificial Neural Networks:A Survey[J].IEEE Transactions on Radiation and Plasma Medical Sciences,2021,5(6):741-760. [170]GUAN J.Artificial Intelligence in Healthcare and Medicine:Promises,Ethical Challenges and Governance[J].Chinese Medical Sciences Journal,2019,34(2):76-83. [171]TOPOL E J.High-performance medicine:the convergence of human and artificial intelligence[J].Nature Medicine,2019,25(1):44-56. [172]BRIGANTI G,LE MOINE O.Artificial Intelligence in Medicine:Today and Tomorrow[J/OL].Front Med(Lausanne),2020,7:27.https://www.frontiersin.org/articles/10.3389/fmed.2020.00027/full. [173]XU J,GLICKSBERG B S,SU C,et al.Federated Learning for Healthcare Informatics[J].Journal of Healthcare Informatics Research,2021,5(1):1-19. [174]LI W,MILLETARÌ F,XU D,et al.Privacy-Preserving Federated Brain Tumour Segmentation[C]//International Workshop on Machine Learning in Medical Imaging.Cham:Springer,2019:133-141. [175]KELLY C J,KARTHIKESALINGAM A,SULEYMAN M,et al.Key challenges for delivering clinical impact with artificial intelligence[J].BMC Medicine,2019,17(1):195. [176]BEEDE E,BAYLOR E,HERSCH F,et al.A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy[C]//Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems.2020:1-12. [177]BENGIO Y,LECUN Y,HINTON G.Deep learning for AI[J].Communications of the ACM,2021,64(7):58-65. [178]MORIN O,VALLIÈRES M,BRAUNSTEIN S,et al.An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication[J].Nature Cancer,2021,2(7):709-722. [179]RICHMAN B.Health Regulation for the Digital Age-Correcting the Mismatch[J].New England Journal of Medicine,2018,379(18):1694-1695. |
[1] | LIU Zerun, ZHENG Hong, QIU Junjie. Smart Contract Vulnerability Detection Based on Abstract Syntax Tree Pruning [J]. Computer Science, 2023, 50(4): 317-322. |
[2] | HAN Xueming, JIA Caiyan, LI Xuanya, ZHANG Pengfei. Dual-attention Network Model on Propagation Tree Structures for Rumor Detection [J]. Computer Science, 2023, 50(4): 22-31. |
[3] | WANG Yali, ZHANG Fan, YU Zeng, LI Tianrui. Aspect-level Sentiment Classification Based on Interactive Attention and Graph Convolutional Network [J]. Computer Science, 2023, 50(4): 196-203. |
[4] | DONG Yongfeng, HUANG Gang, XUE Wanruo, LI Linhao. Graph Attention Deep Knowledge Tracing Model Integrated with IRT [J]. Computer Science, 2023, 50(3): 173-180. |
[5] | HUA Xiaofeng, FENG Na, YU Junqing, HE Yunfeng. Shooting Event Detection of Free Kick in Soccer Video Based on Rule Reasoning [J]. Computer Science, 2023, 50(3): 181-190. |
[6] | MEI Pengcheng, YANG Jibin, ZHANG Qiang, HUANG Xiang. Sound Event Joint Estimation Method Based on Three-dimension Convolution [J]. Computer Science, 2023, 50(3): 191-198. |
[7] | BAI Xuefei, MA Yanan, WANG Wenjian. Segmentation Method of Edge-guided Breast Ultrasound Images Based on Feature Fusion [J]. Computer Science, 2023, 50(3): 199-207. |
[8] | LIU Hang, PU Yuanyuan, LYU Dahua, ZHAO Zhengpeng, XU Dan, QIAN Wenhua. Polarized Self-attention Constrains Color Overflow in Automatic Coloring of Image [J]. Computer Science, 2023, 50(3): 208-215. |
[9] | CHEN Liang, WANG Lu, LI Shengchun, LIU Changhong. Study on Visual Dashboard Generation Technology Based on Deep Learning [J]. Computer Science, 2023, 50(3): 238-245. |
[10] | ZHANG Yi, WU Qin. Crowd Counting Network Based on Feature Enhancement Loss and Foreground Attention [J]. Computer Science, 2023, 50(3): 246-253. |
[11] | YING Zonghao, WU Bin. Backdoor Attack on Deep Learning Models:A Survey [J]. Computer Science, 2023, 50(3): 333-350. |
[12] | ZOU Yunzhu, DU Shengdong, TENG Fei, LI Tianrui. Visual Question Answering Model Based on Multi-modal Deep Feature Fusion [J]. Computer Science, 2023, 50(2): 123-129. |
[13] | WANG Pengyu, TAI Wenxin, LIU Fang, ZHONG Ting, LUO Xucheng, ZHOU Fan. Self-supervised Flight Trajectory Prediction Based on Data Augmentation [J]. Computer Science, 2023, 50(2): 130-137. |
[14] | GUO Nan, LI Jingyuan, REN Xi. Survey of Rigid Object Pose Estimation Algorithms Based on Deep Learning [J]. Computer Science, 2023, 50(2): 178-189. |
[15] | LI Junlin, OUYANG Zhi, DU Nisuo. Scene Text Detection with Improved Region Proposal Network [J]. Computer Science, 2023, 50(2): 201-208. |
|