Skip to main content
SpringerLink
Log in

Deep Learning—Prediction

  • Chapter
  • First Online:
Clinical Applications of Artificial Intelligence in Real-World Data

  • 312 Accesses

Abstract

Deep learning is a subfield of artificial intelligence (AI) that is concerned with developing large and complex neural networks for various tasks. As of today, there exists a wide variety of DL models yielding promising results in many subfields of AI, such as computer vision (CV) and natural language processing (NLP). In this chapter, we provide an overview of deep learning, elaborating on some common model architectures. Furthermore, we describe the advantages and disadvantages of deep learning compared to machine learning. Afterwards, we discuss the application of deep learning models in various clinical tasks, focusing on clinical imaging, electronic health records and genomics. We also provide a brief overview of prediction tasks in deep learning. The final section of this chapter discusses the limitations and challenges of deploying deep learning models in healthcare and medicine, focusing on the lack of explainability in deep learning models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Artificial neural network—An overview. ScienceDirect topics. https://www.sciencedirect.com/topics/neuroscience/artificial-neural-network. Accessed 17 Sept 2022.

  2. Hecht-Nielsen. Theory of the backpropagation neural network. In: International 1989 joint conference on neural networks, vol 1; 1989. p. 593–605. https://doi.org/10.1109/IJCNN.1989.118638

  3. Duchi J, Hazan E, Singer Y. 2011. Adaptive subgradient methods for online learning and stochastic optimization. p. 39.

    Google Scholar 

  4. Tieleman T, Hinton G. Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. In: Presented at the COURSERA: neural networks for machine learning; 2012.

    Google Scholar 

  5. Kingma DP, Ba J. Adam: a method for stochastic optimization. 29 Jan 2017. Accessed 04 Oct 2022. Available http://arxiv.org/abs/1412.6980

  6. Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Mark. 2021;31(3):685–95. https://doi.org/10.1007/s12525-021-00475-2.

    Article  Google Scholar 

  7. Esteva A, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–9. https://doi.org/10.1038/s41591-018-0316-z.

    Article  Google Scholar 

  8. Wang F, Kaushal R, Khullar D. Should health care demand interpretable artificial intelligence or accept ‘black box’ medicine? Ann Intern Med. 2020;172(1):59–60. https://doi.org/10.7326/M19-2548.

    Article  Google Scholar 

  9. Ahmad MA, Eckert C, Teredesai A. Interpretable machine learning in healthcare. In: Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics. New York, NY, USA, Aug 2018. p. 559–60. https://doi.org/10.1145/3233547.3233667

  10. Yang S, Zhu F, Ling X, Liu Q, Zhao P. Intelligent health care: applications of deep learning in computational medicine. Front Genet. 2021;12: 607471. https://doi.org/10.3389/fgene.2021.607471.

    Article  Google Scholar 

  11. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236–46. https://doi.org/10.1093/bib/bbx044.

    Article  Google Scholar 

  12. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. 18 May 2015. https://doi.org/10.48550/arXiv.1505.04597

  13. Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19(1):64. https://doi.org/10.1186/s12874-019-0681-4.

    Article  Google Scholar 

  14. Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Netw. 1989;2(5):359–66. https://doi.org/10.1016/0893-6080(89)90020-8.

    Article  MATH  Google Scholar 

  15. Attia ZI, et al. Age and sex estimation using artificial intelligence from standard 12-lead ECGs. Circ Arrhythm Electrophysiol. 2019;12(9): e007284. https://doi.org/10.1161/CIRCEP.119.007284.

    Article  Google Scholar 

  16. Mahmud S et al. A shallow U-Net architecture for reliably predicting blood pressure (BP) from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. Sensors. 2022;22(3):Art no 3. https://doi.org/10.3390/s22030919

  17. Götz TI, Schmidkonz C, Chen S, Al-Baddai S, Kuwert T, Lang EW. A deep learning approach to radiation dose estimation. Phys Med Ampmathsemicolon Biol. 2020;65(3): 035007. https://doi.org/10.1088/1361-6560/ab65dc.

    Article  Google Scholar 

  18. Lou B, et al. An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction. Lancet Digit Health. 2019;1(3):e136–47. https://doi.org/10.1016/S2589-7500(19)30058-5.

    Article  Google Scholar 

  19. van de Leur R et al. Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning. Neth Heart J Mon J Neth Soc Cardiol Neth Heart Found. 2022; 30(6): 312–18. https://doi.org/10.1007/s12471-022-01670-2

  20. van de Leur RR, et al. Automatic triage of 12-lead ECGs using deep convolutional neural networks. J Am Heart Assoc. 2020;9(10): e015138. https://doi.org/10.1161/JAHA.119.015138.

    Article  Google Scholar 

  21. Clark TG, Bradburn MJ, Love SB, Altman DG. Survival analysis Part I: basic concepts and first analyses. Br J Cancer. 2003;89(2):Art no 2. https://doi.org/10.1038/sj.bjc.6601118

  22. Cox DR. Regression models and life-tables. J R Stat Soc Ser B Methodol. 1972;34(2):187–202. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x.

    Article  MathSciNet  MATH  Google Scholar 

  23. Nagpal C, Yadlowsky S, Rostamzadeh N, Heller K. Deep cox mixtures for survival regression. In: Proceedings of the 6th machine learning for healthcare conference, Oct 2021p. 674–708. Accessed 06 Oct 2022. Available https://proceedings.mlr.press/v149/nagpal21a.html

  24. Sammani A et al. Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks. Eur Eur Pacing Arrhythm Card Electrophysiol J Work Groups Card Pacing Arrhythm Card Cell Electrophysiol Eur Soc Cardiol. 2022;24(10):1645–54. https://doi.org/10.1093/europace/euac054

  25. Avendi M, Kheradvar A, Jafarkhani H. Fully automatic segmentation of heart chambers in cardiac MRI using deep learning. J Cardiovasc Magn Reson. 2016;18(1):P351. https://doi.org/10.1186/1532-429X-18-S1-P351.

    Article  Google Scholar 

  26. Moskalenko V, Zolotykh N, Osipov G. Deep learning for ECG segmentation. In: Advances in neural computation, machine learning, and cognitive research III. Cham. 2020. p. 246–54. https://doi.org/10.1007/978-3-030-30425-6_29

  27. Rodrigues G, et al. Automated large artery occlusion detection in stroke: a single-center validation study of an artificial intelligence algorithm. Cerebrovasc Dis. 2022;51(2):259–64. https://doi.org/10.1159/000519125.

    Article  MathSciNet  Google Scholar 

  28. Beck AH et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011;3(108):108ra113–108ra113. https://doi.org/10.1126/scitranslmed.3002564.

  29. Charoentong P, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017;18(1):248–62. https://doi.org/10.1016/j.celrep.2016.12.019.

    Article  Google Scholar 

  30. Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng D. Early diagnosis of Alzheimer’s disease with deep learning. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI), Apr 2014. p. 1015–18. https://doi.org/10.1109/ISBI.2014.6868045

  31. Esteva A et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):Art no 7639. https://doi.org/10.1038/nature21056

  32. Gulshan V, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10. https://doi.org/10.1001/jama.2016.17216.

    Article  Google Scholar 

  33. Poplin R et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018; 2(3):Art no 3. https://doi.org/10.1038/s41551-018-0195-0

  34. Kooi T, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal. 2017;35:303–12. https://doi.org/10.1016/j.media.2016.07.007.

    Article  Google Scholar 

  35. Jamaludin A, Kadir T, Zisserman A. SpineNet: automatically pinpointing classification evidence in spinal MRIs. In: Medical image computing and computer-assisted intervention—MICCAI 2016, Cham; 2016. p. 166–75. https://doi.org/10.1007/978-3-319-46723-8_20

  36. Cheng J-Z et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in us images and pulmonary nodules in CT scans. Sci Rep. 2016;6(1):Art no 1. https://doi.org/10.1038/srep24454

  37. Liu Y et al. Detecting cancer metastases on gigapixel pathology images. 07 Mar 2017. https://doi.org/10.48550/arXiv.1703.02442

  38. Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J. Mitosis detection in breast cancer histology images with deep neural networks. In: Medical image computing and computer-assisted intervention—MICCAI 2013, Berlin, Heidelberg; 2013, p. 411–18. https://doi.org/10.1007/978-3-642-40763-5_51

  39. Yoo Y, Brosch T, Traboulsee A, Li DKB, Tam R. Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation. In: Machine learning in medical imaging, Cham; 2014, p. 117–24. https://doi.org/10.1007/978-3-319-10581-9_15

  40. Pu B et al. MobileUNet-FPN: a semantic segmentation model for fetal ultrasound four-chamber segmentation in edge computing environments. IEEE J Biomed Health Inform. 2022;1–11. https://doi.org/10.1109/JBHI.2022.3182722

  41. Malali A, Hiriyannaiah S, Siddesh GM, Srinivasa KG, Sanjay NT. Supervised ECG wave segmentation using convolutional LSTM. ICT Express. 2020; 6(3):166–69. https://doi.org/10.1016/j.icte.2020.04.004

  42. Mahmud S, et al. NABNet: a nested attention-guided BiConvLSTM network for a robust prediction of blood pressure components from reconstructed arterial blood pressure waveforms using PPG and ECG signals. Biomed Signal Process Control. 2023;79: 104247. https://doi.org/10.1016/j.bspc.2022.104247.

    Article  Google Scholar 

  43. Häyrinen K, Saranto K, Nykänen P. Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inf. 2008;77(5):291–304. https://doi.org/10.1016/j.ijmedinf.2007.09.001.

    Article  Google Scholar 

  44. Choi E, Bahadori MT, Schuetz A, Stewart WF, Sun J. Doctor AI: predicting clinical events via recurrent neural networks. In: Proceedings of the 1st machine learning for healthcare conference, Dec 2016. p. 301–18. Accessed 26 Sep 2022. Available https://proceedings.mlr.press/v56/Choi16.html

  45. Pham T, Tran T, Phung D, Venkatesh S. DeepCare: a deep dynamic memory model for predictive medicine. Advances in knowledge discovery and data mining, Cham; 2016. p. 30–41. https://doi.org/10.1007/978-3-319-31750-2_3

  46. Nguyen P, Tran T, Wickramasinghe N, Venkatesh S. Deepr: a convolutional net for medical records. IEEE J Biomed Health Inform. 2017;21(1):22–30. https://doi.org/10.1109/JBHI.2016.2633963.

    Article  Google Scholar 

  47. Razavian N, Marcus J, Sontag D. Multi-task prediction of disease onsets from longitudinal laboratory tests. In: Proceedings of the 1st machine learning for healthcare conference, Dec 2016. p. 73–100. Accessed 27 Sep 27 2022. Available https://proceedings.mlr.press/v56/Razavian16.html

  48. Lipton ZC, Kale DC, Elkan C, Wetzel R. Learning to diagnose with LSTM recurrent neural networks. 21 Mar 2017. https://doi.org/10.48550/arXiv.1511.03677

  49. Suresh H, Hunt N, Johnson A, Celi LA, Szolovits P, Ghassemi M. Clinical intervention prediction and understanding with deep neural networks. In: Proceedings of the 2nd machine learning for healthcare conference, Nov 2017, p. 322–37. Accessed 27 Sep 2022. Available https://proceedings.mlr.press/v68/suresh17a.html

  50. Choi Y, Chiu CY-I, Sontag D. Learning low-dimensional representations of medical concepts. AMIA Summits Transl Sci Proc. 2016;2016:41–50.

    Google Scholar 

  51. Dernoncourt F, Lee JY, Uzuner O, Szolovits P. De-identification of patient notes with recurrent neural networks. J Am Med Inform Assoc. 2017;24(3):596–606. https://doi.org/10.1093/jamia/ocw156.

    Article  Google Scholar 

  52. Xiong HY, et al. The human splicing code reveals new insights into the genetic determinants of disease. Science. 2015;347(6218):1254806. https://doi.org/10.1126/science.1254806.

    Article  Google Scholar 

  53. Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol. 2015;33(8):Art no 8. . https://doi.org/10.1038/nbt.3300

  54. Kelley DR, Snoek J, Rinn JL. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. 2016;26(7):990–9. https://doi.org/10.1101/gr.200535.115.

    Article  Google Scholar 

  55. Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning–based sequence model. Nat Methods. 2015;12(10):Art no 10. https://doi.org/10.1038/nmeth.3547

  56. Angermueller C, Lee HJ, Reik W, Stegle O. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol. 2017;18(1):67. https://doi.org/10.1186/s13059-017-1189-z.

    Article  Google Scholar 

  57. Singh R, Lanchantin J, Robins G, Qi Y. DeepChrome: deep-learning for predicting gene expression from histone modifications. Bioinformatics. 2016;32(17):i639–48. https://doi.org/10.1093/bioinformatics/btw427.

    Article  Google Scholar 

  58. Markus AF, Kors JA, Rijnbeek PR. The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. J Biomed Inform. 2021;113: 103655. https://doi.org/10.1016/j.jbi.2020.103655.

    Article  Google Scholar 

  59. Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. 02 Mar 2017. https://doi.org/10.48550/arXiv.1702.08608

  60. Linardatos P, Papastefanopoulos V, Kotsiantis S. Entropy, and undefined 2021, Explainable AI: a review of machine learning interpretability methods. mdpi.com. 2020. https://doi.org/10.3390/e23010018

  61. Ghassemi M, Vector, Beam AL, Ghassemi M, Oakden-Rayner L, The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3(11):e745–e750. https://doi.org/10.1016/S2589-7500(21)00208-9

  62. Liu X, Sanchez P, Thermos S, O’Neil AQ, Tsaftaris SA. Learning disentangled representations in the imaging domain. Med Image Anal. 2022;80: 102516. https://doi.org/10.1016/j.media.2022.102516.

    Article  Google Scholar 

  63. van de Leur RR, et al. Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders. Eur Heart J Digit Health. 2022;3(3):390–404. https://doi.org/10.1093/ehjdh/ztac038.

    Article  Google Scholar 

  64. van de Leur RR et al. Inherently explainable deep neural network-based interpretation of electrocardiograms using variational auto-encoders. Cardiovasc Med. Preprint, 2022. https://doi.org/10.1101/2022.01.04.22268759

  65. Pinto JR, Cardoso JS. Explaining ECG biometrics: is it all in the QRS? p. 12.

    Google Scholar 

  66. Pinto JR, Cardoso JS, Lourenço A. Deep neural networks for biometric identification based on non-intrusive ECG acquisitions. In: Arya KV, editors. The biometric computing, 1st ed. Chapman and Hall/CRC; 2019. p. 217–34. https://doi.org/10.1201/9781351013437-11

  67. Barocas S, Hardt M, Narayanan A. Fairness and machine learning. p 300.

    Google Scholar 

  68. Karimian G, Petelos E, Evers SMAA. The ethical issues of the application of artificial intelligence in healthcare: a systematic scoping review. AI Ethics. 2022. https://doi.org/10.1007/s43681-021-00131-7.

    Article  Google Scholar 

  69. Petkus H, Hoogewerf J, Wyatt JC. What do senior physicians think about AI and clinical decision support systems: quantitative and qualitative analysis of data from specialty societies. Clin Med. 2020;20(3):324. https://doi.org/10.7861/clinmed.2019-0317.

    Article  Google Scholar 

  70. Kamiran F, Calders T. Data preprocessing techniques for classification without discrimination. Knowl Inf Syst. 2012;33(1):1–33. https://doi.org/10.1007/s10115-011-0463-8.

    Article  Google Scholar 

  71. Kamiran F, Calders T. Classifying without discriminating. In: Control and communication 2009 2nd international conference on computer, Feb 2009. pp 1–6. https://doi.org/10.1109/IC4.2009.4909197

  72. Calders T, Kamiran F, Pechenizkiy M. Building classifiers with independency constraints. In: 2009 IEEE international conference on data mining workshops, Dec 2009. p. 13–18. https://doi.org/10.1109/ICDMW.2009.83

  73. Kamiran F, Karim A, Zhang X. Decision theory for discrimination-aware classification. In: 2012 IEEE 12th international conference on data mining, Dec 2012. p. 924–29. https://doi.org/10.1109/ICDM.2012.45

  74. Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R. Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference, New York, NY, USA, Jan 2012. p. 214–26. https://doi.org/10.1145/2090236.2090255

  75. Tramèr F, Zhang F, Juels A, Reiter MK, Ristenpart T. Stealing machine learning models via prediction {APIs}. In: Presented at the 25th USENIX security symposium (USENIX security 16); 2016, p. 601–18. Accessed 07 Nov 2022. Available https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/tramer

  76. Abadi M et al. Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, New York, NY, USA, Oct 2016, p. 308–18. https://doi.org/10.1145/2976749.2978318

  77. Phan N, Wang Y, Wu X, Dou D. Differential privacy preservation for deep auto-encoders: an application of human behavior prediction. In: Thirtieth AAAI conference on artificial intelligence, Feb 2016. Accessed 07 Nov 2022. Available https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12174

  78. Shokri R, Shmatikov V. Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, New York, NY, USA, Oct 2015, p. 1310–21. https://doi.org/10.1145/2810103.2813687

  79. Phong LT, Aono Y, Hayashi T, Wang L, Moriai S. Privacy-preserving deep learning via additively homomorphic encryption. IEEE Trans Inf Forensics Secur. 2018;13(5):1333–45. https://doi.org/10.1109/TIFS.2017.2787987.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. UMCU, Utrecht, Netherlands

    Chris Al Gerges, Melle B. Vessies, Rutger R. van de Leur & René van Es

Authors
  1. Chris Al Gerges
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Melle B. Vessies
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rutger R. van de Leur
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. René van Es
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chris Al Gerges .

Editor information

Editors and Affiliations

  1. Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands

    Folkert W. Asselbergs

  2. Institute of Health Informatics, University College London, London, UK

    Spiros Denaxas

  3. Department of Data Science and Biostatistics, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands

    Daniel L. Oberski

  4. Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Jason H. Moore

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Al Gerges, C., Vessies, M.B., van de Leur, R.R., van Es, R. (2023). Deep Learning—Prediction. In: Asselbergs, F.W., Denaxas, S., Oberski, D.L., Moore, J.H. (eds) Clinical Applications of Artificial Intelligence in Real-World Data. Springer, Cham. https://doi.org/10.1007/978-3-031-36678-9_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-36678-9_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36677-2

  • Online ISBN: 978-3-031-36678-9

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation