1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Biomedical Engineering
  4. Volume 25, 2023
  5. Article

Annual Review of Biomedical Engineering

Volume 25, 2023

Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine

Abstract

Artificial intelligence (AI) and machine learning (ML) methods are currently widely employed in medicine and healthcare. A PubMed search returns more than 100,000 articles on these topics published between 2018 and 2022 alone. Notwithstanding several recent reviews in various subfields of AI and ML in medicine, we have yet to see a comprehensive review around the methods’ use in longitudinal analysis and prediction of an individual patient's health status within a personalized disease pathway. This review seeks to fill that gap. After an overview of the AI and ML methods employed in this field and of specific medical applications of models of this type, the review discusses the strengths and limitations of current studies and looks ahead to future strands of research in this field. We aim to enable interested readers to gain a detailed impression of the research currently available and accordingly plan future work around predictive models for deterioration in health status.

Keyword(s): artificial intelligence, digital health pathways, disease trajectories, machine learning, personalized medicine, time series
Loading

Article metrics loading...

/content/journals/10.1146/annurev-bioeng-110220-030247
2023-06-08
2024-04-28
Loading full text...

Full text loading...

/deliver/fulltext/bioeng/25/1/annurev-bioeng-110220-030247.html?itemId=/content/journals/10.1146/annurev-bioeng-110220-030247&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Eskofier BM, Lee S, Baron M, Simon A, Martindale C et al. 2017. An overview of smart shoes in the internet of health things: gait and mobility assessment in health promotion and disease monitoring. Appl. Sci. 7:986
     [Google Scholar]
  2. 2.
    Kuhlmann L, Lehnertz K, Richardson MP, Schelter B, Zaveri HP. 2018. Seizure prediction—ready for a new era. Nat. Rev. Neurol. 14:618–30
     [Google Scholar]
  3. 3.
    Lehnertz K, Mormann F, Osterhage H, Muller A, Prusseit J et al. 2007. State-of-the-art of seizure prediction. J. Clin. Neurophysiol. 24:147–53
     [Google Scholar]
  4. 4.
    Rasheed K, Qayyum A, Qadir J, Sivathamboo S, Kwan P et al. 2021. Machine learning for predicting epileptic seizures using EEG signals: a review. IEEE Rev. Biomed. Eng. 14:139–55
     [Google Scholar]
  5. 5.
    LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
     [Google Scholar]
  6. 6.
    Baldi P. 2018. Deep learning in biomedical data science. Annu. Rev. Biomed. Data Sci. 1:181–205
     [Google Scholar]
  7. 7.
    Wainberg M, Merico D, Delong A, Frey BJ. 2018. Deep learning in biomedicine. Nat. Biotechnol. 36:829–38
     [Google Scholar]
  8. 8.
    Shoeibi A, Khodatars M, Ghassemi N, Jafari M, Moridian P et al. 2021. Epileptic seizures detection using deep learning techniques: a review. Int. J. Environ. Res. Public Health 18:5780
     [Google Scholar]
  9. 9.
    Banda JM, Seneviratne M, Hernandez-Boussard T, Shah NH. 2018. Advances in electronic phenotyping: from rule-based definitions to machine learning models. Annu. Rev. Biomed. Data Sci. 1:53–68
     [Google Scholar]
  10. 10.
    Chen IY, Joshi S, Ghassemi M, Ranganath R. 2021. Probabilistic machine learning for healthcare. Annu. Rev. Biomed. Data Sci. 4:393–415
     [Google Scholar]
  11. 11.
    Romm EL, Tsigelny IF. 2020. Artificial intelligence in drug treatment. Annu. Rev. Pharmacol. Toxicol. 60:353–69
     [Google Scholar]
  12. 12.
    Shen D, Wu G, Suk HI. 2017. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19:221–48
     [Google Scholar]
  13. 13.
    Chen C, Qin C, Qiu H, Tarroni G, Duan J et al. 2020. Deep learning for cardiac image segmentation: a review. Front. Cardiovasc. Med. 7:25
     [Google Scholar]
  14. 14.
    Marti-Juan G, Sanroma-Guell G, Piella G. 2020. A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease. Comput. Methods Programs Biomed. 189:105348
     [Google Scholar]
  15. 15.
    Goergen CJ, Tweardy MJ, Steinhubl SR, Wegerich SW, Singh K et al. 2022. Detection and monitoring of viral infections via wearable devices and biometric data. Annu. Rev. Biomed. Eng. 24:1–27
     [Google Scholar]
  16. 16.
    van Smeden M, Reitsma JB, Riley RD, Collins GS, Moons KG. 2021. Clinical prediction models: diagnosis versus prognosis. J. Clin. Epidemiol. 132:142–45
     [Google Scholar]
  17. 17.
    Geisser S. 1993. Predictive Inference: An Introduction New York: Chapman and Hall/CRC. , 1st ed..
  18. 18.
    Morgenstern JD, Buajitti E, O'Neill M, Piggott T, Goel V et al. 2020. Predicting population health with machine learning: a scoping review. BMJ Open 10:e037860
     [Google Scholar]
  19. 19.
    Leininger L, DeLeire T. 2017. Predictive modeling for population health management: a practical guide Issue Brief, Mathematica Policy Research, Inc. Princeton, NJ:
  20. 20.
    Bica I, Alaa AM, Lambert C, van der Schaar M. 2021. From real-world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges. Clin. Pharmacol. Ther. 109:87–100
     [Google Scholar]
  21. 21.
    Silva JF, Matos S. 2021. Patient trajectory modelling in longitudinal data: a review on existing solutions. Proceedings of the 2021 IEEE 34th International Symposium on Computer-Based Medical Systems480–85. New York: IEEE
     [Google Scholar]
  22. 22.
    Bellavista 2004. The New International Webster's Comprehensive Dictionary of the English Language Köln, Germany: Bellavista
  23. 23.
    Fjelland R. 2020. Why general artificial intelligence will not be realized. Human. Soc. Sci. Commun. 7:10
     [Google Scholar]
  24. 24.
    Samuel AL. 1959. Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 3:210–29
     [Google Scholar]
  25. 25.
    Duda RO, Hart PE, Stork DG. 2001. Pattern Classification New York: John Wiley & Sons
  26. 26.
    Kshirsagar AV, Chiu YL, Bomback AS, August PA, Viera AJ et al. 2010. A hypertension risk score for middle-aged and older adults. J. Clin. Hypertens. 12:800–8
     [Google Scholar]
  27. 27.
    Bishop CM. 2006. Pattern Recognition and Machine Learning New York: Springer
  28. 28.
    Murphy KP. 2022. Probabilistic Machine Learning: An Introduction Cambridge, MA: The MIT Press
  29. 29.
    Niemann H. 2003. Klassifikation von Mustern Berlin: Springer
  30. 30.
    Quinlan JR. 1986. Induction of decision trees. Mach. Learn. 1:81–106
     [Google Scholar]
  31. 31.
    Opitz D, Maclin R. 1999. Popular ensemble methods: an empirical study. J. Artif. Int. Res. 11:169–98
     [Google Scholar]
  32. 32.
    Rasmussen CE. 2004. Gaussian processes in machine learning. Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2–14, 2003, Tübingen, Germany, August 4–16, 2003, Revised Lectures O Bousquet, U von Luxburg, G Rätsch 63–71. Berlin/Heidelberg: Springer
     [Google Scholar]
  33. 33.
    Rabiner L, Juang B. 1986. An introduction to hidden Markov models. IEEE ASSP Magaz. 3:4–16
     [Google Scholar]
  34. 34.
    Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
     [Google Scholar]
  35. 35.
    Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 58:267–88
     [Google Scholar]
  36. 36.
    Hosmer DW Jr., Lemeshow S, Sturdivant RX. 2013. Applied Logistic Regression New York: John Wiley & Sons
  37. 37.
    Breiman L. 2001. Random forests. Mach. Learn. 45:5–32
     [Google Scholar]
  38. 38.
    Burges CJC. 1998. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2:121–67
     [Google Scholar]
  39. 39.
    Fischer A, Igel C. 2012. An Introduction to Restricted Boltzmann Machines14–36. Berlin/Heidelberg: Springer
  40. 40.
    Doersch C. 2016. Tutorial on variational autoencoders. arXiv:1606.05908 [stat.ML]
  41. 41.
    O'Shea K, Nash R 2015. An introduction to convolutional neural networks. arXiv:1511.08458 [cs.NE]
  42. 42.
    Schmidhuber J. 2015. Deep learning in neural networks: an overview. Neural Netw. 61:85–117
     [Google Scholar]
  43. 43.
    Chung J, Gulcehre C, Cho K, Bengio Y. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555 [cs.NE]
  44. 44.
    Hochreiter S, Schmidhuber J. 1997. Long short-term memory. Neural Comput. 9:1735–80
     [Google Scholar]
  45. 45.
    Medsker LR, Jain LC. 1999. Recurrent Neural Networks: Design and Applications Boca Raton: CRC Press
  46. 46.
    Bromley J, Guyon I, LeCun Y, Säckinger E, Shah R. 1993. Signature verification using a “Siamese” time delay neural network. Proceedings of the 6th International Conference on Neural Information Processing Systems737–44. Denver, CO: Morgan Kaufmann Publishers Inc.
     [Google Scholar]
  47. 47.
    Chicco D. 2021. Siamese neural networks: an overview. Methods Mol. Biol. 2190:73–94
     [Google Scholar]
  48. 48.
    Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L et al. 2017. Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems6000–10. Long Beach, CA: Curran Associates Inc.
     [Google Scholar]
  49. 49.
    Ronneberger O, Fischer P, Brox T. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation234–41. Cham, Switzerland: Springer International Publishing
  50. 50.
    Kautz T, Eskofier BM, Pasluosta CF. 2017. Generic performance measure for multiclass-classifiers. Pattern Recogn. 68:111–25
     [Google Scholar]
  51. 51.
    Zhou Y, Hou Y, Hussain M, Brown SA, Budd T et al. 2020. Machine learning-based risk assessment for cancer therapy-related cardiac dysfunction in 4300 longitudinal oncology patients. J. Am. Heart Assoc. 9:e019628
     [Google Scholar]
  52. 52.
    Chocron A, Oster J, Biton S, Mandel F, Elbaz M et al. 2021. Remote atrial fibrillation burden estimation using deep recurrent neural network. IEEE Trans. Biomed. Eng. 68:2447–55
     [Google Scholar]
  53. 53.
    Lu XH, Liu A, Fuh SC, Lian Y, Guo L et al. 2021. Recurrent disease progression networks for modelling risk trajectory of heart failure. PLOS ONE 16:e0245177
     [Google Scholar]
  54. 54.
    Huvanandana J, Nguyen C, Thamrin C, Tracy M, Hinder M, McEwan AL. 2017. Prediction of intraventricular haemorrhage in preterm infants using time series analysis of blood pressure and respiratory signals. Sci. Rep. 7:46538
     [Google Scholar]
  55. 55.
    Tabrizi PR, Mansoor A, Obeid R, Cerrolaza JJ, Perez DA et al. 2020. Ultrasound-based phenotyping of lateral ventricles to predict hydrocephalus outcome in premature neonates. IEEE Trans. Biomed. Eng. 67:3026–34
     [Google Scholar]
  56. 56.
    Mohan S, Thirumalai C, Srivastava G. 2019. Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7:81542–54
     [Google Scholar]
  57. 57.
    Huang Z, Dong W, Ji L, Duan H 2016. Predictive monitoring of clinical pathways. Expert Syst. Appl. 56:227–41
     [Google Scholar]
  58. 58.
    Mezzatesta S, Torino C, Meo P, Fiumara G, Vilasi A. 2019. A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis. Comput. Methods Programs Biomed. 177:9–15
     [Google Scholar]
  59. 59.
    Kim J, Chae M, Chang HJ, Kim YA, Park E 2019. Predicting cardiac arrest and respiratory failure using feasible artificial intelligence with simple trajectories of patient data. J. Clin. Med. 8:1336
     [Google Scholar]
  60. 60.
    Li Y, Sperrin M, Ashcroft DM, van Staa TP. 2020. Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar. BMJ 371:m3919
     [Google Scholar]
  61. 61.
    Oba Y, Tezuka T, Sanuki M, Wagatsuma Y. Analysis of Health Screening Records Using Interpretations of Predictive Models146–51. Cham, Switzerland: Springer International Publishing
  62. 62.
    Perveen S, Shahbaz M, Saba T, Keshavjee K, Rehman A, Guergachi A. 2020. Handling irregularly sampled longitudinal data and prognostic modeling of diabetes using machine learning technique. IEEE Access 8:21875–85
     [Google Scholar]
  63. 63.
    Xu Y, Xu Y, Saria S. 2016. A Bayesian nonparametric approach for estimating individualized treatment-response curves. Proc. Mach. Learn. Res. 56:282–300
     [Google Scholar]
  64. 64.
    Nitski O, Azhie A, Qazi-Arisar FA, Wang X, Ma S et al. 2021. Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data. Lancet Digit. Health 3:e295–305
     [Google Scholar]
  65. 65.
    Rossi LA, Melstrom LG, Fong Y, Sun V. 2021. Predicting post-discharge cancer surgery complications via telemonitoring of patient-reported outcomes and patient-generated health data. J. Surg. Oncol. 123:1345–52
     [Google Scholar]
  66. 66.
    Alaa AM, Yoon J, Hu S, van der Schaar M. 2018. Personalized risk scoring for critical care prognosis using mixtures of Gaussian processes. IEEE Trans. Biomed. Eng. 65:207–18
     [Google Scholar]
  67. 67.
    Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ et al. 2018. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. 2:749–60
     [Google Scholar]
  68. 68.
    Futoma JD, Sendak MP, Cameron B, Heller KA 2016. Predicting disease progression with a model for multivariate longitudinal clinical data. Proc. Mach. Learn. Res. 56:42–54
     [Google Scholar]
  69. 69.
    Makino M, Yoshimoto R, Ono M, Itoko T, Katsuki T et al. 2019. Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. Sci. Rep. 9:11862
     [Google Scholar]
  70. 70.
    Tomasev N, Glorot X, Rae JW, Zielinski M, Askham H et al. 2019. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572:116–19
     [Google Scholar]
  71. 71.
    Chua HR, Zheng K, Vathsala A, Ngiam KY, Yap HK et al. 2021. Health care analytics with time-invariant and time-variant feature importance to predict hospital-acquired acute kidney injury: observational longitudinal study. J. Med. Internet Res. 23:e30805
     [Google Scholar]
  72. 72.
    Fisher CK, Smith AM, Walsh JR. 2019. Machine learning for comprehensive forecasting of Alzheimer's Disease progression. Sci. Rep. 9:13622
     [Google Scholar]
  73. 73.
    Liu M, Zhang J, Adeli E, Shen D. 2019. Joint classification and regression via deep multi-task multi-channel learning for Alzheimer's disease diagnosis. IEEE Trans. Biomed. Eng. 66:1195–206
     [Google Scholar]
  74. 74.
    Bhagwat N, Viviano JD, Voineskos AN, Chakravarty MM. 2018. Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data. PLOS Comput. Biol. 14:e1006376
     [Google Scholar]
  75. 75.
    Lu L, Elbeleidy S, Baker L, Wang H, Shen L, Heng H. 2021. Improved prediction of cognitive outcomes via globally aligned imaging biomarker enrichments over progressions. IEEE Trans. Biomed. Eng. 68:3336–46
     [Google Scholar]
  76. 76.
    Battineni G, Chintalapudi N, Amenta F. 2019. Machine learning in medicine: performance calculation of dementia prediction by support vector machines (SVM). Informatics Med. Unlocked 16:100200
     [Google Scholar]
  77. 77.
    Zhao Y, Healy BC, Rotstein D, Guttmann CR, Bakshi R et al. 2017. Exploration of machine learning techniques in predicting multiple sclerosis disease course. PLOS ONE 12:e0174866
     [Google Scholar]
  78. 78.
    De Brouwer E, Becker T, Moreau Y, Havrdova EK, Trojano M et al. 2021. Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression. Comput. Methods Programs Biomed. 208:106180
     [Google Scholar]
  79. 79.
    Mohan A, Sun Z, Ghosh S, Li Y, Sathe S et al. 2022. A machine-learning derived Huntington's disease progression model: insights for clinical trial design. Mov. Disord. 37:553–62
     [Google Scholar]
  80. 80.
    Klucken J, Krüger R, Schmidt P, Bloem BR. 2018. Management of Parkinson's disease 20 years from now: towards digital health pathways. J. Parkinsons Dis. 8:S85–94
     [Google Scholar]
  81. 81.
    Severson KA, Chahine LM, Smolensky LA, Dhuliawala M, Frasier M et al. 2021. Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning. Lancet Digit. Health 3:e555–64
     [Google Scholar]
  82. 82.
    Shu ZY, Cui SJ, Wu X, Xu Y, Huang P et al. 2021. Predicting the progression of Parkinson's disease using conventional MRI and machine learning: an application of radiomic biomarkers in whole-brain white matter. Magn. Reson. Med. 85:1611–24
     [Google Scholar]
  83. 83.
    Qiu W, Kuang H, Teleg E, Ospel JM, Sohn SI et al. 2020. Machine learning for detecting early infarction in acute stroke with non-contrast-enhanced CT. Radiology 294:638–44
     [Google Scholar]
  84. 84.
    Chen L, Bentley P, Rueckert D. 2017. Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. Neuroimage Clin. 15:633–43
     [Google Scholar]
  85. 85.
    Viglione SS, Walsh GO. 1975. Proceedings: Epileptic seizure prediction. Electroencephalogr. Clin. Neurophysiol. 39:435–36
     [Google Scholar]
  86. 86.
    Shiao HT, Cherkassky V, Lee J, Veber B, Patterson EE et al. 2017. SVM-based system for prediction of epileptic seizures from iEEG signal. IEEE Trans. Biomed. Eng. 64:1011–22
     [Google Scholar]
  87. 87.
    Heldberg BE, Kautz T, Leutheuser H, Hopfengartner R, Kasper BS, Eskofier BM. 2015. Using wearable sensors for semiology-independent seizure detection—towards ambulatory monitoring of epilepsy. Proceedings of the IEEE Engineering in Medicine and Biology Society, 37th Annual International Conference, Milan, Italy5593–96. New York: IEEE
     [Google Scholar]
  88. 88.
    Avcu MT, Zhang Z, Chan DWS. 2019. Seizure detection using least EEG channels by deep convolutional neural network. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019)1120–24. New York: IEEE
     [Google Scholar]
  89. 89.
    Shoeb A, Guttag J. 2010. Application of machine learning to epileptic seizure detection. Proceedings of the 27th International Conference on International Conference on Machine Learning975–82. Haifa, Israel: Omnipress
     [Google Scholar]
  90. 90.
    Dissanayake T, Fernando T, Denman S, Sridharan S, Fookes C. 2021. Deep learning for patient-independent epileptic seizure prediction using scalp EEG signals. IEEE Sensors J. 21:9377–88
     [Google Scholar]
  91. 91.
    Tiwari V. 2010. MFCC and its applications in speaker recognition. Int. J. Emerg. Technol. 1:19–22
     [Google Scholar]
  92. 92.
    Lim B, Alaa A, van der Schaar M 2018. Forecasting treatment responses over time using recurrent marginal structural networks. Proceedings of the 32nd International Conference on Neural Information Processing Systems7494–504. Montréal, Canada: Curran Associates Inc.
     [Google Scholar]
  93. 93.
    Khan J, Wei JS, Ringnér M, Saal LH, Ladanyi M et al. 2001. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7:673–79
     [Google Scholar]
  94. 94.
    Xiao Y, Wu J, Lin Z, Zhao X. 2018. A deep learning-based multi-model ensemble method for cancer prediction. Comput. Methods Programs Biomed. 153:1–9
     [Google Scholar]
  95. 95.
    Lu J, Song E, Ghoneim A, Alrashoud M. 2020. Machine learning for assisting cervical cancer diagnosis: an ensemble approach. Future Generation Comput. Syst. 106:199–205
     [Google Scholar]
  96. 96.
    Yuan Q, Cai T, Hong C, Du M, Johnson BE et al. 2021. Performance of a machine learning algorithm using electronic health record data to identify and estimate survival in a longitudinal cohort of patients with lung cancer. JAMA Netw. Open 4:e2114723
     [Google Scholar]
  97. 97.
    Courtiol P, Maussion C, Moarii M, Pronier E, Pilcer S et al. 2019. Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nat. Med. 25:1519–25
     [Google Scholar]
  98. 98.
    Thakoor KA, Koorathota SC, Hood DC, Sajda P. 2021. Robust and interpretable convolutional neural networks to detect glaucoma in optical coherence tomography images. IEEE Trans. Biomed. Eng. 68:2456–66
     [Google Scholar]
  99. 99.
    Oh K, Kang HM, Leem D, Lee H, Seo KY, Yoon S. 2021. Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images. Sci. Rep. 11:1897
     [Google Scholar]
  100. 100.
    Hirsch S, Shapiro JL, Turega MA, Frank TL, Niven RM, Frank PI 2001. Using a neural network to screen a population for asthma. Ann. Epidemiol. 11:369–76
     [Google Scholar]
  101. 101.
    Chatzimichail E, Paraskakis E, Sitzimi M, Rigas A. 2013. An intelligent system approach for asthma prediction in symptomatic preschool children. Comput. Math. Methods Med. 2013:240182
     [Google Scholar]
  102. 102.
    Prosperi MC, Marinho S, Simpson A, Custovic A, Buchan IE. 2014. Predicting phenotypes of asthma and eczema with machine learning. BMC Med. Genom. 7:Suppl. 1S7
     [Google Scholar]
  103. 103.
    Finkelstein J, Jeong IC. 2017. Machine learning approaches to personalize early prediction of asthma exacerbations. Ann. N. Y. Acad. Sci. 1387:153–65
     [Google Scholar]
  104. 104.
    Spathis D, Vlamos P. 2019. Diagnosing asthma and chronic obstructive pulmonary disease with machine learning. Health Informatics J. 25:811–27
     [Google Scholar]
  105. 105.
    Burgos-Artizzu XP, Perez-Moreno A, Coronado-Gutierrez D, Gratacos E, Palacio M 2019. Evaluation of an improved tool for non-invasive prediction of neonatal respiratory morbidity based on fully automated fetal lung ultrasound analysis. Sci. Rep. 9:1950
     [Google Scholar]
  106. 106.
    Gradus JL, Rosellini AJ, Horvath-Puho E, Street AE, Galatzer-Levy I et al. 2020. Prediction of sex-specific suicide risk using machine learning and single-payer health care registry data from Denmark. JAMA Psychiatry 77:25–34
     [Google Scholar]
  107. 107.
    Kessler RC, Rose S, Koenen KC, Karam EG, Stang PE et al. 2014. How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys. World Psychiatry 13:265–74
     [Google Scholar]
  108. 108.
    Schultebraucks K, Galatzer-Levy IR. 2019. Machine learning for prediction of posttraumatic stress and resilience following trauma: an overview of basic concepts and recent advances. J. Trauma. Stress 32:215–25
     [Google Scholar]
  109. 109.
    Su D, Zhang X, He K, Chen Y. 2021. Use of machine learning approach to predict depression in the elderly in China: a longitudinal study. J. Affect. Disord. 282:289–98
     [Google Scholar]
  110. 110.
    Miotto R, Li L, Kidd BA, Dudley JT. 2016. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6:26094
     [Google Scholar]
  111. 111.
    Jensen AB, Moseley PL, Oprea TI, Ellesoe SG, Eriksson R et al. 2014. Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat. Commun. 5:4022
     [Google Scholar]
  112. 112.
    Pham T, Tran T, Phung D, Venkatesh S. 2017. Predicting healthcare trajectories from medical records: a deep learning approach. J. Biomed. Inform. 69:218–29
     [Google Scholar]
  113. 113.
    Choi E, Bahadori MT, Schuetz A, Stewart WF, Sun J. 2016. Doctor AI: predicting clinical events via recurrent neural networks. JMLR Workshop Conf. Proc. 56:301–18
     [Google Scholar]
  114. 114.
    Razavian N, Marcus J, Sontag D. 2016. Multi-task prediction of disease onsets from longitudinal laboratory tests. Proc. Mach. Learn. Res. 56:73–100
     [Google Scholar]
  115. 115.
    Lee C, Rashbass J, van der Schaar M. 2021. Outcome-oriented deep temporal phenotyping of disease progression. IEEE Trans. Biomed. Eng. 68:2423–34
     [Google Scholar]
  116. 116.
    Lee C, Yoon J, van der Schaar M. 2020. Dynamic-DeepHit: a deep learning approach for dynamic survival analysis with competing risks based on longitudinal data. IEEE Trans. Biomed. Eng. 67:122–33
     [Google Scholar]
  117. 117.
    Zhou Q, Chen ZH, Cao YH, Peng S. 2021. Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review. NPJ Digit. Med. 4:154
     [Google Scholar]
  118. 118.
    Marwaha JS, Kvedar JC. 2022. Crossing the chasm from model performance to clinical impact: the need to improve implementation and evaluation of AI. NPJ Digit. Med. 5:25
     [Google Scholar]
  119. 119.
    de Hond AAH, Leeuwenberg AM, Hooft L, Kant IMJ, Nijman SWJ et al. 2022. Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. NPJ Digit. Med. 5:2
     [Google Scholar]
  120. 120.
    Kakarmath S, Esteva A, Arnaout R, Harvey H, Kumar S et al. 2020. Best practices for authors of healthcare-related artificial intelligence manuscripts. NPJ Digit. Med. 3:134
     [Google Scholar]
  121. 121.
    Cabitza F, Rasoini R, Gensini GF. 2017. Unintended consequences of machine learning in medicine. JAMA 318:517–18
     [Google Scholar]
  122. 122.
    Jefferies JL, Spencer AK, Lau HA, Nelson MW, Giuliano JD et al. 2021. A new approach to identifying patients with elevated risk for Fabry disease using a machine learning algorithm. Orphanet J. Rare. Dis. 16:518
     [Google Scholar]
  123. 123.
    Dir.-Gen. Health Food Saf 2022. A European Health Data Space: Harnessing the Power of Health Data for People, Patients and Innovation Strasbourg, France: European Commission
  124. 124.
    Pinto MF, Leal A, Lopes F, Dourado A, Martins P, Teixeira CA. 2021. A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction. Sci. Rep. 11:3415
     [Google Scholar]
  125. 125.
    Eskofier BM, Kraus M, Worobets JT, Stefanyshyn DJ, Nigg BM. 2012. Pattern classification of kinematic and kinetic running data to distinguish gender, shod/barefoot and injury groups with feature ranking. Comput. Methods Biomech. Biomed. Engin. 15:467–74
     [Google Scholar]
/content/journals/10.1146/annurev-bioeng-110220-030247
Loading
Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine
Annual Review of Biomedical Engineering 25, 131 (2023); https://doi.org/10.1146/annurev-bioeng-110220-030247
/content/journals/10.1146/annurev-bioeng-110220-030247
/content/journals/10.1146/annurev-bioeng-110220-030247
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error