искусственный интеллект

Прогностические модели в медицине

ОБЗОРЫ ,

А.С. Лучинин

ФГБУН «Кировский НИИ гематологии и переливания крови ФМБА», ул. Красноармейская, д. 72, Киров, Российская Федерация, 610027

Для переписки: Александр Сергеевич Лучинин, канд. мед. наук, ул. Красноармейская, д. 72, Киров, Российская Федерация, 610027; тел.: +7(919)506-87-86; e-mail: glivec@mail.ru

Для цитирования: Лучинин А.С. Прогностические модели в медицине. Клиническая онкогематология. 2023;16(1):27–36.

DOI: 10.21320/2500-2139-2023-16-1-27-36

РЕФЕРАТ

Медицинские прогностические (предиктивные) модели (МПМ) имеют важное значение в современном здравоохранении. Они определяют риски для здоровья и возникновения заболеваний. Целью их создания является улучшение результатов диагностики и лечения. Все МПМ можно разделить на две категории. Диагностические медицинские модели (ДММ) помогают рассчитать индивидуальный риск присутствия заболевания, в то время как прогностические медицинские модели (ПММ) — риск возникновения болезни или его осложнения в будущем. В обзоре обсуждаются характеристики ДММ и ПММ, условия их разработки, критерии применения в медицине, в частности в гематологии, а также проблемы, возникающие на этапе их создания и проверки качества.

Ключевые слова: прогностическая модель, искусственный интеллект.

Получено: 13 сентября 2022 г.

Принято в печать: 7 декабря 2022 г.

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Статистика Plumx русский

ЛИТЕРАТУРА

  1. Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal. Br Med J. 2020;369:m1328. doi: 10.1136/bmj.m1328.
  2. Van Smeden M, Reitsma JB, Riley RD, et al. Clinical prediction models: diagnosis versus prognosis. J Clin Epidemiol. 2021;132:142–5. doi: 10.1016/j.jclinepi.2021.01.009.
  3. Schalling M, Gleiss A, Gisslinger B, et al. Essential thrombocythemia vs. pre-fibrotic/early primary myelofibrosis: discrimination by laboratory and clinical data. Blood Cancer J. 2017;7(12):643. doi: 10.1038/s41408-017-0006-y.
  4. Guncar G, Kukar M, Notar M, et al. An application of machine learning to haematological diagnosis. Sci Rep. 2018;8(1):411. doi: 10.1038/s41598-017-18564-8.
  5. Sehn LH, Berry B, Chhanabhai M, et al. The revised International Prognostic Index (R-IPI) is a better predictor of outcome than the standard IPI for patients with diffuse large B-cell lymphoma treated with R-CHOP. Blood. 2007;109(5):1857–61. doi: 10.1182/blood-2006-08-038257.
  6. Van de Schans SАM, Steyerberg EW, Nijziel MR, et al. Validation, revision and extension of the Follicular Lymphoma International Prognostic Index (FLIPI) in a population-based setting. Ann Oncol. 2009;20(10):1697–702. doi: 10.1093/annonc/mdp053.
  7. Palumbo A, Avet-Loiseau H, Oliva S, et al. Revised International Staging System for Multiple Myeloma: A Report From International Myeloma Working Group. J Clin Oncol. 2015;33(26):2863–9. doi: 10.1200/JCO.2015.61.2267.
  8. Лучинин А.С. Искусственный интеллект в гематологии. Клиническая онкогематология. 2022;15(1):16–27. doi: 10.21320/2500-2139-2022-15-1-16-27.
    [Luchinin AS. Artificial Intelligence in Hematology. Clinical oncohematology. 2022;15(1):16–27. doi: 10.21320/2500-2139-2022-15-1-16-27. (In Russ)]
  9. Zhou L, Meng X, Huang Y, et al. An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors. Nat Mach Intell. 2022;4(5):494–503. doi: 10.1038/s42256-022-00483-7.
  10. Szumilas M. Explaining Odds Ratios. J Can Acad Child Adolesc Psychiatry. 2010;19(3):227–29.
  11. Barraclough H, Simms L, Govindan R. Biostatistics Primer: What a Clinician Ought to Know: Hazard Ratios. J Thorac Oncol. 2011;6(6):978–82. doi: 10.1097/JTO.0b013e31821b10ab.
  12. Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J. 2014;35(29):1925–31. doi: 10.1093/eurheartj/ehu207.
  13. Van Calster B, McLernon DJ, van Smeden M, et al. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17(1):230. doi: 10.1186/s12916-019-1466-7.
  14. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Ann Intern Med. 2019;170(1):51–8. doi: 10.7326/M18-1376.
  15. Moons KGM, Altman DG, Vergouwe Y, Royston P. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. Br Med J. 2009;338:b606. doi: 10.1136/bmj.b606.
  16. Altman DG, Bland JM. Missing data. Br Med J. 2007;334(7590):424. doi: 10.1136/bmj.38977.682025.2C.
  17. Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. Br Med J. 2020;368:m441. doi: 10.1136/bmj.m441.
  18. Jenkins DG, Quintana-Ascencio PF. A solution to minimum sample size for regressions. PloS One. 2020;15(2):e0229345. doi: 10.1371/journal.pone.0229345.
  19. Van Voorhis WCR, Morgan BL. Understanding Power and Rules of Thumb for Determining Sample Sizes. Tutor Quant Meth Psychol. 2007;3(2):43–50. doi: 10.20982/tqmp.03.2.p043.
  20. Peduzzi P, Concato J, Kemper E, et al. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–9. doi: 10.1016/s0895-4356(96)00236-3.
  21. Bujang MA, Sa’at N, Sidik TMITAB, Joo LC. Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data. Malays J Med Sci. 2018;25(4):122–30. doi: 10.21315/mjms2018.25.4.12.
  22. Zhou P-Y, Wong AKC. Explanation and prediction of clinical data with imbalanced class distribution based on pattern discovery and disentanglement. BMC Med Inform Decis Mak. 2021;21(1):16. doi: 10.1186/s12911-020-01356-y.
  23. Pauker SG, Kassirer JP. The Threshold Approach to Clinical Decision Making. N Engl J Med. 1980;302(20):1109–17. doi: 10.1056/NEJM198005153022003.
  24. Lee DK. Data transformation: a focus on the interpretation. Korean J Anesthesiol. 2020;73(6):503–8. doi: 10.4097/kja.20137.
  25. Zhang Z. Variable selection with stepwise and best subset approaches. Ann Transl Med. 2016;4(7):136. doi: 10.21037/atm.2016.03.35.
  26. Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16(4):385395. doi: 10.1002/(sici)1097-0258(19970228)16:4<385::aid-sim380>3.0.co;2-3.
  27. de Hond AAH, Leeuwenberg AM, Hooft L, et al. Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. NPJ Digit Med. 2022;5(1):1–13. doi: 10.1038/s41746-021-00549-7.
  28. Hajian-Tilaki K. Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. Caspian J Intern Med. 2013;4(2):627–35.
  29. Agarwal A, Sharma P, Alshehri M, et al. Classification model for accuracy and intrusion detection using machine learning approach. PeerJ Comput Sci. 2021;7:e437. doi: 10.7717/peerj-cs.437.
  30. Hendriksen JMT, Geersing GJ, Moons KGM, de Groot JАH. Diagnostic and prognostic prediction models. J Thromb Haemost. 2013;11(Suppl 1):129–41. doi: 10.1111/jth.12262.
  31. Huang Y, Li W, Macheret F, et al. A tutorial on calibration measurements and calibration models for clinical prediction models. J Am Med Inform Assoc. 2020;27(4):621–33. doi: 10.1093/jamia/ocz228.
  32. Snell KIE, Archer L, Ensor J, et al. External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb. J Clin Epidemiol. 2021;135:79–89. doi: 10.1016/j.jclinepi.2021.02.011.
  33. Ramspek CL, Teece L, Snell KIE, et al. Lessons learnt when accounting for competing events in the external validation of time-to-event prognostic models. Int J Epidemiol. 2022;51(2):615–25. doi: 10.1093/ije/dyab256.
  34. Van Geloven N, Giardiello D, Bonneville EF, et al. Validation of prediction models in the presence of competing risks: a guide through modern methods. Br Med J. 2022;377:e069249. doi: 10.1136/bmj-2021-069249.
  35. Altman DG, Bland JM. Absence of evidence is not evidence of absence. Br Med J. 1995;311(7003):485. doi: 10.1136/bmj.311.7003.485.
  36. Smith GD, Ebrahim S. Data dredging, bias, or confounding. Br Med J. 2002;325(7378):1437–8. doi: 10.1136/bmj.325.7378.1437.
  37. Lakens D, Adolfi FG, Albers CJ, et al. Justify your alpha. Nat Hum Behav. 2018;2(3):168–71. doi: 10.1038/s41562-018-0311-x.
  38. Benjamin DJ, Berger JO, Johannesson M, et al. Redefine statistical significance. Nat Hum Behav. 2018;2(1):6–10. doi: 10.1038/s41562-017-0189-z.
  39. Van Smeden M, Lash TL, Groenwold RHH. Reflection on modern methods: five myths about measurement error in epidemiological research. Int J Epidemiol. 2020;49(1):338–47. doi: 10.1093/ije/dyz251.
  40. Altman DG, Royston P. The cost of dichotomising continuous variables. Br Med J. 2006;332(7549):1080. doi: 10.1136/bmj.332.7549.1080.
  41. Wynants L, van Smeden M, McLernon DJ, et al. Three myths about risk thresholds for prediction models. BMC Med. 2019;17(1):192. doi: 10.1186/s12916-019-1425-3.
  42. Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006;25(1):127–41. doi: 10.1002/sim.2331.
  43. Vargha A, Rudas T, Delaney HD, Maxwell SE. Dichotomization, Partial Correlation, and Conditional Independence. J Educ Behav Stat. 1996;21(3):264–82. doi: 10.3102/10769986021003264.
  44. Basagana X, Pedersen M, Barrera-Gomez J, et al. Analysis of multicentre epidemiological studies: contrasting fixed or random effects modelling and meta-analysis. Int J Epidemiol. 2018;47(4):1343–54. doi: 10.1093/ije/dyy
  45. Лучинин А.С. Лечение пациентов с впервые диагностированной диффузной В-крупноклеточной лимфомой: обзор литературы и метаанализ. Клиническая онкогематология. 2022;15(2):130–9. doi: 10.21320/2500-2139-2022-15-2-130-139.
    [Luchinin AS. Treatment of Patients with Newly Diagnosed Diffuse Large B-Cell Lymphoma: A Literature Review and Meta-Analysis. Clinical oncohematology. 2022;15(2):130–9. doi: 10.21320/2500-2139-2022-15-2-130-139. (In Russ)]
  46. Riley RD, Collins GS, Ensor J, et al. Minimum sample size calculations for external validation of a clinical prediction model with a time-to-event outcome. Stat Med. 2022;41(7):1280–95. doi: 10.1002/sim.9275.
  47. Riley RD, Snell KIE, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: Part I – continuous outcomes. Stat Med. 2019;38(7):1262–75. doi: 10.1002/sim.7993.
  48. Riley RD, Snell KI, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: Part II – binary and time-to-event outcomes. Stat Med. 2019;38(7):1276–96. doi: 10.1002/sim.7992.
  49. Riley RD, Debray TPA, Collins GS, et al. Minimum sample size for external validation of a clinical prediction model with a binary outcome. Stat Med. 2021;40(19):4230–51. doi: 10.1002/sim.9025.
  50. Sterne JAC, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. Br Med J. 2009;338:b2393. doi: 10.1136/bmj.b2393.
  51. Petrazzini BO, Naya H, Lopez-Bello F, et al. Evaluation of different approaches for missing data imputation on features associated to genomic data. BioData Min. 2021;14(1):44. doi: 10.1186/s13040-021-00274-7.
  52. Sun GW, Shook TL, Kay GL. Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol. 1996;49(8):907–16. doi: 10.1016/0895-4356(96)00025-x.
  53. Heinze G, Dunkler D. Five myths about variable selection. Transpl Int. 2017;30(1):6–10. doi: 10.1111/tri.12895.
  54. Chen R-C, Dewi C, Huang S-W, Caraka RE. Selecting critical features for data classification based on machine learning methods. J Big Data. 2020;7(1):52. doi: 10.1186/s40537-020-00327-4.
  55. Moons KGM, Kengne AP, Grobbee DE, et al. Risk prediction models: II. External validation, model updating, and impact assessment. Heart. 2012;98(9):691–8. doi: 10.1136/heartjnl-2011-301247.
  56. Moons KGM, Altman DG, Reitsma JB, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1-73. doi: 10.7326/M14-0698.
  57. Vasey B, Nagendran M, Campbell B, et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med. 2022;28(5):924–33. doi: 10.1038/s41591-022-01772-9.

Искусственный интеллект в гематологии

ОБЗОРЫ ,

Искусственный интеллект не заменит врача, однако врачи, использующие искусственный

интеллект, заменят тех, кто его не использует.

Dr. Bertalan Mesko, медицинский футурист

А.С. Лучинин

ФГБУН «Кировский НИИ гематологии и переливания крови ФМБА», ул. Красноармейская, д. 72, Киров, Российская Федерация, 610027

Для переписки: Александр Сергеевич Лучинин, канд. мед. наук, ул. Красноармейская, д. 72, Киров, Российская Федерация, 610027; тел.: +7(919)506-87-86; e-mail: glivec@mail.ru

Для цитирования: Лучинин А.С. Искусственный интеллект в гематологии. Клиническая онкогематология. 2022;15(1):16–27.

DOI: 10.21320/2500-2139-2022-15-1-16-27

РЕФЕРАТ

«Искусственный интеллект» — это общий термин, описывающий компьютерные технологии для решения задач, которые требуют применения интеллекта человека, например распознавание человеческого голоса или изображений. Большинство продуктов с использованием искусственного интеллекта, применяемых в здравоохранении, связано с машинным обучением — отраслью информатики и статистики, которая генерирует предсказательные или описательные модели путем обучения на основе данных, а не путем программирования четких правил. Машинное обучение получило широкое распространение в патоморфологии, радиологии, геномике и анализе данных электронных медицинских карт. С учетом имеющейся тенденции технологии искусственного интеллекта, вероятно, будут все больше интегрироваться в исследовательскую и практическую медицину, включая гематологию. Таким образом, искусственный интеллект и машинное обучение заслуживают внимания и понимания со стороны исследователей и клиницистов. В данном обзоре описываются важные терминологические понятия и основные концепции обозначенных технологий, а также приводятся примеры их практического использования в научной и практической работе врача-гематолога.

Ключевые слова: искусственный интеллект, машинное обучение, нейронная сеть.

Получено: 23 сентября 2021 г.

Принято в печать: 15 декабря 2021 г.

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Статистика Plumx русский

ЛИТЕРАТУРА

  1. Muhsen IN, Shyr D, Sung AD, Hashmi SK. Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases. Clin Hematol Intern. 2021;3(1):13–20. doi: 10.2991/chi.k.201130.001.
  2. Radakovich N, Nagy M, Nazha A. Machine learning in haematological malignancies. Lancet Haematol. 2020;7(7):e541–e550. doi: 10.1016/S2352-3026(20)30121-6.
  3. Deo RC. Machine Learning in Medicine. Circulation. 2015;132(20):1920–30. doi: 10.1161/CIRCULATIONAHA.115.001593.
  4. Miotto R, Wang F, Wang S, et al. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236–46. doi: 10.1093/bib/bbx044.
  5. Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–9. doi: 10.1038/s41591-018-0316-z.
  6. Komura D, Ishikawa S. Machine learning approaches for pathologic diagnosis. Virchows Arch. 2019;475(2):131–8. doi: 10.1007/s00428-019-02594-w.
  7. Sha L, Osinski BL, Ho IY, et al. Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images. J Pathol Inform. 2019;10(1):24. doi: 10.4103/jpi.jpi_24_19.
  8. Abramoff MD, Lavin PT, Birch M, et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1(1):39. doi: 10.1038/s41746-018-0040-6.
  9. Benjamens S, Dhunnoo P, Mesko B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med. 2020;3(1):118. doi: 10.1038/s41746-020-00324-0.
  10. Shouval R, Fein JA, Savani B, et al. Machine learning and artificial intelligence in haematology. Br J Haematol. 2021;192(2):239–50. doi: 10.1111/bjh.16915.
  11. Shahid AH, Singh MP. Computational intelligence techniques for medical diagnosis and prognosis: problems and current developments. Biocybern Biomed Eng. 2019;39(3):638–72. doi: 10.1016/j.bbe.2019.05.010.
  12. Морозов С.П., Владзимирский А.В., Кляшторный В.Г. и др. Клинические испытания программного обеспечения на основе интеллектуальных технологий (лучевая диагностика). Лучшие практики лучевой и инструментальной диагностики. Препринт № ЦДТ-2019-1. М., 2019. 34 с.
    [Morozov SP, Vladzimirskii AV, Klyashtornyi VG, et al. Clinical acceptance of software based on artificial intelligence technologies (radiology). Preprint No. CDT-2019-1. Luchshie praktiki luchevoi i instrumental’noi diagnostiki. (Best practices in medical imaging.) Moscow; 2019. 34 (In Russ)]
  13. Shekelle PG, Shetty K, Newberry S, et al. Machine Learning Versus Standard Techniques for Updating Searches for Systematic Reviews: A Diagnostic Accuracy Study. Ann Intern Med. 2017;167(3):213–5. doi: 10.7326/L17-0124.
  14. Kimura K, Tabe Y, Ai T, et al. A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA. Sci Rep. 2019;9(1):13385. doi: 10.1038/s41598-019-49942-z.
  15. Wang Q, Bi S, Sun M, et al. Deep learning approach to peripheral leukocyte recognition. PLoS One. 2019;14(6):e0218808. doi: 10.1371/journal.pone.0218808.
  16. Hegde RB, Prasad K, Hebbar H, Singh BMK. Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images. Biocybern Biomed Eng. 2019;39(2):382–92. doi: 10.1016/j.bbe.2019.01.005.
  17. Syrykh C, Abreu A, Amara N, et al. Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning. NPJ Digit Med. 2020;3(1):63. doi: 10.1038/s41746-020-0272-0.
  18. Achi HE, Belousova T, Chen L, et al. Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning. Ann Clin Lab Sci. 2019;49(2):153–60.
  19. Sheng B, Zhou M, Hua M, et al. A blood cell dataset for lymphoma classification using faster R-CNN. Biotechnol Biotechnol Equip. 2020;34(1):413–20. doi: 10.1080/13102818.2020.1765871.
  20. Xu L, Tetteh G, Lipkova J, et al. Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on (68)Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods. Contrast Media Mol Imaging. 2018;2018:1–11. doi: 10.1155/2018/2391925.
  21. Martinez-Martinez F, Kybic J, Lambert L, Meckova Z. Fully automated classification of bone marrow infiltration in low-dose CT of patients with multiple myeloma based on probabilistic density model and supervised learning. Comput Biol Med. 2016;71:57–66. doi: 10.1016/j.compbiomed.2016.02.001.
  22. Wang L, Zhao Z, Luo Y, et al. Classifying 2-year recurrence in patients with DLBCL using clinical variables with imbalanced data and machine learning methods. Comput Meth Program Biomed. 2020;196:105567. doi: 10.1016/j.cmpb.2020.105567.
  23. Biccler JL, Eloranta S, de Nully Brown P, et al. Optimizing Outcome Prediction in Diffuse Large B-Cell Lymphoma by Use of Machine Learning and Nationwide Lymphoma Registries: A Nordic Lymphoma Group Study. JCO Clin Cancer Inform. 2018;2:1–13. doi: 10.1200/CCI.18.00025.
  24. Guncar G, Kukar M, Notar M, et al. An application of machine learning to haematological diagnosis. Sci Rep. 2018;8(1):411. doi: 10.1038/s41598-017-18564-8.
  25. Breiman L. Random forests. Machine Learning. 2001;45:5–32. doi: 1023/A:1010933404324.
  26. Nazha A, Komrokji RS, Meggendorfer M, et al. A Personalized Prediction Model to Risk Stratify Patients with Myelodysplastic Syndromes. Blood. 2018;132(Suppl 1):793. doi: 10.1182/blood-2018-99-114774.
  27. Hu SB, Wong DJ, Correa A, et al. Prediction of Clinical Deterioration in Hospitalized Adult Patients with Hematologic Malignancies Using a Neural Network Model. PLoS One. 2016;11(8):e0161401. doi: 10.1371/journal.pone.0161401.
  28. Prochazka VK, Matustikova S, Furst T, et al. Bayesian Network Modelling As a New Tool in Predicting of the Early Progression of Disease in Follicular Lymphoma Patients. Blood. 2020;136(Suppl 1):20–1. doi: 10.1182/blood-2020-139830.
  29. Mahmood N, Shahid S, Bakhshi T, et al. Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach. Med Biol Eng Comput. 2020;58(11):2631–40. doi: 10.1007/s11517-020-02245-2.
  30. Gandelman JS, Byrne MT, Mistry AM, et al. Machine learning reveals chronic graft-versus-host disease phenotypes and stratifies survival after stem cell transplant for hematologic malignancies. Haematologica. 2019;104(1):189–96. doi: 10.3324/haematol.2018.193441.
  31. Chen D, Goyal G, Go RS, et al. Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia. JCO Clin Cancer Inform. 2019;3:1–11. doi: 10.1200/CCI.18.00137.
  32. Coombes CE, Abrams ZB, Li S, et al. Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia. J Am Med Inform Assoc. 2020;27(7):1019–27. doi: 10.1093/jamia/ocaa060.
  33. Shah P, Kendall F, Khozin S, et al. Artificial intelligence and machine learning in clinical development: a translational perspective. NPJ Digit Med. 2019;2(1):69. doi: 10.1038/s41746-019-0148-3.
  34. Shouval R, Labopin M, Bondi O, et al. Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation Mortality 100 Days After Transplantation Using a Machine Learning Algorithm: A European Group for Blood and Marrow Transplantation Acute Leukemia Working Party Retrospective Data Mining Study. J Clin Oncol. 2015;33(28):3144–51. doi: 10.1200/JCO.2014.59.1339.
  35. Nazha A, Hu ZH, Wang T, et al. A Personalized Prediction Model for Outcomes after Allogeneic Hematopoietic Cell Transplant in Patients with Myelodysplastic Syndromes. Biol Blood Marrow Transplant. 2020;26(11):2139–46. doi: 10.1016/j.bbmt.2020.08.003.
  36. Bigorra L, Larriba I, Gutierrez-Gallego R. Machine learning algorithms for accurate differential diagnosis of lymphocytosis based on cell population data. Br J Haematol. 2019;184(6):1035–7. doi: 10.1111/bjh.15230.
  37. Nazha A, Sekeres MA, Bejar R, et al. Genomic Biomarkers to Predict Resistance to Hypomethylating Agents in Patients with Myelodysplastic Syndromes Using Artificial Intelligence. JCO Precis Oncol. 2019;3:1–11. doi: 10.1200/po.19.00119.
  38. Milgrom SA, Elhalawani H, Lee J, et al. A PET Radiomics Model to Predict Refractory Mediastinal Hodgkin Lymphoma. Sci Rep. 2019;9(1):1322. doi: 10.1038/s41598-018-37197-z.
  39. Moraes LO, Pedreira CE, Barrena S, et al. A decision-tree approach for the differential diagnosis of chronic lymphoid leukemias and peripheral B-cell lymphomas. Comput Meth Program Biomed. 2019;178:85–90. doi: 10.1016/j.cmpb.2019.06.014.
  40. Ni W, Hu B, Zheng C, et al. Automated analysis of acute myeloid leukemia minimal residual disease using a support vector machine. Oncotarget. 2016;7(44):71915–21. doi: 10.18632/oncotarget.12430.
  41. Fuse K, Uemura S, Tamura S, et al. Patient-based prediction algorithm of relapse after allo-HSCT for acute Leukemia and its usefulness in the decision-making process using a machine learning approach. Cancer Med. 2019;8(11):5058–67. doi: 10.1002/cam4.2401.
  42. Goswami C, Poonia S, Kumar L, Sengupta D. Staging System to Predict the Risk of Relapse in Multiple Myeloma Patients Undergoing Autologous Stem Cell Transplantation. Front Oncol. 2019;9:633. doi: 10.3389/fonc.2019.00633.
  43. Gal O, Auslander N, Fan Y, Meerzaman D. Predicting Complete Remission of Acute Myeloid Leukemia: Machine Learning Applied to Gene Expression. Cancer Inform. 2019;18:1–5. doi: 10.1177/1176935119835544.
  44. Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett. 2020;471:61–71. doi: 10.1016/j.canlet.2019.12.007.
  45. Ubels J, Sonneveld P, van Beers EH, et al. Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects. Nat Commun. 2018;9(1):2943. doi: 10.1038/s41467-018-05348-5.
  46. Shain KH, Hart D, Silva AS, et al. Reinforcement Learning to Optimize the Treatment of Multiple Myeloma. Blood. 2019;134(Suppl_1):5511. doi: 10.1182/blood-2019-132234.
  47. Mateos MV, Blacklock H, Schjesvold F, et al. Pembrolizumab plus pomalidomide and dexamethasone for patients with relapsed or refractory multiple myeloma (KEYNOTE-183): a randomised, open-label, phase 3 trial. Lancet Haematol. 2019;6(9):e459–e469. doi: 10.1016/S2352-3026(19)30110-3.
  48. Liao JJZ, Farooqui MZH, Marinello P, et al. Using artificial intelligence tools in answering important clinical questions: The KEYNOTE-183 multiple myeloma experience. Contemp Clin Trials. 2020;99:106179. doi: 10.1016/j.cct.2020.106179.
  49. Третье мнение. AI для клинической лабораторной диагностики (электронный документ). Доступно по: https://thirdopinion.ai/ru#rec Ссылка активна на 06.10.2021.
    [Third Opinion. AI for clinical laboratory diagnostics (Internet). Available from: https://thirdopinion.ai/ru#rec354556522 (accessed 06.10.2021). (In Russ)]