Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
20 May 2024
20 May 2024
Historique:
received:
17
11
2023
accepted:
03
05
2024
medline:
20
5
2024
pubmed:
20
5
2024
entrez:
19
5
2024
Statut:
epublish
Résumé
The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1883 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,460 UK Biobank. Importantly, we observed discriminative improvements over basic demographic predictors for 1774 (94.3%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1347 (89.8%) of 1500 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.
Identifiants
pubmed: 38763986
doi: 10.1038/s41467-024-48568-8
pii: 10.1038/s41467-024-48568-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4257Subventions
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : Project-ID 437531118 - SFB 1470
Informations de copyright
© 2024. The Author(s).
Références
Sindi, S. et al. The CAIDE Dementia Risk Score App: The development of an evidence-based mobile application to predict the risk of dementia. Alzheimers Dement 1, 328–333 (2015).
Lindström, J. & Tuomilehto, J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diab. Care 26, 725–731 (2003).
Goff, D. C. et al. 2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk. Circulation 129, S49–S73 (2014).
pubmed: 24222018
Hippisley-Cox, J., Coupland, C. & Brindle, P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ 357, j2099 (2017).
pubmed: 28536104
pmcid: 5441081
Steyerberg, E. W. et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 10, e1001381 (2013).
pubmed: 23393430
pmcid: 3564751
Hampton, J. R., Harrison, M. J., Mitchell, J. R., Prichard, J. S. & Seymour, C. Relative contributions of history-taking, physical examination, and laboratory investigation to diagnosis and management of medical outpatients. Br. Med J. 2, 486–489 (1975).
pubmed: 1148666
pmcid: 1673456
Danish eHealth Portal. Danish eHealth Portal. Danish eHealth Portal. 2001. https://www.sundhed.dk/borger/service/om-sundheddk/om-organisationen/ehealth-in-denmark/background/ .
e-Health Record. e-Health Record. e-Health Record. 2005. https://e-estonia.com/solutions/healthcare/e-health-records/ .
Clalit Research Institute. Clalit Health Services. Clalit Health Services. 2010. http://clalitresearch.org/about-us/our-data/ (accessed 2010).
National Electronic Health Record. National Electronic Health Record. National Electronic Health Record. 2011. https://www.ihis.com.sg/nehr/about-nehr .
My Health Record. My Health Record. My Health Record. 2016. https://www.myhealthrecord.gov.au/ .
Wood, A. et al. Linked electronic health records for research on a nationwide cohort of more than 54 million people in England: data resource. BMJ 373, n826 (2021).
pubmed: 33827854
Rush, R. Taking Note. N. Engl. J. Med 381, 9 (2019).
pubmed: 31269363
Tsang, G., Zhou, S.-M. & Xie, X. Modeling Large Sparse Data for Feature Selection: Hospital Admission Predictions of the Dementia Patients Using Primary Care Electronic Health Records. IEEE J. Transl. Eng. Health Med 9, 3000113 (2021).
pubmed: 33354439
Langham J. et al. Predicting risk of dementia with machine learning and survival models using routine primary care records. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2021: 3036–3042.
Rajkomar, A. et al. Scalable and accurate deep learning with electronic health records. npj Digital Med. 1, 1–10 (2018).
Appelbaum, L. et al. Development and validation of a pancreatic cancer risk model for the general population using electronic health records: An observational study. Eur. J. Cancer 143, 19–30 (2021).
pubmed: 33278770
Kronzer, V. L. et al. Investigating the impact of disease and health record duration on the eMERGE algorithm for rheumatoid arthritis. J. Am. Med Inf. Assoc. 27, 601–605 (2020).
Sekelj, S. et al. Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study. Eur. J. Prev. Cardiol. 28, 598–605 (2021).
pubmed: 34021576
Miotto, R., Li, L., Kidd, B. A. & Dudley, J. T. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Sci. Rep. 6, 26094 (2016).
pubmed: 27185194
pmcid: 4869115
Estiri, H. et al. Predicting COVID-19 mortality with electronic medical records. NPJ Digit Med 4, 15 (2021).
pubmed: 33542473
pmcid: 7862405
Wu J., Nadarajah R., Raveendra K., Cowan J. C., & Gale C. P. FIND-AF: a widely applicable artificial intelligence algorithm to target systematic screening for atrial fibrillation in older individuals through primary care electronic health records. Europace 2022; 24. https://doi.org/10.1093/europace/euac053.565 .
Bagheri A. et al. Multimodal Learning for Cardiovascular Risk Prediction using EHR Data. arXiv [cs.LG]. 2020; published online Aug 27. http://arxiv.org/abs/2008.11979 .
Ben Miled, Z. et al. Predicting dementia with routine care EMR data. Artif. Intell. Med 102, 101771 (2020).
pubmed: 31980108
Zhao, J. et al. Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction. Sci. Rep. 9, 717 (2019).
pubmed: 30679510
pmcid: 6345960
Jin, B. et al. Predicting the Risk of Heart Failure With EHR Sequential Data Modeling. IEEE Access Undefined 6, 9256–9261 (2018).
Hill, N. R. et al. Predicting atrial fibrillation in primary care using machine learning. PLoS One 14, e0224582 (2019).
pubmed: 31675367
pmcid: 6824570
Tiwari, P. et al. Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation. JAMA Netw. Open 3, e1919396 (2020).
pubmed: 31951272
pmcid: 6991266
Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102–1110 (2013).
pubmed: 24270849
pmcid: 3969265
Bush, W. S., Oetjens, M. T. & Crawford, D. C. Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat. Rev. Genet 17, 129–145 (2016).
pubmed: 26875678
Zhang, Y. et al. High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP). Nat. Protoc. 14, 3426–3444 (2019).
pubmed: 31748751
pmcid: 7323894
Zheng, N. S. et al. PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records. J. Am. Med Inf. Assoc. 27, 1675–1687 (2020).
Rasmy, L., Xiang, Y., Xie, Z., Tao, C. & Zhi, D. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. npj Digital Med. 4, 1–13 (2021).
Li, Y. et al. BEHRT: Transformer for Electronic Health Records. Sci. Rep. 10, 1–12 (2020).
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
pubmed: 30305743
pmcid: 6786975
Cox, D. R. Regression Models and Life-Tables. J. R. Stat. Soc. Ser. B Stat. Methodol. 34, 187–202 (1972).
All of Us Research Program Investigators, Denny, J. C. et al. The ‘All of Us’ Research Program. N. Engl. J. Med 381, 668–676 (2019).
Sudlow, C. et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med 12, e1001779 (2015).
pubmed: 25826379
pmcid: 4380465
Wu, P. et al. Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation. JMIR Med Inf. 7, e14325 (2019).
Ramirez, A. H. et al. The All of Us Research Program: Data quality, utility, and diversity. Patterns (N. Y) 3, 100570 (2022).
pubmed: 36033590
Martinez, F. J. et al. A New Approach for Identifying Patients with Undiagnosed Chronic Obstructive Pulmonary Disease. Am. J. Respir. Crit. Care Med 195, 748–756 (2017).
pubmed: 27783539
pmcid: 5363964
Charlson, M. E., Pompei, P., Ales, K. L. & MacKenzie, C. R. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J. Chronic Dis. 40, 373–383 (1987).
pubmed: 3558716
Castro, J., Gómez, D. & Tejada, J. Polynomial calculation of the Shapley value based on sampling. Comput Oper. Res 36, 1726–1730 (2009).
Finlayson, S. G. et al. The Clinician and Dataset Shift in Artificial Intelligence. N. Engl. J. Med 385, 283–286 (2021).
pubmed: 34260843
pmcid: 8665481
Wong, A. et al. External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients. JAMA Intern Med 181, 1065–1070 (2021).
pubmed: 34152373
Guo, L. L. et al. Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine. Sci. Rep. 12, 2726 (2022).
pubmed: 35177653
pmcid: 8854561
National Institute for health and Care Excellence (NICE). Cardiovascular disease: risk assessment and reduction, including lipid modification. 2014; published online July 18. https://www.nice.org.uk/guidance/cg181 (accessed Sept 16, 2022).
SCORE2 working group and ESC Cardiovascular risk collaboration. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur. Heart J. 42, 2439–2454 (2021).
Collins, R. et al. Interpretation of the evidence for the efficacy and safety of statin therapy. Lancet 388, 2532–2561 (2016).
pubmed: 27616593
Ference, B. A. et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur. Heart J. 38, 2459–2472 (2017).
pubmed: 28444290
pmcid: 5837225
Vaduganathan, M. et al. Estimating lifetime benefits of comprehensive disease-modifying pharmacological therapies in patients with heart failure with reduced ejection fraction: a comparative analysis of three randomised controlled trials. Lancet 396, 121–128 (2020).
pubmed: 32446323
Adelson, K. et al. Standardized Criteria for Palliative Care Consultation on a Solid Tumor Oncology Service Reduces Downstream Health Care Use. J. Oncol. Pr. 13, e431–e440 (2017).
Weissman, D. E. & Meier, D. E. Identifying patients in need of a palliative care assessment in the hospital setting: a consensus report from the Center to Advance Palliative Care. J. Palliat. Med 14, 17–23 (2011).
pubmed: 21133809
Centeno, C. & Arias-Casais, N. Global palliative care: from need to action. Lancet Glob. Health 7, e815–e816 (2019).
pubmed: 31129121
de Lemos, J. A. et al. Multimodality Strategy for Cardiovascular Risk Assessment: Performance in 2 Population-Based Cohorts. Circulation 135, 2119–2132 (2017).
pubmed: 28360032
pmcid: 5486874
Steinfeldt, J. et al. Neural network-based integration of polygenic and clinical information: development and validation of a prediction model for 10-year risk of major adverse cardiac events in the UK Biobank cohort. Lancet Digit Health 4, e84–e94 (2022).
pubmed: 35090679
Buergel T., et al. Metabolomic profiles predict individual multidisease outcomes. Nat. Med. https://doi.org/10.1038/s41591-022-01980-3 2022.
Vayena, E. Value from health data: European opportunity to catalyse progress in digital health. Lancet 397, 652–653 (2021).
pubmed: 33571452
Denaxas, S. et al. A semi-supervised approach for rapidly creating clinical biomarker phenotypes in the UK Biobank using different primary care EHR and clinical terminology systems. JAMIA Open 3, 545–556 (2020).
pubmed: 33619467
pmcid: 7717266
Fry, A. et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am. J. Epidemiol. 186, 1026–1034 (2017).
pubmed: 28641372
pmcid: 5860371
Wei, W.-Q. et al. Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record. PLoS One 12, e0175508 (2017).
pubmed: 28686612
pmcid: 5501393
Moons, K. G. M. et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann. Intern. Med. 162, W1–W73 (2015).
pubmed: 25560730
Stekhoven, D. J. & Bühlmann, P. MissForest-non-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112–118 (2012).
pubmed: 22039212
miceforest. PyPI. https://pypi.org/project/miceforest/ (accessed July 6, 2022).
Katzman J. L. et al. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC. Med. Res. Methodol. 18, 24 (2018).
Ba J. L., Kiros J. R. & Hinton G. E. Layer Normalization. arXiv [stat.ML]. 2016; published online July 21. http://arxiv.org/abs/1607.06450 .
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. J. Mach. Learn Res 15, 1929–1958 (2014).
Kingma D. P. & Ba J. L. Adam: a Method for stochastic optimization. In International Conference on Learning Representations 2015 (ICLR, 2015).
Paszke, A. et al. Automatic differentiation in PyTorch. Adv. Neural Inf. Process. Syst. 30, 1–4 (2017).
Machine Learning CO2 impact calculator. https://mlco2.github.io/impact/ (accessed May 10, 2023).
Harrell, F. E. et al. Evaluating the yield of medical tests. JAMA 247, 2543–2546 (1982).
pubmed: 7069920
lifelines 0.25.8. 2021. https://lifelines.readthedocs.io/en/latest/ (accessed Feb 3, 2021).
How does All of Us assess diversity? What communities does All of Us consider ‘underrepresented in biomedical research?’ https://www.researchallofus.org/faq/how-does-all-of-us-assess-diversity-what-communities-does-all-of-us-consider-underrepresented-in-biomedical-research/ (accessed May 5, 2023).