1932
0

Annual Review of Biomedical Data Science

Volume 7, 2024

Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives

Abstract

Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.

Keyword(s): disease co-occurrence, disease trajectories, healthcare data, multimorbidity
Loading

Article metrics loading...

/content/journals/10.1146/annurev-biodatasci-110123-041001
2024-08-23
2025-06-22
Loading full text...

Full text loading...

/deliver/fulltext/biodatasci/7/1/annurev-biodatasci-110123-041001.html?itemId=/content/journals/10.1146/annurev-biodatasci-110123-041001&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Johnston MC, Crilly M, Black C, Prescott GJ, Mercer SW. 2019.. Defining and measuring multimorbidity: a systematic review of systematic reviews. . Eur. J. Public Health 29:(1):182–89
     [Crossref] [Google Scholar]
  2. 2.
    Langenberg C, Hingorani AD, Whitty CJM. 2023.. Biological and functional multimorbidity—from mechanisms to management. . Nat. Med. 29:(7):1649–57
     [Crossref] [Google Scholar]
  3. 3.
    Nguyen H, Manolova G, Daskalopoulou C, Vitoratou S, Prince M, Prina AM. 2019.. Prevalence of multimorbidity in community settings: a systematic review and meta-analysis of observational studies. . J. Comorbidity 9::2235042X19870934
     [Crossref] [Google Scholar]
  4. 4.
    Pearson-Stuttard J, Ezzati M, Gregg EW. 2019.. Multimorbidity—a defining challenge for health systems. . Lancet Public Health 4:(12):e599–600
     [Crossref] [Google Scholar]
  5. 5.
    Hidalgo CA, Blumm N, Barabási A-L, Christakis NA. 2009.. A dynamic network approach for the study of human phenotypes. . PLOS Comput. Biol. 5:(4):e1000353
     [Crossref] [Google Scholar]
  6. 6.
    van den Akker M, Buntinx F, Knottnerus JA. 1996.. Comorbidity or multimorbidity: What's in a name?. Eur. J. Gen. Pract. 2:(2):65–70
     [Crossref] [Google Scholar]
  7. 7.
    Hu JX, Thomas CE, Brunak S. 2016.. Network biology concepts in complex disease comorbidities. . Nat. Rev. Genet. 17:(10):615–29
     [Crossref] [Google Scholar]
  8. 8.
    Jensen PB, Jensen LJ, Brunak S. 2012.. Mining electronic health records: towards better research applications and clinical care. . Nat. Rev. Genet. 13:(6):395–405
     [Crossref] [Google Scholar]
  9. 9.
    Ohlsson M, Peterson C, Dictor M. 2001.. Using Hidden Markov Models to characterize disease trajectories. Paper presented at the 4th International Conference on Neural Networks and Expert Systems in Medicine and Healthcare, Milos Island, Greece:, June 20–22
    [Google Scholar]
  10. 10.
    Hyman RB, Corbin JM, eds. 2001.. Chronic Illness: Research and Theory for Nursing Practice. New York:: Springer
     [Google Scholar]
  11. 11.
    Henly SJ, Wyman JF, Findorff MJ. 2011.. Health and illness over time: the trajectory perspective in nursing science. . Nurs. Res. 60:(Suppl. 3):S5–14
     [Crossref] [Google Scholar]
  12. 12.
    Hawkins NM, Jhund PS, McMurray JJV, Capewell S. 2012.. Heart failure and socioeconomic status: accumulating evidence of inequality. . Eur. J. Heart Fail. 14:(2):138–46
     [Crossref] [Google Scholar]
  13. 13.
    Aguayo-Orozco A, Haue AD, Jørgensen IF, Westergaard D, Moseley PL, et al. 2021.. Optimizing drug selection from a prescription trajectory of one patient. . NPJ Digit. Med. 4:(1):150
     [Crossref] [Google Scholar]
  14. 14.
    Rodríguez CL, Haue AD, Mazzoni G, Eriksson R, Biel JH, et al. 2023.. Drug dosage modifications in 24 million in-patient prescriptions covering eight years: a Danish population-wide study of polypharmacy. . PLOS Digit. Health 2:(9):e0000336
     [Crossref] [Google Scholar]
  15. 15.
    Weber GM, Zhang HG, L'Yi S, Bonzel C-L, Hong C, et al. 2021.. International changes in COVID-19 clinical trajectories across 315 hospitals and 6 countries: retrospective cohort study. . J. Med. Internet Res. 23:(10):e31400
     [Crossref] [Google Scholar]
  16. 16.
    Jensen AB, Moseley PL, Oprea TI, Ellesøe SG, Eriksson R, et al. 2014.. Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. . Nat. Commun. 5:(1):4022
     [Crossref] [Google Scholar]
  17. 17.
    Beck MK, Westergaard D, Jensen AB, Groop L, Brunak S. 2017.. Temporal order of disease pairs affects subsequent disease trajectories: the case of diabetes and sleep apnea. . Pac. Symp. Biocomput. 2017::380–89
     [Google Scholar]
  18. 18.
    Westergaard D, Moseley P, Sørup FKH, Baldi P, Brunak S. 2019.. Population-wide analysis of differences in disease progression patterns in men and women. . Nat. Commun. 10:(1):666
     [Crossref] [Google Scholar]
  19. 19.
    Nielsen AB, Thorsen-Meyer H-C, Belling K, Nielsen AP, Thomas CE, et al. 2019.. Survival prediction in intensive-care units based on aggregation of long-term disease history and acute physiology: a retrospective study of the Danish National Patient Registry and electronic patient records. . Lancet Digit. Health 1:(2):e78–89
     [Crossref] [Google Scholar]
  20. 20.
    Placido D, Yuan B, Hjaltelin JX, Zheng C, Haue AD, et al. 2023.. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. . Nat. Med. 29:(5):1113–22
     [Crossref] [Google Scholar]
  21. 21.
    Hajat C, Stein E. 2018.. The global burden of multiple chronic conditions: a narrative review. . Prev. Med. Rep. 12::284–93
     [Crossref] [Google Scholar]
  22. 22.
    Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. 2012.. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. . Lancet 380:(9836):37–43
     [Crossref] [Google Scholar]
  23. 23.
    Loza E, Jover JA, Rodriguez L, Carmona L. 2009.. Multimorbidity: prevalence, effect on quality of life and daily functioning, and variation of this effect when one condition is a rheumatic disease. . Semin. Arthritis Rheum. 38:(4):312–19
     [Crossref] [Google Scholar]
  24. 24.
    Souza DLB, Oliveras-Fabregas A, Minobes-Molina E, de Camargo Cancela M, Galbany-Estragués P, Jerez-Roig J. 2021.. Trends of multimorbidity in 15 European countries: a population-based study in community-dwelling adults aged 50 and over. . BMC Public Health 21:(1):76
     [Crossref] [Google Scholar]
  25. 25.
    Ward BW, Schiller JS, Goodman RA. 2014.. Multiple chronic conditions among US adults: a 2012 update. . Prev. Chronic Dis. 11::130389
     [Crossref] [Google Scholar]
  26. 26.
    van Oostrom SH, Gijsen R, Stirbu I, Korevaar JC, Schellevis FG, et al. 2016.. Time trends in prevalence of chronic diseases and multimorbidity not only due to aging: data from general practices and health surveys. . PLOS ONE 11:(8):e0160264
     [Crossref] [Google Scholar]
  27. 27.
    Kannan V, Swartz F, Kiani NA, Silberberg G, Tsipras G, et al. 2016.. Conditional disease development extracted from longitudinal health care cohort data using layered network construction. . Sci. Rep. 6:(1):26170
     [Crossref] [Google Scholar]
  28. 28.
    Makovski TT, Schmitz S, Zeegers MP, Stranges S, van den Akker M. 2019.. Multimorbidity and quality of life: systematic literature review and meta-analysis. . Ageing Res. Rev. 53::100903
     [Crossref] [Google Scholar]
  29. 29.
    Wang L, Si L, Cocker F, Palmer AJ, Sanderson K. 2018.. A systematic review of cost-of-illness studies of multimorbidity. . Appl. Health Econ. Health Policy 16:(1):15–29
     [Crossref] [Google Scholar]
  30. 30.
    Nunes BP, Flores TR, Mielke GI, Thumé E, Facchini LA. 2016.. Multimorbidity and mortality in older adults: a systematic review and meta-analysis. . Arch. Gerontol. Geriatr. 67::130–38
     [Crossref] [Google Scholar]
  31. 31.
    Cezard G, McHale CT, Sullivan F, Bowles JKF, Keenan K. 2021.. Studying trajectories of multimorbidity: a systematic scoping review of longitudinal approaches and evidence. . BMJ Open 11:(11):e048485
     [Crossref] [Google Scholar]
  32. 32.
    Ashworth M, Durbaba S, Whitney D, Crompton J, Wright M, Dodhia H. 2019.. Journey to multimorbidity: longitudinal analysis exploring cardiovascular risk factors and sociodemographic determinants in an urban setting. . BMJ Open 9:(12):e031649
     [Crossref] [Google Scholar]
  33. 33.
    Jørgensen IF, Muse VP, Aguayo-Orozco A, Brunak S, Sørensen SS. 2024.. Stratification of kidney transplant recipients into five subgroups based on temporal disease trajectories. . Transplantation Direct. 10:(2):e1576
     [Crossref] [Google Scholar]
  34. 34.
    Thygesen JH, Tomlinson C, Hollings S, Mizani MA, Handy A, et al. 2022.. COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records. . Lancet Digit. Health 4:(7):e542–57
     [Crossref] [Google Scholar]
  35. 35.
    Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, et al. 2011.. Using electronic patient records to discover disease correlations and stratify patient cohorts. . PLOS Comput. Biol. 7:(8):e1002141
     [Crossref] [Google Scholar]
  36. 36.
    Jensen K, Soguero-Ruiz C, Oyvind Mikalsen K, Lindsetmo R-O, Kouskoumvekaki I, et al. 2017.. Analysis of free text in electronic health records for identification of cancer patient trajectories. . Sci. Rep. 7:(1):46226
     [Crossref] [Google Scholar]
  37. 37.
    Hjaltelin JX, Novitski SI, Jørgensen IF, Johansen JS, Chen IM, et al. 2023.. Pancreatic cancer symptom trajectories from Danish registry data and free text in electronic health records. . eLife 12::e84919
     [Crossref] [Google Scholar]
  38. 38.
    Yang H, Pawitan Y, Fang F, Czene K, Ye W. 2022.. Biomarkers and disease trajectories influencing women's health: results from the UK Biobank cohort. . Phenomics 2:(3):184–93
     [Crossref] [Google Scholar]
  39. 39.
    Ploner T, Heß S, Grum M, Drewe-Boss P, Walker J. 2020.. Using gradient boosting with stability selection on health insurance claims data to identify disease trajectories in chronic obstructive pulmonary disease. . Stat. Methods Med. Res. 29:(12):3684–94
     [Crossref] [Google Scholar]
  40. 40.
    Künnapuu K, Ioannou S, Ligi K, Kolde R, Laur S, et al. 2022.. Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. . JAMIA Open 5:(1):ooac021
     [Crossref] [Google Scholar]
  41. 41.
    Xia D, Han X, Zeng Y, Wang J, Xu K, et al. 2024.. Disease trajectory of high neuroticism and the relevance to psychiatric disorders: a retro-prospective cohort study. . Acta Psychiatr. Scand. 149:(2):133–46
     [Crossref] [Google Scholar]
  42. 42.
    Haendel MA, Chute CG, Robinson PN. 2018.. Classification, ontology, and precision medicine. . N. Engl. J. Med. 379:(15):1452–62
     [Crossref] [Google Scholar]
  43. 43.
    Boerma T, Harrison J, Jakob R, Mathers C, Schmider A, Weber S. 2016.. Revising the ICD: explaining the WHO approach. . Lancet 388:(10059):2476–77
     [Crossref] [Google Scholar]
  44. 44.
    Knibbs GH. 1929.. The International Classification of Disease and Causes of Death and its revision. . Med. J. Aust. 1:(1):2–12
     [Crossref] [Google Scholar]
  45. 45.
    Hofmans-Okkes I, Lamberts H. 1996.. The International Classification of Primary Care (ICPC): new applications in research and computer-based patient records in family practice. . Fam. Pract. 13:(3):294–302
     [Crossref] [Google Scholar]
  46. 46.
    O'Halloran J, Miller GC, Britt H. 2004.. Defining chronic conditions for primary care with ICPC-2. . Fam. Pract. 21:(4):381–86
     [Crossref] [Google Scholar]
  47. 47.
    Aguado A, Moratalla-Navarro F, López-Simarro F, Moreno V. 2020.. MorbiNet: multimorbidity networks in adult general population. Analysis of type 2 diabetes mellitus comorbidity. . Sci. Rep. 10::2416
     [Crossref] [Google Scholar]
  48. 48.
    Hassaine A, Canoy D, Solares JRA, Zhu Y, Rao S, et al. 2020.. Learning multimorbidity patterns from electronic health records using Non-negative Matrix Factorisation. . J. Biomed. Inform. 112::103606
     [Crossref] [Google Scholar]
  49. 49.
    Chisholm J. 1990.. The Read clinical classification. . BMJ 300:(6732):1092
     [Crossref] [Google Scholar]
  50. 50.
    Sahl Andersen J, De Fine Olivarius N, Krasnik A. 2011.. The Danish National Health Service Register. . Scand. J. Public Health 39:(Suppl. 7):34–37
     [Crossref] [Google Scholar]
  51. 51.
    Bojesen AB, Mortensen FV, Kirkegård J. 2023.. Real-time identification of pancreatic cancer cases using artificial intelligence developed on Danish nationwide registry data. . JCO Clin. Cancer Inform. 2023::e2300084
     [Crossref] [Google Scholar]
  52. 52.
    Haug N, Deischinger C, Gyimesi M, Kautzky-Willer A, Thurner S, Klimek P. 2020.. High-risk multimorbidity patterns on the road to cardiovascular mortality. . BMC Med. 18:(1):44
     [Crossref] [Google Scholar]
  53. 53.
    Giannoula A, Gutierrez-Sacristán A, Bravo Á, Sanz F, Furlong LI. 2018.. Identifying temporal patterns in patient disease trajectories using dynamic time warping: a population-based study. . Sci. Rep. 8:(1):4216
     [Crossref] [Google Scholar]
  54. 54.
    Han X, Hou C, Yang H, Chen W, Ying Z, et al. 2021.. Disease trajectories and mortality among individuals diagnosed with depression: a community-based cohort study in UK Biobank. . Mol. Psychiatry 26:(11):6736–46
     [Crossref] [Google Scholar]
  55. 55.
    Shang X, Zhang X, Huang Y, Zhu Z, Zhang X, et al. 2022.. Temporal trajectories of important diseases in the life course and premature mortality in the UK Biobank. . BMC Med. 20:(1):185
     [Crossref] [Google Scholar]
  56. 56.
    Hagmann M, Baty F, Rassouli F, Maeder MT, Brutsche MH. 2023.. Gender-specific disease trajectories prior to the onset of COPD allow individualized screening and early intervention. . PLOS ONE 18:(7):e0288237
     [Crossref] [Google Scholar]
  57. 57.
    Moreno GR, Restocchi V, Fleuriot JD, Anand A, Mercer SW, Guthrie B. 2024.. Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups. . eBioMedicine 102::105081
     [Crossref] [Google Scholar]
  58. 58.
    Shi X, Nikolic G, Van Pottelbergh G, van den Akker M, Vos R, De Moor B. 2021.. Development of multimorbidity over time: an analysis of Belgium primary care data using Markov chains and Weighted Association Rule Mining. . J. Gerontol. A. Biol. Sci. Med. Sci. 76:(7):1234–41
     [Crossref] [Google Scholar]
  59. 59.
    Beck MK, Jensen AB, Nielsen AB, Perner A, Moseley PL, Brunak S. 2016.. Diagnosis trajectories of prior multi-morbidity predict sepsis mortality. . Sci. Rep. 6:(1):36624
     [Crossref] [Google Scholar]
  60. 60.
    Dervić E, Sorger J, Yang L, Leutner M, Kautzky A, et al. 2024.. Unraveling cradle-to-grave disease trajectories from multilayer comorbidity networks. . NPJ Digit. Med. 7::56
     [Crossref] [Google Scholar]
  61. 61.
    Hou C, Zeng Y, Chen W, Han X, Yang H, et al. 2022.. Medical conditions associated with coffee consumption: disease-trajectory and comorbidity network analyses of a prospective cohort study in UK Biobank. . Am. J. Clin. Nutr. 116:(3):730–40
     [Crossref] [Google Scholar]
  62. 62.
    Jia Y, Li D, You Y, Yu J, Jiang W, et al. 2023.. Multi-system diseases and death trajectory of metabolic dysfunction-associated fatty liver disease: findings from the UK Biobank. . BMC Med. 21:(1):398
     [Crossref] [Google Scholar]
  63. 63.
    Siggaard T, Reguant R, Jørgensen IF, Haue AD, Lademann M, et al. 2020.. Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients. . Nat. Commun. 11:(1):4952
     [Crossref] [Google Scholar]
  64. 64.
    Haue AD, Armenteros JJA, Holm PC, Eriksson R, Moseley PL, et al. 2022.. Temporal patterns of multi-morbidity in 570157 ischemic heart disease patients: a nationwide cohort study. . Cardiovasc. Diabetol. 21:(1):87
     [Crossref] [Google Scholar]
  65. 65.
    Holm NN, Frølich A, Andersen O, Juul-Larsen HG, Stockmarr A. 2023.. Longitudinal models for the progression of disease portfolios in a nationwide chronic heart disease population. . PLOS ONE 18:(4):e0284496
     [Crossref] [Google Scholar]
  66. 66.
    Hjaltelin JX, Currant H, Jørgensen IF, Brunak S. 2023.. Visualising disease trajectories from population-wide data. . Front. Bioinform. 3::1112113
     [Crossref] [Google Scholar]
  67. 67.
    Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, et al. 2015.. Meta-analysis of the heritability of human traits based on fifty years of twin studies. . Nat. Genet. 47:(7):702–9
     [Crossref] [Google Scholar]
  68. 68.
    van Rheenen W, Peyrot WJ, Schork AJ, Lee SH, Wray NR. 2019.. Genetic correlations of polygenic disease traits: from theory to practice. . Nat. Rev. Genet. 20:(10):567–81
     [Crossref] [Google Scholar]
  69. 69.
    Jia G, Li Y, Zhang H, Chattopadhyay I, Boeck Jensen A, et al. 2019.. Estimating heritability and genetic correlations from large health datasets in the absence of genetic data. . Nat. Commun. 10:(1):5508
     [Crossref] [Google Scholar]
  70. 70.
    Westergaard D, Jørgensen FH, Waaben J, Lademann M, Hansen TF, et al. 2023.. Uncovering the heritable components of multimorbidities and disease trajectories: a nationwide cohort study. . medRxiv 2023.02.08.23285642. https://doi.org/10.1101/2023.02.08.23285642
  71. 71.
    Barabási A-L, Gulbahce N, Loscalzo J. 2011.. Network medicine: a network-based approach to human disease. . Nat. Rev. Genet. 12:(1):56–68
     [Crossref] [Google Scholar]
  72. 72.
    Furlong LI. 2013.. Human diseases through the lens of network biology. . Trends Genet. 29:(3):150–59
     [Crossref] [Google Scholar]
  73. 73.
    Sánchez-Valle J, Valencia A. 2023.. Molecular bases of comorbidities: present and future perspectives. . Trends Genet. 39:(10):773–86
     [Crossref] [Google Scholar]
  74. 74.
    Sánchez-Valle J, Tejero H, Fernández JM, Juan D, Urda-García B, et al. 2020.. Interpreting molecular similarity between patients as a determinant of disease comorbidity relationships. . Nat. Commun. 11:(1):2854
     [Crossref] [Google Scholar]
  75. 75.
    Ibáñez K, Boullosa C, Tabarés-Seisdedos R, Baudot A, Valencia A. 2014.. Molecular evidence for the inverse comorbidity between central nervous system disorders and cancers detected by transcriptomic meta-analyses. . PLOS Genet. 10:(2):e1004173
     [Crossref] [Google Scholar]
  76. 76.
    Tabarés-Seisdedos R, Dumont N, Baudot A, Valderas JM, Climent J, et al. 2011.. No paradox, no progress: inverse cancer comorbidity in people with other complex diseases. . Lancet Oncol. 12:(6):604–8
     [Crossref] [Google Scholar]
  77. 77.
    Tabares-Seisdedos R, Rubenstein J. 2013.. Inverse cancer comorbidity: a serendipitous opportunity to gain insight into CNS disorders. . Nat. Rev. Neurosci. 14::293–304
     [Crossref] [Google Scholar]
  78. 78.
    Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, et al. 2015.. Uncovering disease-disease relationships through the incomplete interactome. . Science 347:(6224):1257601
     [Crossref] [Google Scholar]
  79. 79.
    Bang S, Shin H, Kim J-H. 2016.. Causality modeling for directed disease network. . Bioinformatics 32:(17):i437–44
     [Crossref] [Google Scholar]
  80. 80.
    Vlietstra WJ, Vos R, van den Akker M, van Mulligen EM, Kors JA. 2020.. Identifying disease trajectories with predicate information from a knowledge graph. . J. Biomed. Semant. 11:(1):9
     [Crossref] [Google Scholar]
  81. 81.
    Chmiel A, Klimek P, Thurner S. 2014.. Spreading of diseases through comorbidity networks across life and gender. . New J. Phys. 16:(11):115013
     [Crossref] [Google Scholar]
  82. 82.
    Kuan V, Denaxas S, Patalay P, Nitsch D, Mathur R, et al. 2023.. Identifying and visualising multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study. . Lancet Digit. Health 5:(1):e16–27
     [Crossref] [Google Scholar]
  83. 83.
    Henning MAS, Reguant R, Jørgensen IF, Andersen RK, Ibler KS, et al. 2022.. The temporal association of hyperhidrosis and its comorbidities—a nationwide hospital-based cohort study. . J. Eur. Acad. Dermatol. Venereol. 36:(12):2504–11
     [Crossref] [Google Scholar]
  84. 84.
    Kjærsgaard Andersen R, Jørgensen IF, Reguant R, Jemec GBE, Brunak S. 2020.. Disease trajectories for hidradenitis suppurativa in the Danish population. . JAMA Dermatol. 156:(7):780–86
     [Crossref] [Google Scholar]
  85. 85.
    Hanauer DA, Ramakrishnan N. 2013.. Modeling temporal relationships in large scale clinical associations. . J. Am. Med. Inform. Assoc. 20:(2):332–41
     [Crossref] [Google Scholar]
  86. 86.
    Hemingway H, Asselbergs FW, Danesh J, Dobson R, Maniadakis N, et al. 2018.. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. . Eur. Heart J. 39:(16):1481–95
     [Crossref] [Google Scholar]
  87. 87.
    Vetrano DL, Roso-Llorach A, Fernández S, Guisado-Clavero M, Violán C, et al. 2020.. Twelve-year clinical trajectories of multimorbidity in a population of older adults. . Nat. Commun. 11:(1):3223
     [Crossref] [Google Scholar]
  88. 88.
    Jørgensen IF, Brunak S. 2021.. Time-ordered comorbidity correlations identify patients at risk of mis- and overdiagnosis. . NPJ Digit. Med. 4:(1):12
     [Crossref] [Google Scholar]
  89. 89.
    Giannoula A, Comas M, Castells X, Estupiñán-Romero F, Bernal-Delgado E, et al. 2024.. Exploring long-term breast cancer survivors’ care trajectories using dynamic time warping-based unsupervised clustering. . J. Am. Med. Inform. Assoc. 31:(4):820–31
     [Crossref] [Google Scholar]
  90. 90.
    Lademann M, Lademann M, Boeck Jensen A, Brunak S. 2019.. Incorporating symptom data in longitudinal disease trajectories for more detailed patient stratification. . Int. J. Med. Inf. 129::107–13
     [Crossref] [Google Scholar]
  91. 91.
    Topol EJ. 2019.. High-performance medicine: the convergence of human and artificial intelligence. . Nat. Med. 25:(1):44–56
     [Crossref] [Google Scholar]
  92. 92.
    Muse VP, Aguayo-Orozco A, Balaganeshan SB, Brunak S. 2023.. Population-wide analysis of hospital laboratory tests to assess seasonal variation and temporal reference interval modification. . Patterns 4:(8):100778
     [Crossref] [Google Scholar]
  93. 93.
    Li Y, Rao S, Solares JRA, Hassaine A, Ramakrishnan R, et al. 2020.. BEHRT: transformer for electronic health records. . Sci. Rep. 10:(1):7155
     [Crossref] [Google Scholar]
  94. 94.
    Shickel B, Tighe PJ, Bihorac A, Rashidi P. 2018.. Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. . IEEE J. Biomed. Health Inform. 22:(5):1589–604
     [Crossref] [Google Scholar]
  95. 95.
    Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, et al. 2018.. Scalable and accurate deep learning with electronic health records. . NPJ Digit. Med. 1::18
     [Crossref] [Google Scholar]
  96. 96.
    Allam A, Feuerriegel S, Rebhan M, Krauthammer M. 2021.. Analyzing patient trajectories with artificial intelligence. . J. Med. Internet Res. 23:(12):e29812
     [Crossref] [Google Scholar]
  97. 97.
    Goroll AH. 2017.. Emerging from EHR purgatory—moving from process to outcomes. . N. Engl. J. Med. 376::2004–6
     [Crossref] [Google Scholar]
  98. 98.
    Hulsen T, Jamuar SS, Moody AR, Karnes JH, Varga O, et al. 2019.. From big data to precision medicine. . Front. Med. 6::34
     [Crossref] [Google Scholar]
  99. 99.
    Gribsholt SB, Pedersen L, Richelsen B, Thomsen RW. 2019.. Validity of ICD-10 diagnoses of overweight and obesity in Danish hospitals. . Clin. Epidemiol. 11::845–54
     [Crossref] [Google Scholar]
  100. 100.
    Johnson AEW, Ghassemi MM, Nemati S, Niehaus KE, Clifton DA, Clifford GD. 2016.. Machine learning and decision support in critical care. . Proc. IEEE 104:(2):444–66
     [Crossref] [Google Scholar]
  101. 101.
    Sutherland SM, Chawla LS, Kane-Gill SL, Hsu RK, Kramer AA, et al. 2016.. Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15th ADQI Consensus Conference. . Can. J. Kidney Health Dis. 3::11
     [Crossref] [Google Scholar]
  102. 102.
    Jørgensen IF, Aguayo-Orozco A, Lademann M, Brunak S. 2020.. Age-stratified longitudinal study of Alzheimer's and vascular dementia patients. . Alzheimer's Dement. 16:(6):908–17
     [Crossref] [Google Scholar]
  103. 103.
    Ahlqvist E, Storm P, Käräjämäki A, Martinell M, Dorkhan M, et al. 2018.. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. . Lancet Diabetes Endocrinol. 6:(5):361–69
     [Crossref] [Google Scholar]
  104. 104.
    McCarthy MI. 2017.. Painting a new picture of personalised medicine for diabetes. . Diabetologia 60:(5):793–99
     [Crossref] [Google Scholar]
  105. 105.
    Wesolowska-Andersen A, Brorsson CA, Bizzotto R, Mari A, Tura A, et al. 2022.. Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: an IMI DIRECT study. . Cell Rep. Med. 3:(1):100477
     [Crossref] [Google Scholar]
  106. 106.
    Rusanov A, Weiskopf NG, Wang S, Weng C. 2014.. Hidden in plain sight: bias towards sick patients when sampling patients with sufficient electronic health record data for research. . BMC Med. Inform. Decis. Mak. 14:(1):51
     [Crossref] [Google Scholar]
  107. 107.
    Weiskopf NG, Rusanov A, Weng C. 2013.. Sick patients have more data: the non-random completeness of electronic health records. . AMIA Annu. Symp. Proc. 2013::1472–77
     [Google Scholar]
  108. 108.
    Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, et al. 2017.. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. . Am. J. Epidemiol. 186:(9):1026–34
     [Crossref] [Google Scholar]
  109. 109.
    Schmidt M, Schmidt SAJ, Sandegaard JL, Ehrenstein V, Pedersen L, Sørensen HT. 2015.. The Danish National Patient Registry: a review of content, data quality, and research potential. . Clin. Epidemiol. 7::449–90
     [Crossref] [Google Scholar]
  110. 110.
    Thygesen SK, Christiansen CF, Christensen S, Lash TL, Sørensen HT. 2011.. The predictive value of ICD-10 diagnostic coding used to assess Charlson comorbidity index conditions in the population-based Danish National Registry of Patients. . BMC Med. Res. Methodol. 11:(1):83
     [Crossref] [Google Scholar]
  111. 111.
    Burns EM, Rigby E, Mamidanna R, Bottle A, Aylin P, et al. 2012.. Systematic review of discharge coding accuracy. . J. Public Health 34:(1):138–48
     [Crossref] [Google Scholar]
  112. 112.
    Bagley SC, Altman RB. 2016.. Computing disease incidence, prevalence and comorbidity from electronic medical records. . J. Biomed. Inform. 63::108–11
     [Crossref] [Google Scholar]
  113. 113.
    Lynge E, Sandegaard JL, Rebolj M. 2011.. The Danish National Patient Register. . Scand. J. Public Health 39:(Suppl. 7):30–33
     [Crossref] [Google Scholar]
  114. 114.
    Schwartz LM, Woloshin S. 1999.. Changing disease definitions: implications for disease prevalence. Analysis of the Third National Health and Nutrition Examination Survey, 1988–1994. . Eff. Clin. Pract. 2:(2):76–85
     [Google Scholar]
  115. 115.
    Schmidt M, Jacobsen JB, Lash TL, Bøtker HE, Sørensen HT. 2012.. 25 year trends in first time hospitalisation for acute myocardial infarction, subsequent short and long term mortality, and the prognostic impact of sex and comorbidity: a Danish nationwide cohort study. . BMJ 344::e356
     [Crossref] [Google Scholar]
  116. 116.
    Brodersen J, Schwartz LM, Heneghan C, O'Sullivan JW, Aronson JK, Woloshin S. 2018.. Overdiagnosis: what it is and what it isn't. . BMJ Evid. Based Med. 23:(1):1–3
     [Crossref] [Google Scholar]
  117. 117.
    Jespersgaard C, Syed A, Chmura P, Løngreen P. 2020.. Supercomputing and secure cloud infrastructures in biology and medicine. . Annu. Rev. Biomed. Data Sci. 3::391–410
     [Crossref] [Google Scholar]
  118. 118.
    Nedelec T, Couvy-Duchesne B, Monnet F, Daly T, Ansart M, et al. 2022.. Identifying health conditions associated with Alzheimer's disease up to 15 years before diagnosis: an agnostic study of French and British health records. . Lancet Digit. Health 4:(3):e169–78
     [Crossref] [Google Scholar]
  119. 119.
    Gao Y, Sharma T, Cui Y. 2023.. Addressing the challenge of biomedical data inequality: an artificial intelligence perspective. . Annu. Rev. Biomed. Data Sci. 6::153–71
     [Crossref] [Google Scholar]
  120. 120.
    Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. 2014.. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. . Health Aff. 33:(7):1123–31
     [Crossref] [Google Scholar]
  121. 121.
    van Helden P. 2013.. Data-driven hypotheses. . EMBO Rep. 14:(2):104
     [Crossref] [Google Scholar]
  122. 122.
    Hu JX, Helleberg M, Jensen AB, Brunak S, Lundgren J. 2019.. A large-cohort, longitudinal study determines precancer disease routes across different cancer types. . Cancer Res. 79:(4):864–72
     [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-110123-041001
Loading
Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives
Annual Review of Biomedical Data Science 7, 251 (2024); https://doi.org/10.1146/annurev-biodatasci-110123-041001
/content/journals/10.1146/annurev-biodatasci-110123-041001
/content/journals/10.1146/annurev-biodatasci-110123-041001
Loading

Data & Media loading...

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