Top

BMC Medical Informatics and Decision Making

Published in:

Open Access 01-12-2019 | Research article

Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome

Authors: Andreas Philipp Hassler, Ernestina Menasalvas, Francisco José García-García, Leocadio Rodríguez-Mañas, Andreas Holzinger

Published in: BMC Medical Informatics and Decision Making | Issue 1/2019

Abstract

Background

Increasing life expectancy results in more elderly people struggling with age related diseases and functional conditions. This poses huge challenges towards establishing new approaches for maintaining health at a higher age. An important aspect for age related deterioration of the general patient condition is frailty. The frailty syndrome is associated with a high risk for falls, hospitalization, disability, and finally increased mortality. Using predictive data mining enables the discovery of potential risk factors and can be used as clinical decision support system, which provides the medical doctor with information on the probable clinical patient outcome. This enables the professional to react promptly and to avert likely adverse events in advance.

Methods

Medical data of 474 study participants containing 284 health related parameters, including questionnaire answers, blood parameters and vital parameters from the Toledo Study for Healthy Aging (TSHA) was used. Binary classification models were built in order to distinguish between frail and non-frail study subjects.

Results

Using the available TSHA data and the discovered potential predictors, it was possible to design, develop and evaluate a variety of different predictive models for the frailty syndrome. The best performing model was the support vector machine (SVM, 78.31%). Moreover, a methodology was developed, making it possible to explore and to use incomplete medical data and further identify potential predictors and enable interpretability.

Conclusions

This work demonstrates that it is feasible to use incomplete, imbalanced medical data for the development of a predictive model for the frailty syndrome. Moreover, potential predictive factors have been discovered, which were clinically approved by the clinicians. Future work will improve prediction accuracy, especially with regard to separating the group of frail patients into frail and pre-frail ones and analyze the differences among them.

Available only for authorised users

Economic Policy Committee and others. The 2009 Ageing Report: economic and budgetary projections for the EU-27 Member States (2008-2060). European Economy. 2009; 2. https://doi.org/10.2765/80301.

Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, Ezzati M, Shibuya K, Salomon JA, Abdalla S, et al.Disability-adjusted life years (dalys) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2013; 380(9859):2197–223.CrossRef

Kleinberger T, Becker M, Ras E, Holzinger A, Müller P. Ambient intelligence in assisted living: Enable elderly people to handle future interfaces. 2007:103–112. https://doi.org/10.1007/978-3-540-73281-5_11.

Bellazzi R, Zupan B. Predictive data mining in clinical medicine: Current issues and guidelines. Int J Med Inform. 2008; 77(2):81–97. https://doi.org/10.1016/j.ijmedinf.2006.11.006.PubMedCrossRef

Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, McBurnie MA. Frailty in older adults: Evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001; 56(3):146–57. https://doi.org/10.1093/gerona/56.3.m146.CrossRef

Rodriguez-Mañas L, Fried LP. Frailty in the clinical scenario. Lancet. 2015; 385(9968):7–9. https://doi.org/10.1016/s0140-6736(14)61595-6.CrossRef

Safran C, Bloomrosen M, Hammond WE, Labkoff S, Markel-Fox S, Tang PC, Detmer DE. Toward a national framework for the secondary use of health data: An american medical informatics association white paper. J Am Med Inform Assoc. 2007; 14(1):1–9. https://doi.org/10.1197/jamia.m2273.PubMedPubMedCentralCrossRef

Jaspers MWM, Smeulers M, Vermeulen H, Peute LW. Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings. J Am Med Inform Assoc. 2011; 18(3):327–34. https://doi.org/10.1136/amiajnl-2011-000094.PubMedPubMedCentralCrossRef

Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR, Samsa G, Hasselblad V, Williams JW, Musty MD, et al. Effect of clinical decision-support systemsa systematic review. Ann Intern Med. 2012; 157(1):29–43.PubMedCrossRef

10.

Bose NK, Liang P. Neural network fundamentals with graphs, algorithms, and applications. New York: McGraw-Hill Inc.; 1996.

11.

Schmidhuber J. Deep learning in neural networks: An overview. Neural Netw. 2015; 61:85–117.PubMedCrossRef

12.

Rish I, et al.An empirical study of the naive bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol 3. New York: IBM: 2001. p. 41–6.

13.

Izenman AJ. Linear Discriminant Analysis. In: Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. New York: Springer: 2008. p. 237–80. https://doi.org/10.1007/978-0-387-78189-1_8.CrossRef

14.

Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B. Support vector machines. IEEE Intell Syst Appl. 1998; 13(4):18–28. https://doi.org/10.1109/5254.708428.CrossRef

15.

Breiman L. Classification and Regression Trees; 2017. https://doi.org/10.1201/9781315139470. https://doi.org/10.1201%2F9781315139470.

16.

Holzinger A. Interactive machine learning for health informatics: when do we need the human-in-the-loop?Brain Inform. 2016; 3(2):119–31.PubMedPubMedCentralCrossRef

17.

Ensrud KE, Ewing SK, Taylor BC, et al.Comparison of 2 frailty indexes for prediction of falls, disability, fractures, and death in older women. Arch Intern Med. 2008; 168(4):382–9. https://doi.org/10.1001/archinternmed.2007.113.PubMedCrossRef

18.

Drubbel I, de Wit NJ, Bleijenberg N, Eijkemans RJC, Schuurmans MJ, Numans ME. Prediction of adverse health outcomes in older people using a frailty index based on routine primary care data. J Gerontol A. 2013; 68(3):301–8. https://doi.org/10.1093/gerona/gls161.CrossRef

19.

Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling the concepts of disability, frailty, and comorbidity: Implications for improved targeting and care. J Gerontol A. 2004; 59(3):255–63. https://doi.org/10.1093/gerona/59.3.M255.CrossRef

20.

Makary MA, Segev DL, Pronovost PJ, Syin D, Bandeen-Roche K, Patel P, Takenaga R, Devgan L, Holzmueller CG, Tian J, et al.Frailty as a predictor of surgical outcomes in older patients. J Am Coll Surg. 2010; 210(6):901–8.PubMedCrossRef

21.

Kim SW, Han HS, Jung HW, Kim KI, Hwang DW, Kang SB, Kim CH. Multidimensional frailty score for the prediction of postoperative mortality risk. JAMA Surg. 2014; 149(7):633–40. https://doi.org/10.1001/jamasurg.2014.241. data/journals/surg/930571/soi140018.pdf.PubMedCrossRef

22.

Fiatarone MA, O’neill EF, Ryan ND, Clements KM, Solares GR, Nelson ME, Roberts SB, Kehayias JJ, Lipsitz LA, Evans WJ. Exercise training and nutritional supplementation for physical frailty in very elderly people. N Engl J Med. 1994; 330(25):1769–75. https://doi.org/10.1056/NEJM199406233302501.PubMedCrossRef

23.

Wolf SL, Barnhart HX, Kutner NG, McNeely E, Coogler C, Xu T. Reducing frailty and falls in older persons: An investigation of tai chi and computerized balance training. J Am Geriatr Soc; 44(5):489–497. https://doi.org/10.1111/j.1532-5415.1996.tb01432.x. https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1532-5415.1996.tb01432.x.

24.

Collard RM. Frailty & late-life depression: a delicate balance.Radboud University Nijmegen; 2015. https://repository.ubn.ru.nl/handle/2066/144024.

25.

Bouillon K, Kivimäki M, Hamer M, Shipley MJ, Akbaraly TN, Tabak A, Singh-Manoux A, Batty GD. Diabetes risk factors, diabetes risk algorithms, and the prediction of future frailty: The whitehall ii prospective cohort study. J Am Med Dir Assoc. 2013; 14(11):851–18516. https://doi.org/10.1016/j.jamda.2013.08.016.PubMedPubMedCentralCrossRef

26.

Bandeen-Roche K, Xue Q-L, Ferrucci L, Walston J, Guralnik JM, Chaves P, Zeger SL, Fried LP. Phenotype of frailty: Characterization in the women’s health and aging studies. J Gerontol A. 2006; 61(3):262–6. https://doi.org/10.1093/gerona/61.3.262.CrossRef

27.

Walston J, McBurnie M, Newman A, et al.Frailty and activation of the inflammation and coagulation systems with and without clinical comorbidities: Results from the cardiovascular health study. Arch Intern Med. 2002; 162(20):2333–41. https://doi.org/10.1001/archinte.162.20.2333.PubMedCrossRef

28.

Baylis D, Bartlett DB, Syddall HE, Ntani G, Gale CR, Cooper C, Lord JM, Sayer AA. Immune-endocrine biomarkers as predictors of frailty and mortality: a 10-year longitudinal study in community-dwelling older people. AGE. 2013; 35(3):963–71. https://doi.org/10.1007/s11357-012-9396-8.PubMedCrossRef

29.

Garcia-Garcia FJ, Avila GG, Alfaro-Acha A, Andres MSA, de la Torre Lanza MDLA, Aparicio MVE, Aparicio SH, Zugasti JLL, Reus MG-S, Rodriguez-Artalejo F, Rodriguez-Manas L. The prevalence of frailty syndrome in an older population from spain. the toledo study for healthy aging. J Nutr, Health Aging. 2011; 15(10):852–6. https://doi.org/10.1007/s12603-011-0075-8.CrossRef

30.

Yesavage JA, Sheikh JI. 9/geriatric depression scale (GDS). Clin Gerontol. 1986; 5(1-2):165–73.CrossRef

31.

Yesavage JA, Brink TL, Rose TL, Lum O, Huang V, Adey M, Leirer VO. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res. 1983; 17(1):37–49.CrossRef

32.

KATZ S. Assessing self-maintenance: Activities of daily living, mobility, and instrumental activities of daily living. J Am Geriatr Soc. 1983; 31(12):721–7. https://doi.org/10.1111/j.1532-5415.1983.tb03391.x.PubMedCrossRef

33.

Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Nurs Res. 1970; 19(3):278. https://doi.org/10.1097/00006199-197005000-00029.CrossRef

34.

Folstein MF, Folstein SE, McHugh PR. “mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975; 12(3):189–98.PubMedCrossRef

35.

Cockrell JR, Folstein MF. Mini-mental state examination. Principles and practice of geriatric psychiatry. 2002;:140–141. https://doi.org/10.1002/0470846410.ch27(ii).

36.

Washburn RA, Smith KW, Jette AM, Janney CA. The physical activity scale for the elderly (PASE): Development and evaluation. J Clin Epidemiol. 1993; 46(2):153–62. https://doi.org/10.1016/0895-4356(93)90053-4.PubMedCrossRef

37.

Wartner S, Girardi D, Wiesinger-Widi M, Trenkler J, Kleiser R, Holzinger A. Ontology-guided principal component analysis: Reaching the limits of the doctor-in-the-loop. In: International Conference on Information Technology in Bio-and Medical Informatics. Springer: 2016. p. 22–33. https://doi.org/10.1007/978-3-319-43949-5_2. https://doi.org/10.1007%2F978-3-319-43949-5_2.

38.

Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo AI, et al.Fast discovery of association rules. Adv Knowl Disc Data Min. 1996; 12(1):307–28.

39.

Eknoyan Garabed. Adolphe Quetelet (1796–1874)–the average man and indices of obesity. Nephrol Dial Transplant. 2007; 23(1):47–51.PubMedCrossRef

40.

Shah AD, Bartlett JW, Carpenter J, Nicholas O, Hemingway H. Comparison of random forest and parametric imputation models for imputing missing data using MICE: A CALIBER study. Am J Epidemiol. 2014; 179(6):764–74. https://doi.org/10.1093/aje/kwt312.PubMedPubMedCentralCrossRef

41.

van Buuren S, Groothuis-Oudshoorn K. mice: Multivariate imputation by chained equations in r. J Stat Softw. 2011; 45(3). https://doi.org/10.18637/jss.v045.i03.

42.

Kursa MB, Rudnicki WR. Feature selection with the boruta package. J Stat Softw. 2010; 36(11):1–13.CrossRef

43.

Le J. The 10 algorithms machine learning engineers need to know. KDnuggets Analytics Big Data Data Min Data Sci. 2016. https://www.kdnuggets.com/2016/08/10-algorithms-machine-learning-engineers.html. Accessed 15 Aug 2017.

44.

Bradley AP. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn. 1997; 30(7):1145–59.CrossRef

45.

Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Inf Process Manag. 2009; 45(4):427–37.CrossRef

46.

Lippi G, Jansen-Duerr P, Viña J, Durrance-Bagale A, Abugessaisa I, Gomez-Cabrero D, Tegnér J, Grillari J, Erusalimsky J, Sinclair A, Rodriguez-Manãs L, on behalf of the FRAILOMIC consorti. Laboratory biomarkers and frailty: presentation of the FRAILOMIC initiative. Clin Chem Lab Med (CCLM). 2015; 53(10). https://doi.org/10.1515/cclm-2015-0147.

47.

Caraviello D, Weigel K, Craven M, Gianola D, Cook N, Nordlund K, Fricke P, Wiltbank M. Analysis of reproductive performance of lactating cows on large dairy farms using machine learning algorithms. J Dairy Sci. 2006; 89(12):4703–22.PubMedCrossRef

48.

Kotsiantis SB, Zaharakis I, Pintelas P. Supervised machine learning: A review of classification techniques. In: Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies. Amsterdam: IOS Press; 2007. p. 3–24. http://dl.acm.org/citation.cfm?id=1566770.1566773.

49.

Hudec M, Bednárová E, Holzinger A. Augmenting statistical data dissemination by short quantified sentences of natural language. J Off Stat (JOS). 2018; 34(4):981. https://doi.org/10.2478/jos-2018-0048.CrossRef

50.

Bellazzi R, Ferrazzi F, Sacchi L. Predictive data mining in clinical medicine: a focus on selected methods and applications. Wiley Interdiscip Rev Data Min Knowl Disc. 2011; 1(5):416–30. https://doi.org/10.1002/widm.23.CrossRef

51.

Buuren S. Flexible Imputation of Missing Data; 2012. https://doi.org/10.1201/b11826. https://doi.org/10.1201%2Fb11826.

52.

Holzinger A. Introduction to machine learning & knowledge extraction (make). In: Machine Learning and Knowledge Extraction, vol 1: 2017. p. 1–20. https://doi.org/10.3390/make1010001.

53.

Holzinger A, Plass M, Kickmeier-Rust M, Holzinger K, Crişan GC, Pintea C-M, Palade V. Interactive machine learning: experimental evidence for the human in the algorithmic loop. Appl Intell. 2018. https://doi.org/10.1007/s10489-018-1361-5.

54.

Kursa MB, Jankowski A, Rudnicki WR. Boruta–a system for feature selection. Fundam Informaticae. 2010; 101(4):271–85.CrossRef

Title: Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome
Authors: Andreas Philipp Hassler
Ernestina Menasalvas
Francisco José García-García
Leocadio Rodríguez-Mañas
Andreas Holzinger
Publication date: 01-12-2019
Publisher: BioMed Central
Published in: BMC Medical Informatics and Decision Making / Issue 1/2019
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-019-0747-6

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2019

The effect of computerized decision support systems on cardiovascular risk factors: a systematic review and meta-analysis

A clustering approach for detecting implausible observation values in electronic health records data

Patient decision aids: a content analysis based on a decision tree structure

The past, present and future of opioid withdrawal assessment: a scoping review of scales and technologies

Development and validation of a patient decision aid for prostate Cancer therapy: from paternalistic towards participative shared decision making

Using decision fusion methods to improve outbreak detection in disease surveillance