Abstract
Chronic wounds affect 6.5 million Americans, are complex conditions to manage and cost $28–$32 billion annually. Although digital solutions exist for non-expert clinicians to accurately segment tissues, analyze affected tissues or efficiently document their wound assessment results, there exists a lack of decision support for non-expert clinicians who usually provide most wound assessments and care decisions at the point of care (POC). We designed a machine learning (ML) system that can accurately predict wound care decisions based on labeled wound image data. The care decisions we predict are based on guidelines for standard wound care and are labeled as: continue the treatment, request a change in treatment, or refer patient to a specialist. In this paper, we demonstrate how our final ML solution using XGboost (XGB) algorithm achieved on average an overall performance of F-1 = .782 using labels given by an expert and a novice decision maker. The key contribution of our research lies in the ability of the ML artifact to use only those wound features (predictors) that require less expertise for novice users when examining wounds to make standard of care decisions (predictions).
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References
Fife, C.E., et al.: Wound care outcomes and associated cost among patients treated in US outpatient wound centers: data from the US wound registry. Wounds 24(1), 10 (2012)
Han, G., Ceilley, R.: Chronic wound healing: a review of current management and Treatments. Adv. Ther. 34(3), 599–610 (2017). https://doi.org/10.1007/s12325-017-0478-y
Nussbaum, S.R., et al.: An economic evaluation of the impact, cost, and medicare policy implications of chronic nonhealing wounds. Value Health 21(1), 27–32 (2018)
Benskin, L.: A review of the literature informing affordable, available wound management choices for rural areas of tropical developing countries. Ostomy/Wound Manage. 59(10), 20–41 (2013)
McIntosh, C., Ousey, K.: A survey of nurses’ and podiatrists’ attitudes, skills and knowledge of lower extremity wound care. Wounds UK 4(1) (2008)
Wu, S.C., Marston, W., Armstrong, D.G.: Wound care: the role of advanced wound healing technologies. J. Vasc. Surg. 52(3 Suppl), 59S–66S (2010)
Jeffcoate, W.J., van Houtum, W.H.: Amputation as a marker of the quality of foot care in diabetes. Diabetologia 47(12), 2051–2058 (2004)
Järbrink, K., et al.: The humanistic and economic burden of chronic wounds: a protocol for a systematic review. Syst. Rev. 6(1), 15 (2017)
Frykberg, R.G., Banks, J.: Challenges in the Treatment of Chronic Wounds. Adv. Wound Care (New Rochelle) 4(9), 560–582 (2015)
Kottner, J., et al.: Prevention and treatment of pressure ulcers/injuries: the protocol for the second update of the international Clinical Practice Guideline 2019. J. Tissue Viability 28(2), 51–58 (2019)
Franks, P.J., et al.: Management of patients with venous leg ulcers: challenges and current best practice. J. Wound Care 25(Sup6), S1–S67 (2016)
Hingorani, A., et al.: The management of diabetic foot: a clinical practice guideline by the Society for Vascular Surgery in collaboration with the American Podiatric Medical Association and the Society for Vascular Medicine. J. Vasc. Surg. 63(2), 3S–21S (2016)
Beeckman, D., et al.: A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial. Int. J. Nurs. Stud. 50(4), 475–486 (2013)
Christie, J., et al.: Do systematic reviews address community healthcare professionals’ wound care uncertainties? Results from evidence mapping in wound care. PLoS ONE 13(1), e0190045 (2018)
Thompson, C., Dowding, D.: Responding to uncertainty in nursing practice. Int. J. Nurs. Stud. 38(5), 609–615 (2001)
Balsa, A.I., et al.: Clinical uncertainty and healthcare disparities. Am. JL Med. 29, 203 (2003)
French, B.: Uncertainty and information need in nursing. Nurse Educ. Today 26(3), 245–252 (2006)
Agu, E., et al.: The smartphone as a medical device: Assessing enablers, benefits and challenges. In: 2013 IEEE International Workshop of Internet-of-Things Networking and Control (IoT-NC). IEEE (2013)
Nasi, G., Cucciniello, M., Guerrazzi, C.: The role of mobile technologies in health care processes: the case of cancer supportive care. J. Med. Internet Res. 17(2), e26 (2015)
Couch, K.S.: The Expanding Role of the Nurse & the NP in Chronic Wound Care
Logan, G.J.B.j.o.c.n.: Clinical judgment and decision-making in wound assessment and management: is experience enough? Br. J. Community Nurs. 20(Sup3), S21–S28 (2015)
Gillespie, B.M., et al.: Health professionals’ decision-making in wound management: a grounded theory. J. Adv. Nurs. 71(6), 1238–1248 (2015)
Hedberg, B., Larsson, U.S.: Environmental elements affecting the decision-making process in nursing practice. J. Clin. Nurs. 13(3), 316–324 (2004)
Jung, Hoill., Yang, JungGi., Woo, Ji-In., Lee, Byung-Mun., Ouyang, Jinsong., Chung, Kyungyong, Lee, YoungHo: Evolutionary rule decision using similarity based associative chronic disease patients. Cluster Comput. 18(1), 279–291 (2014). https://doi.org/10.1007/s10586-014-0376-x
Chakraborty, C., et al.: Telemedicine supported chronic wound tissue prediction using classification approaches. J. Med. Syst. 40(3), 68 (2016)
Jung, K., et al.: Rapid identification of slow healing wounds. Wound Repair Regener. 24(1), 181–188 (2016)
Klinker, K., Wiesche, M., Krcmar, H.: Conceptualizing passive trust: the case of smart glasses in healthcare. in European Conference on Information Systems (2019)
Peffers, K., et al.: A design science research methodology for information systems research. J. Manage. Inform. Syst. 24(3), 45–77 (2007)
(WUWHS), W.U.o.W.H.S.: Advances in Wound Care: The Triangle of Wound Assessment. Florence Congress: Wounds International (2016)
Bates-Jensen, B.M., Vredevoe, D.L., Brecht, M.-L.J.D.: Validity and reliability of the pressure sore status tool. Decubitus 5(6), 20–28 (1992)
The National Pressure Ulcer Advisory Panel: Prevention and Treatment of Pressure Ulcers: Clinical Practice Guideline (2019). http://www.npuap.org/resources/educational-and-clinical-resources/prevention-and-treatment-of-pressure-ulcers-clinical-practice-guideline/
Mills, J.L., Sr., et al.: The Society for Vascular Surgery Lower Extremity Threatened Limb Classification System: risk stratification based on wound, ischemia, and foot infection (WIfI). J. Vasc. Surg. 59(1), 220–34 e1-2 (2014)
Thompson, N., et al.: Reliability and validity of the revised photographic wound assessment tool on digital images taken of various types of chronic wounds. Adv. Skin Wound Care 26(8), 360–373 (2013)
Mombini, H., et al.: Design of a rule-based decision model for assessment of chronic wounds. In: Online Proceedings of 14th International Conference on Design Science Research in Information Systems and Technology (2019)
Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)
Sheehan, P., et al.: Percent change in wound area of diabetic foot ulcers over a 4-week period is a robust predictor of complete healing in a 12-week prospective trial. Diabetes Care 26(6), 1879–1882 (2003)
Cardinal, M., et al.: Early healing rates and wound area measurements are reliable predictors of later complete wound closure. Wound Repair Regener. 16(1), 19–22 (2008)
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Mombini, H. et al. (2020). Design of a Machine Learning System for Prediction of Chronic Wound Management Decisions. In: Hofmann, S., Müller, O., Rossi, M. (eds) Designing for Digital Transformation. Co-Creating Services with Citizens and Industry. DESRIST 2020. Lecture Notes in Computer Science(), vol 12388. Springer, Cham. https://doi.org/10.1007/978-3-030-64823-7_2
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DOI: https://doi.org/10.1007/978-3-030-64823-7_2
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