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Machine learning model for healthcare investments predicting the length of stay in a hospital & mortality rate

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Abstract

The demand for healthcare workers and infrastructure from an alarmingly growing patient population may contribute to the increased Length of Stay (LOS) in Hospital and Mortality rate. The shortage of doctors, nurses, and hospital beds may be blamed for this increase. As Constant patient monitoring is essential and the better hospital management and administration are necessary, therefore this research aimed foremost, to develop a machine learning model to predict long-term outcomes like Length of Stay (LOS), mortality rate of a patient admitted into the hospital. We used Machine Learning (ML) in the National Hospital Care Research Database (NHCRD) to create minimum feature-based predictive modeling with adequate performance. Unlike other approaches, ours requires the patient’s profile, tests reports at the time of admission and treatment history to accurately predict outcomes like the length of stay and mortality rate, making our technique novel with 98% accuracy, 98% precision, 95% AUROC Score, 94% F1 Score, 0.97 Recall, 0.95 Train Accuracy, and 0.90 Test Accuracy with the Support Vector Machine Algorithm. The ratio of training data to testing data was divided in the ratio 8:2 then the Machine Learning methods were applied. Descriptive statistical graphs, feature significance, precision-recall curve, accuracy plots, and Area Under the Curve (AUC), Accuracy, Precision, Recall, F1-Score, Mean Squared Error, Mean Absolute Error and Root Mean Squared Error were used to evaluate different machine learning methods like Random Forests (RF), Logistic Regression (LR), Gradient Boosting (GB), Decision Tree (DT), Naive Bayes (NB), Artificial Neural Network (ANN), and Ensemble Learning Techniques (EL), etc. Adopting the proposed framework, which considers the imbalanced dataset for classification-based methods based on electronic healthcare records, may allow us to apply Machine Learning to forecast patient length of stay and mortality rate in the hospital’s clinical information system. This Prediction Model will help hospitals and healthcare professionals better manage these resources and save lives by proving the utility of ML algorithms in aiding with data-driven decision-making and allowing early treatments, resource planning and finances.

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References

  1. Alghatani K, Ammar N, Rezgui A, Shaban-Nejad A (2021) Predicting intensive care unit length of stay and mortality using patient vital signs: machine learning model development and validation. JMIR Med Inform 9(5):e21347

    Article  PubMed  PubMed Central  Google Scholar 

  2. Alsinglawi B, Alshari O, Alorjani M, Mubin O, Alnajjar F, Novoa M, Darwish O (2022) An explainable machine learning framework for lung cancer hospital length of stay prediction. Sci Rep 12(1):607. https://doi.org/10.1038/s41598-021-04608-7

  3. Alsinglawi B, Alshari O, Alorjani M et al (2022) An explainable machine learning framework for lung cancer hospital length of stay prediction. Sci Rep 12:607. https://doi.org/10.1038/s41598-021-04608-7

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  4. Anan K, Kawamura K, Suga M, Ichikado K (2018) Clinical differences between pulmonary and extrapulmonary acute respiratory distress syndrome: a retrospective cohort study of prospectively collected data in Japan. J Thorac Dis 10:5796–5803

    Article  PubMed  PubMed Central  Google Scholar 

  5. Bacchi S, Gluck S, Tan Y, Chim I, Cheng J et al (2020) Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study. Intern Emerg Med 15(6):989–995

    Article  PubMed  Google Scholar 

  6. Baek H, Cho M, Kim S, Hwang H, Song M, Yoo S (2018) Analysis of length of hospital stay using electronic health records: a statistical and data mining approach. PLoS ONE 13:e0195901. https://doi.org/10.1371/journal.pone.0195901

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Baek H, Cho M, Kim S, Hwang H, Song M, Yoo S (2018) Analysis of length of hospital stay using electronic health records: a statistical and data mining approach. PLoS ONE 13(4):e0195901. https://doi.org/10.1371/journal.pone.0195901

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bulgarelli L, Deliberato RO, Johnson AEW (2020) Prediction on critically ill patients: the role of “big data”. J Crit Care 60:64–68

    Article  PubMed  Google Scholar 

  9. Cheng-Chang Y, Adebayo BO, Lung C, Jia-Hung C, Chien-Tai H, Yi-Ting H, Chen-Chih C (2023) Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic emergency using artificial neural networks. Front Neurol 14(2023):1664–2295. https://doi.org/10.3389/fneur.2023.1085178 https://www.frontiersin.org/articles/10.3389/fneur.2023.1085178

    Article  Google Scholar 

  10. Chrusciel J, Girardon F, Roquette L et al (2021) The prediction of hospital length of stay using unstructured data. BMC Med Inform Decis Mak 21:351. https://doi.org/10.1186/s12911-021-01722-4

    Article  PubMed  PubMed Central  Google Scholar 

  11. Chuang MT, Hu YH, Lo CL (2018) Predicting the prolonged length of stay of general surgery patients: a supervised learning approach. Int Trans Oper Res 25(1):75–90

    Article  Google Scholar 

  12. Daghistani TA, Elshawi R, Sakr S, Ahmed AM, Thwayee AA et al (2019) Predictors of inhospital length of stay among cardiac patients: a machine learning approach. Int J Cardiol 288:140–147

    Article  PubMed  Google Scholar 

  13. Delahanty RJ, Kaufman D, Jones SS (2018) Development and evaluation of an automated machine learning algorithm for inhospital mortality risk adjustment among critical care patients. Crit Care Med 46:e481–e488

    Article  PubMed  Google Scholar 

  14. Maguire PA, Taylor IC, Stout RW (1986) Elderly patients in acute medical wards: factors predicting length of stay in hospital. Br Med J (Clin Res Ed) 292(6530):1251–1253. https://doi.org/10.1136/bmj.292.6530.1251

  15. Gumaei A, Rakhami MA, Rahhal MMA, Albogamy FRH, Maghayreh EA et al (2002) Prediction of COVID-19 confirmed cases using gradient boosting regression method. Comput Mater Contin 66(1):315–329

    Google Scholar 

  16. Guo A, Lu J, Tan H et al (2021) Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study. Sci Rep 11:7310. https://doi.org/10.1038/s41598-021-86853-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Gutierrez G (2020) Artificial intelligence in the intensive care unit. Crit Care 24:101

    Article  PubMed  PubMed Central  Google Scholar 

  18. Houthooft R et al (2015) Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores. Artif Intell Med 63:191–207

    Article  PubMed  Google Scholar 

  19. Hughes AH, Horrocks D, Leung C et al (2021) The increasing impact of length of stay “outliers” on length of stay at an urban academic hospital. BMC Health Serv Res 21:940. https://doi.org/10.1186/s12913-021-06972-6

    Article  PubMed  PubMed Central  Google Scholar 

  20. Komorowski M (2019) Artificial intelligence in intensive care: are we there yet? Intensive Care Med 45:1298–1300

    Article  PubMed  Google Scholar 

  21. Lagoe RJ, Abbott JH, Littau SA (2021) Reducing hospital lengths of stay: a five-year study. Case Reports in Clinical Medicine 10(6):160–167. https://doi.org/10.4236/crcm.2021.106020

  22. Laupland KB, Kirkpatrick AW, Kortbeek JB, Zuege DJ (2006) Long-term mortality outcome associated with prolonged admission to the ICU. Chest 129:954–959

    Article  PubMed  Google Scholar 

  23. Leong MQ, Lim CW, Lai YF (2021) Comparison of hospital at-home models: a systematic review of reviews. BMJ Open 11:e043285. https://doi.org/10.1136/bmjopen-2020-04328

    Article  PubMed  PubMed Central  Google Scholar 

  24. Lisk R, Uddin M, Parbhoo A et al (2019) Predictive model of length of stay in hospital among older patients. Aging Clin Exp Res 31:993–999. https://doi.org/10.1007/s40520-018-1033-7

    Article  PubMed  Google Scholar 

  25. Love BC (2002) Comparing supervised and unsupervised category learning. Psychon Bull Rev 9(4):829–835

    Article  PubMed  Google Scholar 

  26. Lu J et al (2018) Lactate dehydrogenase is associated with 28-day mortality in patients with sepsis: a retrospective observational study. J Surg Res 228:314–321

    Article  CAS  PubMed  Google Scholar 

  27. Ma X, Si Y, Wang Z, Wang Y (2020) Length of stay prediction for ICU patients using individualized single classification algorithm. Comput Methods Prog Biomed 186:105224

    Article  Google Scholar 

  28. Mamdani M, Slutsky AS (2021) Artificial intelligence in intensive care medicine. Intensive Care Med 47:147–149

    Article  PubMed  Google Scholar 

  29. Attrill S, White S, Murray J, Hammond S, Doeltgen S (2018) Impact of oropharyngeal dysphagia on healthcare cost and length of stay in hospital: a systematic review. BMC Health Serv Res 18(1):1–18. https://doi.org/10.1186/s12913-018-3376-3

  30. Mekhaldi RN, Caulier P, Chaabane S, Chraibi A, Piechowiak S (2020) Using machine learning models to predict the length of stay in a hospital setting. In: Rocha Á, Adeli H, Reis L, Costanzo S, Orovic I, Moreira F (eds) Trends and innovations in information systems and technologies. WorldCIST 2020. Advances in intelligent systems and computing, vol 1159. Springer, Cham. https://doi.org/10.1007/978-3-030-45688-7_21

    Chapter  Google Scholar 

  31. Mitchell R, Banks C (2020) Emergency departments and the COVID-19 pandemic: making the most of limited resources. Emerg Med J 37(5):258–259

    PubMed  Google Scholar 

  32. Möllers T, Stocker H, Wei W, Perna L, Brenner H (2019) Length of hospital stay and dementia: a systematic review of observational studies. Int J Geriatr Psychiatry 34:8–21. https://doi.org/10.1002/gps.4993

    Article  PubMed  Google Scholar 

  33. Morton A, Marzban E, Giannoulis G, Patel A, Aparasu R et al (2014) A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients. In: Int. conf. on machine learning and applications, Detroit, MI, USA, pp 1–5

    Google Scholar 

  34. Nadeem MW, Ghamdi MAA, Hussain M, Khan MA, Khalid KM et al (2020) Brain tumor analysis empowered with deep learning: a review, taxonomy, and future challenges. Brain Sci 10(2):118–134

    Article  PubMed  PubMed Central  Google Scholar 

  35. Mekhaldi RN, Caulier P, Chaabane S, Chraibi A, Piechowiak S (2020) Using machine learning models to predict the length of stay in a hospital setting. In World conference on information systems and technologies. Springer International Publishing, Cham, pp. 202–211. https://doi.org/10.1007/978-3-030-45688-7_21

  36. Patel A, Johnson M, Aparasu R (2013) Predicting in-hospital mortality and hospital length of stay in diabetic patients. Value Health 16(3):A17–A25

    Article  Google Scholar 

  37. Pirracchio R et al (2015) Mortality prediction in intensive care units with the super ICU learner algorithm (SICULA): a populationbased study. Lancet Respir Med 3:42–52

    Article  PubMed  Google Scholar 

  38. Rongali S et al (2020) Learning latent space representations to predict patient outcomes: model development and validation. J Med Internet Res 22:e16374

    Article  PubMed  PubMed Central  Google Scholar 

  39. Shi T, Horvath S (2006) Unsupervised learning with random forest predictors. J Comput Graph Stat 15:118–138

    Article  MathSciNet  Google Scholar 

  40. Shillan D, Sterne JAC, Champneys A, Gibbison B (2019) Use of machine learning to analyse routinely collected intensive care unit data: a systematic review. Crit Care 23:284

    Article  PubMed  PubMed Central  Google Scholar 

  41. Siddique SM, Tipton K, Leas B et al (2021) Interventions to reduce hospital length of stay in high-risk populations: a systematic review. JAMA Netw Open 4(9):e2125846. https://doi.org/10.1001/jamanetworkopen.2021.25846

    Article  PubMed  PubMed Central  Google Scholar 

  42. Sridhar S, Whitaker B, Mouat-Hunter A, McCrory B (2022) Predicting length of stay using machine learning for total joint replacements performed at a rural community hospital. PLoS ONE 17(11):e0277479. https://doi.org/10.1371/journal.pone.0277479

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Stone K, Zwiggelaar R, Jones P, Mac Parthaláin N (2022) A systematic review of the prediction of hospital length of stay: towards a unified framework. PLOS Digit Health 1(4):e0000017. https://doi.org/10.1371/journal.pdig.0000017

    Article  PubMed  PubMed Central  Google Scholar 

  44. Tomovic S (2021) Patient length of stay analysis with machine learning algorithms. Comput Sci Inf Syst 18:16–16. https://doi.org/10.2298/CSIS200422016T

    Article  Google Scholar 

  45. Uddin S, Khan A , Hossain ME, Moni MA (2019) Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak 19(1):1–16

  46. Vengadakrishnan K, Koushik AK (2015) A study of the clinical profile of acute pancreatitis and its correlation with severity indices. Int J Health Sci 9:410–417

    CAS  Google Scholar 

  47. Zolbanin HM, Davazdahemami B, Delen D, Zadeh AH (2020) Data analytics for the sustainable use of resources in hospitals: predicting the length of stay for patients with chronic diseases. Inf Manag 13(4):103282–103299

    Google Scholar 

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  1. Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh, India

    Aashi Singh Bhadouria & Ranjeet Kumar Singh

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  1. Aashi Singh Bhadouria
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Bhadouria, A.S., Singh, R.K. Machine learning model for healthcare investments predicting the length of stay in a hospital & mortality rate. Multimed Tools Appl 83, 27121–27191 (2024). https://doi.org/10.1007/s11042-023-16474-8

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