Abstract
Length of stay (LOS) and discharge destination predictions are key parts of the discharge planning process for general medical hospital inpatients. It is possible that machine learning, using natural language processing, may be able to assist with accurate LOS and discharge destination prediction for this patient group. Emergency department triage and doctor notes were retrospectively collected on consecutive general medical and acute medical unit admissions to a single tertiary hospital from a 2-month period in 2019. These data were used to assess the feasibility of predicting LOS and discharge destination using natural language processing and a variety of machine learning models. 313 patients were included in the study. The artificial neural network achieved the highest accuracy on the primary outcome of predicting whether a patient would remain in hospital for > 2 days (accuracy 0.82, area under the received operator curve 0.75, sensitivity 0.47 and specificity 0.97). When predicting LOS as an exact number of days, the artificial neural network achieved a mean absolute error of 2.9 and a mean squared error of 16.8 on the test set. For the prediction of home as a discharge destination (vs any non-home alternative), all models performed similarly with an accuracy of approximately 0.74. This study supports the feasibility of using natural language processing to predict general medical inpatient LOS and discharge destination. Further research is indicated with larger, more detailed, datasets from multiple centres to optimise and examine the accuracy that may be achieved with such predictions.
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References
Lin C-J, Cheng S-J, Shih S-C, Chu C-H, Tjung J-J (2012) Discharge planning. Int J Gerontol 6(4):237–240. https://doi.org/10.1016/j.ijge.2012.05.001
Goncalves-Bradley DC, Lannin NA, Clemson LM, Cameron ID, Shepperd S (2016) Discharge planning from hospital. Cochrane Database Syst Rev (1):CD000313. 10.1002/14651858.CD000313.pub5
Daghistani T, Elshawi R, Sakr S, Ahmed A, Al-Thwayee A, Al-Mallah M (2019) Predictors of in-hospital length of stay among cardiac patients: a machine learning approach. Int J Cardiol. https://doi.org/10.1016/j.ijcard.2019.01.046
Maharlou H, Kalhori S, Shahbazi S, Ravangard R (2018) Predicting length of stay in intensive care units after cardiac surgery: comparison of artificial neural networks and adaptive neuro-fuzzy system. Healthc Inform Res 24(2):109–117
Waring J, Marshall F, Bishop S, Sahota O, Walker M, Currie G, Fisher R, Avery T (2014) An ethnographic study of knowledge sharing across the boundaries between care processes, services and organisations: the contributions to 'safe' hospital discharge. Health Serv Deliv Res 2(29):1–160. https://doi.org/10.3310/hsdr02290
Liu Y, Phillips M, Codde J (2001) Factors influencing patients' length of stay. Aust Health Rev 24(2):63–70
Khosravizadeh O, Vatankhah S, Bastani P, Kalhor R, Alirezaei S, Doosty F (2016) Factors affecting length of stay in teaching hospitals of a middle-income country. Electron Physician 8(10):3042–3047. https://doi.org/10.19082/3042
Deo R (2015) Machine learning in medicine. Circulation. 10.1161/CIRCULATIONAHA.115.001593
Schwartz N, Sakhnini A, Bisharat N (2018) Predictive modeling of inpatient mortality in departments of internal medicine. Intern Emerg Med 13(2):205–211. https://doi.org/10.1007/s11739-017-1784-8
Elbattah M, Molloy O (2018) Using machine learning to predict length of stay and discharge destination for hip-fracture patients. In: Bi Y, Kapoor S, Bhatia R (eds) Proceedings of SAI intelligent systems conference (IntelliSys), Lecture notes in networks and systems, vol 15. Springer, Cham
Mulestein W, Akagi D, Davies J, Chambless L (2018) Predicting inpatient length of stay after brain tumor surgery: developing machine learning ensembles to improve predictive performance. Neurosurgery. 10.1093/neuros/nyy343. [Epub ahead of print]
Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J (2019) A guide to deep learning in healthcare. Nat Med 25(1):24–29. https://doi.org/10.1038/s41591-018-0316-z
Mazzali C, Duca P (2015) Use of administrative data in healthcare research. Intern Emerg Med 10(4):517–524. https://doi.org/10.1007/s11739-015-1213-9
Yong TY, Li JY, Roberts S, Hakendorf P, Ben-Tovim DI, Thompson CH (2011) The selection of acute medical admissions for a short-stay unit. Intern Emerg Med 6(4):321–327. https://doi.org/10.1007/s11739-010-0490-6
Sidey-Gibbons JAM, Sidey-Gibbons CJ (2019) Machine learning in medicine: a practical introduction. BMC Med Res Methodol 19(1):64. https://doi.org/10.1186/s12874-019-0681-4
Youden WJ (1950) Index for rating diagnostic tests. Cancer 3:32–35
Chambless L, Thompson R, Weaver K, Morone P, Kallos J, Akagi D, Muhlestein W (2017) Using a guided machine learning ensemble model to predict discharge disposition following meningioma resection. J Neurol Surg Part B Skull Base 79(02):123–130. https://doi.org/10.1055/s-0037-1604393
Hollon T, Parikh A, Pandian B, Tarpeh J, Orringer D, Barkan A, McKean E, Sullivan S (2018) A machine learning approach to predict early outcomes after pituitary adenoma surgery. Neurosurg Focus 45(5):E8
Senders JT, Staples PC, Karhade AV, Zaki MM, Gormley WB, Broekman MLD, Smith TR, Arnaout O (2018) Machine learning and neurosurgical outcome prediction: a systematic review. World Neurosurg 109(476–486):e471. https://doi.org/10.1016/j.wneu.2017.09.149
Ramkumar PN, Navarro SM, Haeberle HS, Karnuta JM, Mont MA, Iannotti JP, Patterson BM, Krebs VE (2019) Development and validation of a machine learning algorithm after primary total hip arthroplasty: applications to length of stay and payment models. J Arthroplasty 34(4):632–637. https://doi.org/10.1016/j.arth.2018.12.030
Navarro SM, Wang EY, Haeberle HS, Mont MA, Krebs VE, Patterson BM, Ramkumar PN (2018) Machine learning and primary total knee arthroplasty: patient forecasting for a patient-specific payment model. J Arthroplasty 33(12):3617–3623. https://doi.org/10.1016/j.arth.2018.08.028
Chuang M-T, Hu Y-h, Lo C-L (2018) Predicting the prolonged length of stay of general surgery patients: a supervised learning approach. Int Trans Oper Res 25(1):75–90. https://doi.org/10.1111/itor.12298
Yakovlev A, Metsker O, Kovalchuk S, Bologova E (2018) Prediction of in-hospital mortality and length of stay in acute coronary syndrome patients using machine-learning methods. J Am Coll Cardiol. 10.1016/s0735–1097(18)30783–6
Goto T, Camargo CA Jr, Faridi MK, Yun BJ, Hasegawa K (2018) Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED. Am J Emerg Med 36(9):1650–1654. https://doi.org/10.1016/j.ajem.2018.06.062
Turgeman L, May JH, Sciulli R (2017) Insights from a machine learning model for predicting the hospital Length of Stay (LOS) at the time of admission. Expert Syst Appl 78:376–385. https://doi.org/10.1016/j.eswa.2017.02.023
Morton A, Marzban E, Giannoulis G, Patel A, Aparasu R, Kakadiaris IA (2014) A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients. In: Paper presented at the 2014 13th International Conference on Machine Learning and Applications
Chary M, Parikh S, Manini AF, Boyer EW, Radeos M (2019) A review of natural language processing in medical education. West J Emerg Med 20(1):78–86. https://doi.org/10.5811/westjem.2018.11.39725
Rajput A (2019) Natural language processing, sentiment analysis and clinical analytics. arXiv:1902.00679
Wong A, Plasek JM, Montecalvo SP, Zhou L (2018) Natural language processing and its implications for the future of medication safety: a narrative review of recent advances and challenges. Pharmacotherapy 38(8):822–841. https://doi.org/10.1002/phar.2151
Bacchi S, Oakden-Rayner L, Zerner T, Kleinig T, Patel S, Jannes J (2019) Deep learning natural language processing successfully predicts the cerebrovascular cause of transient ischemic attack-like presentations. Stroke 50(3):758–760. https://doi.org/10.1161/STROKEAHA.118.024124
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This research did not receive any specific Grant from funding agencies in the public, commercial, or not-for-profit sectors. SG is supported by a Royal Adelaide Hospital Research Committee A.R Clarkson Scholarship.
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Bacchi, S., Gluck, S., Tan, Y. et al. Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study. Intern Emerg Med 15, 989–995 (2020). https://doi.org/10.1007/s11739-019-02265-3
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DOI: https://doi.org/10.1007/s11739-019-02265-3