Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection

Abstract

Objective Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept.

Materials and Methods A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble.

Results Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced.

Conclusion Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

Keywords

machine learning - meningioma - disposition - outcomes - predictive modeling

• References

• 1 Centers for Medicare & Medicaid Services. Bundled Payments for Care Improvement (BPCI) Initiative: General Information. Available at: https://innovation.cms.gov/initiatives/bundled-payments/ . Accessed November 14, 2016
• 2 Barsoum WK, Murray TG, Klika AK. , et al. Predicting patient discharge disposition after total joint arthroplasty in the United States. J Arthroplasty 2010; 25 (06) 885-892
• 3 AlHilli MM, Tran CW, Langstraat CL. , et al. Risk-scoring model for prediction of non-home discharge in epithelial ovarian cancer patients. J Am Coll Surg 2013; 217 (03) 507-515
• 4 Hagino T, Ochiai S, Sato E, Watanabe Y, Senga S, Haro H. Prognostic prediction in patients with hip fracture: risk factors predicting difficulties with discharge to own home. J Orthop Traumatol 2011; 12 (02) 77-80
• 5 Hyder JA, Wakeam E, Habermann EB, Hess EP, Cima RR, Nguyen LL. Derivation and validation of a simple calculator to predict home discharge after surgery. J Am Coll Surg 2014; 218 (02) 226-236
• 6 Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. New York: Springer Science & Business Media; 2009
• 7 Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 2014; 13: 8-17
• 8 Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet 2015; 16 (06) 321-332
• 9 Petricoin EF, Liotta LA. SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer. Curr Opin Biotechnol 2004; 15 (01) 24-30
• 10 Bocchi L, Coppini G, Nori J, Valli G. Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks. Med Eng Phys 2004; 26 (04) 303-312
• 11 Ouyang Z, Zhou Q, Wong WH. ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells. Proc Natl Acad Sci U S A 2009; 106 (51) 21521-21526
• 12 Obermeyer Z, Emanuel EJ. Predicting the future – big data, machine learning, and clinical medicine. N Engl J Med 2016; 375 (13) 1216-1219
• 13 Bibault JE, Giraud P, Burgun A. Big data and machine learning in radiation oncology: state of the art and future prospects. Cancer Lett 2016; 382 (01) 110-117
• 14 Taylor RA, Pare JR, Venkatesh AK. , et al. Prediction of in-hospital mortality in emergency department patients with sepsis: a local big data-driven, machine learning approach. Acad Emerg Med 2016; 23 (03) 269-278
• 15 Temple MW, Lehmann CU, Fabbri D. Predicting discharge dates from the NICU using progress note data. Pediatrics 2015; 136 (02) e395-e405
• 16 Young J, Kempton MJ, McGuire P. Using machine learning to predict outcomes in psychosis. Lancet Psychiatry 2016; 3 (10) 908-909
• 17 Valdes G, Solberg TD, Heskel M, Ungar L, Simone II CB. Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy. Phys Med Biol 2016; 61 (16) 6105-6120
• 18 Tighe PJ, Harle CA, Hurley RW, Aytug H, Boezaart AP, Fillingim RB. Teaching a machine to feel postoperative pain: combining high-dimensional clinical data with machine learning algorithms to forecast acute postoperative pain. Pain Med 2015; 16 (07) 1386-1401
• 19 Deo RC. Machine learning in medicine. Circulation 2015; 132 (20) 1920-1930
• 20 Oermann EK, Kress MA, Collins BT. , et al. Predicting survival in patients with brain metastases treated with radiosurgery using artificial neural networks. Neurosurgery 2013; 72 (06) 944-951 , discussion 952
• 21 Akbari H, Macyszyn L, Da X. , et al. Imaging surrogates of infiltration obtained via multiparametric imaging pattern analysis predict subsequent location of recurrence of glioblastoma. Neurosurgery 2016; 78 (04) 572-580
• 22 Emblem KE, Pinho MC, Zöllner FG. , et al. A generic support vector machine model for preoperative glioma survival associations. Radiology 2015; 275 (01) 228-234
• 23 Shi HY, Hwang SL, Lee KT, Lin CL. In-hospital mortality after traumatic brain injury surgery: a nationwide population-based comparison of mortality predictors used in artificial neural network and logistic regression models. J Neurosurg 2013; 118 (04) 746-752
• 24 Rokach L. Ensemble-based classifiers. Artif Intell Rev 2010; 33 (01) 1-39
• 25 Kang J, Schwartz R, Flickinger J, Beriwal S. Machine learning approaches for prediction radiation therapy outcomes: a clinician's perspective. Int J Radiat Oncol Biol Phys 2015; 93 (05) 1127-1135
• 26 Breiman L. Random forests. Mach Learn 2001; 45: 5-32
• 27 Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat 2001; 29 (05) 1189-1232
• 28 Darcy AM, Louie AK, Roberts LW. Machine learning and the profession of medicine. JAMA 2016; 315 (06) 551-552
• 29 Tanwani AK, Afridi J, Shafiq MZ, Farooq M. Guidelines to select machine learning scheme for classification of biomedical datasets. In: Pizzuti C, Ritchie MD, Giacobini M. , eds. Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics. . Berlin, Heidelberg: Springer Verlag; 2009: 128-139
• 30 Foster KR, Koprowski R, Skufca JD. Machine learning, medical diagnosis, and biomedical engineering research - commentary. Biomed Eng Online 2014; 13: 94
• 31 Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996; 49 (12) 1373-1379
• 32 Ye QH, Qin LX, Forgues M. , et al. Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning. Nat Med 2003; 9 (04) 416-423