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Machine learning and modeling: Data, validation, communication challenges

dc.contributor.authorEl Naqa, Issam
dc.contributor.authorRuan, Dan
dc.contributor.authorValdes, Gilmer
dc.contributor.authorDekker, Andre
dc.contributor.authorMcNutt, Todd
dc.contributor.authorGe, Yaorong
dc.contributor.authorWu, Q. Jackie
dc.contributor.authorOh, Jung Hun
dc.contributor.authorThor, Maria
dc.contributor.authorSmith, Wade
dc.contributor.authorRao, Arvind
dc.contributor.authorFuller, Clifton
dc.contributor.authorXiao, Ying
dc.contributor.authorManion, Frank
dc.contributor.authorSchipper, Matthew
dc.contributor.authorMayo, Charles
dc.contributor.authorMoran, Jean M.
dc.contributor.authorTen Haken, Randall
dc.date.accessioned2018-11-20T15:32:34Z
dc.date.available2019-12-02T14:55:09Zen
dc.date.issued2018-10
dc.identifier.citationEl Naqa, Issam; Ruan, Dan; Valdes, Gilmer; Dekker, Andre; McNutt, Todd; Ge, Yaorong; Wu, Q. Jackie; Oh, Jung Hun; Thor, Maria; Smith, Wade; Rao, Arvind; Fuller, Clifton; Xiao, Ying; Manion, Frank; Schipper, Matthew; Mayo, Charles; Moran, Jean M.; Ten Haken, Randall (2018). "Machine learning and modeling: Data, validation, communication challenges." Medical Physics 45(10): e834-e840.
dc.identifier.issn0094-2405
dc.identifier.issn2473-4209
dc.identifier.urihttps://hdl.handle.net/2027.42/146326
dc.publisherMcGraw‐Hill
dc.publisherWiley Periodicals, Inc.
dc.subject.otherradiation oncology
dc.subject.othermachine learning
dc.subject.otherbig data
dc.titleMachine learning and modeling: Data, validation, communication challenges
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146326/1/mp12811_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146326/2/mp12811.pdf
dc.identifier.doi10.1002/mp.12811
dc.identifier.sourceMedical Physics
dc.identifier.citedreferenceJiang B, Zhang X, Cai T. Estimating the confidence interval for prediction errors of support vector machine classifiers. J Mach Learn Res. 2008; 9: 521 – 540.
dc.identifier.citedreferenceGyörfi LS, Ottucsák GG, Walk H. Machine Learning for Financial Engineering. Singapore; London: World Scientific; 2012.
dc.identifier.citedreferenceSheng Y, Ge Y, Yuan L, Li T, Yin FF, Wu QJ. Outlier identification in radiation therapy knowledge‐based planning: a study of pelvic cases. Med Phys. 2017; 44: 5617 – 5626.
dc.identifier.citedreferenceEl Naqa I, Bradley JD, Lindsay PE, Hope AJ, Deasy JO. Predicting radiotherapy outcomes using statistical learning techniques. Phys Med Biol. 2009; 54: S9 – S30.
dc.identifier.citedreferenceBritannica TEOE. Occam’s razor. In Encyclopædia Britannica,. London, UK: Encyclopædia Britannica, inc.; 2015.
dc.identifier.citedreferenceGoodfellow I, Bengio Y, Courville A. Deep Learning. Cambridge, MA: MIT Press; 2017.
dc.identifier.citedreferenceCherkassky VS, Mulier F. Learning From Data: Concepts, Theory, and Methods. 2nd ed. Hoboken, NJ: IEEE Press: Wiley‐Interscience; 2007.
dc.identifier.citedreferenceLian J, Yuan L, Ge Y, et al. Modeling the dosimetry of organ‐at‐risk in head and neck IMRT planning: an intertechnique and interinstitutional study. Med Phys. 2013; 40: 121704.
dc.identifier.citedreferenceGammerman A, Vovk V. Prediction algorithms and confidence measures based on algorithmic randomness theory. Theoret Comput Sci. 2002; 287: 209 – 217.
dc.identifier.citedreferenceMitra S. Introduction to Machine Learning and Bioinformatics. Boca Raton: CRC Press; 2008.
dc.identifier.citedreferenceBradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 1997; 30: 1145 – 1159.
dc.identifier.citedreferenceJavanmard A, Montanari A. Confidence intervals and hypothesis testing for high‐dimensional regression. J Mach Learn Res. 2014; 15: 2869 – 2909.
dc.identifier.citedreferenceHoeting JA, Madigan D, Raftery AE, Volinsky CT. Bayesian model averaging: a tutorial. Stat Sci. 1999; 14: 382 – 401.
dc.identifier.citedreferenceYuan L, Ge Y, Lee WR, Yin FF, Kirkpatrick JP, Wu QJ. Quantitative analysis of the factors which affect the interpatient organ‐at‐risk dose sparing variation in IMRT plans. Med Phys. 2012; 39: 6868 – 6878.
dc.identifier.citedreferenceCooper GF, Aliferis CF, Ambrosino R, et al. An evaluation of machine‐learning methods for predicting pneumonia mortality. Artif Intell Med. 1997; 9: 107 – 138.
dc.identifier.citedreferenceCooper GF, Abraham V, Aliferis CF, et al. Predicting dire outcomes of patients with community acquired pneumonia. J Biomed Inform. 2005; 38: 347 – 366.
dc.identifier.citedreferenceCaruana R, Lou Y, Gehrke J, Koch P, Sturm M, Elhadad N. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30‐day readmission. Paper presented at: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2015.
dc.identifier.citedreferenceLou Y, Caruana R, Gehrke J. Intelligible models for classification and regression. Paper presented at: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining 2012.
dc.identifier.citedreferenceValdes G, Luna JM, Eaton E, Simone CB. MediBoost: a patient stratification tool for interpretable decision making in the era of precision medicine. Sci Rep. 2016; 6.
dc.identifier.citedreferenceRibeiro MT, Singh S, Guestrin C. Model‐agnostic interpretability of machine learning. arXiv preprint arXiv:160605386. 2016.
dc.identifier.citedreferenceLuo Y, El Naqa I, McShan DL, et al. Unraveling biophysical interactions of radiation pneumonitis in non‐small‐cell lung cancer via Bayesian network analysis. Radiother Oncol. 2017; 123: 85 – 92.
dc.identifier.citedreferenceKang J, Schwartz R, Flickinger J, Beriwal S. Machine learning approaches for predicting radiation therapy outcomes: a clinician’s perspective. Int J Radiat Oncol Biol Phys. 2015; 93: 1127 – 1135.
dc.identifier.citedreferenceSingh H, Spitzmueller C, Petersen NJ, Sawhney MK, Sittig DF. Information overload and missed test results in electronic health record‐based settings. JAMA Intern Med. 2013; 173: 702 – 704.
dc.identifier.citedreferenceDuncan J. Information overload: when less is more in medical imaging. De Gruyter. 2017; 4: 179 – 183.
dc.identifier.citedreferenceLambin P, van Stiphout RG, Starmans MH, et al. Predicting outcomes in radiation oncology–multifactorial decision support systems. Nat Rev Clin Oncol. 2013; 10: 27 – 40.
dc.identifier.citedreferenceSmith WP, Kim M, Holdsworth C, Liao J, Phillips MH. Personalized treatment planning with a model of radiation therapy outcomes for use in multiobjective optimization of IMRT plans for prostate cancer. Radiat Oncol. 2016; 11: 38.
dc.identifier.citedreferenceMayo CS, Moran JM, Bosch W, et al. AAPM TG‐263: standardizing nomenclatures in radiation oncology. Int J Radiat Oncol Biol Phys. 2017; 99: E552.
dc.identifier.citedreferenceBreiman L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Statist Sci. 2001; 16: 199 – 231.
dc.identifier.citedreferenceDekker A, Vinod S, Holloway L, et al. Rapid learning in practice: a lung cancer survival decision support system in routine patient care data. Radiother Oncol. 2014; 113: 47 – 53.
dc.identifier.citedreferenceFigueroa RL, Zeng‐Treitler Q, Kandula S, Ngo LH. Predicting sample size required for classification performance. BMC Med Inform Decis Mak. 2012; 12: 8 – 8.
dc.identifier.citedreferenceWu Y. Elastic net for cox’s proportional hazards model with a solution path algorithm. Stat Sin. 2012; 22: 27 – 294.
dc.identifier.citedreferenceJapkowicz N, Shah M. Performance evaluation in machine learning. In: El Naqa I, Li R, Murphy MJ, eds. Machine Learning in Radiation Oncology: Theory and Applications. Switzerland: Springer‐Verlag; 2015: 41 – 56.
dc.identifier.citedreferencePage P. Beyond statistical significance: clinical interpretation of rehabilitation research literature. Int J Sports Phys Ther. 2014; 9: 726 – 736.
dc.identifier.citedreferenceHarris RF. Rigor Mortis: How Sloppy Science Creates Worthless Cures, Crushes Hope, and Wastes Billions. New York: Basic Books; 2017.
dc.identifier.citedreferenceHastie T, Tibshirani R, Friedman JH. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. New York, NY: Springer; 2009.
dc.identifier.citedreferenceVallieres M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG‐PET and MRI texture features for the prediction of lung metastases in soft‐tissue sarcomas of the extremities. Phys Med Biol. 2015; 60: 5471 – 5496.
dc.identifier.citedreferenceSamuel AL. Some studies in machine learning using the game of checkers. IBM J Res Dev. 1959; 3: 210 – 229.
dc.identifier.citedreferenceMitchell TM. Machine Learning. New York, NY: McGraw‐Hill; 1997.
dc.identifier.citedreferenceAlpaydin E. Introduction to Machine Learning. 3rd ed. Cambridge, MA: The MIT Press; 2014.
dc.identifier.citedreferenceBishop CM. Pattern Recognition and Machine Learning. New York, NY: Springer; 2006.
dc.identifier.citedreferenceApolloni B. Machine Learning and Robot Perception. Berlin: Springer‐Verlag; 2005.
dc.identifier.citedreferenceAo S‐I, Rieger BB, Amouzegar MA. Machine Learning and Systems Engineering. Dordrecht, NY: Springer; 2010.
dc.identifier.citedreferenceYang ZR. Machine Learning Approaches to Bioinformatics. Hackensack, NJ: World Scientific; 2010.
dc.identifier.citedreferenceCleophas TJ. Machine Learning in Medicine. New York, NY: Springer; 2013.
dc.identifier.citedreferenceMalley JD, Malley KG, Pajevic S. Statistical Learning for Biomedical Data. Cambridge: Cambridge University Press; 2011.
dc.identifier.citedreferenceFriedman J, Hastie T, Tibshirani R. The Elements of Statistical Learning. Vol 1: Berlin: Springer series in statistics Springer; 2001.
dc.identifier.citedreferenceLustberg T, van Soest J, Gooding M, et al. Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiother Oncol. 2018; 126: 312 – 317.
dc.identifier.citedreferenceChapelle O, Schlkopf B, Zien A. Semi‐Supervised Learning. Cambridge, MA: The MIT Press; 2010.
dc.identifier.citedreferencePark SH, Gao Y, Shi Y, Shen D. Interactive prostate segmentation using atlas‐guided semi‐supervised learning and adaptive feature selection. Med Phys. 2014; 41: 111715.
dc.identifier.citedreferenceSoares I, Dias J, Rocha H, Khouri L, Do Carmo Lopes M, Ferreira B. Semi‐supervised self‐training approaches in small and unbalanced datasets: application to xerostomia radiation side‐effect. In Kyriacou E, Christofides S, Pattichis CS, eds. XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016: MEDICON 2016, March 31st‐April 2nd 2016, Paphos, Cyprus. Cham: Springer International Publishing; 2016: 828 – 833.
dc.identifier.citedreferenceCollins GS, Reitsma JB, Altman DG, Moons KM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod): the tripod statement. Ann Intern Med. 2015; 162: 55 – 63.
dc.identifier.citedreferenceLuo W, Phung D, Tran T, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res. 2016; 18: e323.
dc.identifier.citedreferenceWilloughby TR, Starkschall G, Janjan NA, Rosen II. Evaluation and scoring of radiotherapy treatment plans using an artificial neural network. Int J Radiat Oncol Biol Phys. 1996; 34: 923 – 930.
dc.identifier.citedreferenceRowbottom CG, Webb S, Oldham M. Beam‐orientation customization using an artificial neural network. Phys Med Biol 1999; 44: 2251 – 2262.
dc.identifier.citedreferenceWells DM, Niederer J. A medical expert system approach using artificial neural networks for standardized treatment planning. Int J Radiat Oncol Biol Phys 1998; 41: 173 – 182.
dc.identifier.citedreferenceGulliford SL, Webb S, Rowbottom CG, Corne DW, Dearnaley DP. Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate. Radiother Oncol. 2004; 71: 3 – 12.
dc.identifier.citedreferenceMunley MT, Lo JY, Sibley GS, Bentel GC, Anscher MS, Marks LB. A neural network to predict symptomatic lung injury. Phys Med Biol. 1999; 44: 2241 – 2249.
dc.identifier.citedreferenceSu M, Miften M, Whiddon C, Sun X, Light K, Marks L. An artificial neural network for predicting the incidence of radiation pneumonitis. Med Phys. 2005; 32: 318 – 325.
dc.identifier.citedreferenceNAqA IE, Deasy JO, Mu Y, et al. Datamining approaches for modeling tumor control probability. Acta Oncol. 2010; 49: 1363 – 1373.
dc.identifier.citedreferenceValdes G, Solberg TD, Heskel M, Ungar L, Simone CB II. 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: 6105.
dc.identifier.citedreferenceEl Naqa I, Bradley J, Blanco AI, et al. Multivariable modeling of radiotherapy outcomes, including dose–volume and clinical factors. Int J Radiat Oncol Biol Phys. 2006; 64: 1275 – 1286.
dc.identifier.citedreferenceOberije C, Nalbantov G, Dekker A, et al. A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making. Radiother Oncol. 2014; 112: 37 – 43.
dc.identifier.citedreferenceBradley J, Deasy JO, Bentzen S, El Naqa I. Dosimetric correlates for acute esophagitis in patients treated with radiotherapy for lung carcinoma. Int J Radiat Oncol Biol Phys. 2004; 58: 1106 – 1113.
dc.identifier.citedreferenceHope AJ, Lindsay PE, El Naqa I, et al. Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters. Int J Radiat Oncol Biol Phys. 2006; 65: 112 – 124.
dc.identifier.citedreferenceZhou Z, Folkert M, Cannon N, et al. Predicting distant failure in early stage NSCLC treated with SBRT using clinical parameters. Radiother Oncol. 2016; 119: 501 – 504.
dc.identifier.citedreferenceKalet AM, Gennari JH, Ford EC, Phillips MH. Bayesian network models for error detection in radiotherapy plans. Phys Med Biol. 2015; 60: 2735 – 2749.
dc.identifier.citedreferenceValdes G, Scheuermann R, Hung C, Olszanski A, Bellerive M, Solberg T. A mathematical framework for virtual IMRT QA using machine learning. Med Phys. 2016; 43: 4323 – 4334.
dc.identifier.citedreferenceValdes G, Morin O, Valenciaga Y, Kirby N, Pouliot J, Chuang C. Use of TrueBeam developer mode for imaging QA. J Appl Clin Med Phys. 2015; 16: 322 – 333.
dc.identifier.citedreferenceCarlson JN, Park JM, Park S‐Y, Park JI, Choi Y, Ye S‐J. A machine learning approach to the accurate prediction of multi‐leaf collimator positional errors. Phys Med Biol. 2016; 61: 2514.
dc.identifier.citedreferenceLi Q, Chan MF. Predictive time‐series modeling using artificial neural networks for Linac beam symmetry: an empirical study. Ann N Y Acad Sci. 2017; 1387: 84 – 94.
dc.identifier.citedreferenceMoore KL, Brame RS, Low DA, Mutic S. Experience‐based quality control of clinical intensity‐modulated radiotherapy planning. Int J Radiat Oncol Biol Phys. 2011; 81: 545 – 551.
dc.identifier.citedreferenceWu B, Ricchetti F, Sanguineti G, et al. Data‐driven approach to generating achievable dose‐volume histogram objectives in intensity‐modulated radiotherapy planning. Int J Radiat Oncol Biol Phys. 2011; 79: 1241 – 1247.
dc.identifier.citedreferenceWu B, Ricchetti F, Sanguineti G, et al. Patient geometry‐driven information retrieval for IMRT treatment plan quality control. Med Phys. 2009; 36: 5497 – 5505.
dc.identifier.citedreferenceGuidi G, Maffei N, Meduri B, et al. A machine learning tool for re‐planning and adaptive RT: a multicenter cohort investigation. Physica Med. 2016; 32: 1659 – 1666.
dc.identifier.citedreferenceGuidi G, Maffei N, Vecchi C, et al. A support vector machine tool for adaptive tomotherapy treatments: prediction of head and neck patients criticalities. Physica Med. 2015; 31: 442 – 451.
dc.identifier.citedreferenceRuan D, Keall P. Online prediction of respiratory motion: multidimensional processing with low‐dimensional feature learning. Phys Med Biol. 2010; 55: 3011 – 3025.
dc.identifier.citedreferenceIsaksson M, Jalden J, Murphy MJ. On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications. Med Phys. 2005; 32: 3801 – 3809.
dc.identifier.citedreferenceEl Naqa I, Li R, Murphy MJ, eds. Machine Learning in Radiation Oncology: Theory and Application. Switzerland: Springer International Publishing; 2015. https://doi.org/10.1007/978-3-319-18305-3.
dc.identifier.citedreferenceKohannim O, Hua X, Hibar DP, et al. Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiol Aging. 2010; 31: 1429 – 1442.
dc.identifier.citedreferenceHead ML, Holman L, Lanfear R, Kahn AT, Jennions MD. The extent and consequences of P‐hacking in science. PLoS Biol. 2015; 13: e1002106.
dc.identifier.citedreferenceSmith WP, Phillips MH. Comment on “ROC analysis in patient specific quality assurance” [Med. Phys. 40(4), 042103 (7 pp.) (2013)]. Med Phys. 2015; 42: 4411 – 4412.
dc.identifier.citedreferenceHand DJ. Measuring classifier performance: a coherent alternative to the area under the ROC curve. Mach Learn. 2009; 77: 103 – 123.
dc.identifier.citedreferencePhillips MH, Smith WP, Parvathaneni U, Laramore GE. Role of positron emission tomography in the treatment of occult disease in head‐and‐neck cancer: a modeling approach. Int J Radiat Oncol Biol Phys. 2011; 79: 1089 – 1095.
dc.identifier.citedreferenceHalligan S, Altman DG, Mallett S. Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: a discussion and proposal for an alternative approach. Eur Radiol. 2015; 25: 932 – 939.
dc.identifier.citedreferenceRice ME, Harris GT. Comparing effect sizes in follow‐up studies: ROC Area, Cohen’s d, and r. Law Hum Behav. 2005; 29: 615 – 620.
dc.identifier.citedreferenceKraemer HC, Kupfer DJ. Size of treatment effects and their importance to clinical research and practice. Biol Psychiat. 2006; 59: 990 – 996.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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