Raag, Agrawal, Sudhakaran, Prabakaran (2020). Big data in digital healthcare: Lessons learnt and recommendations for general practice. Heredity, 124(4), 525–534.
Google Scholar | Crossref | Medline Stephen, Bates, Trevor, Hastie, Robert, Tibshirani (2021). Cross-validation: What does it estimate and how well does it do it? arXiv Preprint ArXiv:2104.00673.
Google Scholar Bentler, Ruth A (2000). List equivalency and test-retest reliability of the speech in noise test. American Journal of Audiology. Heredity, 9(2), 84–100. doi: 10.1044/1059-0889(2000/010).
Google Scholar | Crossref | Medline Blamey, Peter, Françoise, Artieres, Deniz, Başkent, François, Bergeron, Andy, Beynon, Elaine, Burke, Norbert, Dillier, Richard, Dowell, Bernard, Fraysse, Gallégo, Stéphane, Govaerts Paul J, Green Kevin, Huber Alexander M, Kleine-Punte Andrea, Maat Bert, Marx Mathieu, Mawman Deborah, Mosnier Isabelle, Fitzgerald O'Connor Alec, O'Leary Stephen, Rousset Alexandra, Schauwers Karen, Skarzynski Henryk, Skarzynski Piotr H, Sterkers Olivier, Terranti Assia, Truy Eric, Van de Heyning Paul, Venail Fréderic, Vincent Christophe, & Lazard Diane S (2013). Factors affecting auditory performance of postlinguistically deaf adults using cochlear implants: An update with 2251 patients. Audiology and Neurotology, 18(1), 36–47.
Google Scholar | Crossref | Medline | ISI Blamey, Peter J, Brian C, Pyman, Graeme M, Clark, Richard C, Dowell, Michael, Gordon, Alison M, Brown, Rodney D, Hollow (1992). Factors predicting postoperative sentence scores in postlinguistically deaf adult cochlear implant patients. Annals of Otology, Rhinology & Laryngology, 101(4), 342–348.
Google Scholar | SAGE Journals | ISI Bleeker, S. E., HA, Moll, EW, Steyerberg, ART, Donders, Gerarda, Derksen-Lubsen, DE, Grobbee, KGM, Moons (2003). External validation is necessary in prediction research:: A clinical example. Journal of Clinical Epidemiology, 56(9), 826–832.
Google Scholar | Crossref | Medline Isabelle, Boisvert, Mariana, Reis, Agnes, Au, Cowan, Robert, Richard, C (2020). Cochlear implantation outcomes in adults: A scoping review. PloS One, 15(5), e0232421.
Google Scholar | Medline Botros, Andrew, Rami, Banna, Saji, Maruthurkkara (2013). The next generation of nucleus® fitting: A multiplatform approach towards universal cochlear implant management. International Journal of Audiology, 52(7), 485–494.
Google Scholar | Crossref | Medline Walter, Bouwmeester, Zuithoff, Nicolaas PA, Geerlings, Susan, Vergouwe, Mirjam I, Steyerberg, Yvonne, Altman, Ewout W, Moons, Douglas G, M, Karel G (2012). Reporting and methods in clinical prediction research: A systematic review. PLoS Med, 9(5), e1001221SEP.
Google Scholar | Crossref Breiman, Leo (2001). Random forests. Machine Learning, 45(1), 5–32.
Google Scholar | Crossref | ISI Tianqi, Chen, Carlos, Guestrin (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
Google Scholar | Crossref Ching, Teresa YC, Harvey, Dillon, Greg, Leigh, Linda, Cupples (2018). Learning from the longitudinal outcomes of children with hearing impairment (LOCHI) study: Summary of 5-year findings and implications. International Journal of Audiology, 57(sup2), S105–S111.
Google Scholar | Crossref | Medline Chollet, François (2015). Keras. https://keras.io.
Google Scholar Collins, Gary S, Johannes B, Reitsma, Douglas G, Altman, Karel GM, Moons (2015). Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) the TRIPOD statement. Circulation, 131(2), 211–219.
Google Scholar | Crossref | Medline Cortes, Corinna, Lawrence D, Jackel, Wan-Ping, Chiang (1995). Limits on learning machine accuracy imposed by data quality. In KDD, Vol. 95, 57–62.
Google Scholar Crowson, Matthew G, Jonathan, Ranisau, Antoine, Eskander, Aaron, Babier, Bin, Xu, Russel R, Kahmke, Joseph M, Chen, Timothy CY, Chan (2020a). A contemporary review of machine learning in otolaryngology–head and neck surgery. The Laryngoscope, 130(1), 45–51.
Google Scholar | Crossref | Medline Crowson, Matthew G, Vincent, Lin, Joseph M, Chen, Timothy CY, Chan (2020b). Machine learning and cochlear implantation. A structured review of opportunities and challenges. Otology & Neurotology, 41(1), e36–e45.
Google Scholar | Crossref | Medline Crowson, Matthew G, Peter, Dixon, Rafid, Mahmood, Lee, Jong Wook, David, Shipp, Lin, V, Chen, J, Chan, TCY (2020c). Predicting postoperative cochlear implant performance using supervised machine learning. Otology & Neurotology: Official Publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology.
Google Scholar Figueroa, Rosa L, Qing, Zeng-Treitler, Sasikiran, Kandula, Long H, Ngo (2012). Predicting sample size required for classification performance. BMC Medical Informatics and Decision Making, 12(1), 1–10.
Google Scholar | Crossref | Medline Friedman, Jerome H (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
Google Scholar | Crossref Xavier, Glorot, Antoine, Bordes, Yoshua, Bengio (2011). Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, 315–323.
Google Scholar Benjamin, Goudey, Kerrie, Plant, Isabell, Kiral, Antonio, Jimeno-Yepes, Annalisa, Swan, Manoj, Gambhir, Andreas, Buechnerc, Eugen, Kludt, Robert H, Sucher, Gifford, Cathy, Rottier, Rene, Anjomshoa, Hamideh (2021). A multi-center analysis of factors associated with hearing outcome for 2735 adults with cochlear implants. Trends in Hearing, 25.
Google Scholar Hahlbrock, Karl-Heinz (1953). Über sprachaudiometrie und neue Wörterteste. Archiv Für Ohren-, Nasen- Und Kehlkopfheilkunde, 162(5), 394–431.
Google Scholar | Crossref | Medline Hahlbrock, Karl Heinz (1960). Kritische betrachtungen und vergleichende untersuchungen der schubertschen und freiburger sprachteste. Zeitschrift Fur Laryngologie, Rhinologie, Otologie Und Ihre Grenzgebiete, 39, 100–101.
Google Scholar Harrell Jr, Frank E (2015). Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Springer.
Google Scholar Holden, Laura K, Charles C, Finley, Jill B, Firszt, Timothy A, Holden, Christine, Brenner, Lisa G, Potts, Brenda D, Gotter, Sallie S, Vanderhoof, Karen, Mispagel, Heydebrand, Gitry (2013). Factors affecting open-set word recognition in adults with cochlear implants. Ear and Hearing, 34(3), 342.
Google Scholar | Crossref | Medline | ISI James, Chris J, Chadlia, Karoui, Marie-Laurence, Laborde, Beno, Lepage, Charles-Édouard, Molinier, Marjorie, Tartayre, Bernard, Escudé, Olivier, Deguine, Mathieu, Marx, Bernard, Fraysse (2019). Early sentence recognition in adult cochlear implant users. Ear and Hearing, 40(4), 905–917.
Google Scholar | Crossref | Medline Jordan, Michael I, , Tom M (2015). Machine learning: Trends, perspectives, and prospects. Science (New York, N.Y.), 349(6245), 255–260.
Google Scholar | Crossref | Medline | ISI Kelly, Christopher J, Alan, Karthikesalingam, Mustafa, Suleyman, Greg, Corrado, Dominic, King (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), 1–9.
Google Scholar | Crossref | Medline Kim, Hosung, Woo Seok, Kang, Hong Ju, Park, Jee Yeon, Lee, Jun Woo, Park, Yehree, Kim, Ji Won, Seo, Min Young, Kwak, Byung Chul, Kang, Chan Joo, Yang, Ben A. Duffy, Young Sang Cho, Sang-Youp Lee, Myung Whan Suh, Il Joon Moon, Joong Ho Ahn, Yang-Sun Cho, Seung Ha Oh, & Jong Woo Chung (2018). Cochlear implantation in postlingually deaf adults is time-sensitive towards positive outcome: Prediction using advanced machine learning techniques. Scientific Reports, 8(1), 18004.
Google Scholar | Crossref | Medline Lazard, Diane S, Christophe, Vincent, Frédéric, Venail, Paul, Vande Heyning, Eric, Truy, Olivier, Sterkers, Piotr H, Skarzynski, Henryk, Skarzynski, Karen, Schauwers, Stephen, O’Leary, Mawman Deborah, Maat Bert, Kleine-Punte Andrea, Huber Alexander M, Green Kevin, Govaerts Paul J, Fraysse Bernard, Dowell Richard, Dillier Norbert, Burke Elaine, Beynon Andy, Bergeron François, Başkent Deniz, Artières Françoise, Blamey Peter J (2012). Pre-, per-and postoperative factors affecting performance of postlinguistically deaf adults using cochlear implants: A new conceptual model over time. OnePloS One, 7(11), 1–1.
Google Scholar | Crossref | ISI Lim, Gilbert, Valentina, Bellemo, Yuchen, Xie, Xin Q, Lee, Michelle YT, Yip, Daniel SW, Ting (2020). Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: A review. Eye and Vision, 7, 1–13.
Google Scholar | Crossref Marchetti, Michael A, Konstantinos, Liopyris, Stephen W, Dusza, Noel CF, Codella, David A, Gutman, Brian, Helba, Aadi, Kalloo, Allan C, Halpern (2020). Computer algorithms show potential for improving dermatologists’ accuracy to diagnose cutaneous melanoma: Results of the international skin imaging collaboration 2017. Journal of the American Academy of Dermatology, 82(3), 622–627.
Google Scholar | Crossref | Medline Moons, Karel GM, Douglas G, Altman, Yvonne, Vergouwe, Patrick, Royston (2009). Prognosis and prognostic research: Application and impact of prognostic models in clinical practice. BMJ (Clinical Research Ed.), 338(1.
Google Scholar Moulin, Annie, André, Bernard, Laurent, Tordella, Judith, Vergne, Annie, Gisbert, Christian, Martin, Céline, Richard (2017). Variability of word discrimination scores in clinical practice and consequences on their sensitivity to hearing loss. European Archives of Oto-Rhino-Laryngology, 274(5), 2117–2124.
Google Scholar | Crossref | Medline Obermeyer, Ziad, Emanuel, Ezekiel J (2016). Predicting the future big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216–1219.
Google Scholar | Crossref Pedregosa, F, G, Varoquaux, A, Gramfort, V, Michel, B, Thirion, O, Grisel, M, Blondel, P, Prettenhofer, R, Weiss, V, Dubourg, J, Vanderplas, A, Passos, D, Cournapeau, M, Brucher, M, Perrot, E, Duchesnay (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12, 2825–2830.
Google Scholar | ISI Peterson, Gordon E, , Ilse (1962). Revised CNC lists for auditory tests. Journal of Speech and Hearing Disorders, 27(1), 62–70.
Google Scholar | Crossref | Medline Plant, Kerrie, Hugh, McDermott, Richard, Van Hoesel, Pamela, Dawson, Robert, Cowan (2016). Factors predicting postoperative unilateral and bilateral speech recognition in adult cochlear implant recipients with acoustic hearing. Ear and Hearing, 37(2), 153–163.
Google Scholar | Crossref | Medline Ramspek, Chava L, Kitty J, Jager, Friedo W, Dekker, Carmine, Zoccali, Merel, van Diepen (2021). External validation of prognostic models: What, why, how, when and where. Clinical Kidney Journal, 14(1), 49–58.
Google Scholar | Crossref | Medline Roditi, Rachel E, Sarah F, Poissant, Eva M, Bero, Daniel J, Lee (2009). A predictive model of cochlear implant performance in postlingually deafened adults. Otology & Neurotology, 30(4), 449–454.
Google Scholar | Crossref | Medline Royston, Patrick, Karel GM, Moons, Douglas G, Altman, Yvonne, Vergouwe (2009). Prognosis and prognostic research: Developing a prognostic model. BMJ (Clinical Research Ed.), 338, 1373–1377.
Google Scholar | Crossref Sheng, Victor S, Foster, Provost, Panagiotis G, Ipeirotis (2008). Get another label? improving data quality and data mining using multiple, noisy labelers. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 614–622.
Google Scholar | Crossref Siontis, George CM, Ioanna, Tzoulaki, Peter J, Castaldi, John PA, Ioannidis (2015). External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination. Journal of Clinical Epidemiology, 68(1), 25–34.
Google Scholar | Crossref | Medline Smulders, Yvette E, Thomas, Hendriks, Inge, Stegeman, Robert H, Eikelboom, Cathy, Sucher, Gemma, Upson, Browne, Chester, Jayakody, Ronel, Santa Maria, Dona, Atlas, Peter L, Marcus D, Atlas, & Peter L Friedland. (2018). Predicting sequential bilateral cochlear implantation performance in postlingually deafened adults; A retrospective cohort study. Clinical Otolaryngology, 43(6), 1500–1507.
Google Scholar | Crossref | Medline Srigley, John R, Tom, McGowan, Andrea, MacLean, Marilyn, Raby, Jillian, Ross, Sarah, Kramer, Carol, Sawka (2009). Standardized synoptic cancer pathology reporting: A population-based approach. Journal of Surgical Oncology, 99(8), 517–524.
Google Scholar | Crossref | Medline | ISI Tang, Cheng, Damien, Garreau, Ulrike, von Luxburg (2018). When do random forests fail? In NeurIPS, 31, 2987–2997.
Google Scholar Tsimpida, Dialechti, Evangelos, Kontopantelis, Darren, Ashcroft, Maria, Panagioti (2020). Comparison of self-reported measures of hearing with an objective audiometric measure in adults in the English longitudinal study of ageing. JAMA Network Open, 3(8), e2015009.
Google Scholar | Crossref | Medline Williams, Christopher L, Roger, Bjugn, Lewis A, Hassell (2015). Current status of discrete data capture in synoptic surgical pathology and cancer reporting. Pathology and Laboratory Medicine International, 7, 11.
Google Scholar Woolson, RF (2007). Wilcoxon signed-rank test. Wiley Encyclopedia of Clinical Trials, 2007, 1–3.
Google Scholar Wright, Adam, Thu-Trang T, Hickman, Dustin, McEvoy, Skye, Aaron, Angela, Ai, Jan Marie, Andersen, Salman, Hussain, Rachel, Ramoni, Julie, Fiskio, Dean F, Sittig, & David W Bates (2016). Analysis of clinical decision support system malfunctions: A case series and survey. Journal of the American Medical Informatics Association, 23(6), 1068–1076.