TY - JOUR AU - Simss, L. AU - Barraclough, H. AU - Govindan, R. PY - 2013 DA - 2013// TI - Biostatistics primer: what a clinician ought to know-prognostic and predictive factors JO - J Thorac Oncol VL - 8 UR - https://doi.org/10.1097/JTO.0b013e318292bdcd DO - 10.1097/JTO.0b013e318292bdcd ID - Simss2013 ER - TY - JOUR AU - Atashi, A. AU - Sarbaz, M. AU - Marashi, S. AU - Hajialiasgari, F. AU - Eslami, S. PY - 2018 DA - 2018// TI - Intensive care decision making: using prognostic models for resource allocation JO - Stud Health Technol Inform VL - 251 ID - Atashi2018 ER - TY - BOOK AU - Smith, P. G. AU - Morrow, R. H. AU - Ross, D. A. PY - 2015 DA - 2015// TI - Field Trials of Health Interventions: A Toolbox. 3rd Ed. Oxford UR - https://doi.org/10.1093/med/9780198732860.001.0001 DO - 10.1093/med/9780198732860.001.0001 ID - Smith2015 ER - TY - JOUR AU - Pirracchio, R. AU - Petersen, M. L. AU - Carone, M. AU - Rigon, M. R. AU - Chevret, S. AU - Mark, J. PY - 2015 DA - 2015// TI - Van der LAAN. Mortality prediction in the ICU: can we do better? Results from the super ICU learner algorithm (SICULA) project, a population-based study JO - Lancet Respir Med VL - 3 UR - https://doi.org/10.1016/S2213-2600(14)70239-5 DO - 10.1016/S2213-2600(14)70239-5 ID - Pirracchio2015 ER - TY - JOUR AU - Awad, A. AU - Bader-El-Den, M. AU - McNicholas, J. AU - Briggs, J. PY - 2017 DA - 2017// TI - Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach JO - Int J Med Inf VL - 108 UR - https://doi.org/10.1016/j.ijmedinf.2017.10.002 DO - 10.1016/j.ijmedinf.2017.10.002 ID - Awad2017 ER - TY - JOUR AU - Lipshutz, A. K. M. AU - Feiner, J. R. AU - Grimes, B. AU - Gropper, M. A. PY - 2016 DA - 2016// TI - Predicting mortality in the intensive care unit: a comparison of the university health consortium expected probability of mortality and the mortality prediction model III JO - J Intensive Care VL - 4 UR - https://doi.org/10.1186/s40560-016-0158-z DO - 10.1186/s40560-016-0158-z ID - Lipshutz2016 ER - TY - BOOK AU - Lee, J. AU - Dubin, J. A. AU - Maslove, D. M. PY - 2016 DA - 2016// TI - Mortality prediction in the ICU. In: Secondary Analysis of Electronic Health Records PB - Springer CY - Cham UR - https://doi.org/10.1007/978-3-319-43742-2_21 DO - 10.1007/978-3-319-43742-2_21 ID - Lee2016 ER - TY - JOUR AU - Pirovano, M. AU - Maltoni, M. AU - Nanni, O. PY - 1999 DA - 1999// TI - A new palliative prognostic score: a first step for the staging of terminally ill Cancer patients JO - J Pain Symptom Manag VL - 17 UR - https://doi.org/10.1016/S0885-3924(98)00145-6 DO - 10.1016/S0885-3924(98)00145-6 ID - Pirovano1999 ER - TY - JOUR AU - Morita, T. AU - Tsunoda, J. AU - Inoue, S. AU - Chihara, S. PY - 1999 DA - 1999// TI - The palliative prognostic index: a scoring system for survival prediction of terminally ill cancer patients JO - Support Care Cancer VL - 7 UR - https://doi.org/10.1007/s005200050242 DO - 10.1007/s005200050242 ID - Morita1999 ER - TY - STD TI - Wagner DP, Draper EA. Acute physiology and chronic health evaluation (APACHE II) and medicare reimbursement. Health Care Financ Rev. 1984:91–105. ID - ref10 ER - TY - JOUR AU - Gall, L. J. R. AU - Lemeshow, S. AU - Saulnier, F. PY - 1993 DA - 1993// TI - A new simplified acute physiology score (SAPSII) based on a European/north American multicenter study JO - JAMA. VL - 270 UR - https://doi.org/10.1001/jama.1993.03510240069035 DO - 10.1001/jama.1993.03510240069035 ID - Gall1993 ER - TY - JOUR AU - Ramchandran, K. J. AU - Shega, J. W. AU - Roenn, J. V. AU - Schumacher, M. AU - Szmuilowicz, E. AU - Rademaker, A. AU - Weitner, B. B. AU - Loftus, P. D. AU - Chu, I. M. AU - Weitzman, S. PY - 2013 DA - 2013// TI - A predictive model to identify hospitalized cancer patients at risk for 30-day mortality based on admission criteria via the electronic medical record JO - Cancer. VL - 119 UR - https://doi.org/10.1002/cncr.27974 DO - 10.1002/cncr.27974 ID - Ramchandran2013 ER - TY - STD TI - Lee J, Maslove DM, Dubin JA. Personalized mortality prediction driven by electronic medical data and a patient similarity metric. PLoS ONE. 2015;10(5):e0127428. ID - ref13 ER - TY - STD TI - Sharafoddini A, Dubin JA, Lee J. Patient similarity in prediction models based on health data: a scoping review. JMIR Med Inform. 2017;5(1):e7. ID - ref14 ER - TY - BOOK AU - Wojtusiak, J. AU - Elashkar, E. AU - Nia, R. M. PY - 2017 DA - 2017// TI - C-Lace: Computational model to predict 30-day post-hospitalization mortality ID - Wojtusiak2017 ER - TY - JOUR AU - Kim, S. AU - Kim, W. AU - Park, R. W. PY - 2011 DA - 2011// TI - A comparison of intensive care unit mortality prediction models through the use of data mining techniques JO - Healthc Inform Res VL - 17 UR - https://doi.org/10.4258/hir.2011.17.4.232 DO - 10.4258/hir.2011.17.4.232 ID - Kim2011 ER - TY - CHAP AU - Hoogendoorn, M. AU - Hassouni, A. AU - Mok, K. AU - Ghassemi, M. AU - Szolovits, P. PY - 2016 DA - 2016// TI - Prediction using patient comparison vs. modeling: a case study for mortality prediction BT - 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ID - Hoogendoorn2016 ER - TY - STD TI - Morid MA, Liu Sheng OR, Abdelrahman S. PPMF: A patient-based predictive modeling framework for early ICU mortality prediction. arXiv preprint arXiv:1704.07499. 2017. ID - ref18 ER - TY - JOUR AU - Wallington, M. AU - Saxon, E. B. AU - Bomb, M. PY - 2016 DA - 2016// TI - 30-day mortality after systemic anticancer treatment for breast and lung cancer in England: a population-based, observational study JO - Lancet Oncol VL - 17 UR - https://doi.org/10.1016/S1470-2045(16)30383-7 DO - 10.1016/S1470-2045(16)30383-7 ID - Wallington2016 ER - TY - JOUR AU - Jochems, A. AU - El-Niqa, I. AU - Kessler, M. PY - 2018 DA - 2018// TI - A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy JO - Acta Oncol VL - 57 UR - https://doi.org/10.1080/0284186X.2017.1385842 DO - 10.1080/0284186X.2017.1385842 ID - Jochems2018 ER - TY - STD TI - Carneiro G, Oakden-Rayner L, Bradley AP, Nascimento J, Palmer L. Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography. IEEE Int Symp Biomed Imaging. 2017. p. 130–4. ID - ref21 ER - TY - JOUR AU - Saad, M. AU - Choi, T. S. PY - 2017 DA - 2017// TI - Computer-assisted subtyping and prognosis for non-small cell lung cancer patients with unresectable tumor JO - Comput Med Imaging Graph VL - 67 UR - https://doi.org/10.1016/j.compmedimag.2018.04.003 DO - 10.1016/j.compmedimag.2018.04.003 ID - Saad2017 ER - TY - JOUR AU - Aerts, H. J. W. L. AU - Velazquez, E. R. AU - Leijenaar, R. T. H. PY - 2014 DA - 2014// TI - Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach JO - Nat Commun VL - 5 UR - https://doi.org/10.1038/ncomms5006 DO - 10.1038/ncomms5006 ID - Aerts2014 ER - TY - JOUR AU - Tsutani, Y. AU - Miyata, Y. AU - Yamanaka, T. PY - 2013 DA - 2013// TI - Solid tumors versus mixed tumors with a ground glass opacity component in patients with clinical stage 1A lung adenocarcinoma: prognostic comparison using high-resolution computed tomography findings JO - J Thorac Cardiovasc Surg VL - 146 UR - https://doi.org/10.1016/j.jtcvs.2012.11.019 DO - 10.1016/j.jtcvs.2012.11.019 ID - Tsutani2013 ER - TY - JOUR AU - Hattori, A. AU - Suzuki, K. AU - Maeyashiki, T. PY - 2014 DA - 2014// TI - The presence of air bronchogram is a novel predictor of negative nodal involvement in radiologically pure-solid lung cancer JO - Eur J Cardiothorac Surg VL - 45 UR - https://doi.org/10.1093/ejcts/ezt467 DO - 10.1093/ejcts/ezt467 ID - Hattori2014 ER - TY - BOOK AU - Bakr, S. AU - Gevaert, O. AU - Echegaray, S. PY - 2017 DA - 2017// TI - Data for NSCLC Radiogenomics Collection. The Cancer Imaging Archive ID - Bakr2017 ER - TY - STD TI - Saad M, Lee IH, Choi TS. Automated delineation of non-small cell lung cancer: a step towards quantitative reasoning in medical decision science. Int J Imaging Syst Technol. 2019:1–16. ID - ref27 ER - TY - STD TI - Hazra A, Bera N, Mandal A. Predicting lung cancer survivability using SVM and Logistic Regression Algorithms. Int J Comp Appl. 2017:174(2). ID - ref28 ER - TY - JOUR AU - Rodirigo, H. AU - Tsokos, C. P. PY - 2017 DA - 2017// TI - Artificial neural network model for predicting lung cancer survival JO - JDAIP. VL - 5 UR - https://doi.org/10.4236/jdaip.2017.51003 DO - 10.4236/jdaip.2017.51003 ID - Rodirigo2017 ER - TY - BOOK AU - Kriegeskorte, N. PY - 2015 DA - 2015// TI - Cross validation in brain imaging analysis ID - Kriegeskorte2015 ER - TY - JOUR AU - Louis, M. PY - 2013 DA - 2013// TI - Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension JO - Crit Care Med VL - 41 UR - https://doi.org/10.1097/CCM.0b013e3182772adb DO - 10.1097/CCM.0b013e3182772adb ID - Louis2013 ER - TY - JOUR AU - Manish, K. G. AU - Pardeep, K. AU - Jugal, K. PY - 2010 DA - 2010// TI - Understanding survival analysis: Kaplan-Meier estimate JO - Int J Ayurveda Res VL - 1 UR - https://doi.org/10.4103/0974-7788.76794 DO - 10.4103/0974-7788.76794 ID - Manish2010 ER - TY - JOUR AU - Christensen, E. PY - 1987 DA - 1987// TI - Multivariate survival analysis using Cox’s regression model JO - Hepatology. VL - 7 UR - https://doi.org/10.1002/hep.1840070628 DO - 10.1002/hep.1840070628 ID - Christensen1987 ER - TY - JOUR AU - Brzezniak, C. AU - Satram-Hoang, S. AU - Goerts, H. P. PY - 2015 DA - 2015// TI - Survival and racial differences of non-small cell lung cancer in the United States military JO - J Gen Intern Med VL - 30 UR - https://doi.org/10.1007/s11606-015-3280-z DO - 10.1007/s11606-015-3280-z ID - Brzezniak2015 ER - TY - JOUR AU - Lara, J. D. AU - Brunson, A. AU - Riess, J. W. PY - 2017 DA - 2017// TI - Clinical predictors of survival in young patients with small cell lung cancer: results from the California Cancer registry JO - Lung Cancer VL - 112 UR - https://doi.org/10.1016/j.lungcan.2017.08.015 DO - 10.1016/j.lungcan.2017.08.015 ID - Lara2017 ER - TY - JOUR AU - Veisani, Y. AU - Delpisheh, A. AU - Sayehmiri, K. PY - 2013 DA - 2013// TI - Demographic and histological predictors of survival in patients with gastric and esophageal carcinoma JO - Iranian Red Crescent Med J VL - 15 UR - https://doi.org/10.5812/ircmj.11847 DO - 10.5812/ircmj.11847 ID - Veisani2013 ER - TY - JOUR AU - Grove, O. AU - Berglund, A. E. AU - Schabath, M. B. PY - 2015 DA - 2015// TI - Quantitative computed tomographic descriptor associate with tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma JO - PLoS One VL - 10 UR - https://doi.org/10.1371/journal.pone.0118261 DO - 10.1371/journal.pone.0118261 ID - Grove2015 ER - TY - BOOK AU - Yi, X. AU - Walia, E. AU - Babyn, P. PY - 2019 DA - 2019// TI - Generative adversarial network in medical imaging: a review. Computer Science, Mathematics, Medicine. Medical Image Analysis UR - https://doi.org/10.1016/j.media.2019.101552 DO - 10.1016/j.media.2019.101552 ID - Yi2019 ER - TY - JOUR AU - Sandfort, V. AU - Yan, K. AU - Pickhardt, P. J. PY - 2019 DA - 2019// TI - Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks JO - Sci Rep VL - 9 UR - https://doi.org/10.1038/s41598-019-52737-x DO - 10.1038/s41598-019-52737-x ID - Sandfort2019 ER - TY - JOUR AU - Frid-Adar, M. AU - Diamant, I. AU - Klang, E. PY - 2018 DA - 2018// TI - GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification JO - Neurocomputing VL - 321 UR - https://doi.org/10.1016/j.neucom.2018.09.013 DO - 10.1016/j.neucom.2018.09.013 ID - Frid-Adar2018 ER -