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Improving palliative care with deep learning
Anand Avati, Kenneth Jung, Stephanie Harman and 3 more
BMC Medical Informatics and Decision Making · 2018
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- 1.
Where do americans die?Available: https://palliative.stanford.edu/home-hospice-home-care-of-the-dying-patient/where-do-americans-die/. Accessed 15 Apr 2017.
Not in our library
- 2.
Dumanovsky T, Augustin R, Rogers M, Lettang K, Meier DE, Sean Morrison R. Special Report The Growth of Palliative Care in U.S. Hospitals: A Status Report. J Palliat Med. 2016;19(1):8-15.
Not in our library10.1089/jpm.2015.0351
- 3.
palliative care, report card. 2015. Available: https://web.archive.org/web/20170316014732/https://reportcard.capc.org/#key-findings. Accessed 15 Apr 2017.
Not in our library
- 4.
Spetz J, Dudley N, Trupin L, Rogers M, Meier DE, Dumanovsky T. Few Hospital Palliative Care Programs Meet National Staffing Recommendations. Health Aff (Project Hope). 2016; 9(9):1690–7.
Not in our library10.1377/hlthaff.2016.0113
- 5.
Christakis N, Lamont E. Extent and determinants of error in doctors’ prognoses in terminally ill patients: prospective cohort study. BMJ Clin Res. 2000;320(7233):469-72.
Not in our library10.1136/bmj.320.7233.469
- 6.
most physicians would forgo aggressive treatment for themselves at the end of life, study finds. 2014. Available: https://med.stanford.edu/news/all-news/2014/05/most-physicians-would-forgo-aggressive-treatment-for-themselves-.html. Accessed 15 Apr 2017.
Not in our library
- 7.
Kutner JS, Steiner JF, Corbett KK, Jahnigen DW, Barton PL. Information needs in terminal illness. Soc Sci Med. 1999;48(10):1341-52.
Not in our library10.1016/s0277-9536(98)00453-5
- 8.
Steinhauser KE, Christakis NA, Clipp EC, Mcneilly M, Grambow S, Parker J, Tulsky JA. Preparing for the End of Life: Preferences of Patients, Families, Physicians, and Other Care Providers. J Pain Symptom Manag J Pain Symptom Manage. 2001;2222:727.
Not in our library10.1016/s0885-3924(01)00334-7
- 9.
Selby D, Chakraborty A, Lilien T, Stacey E, Zhang L, Myers J. Clinician Accuracy When Estimating Survival Duration: The Role of the Patient’s Performance Status and Time-Based Prognostic Categories. J Pain Symptom Manag. 2011;42(4):578-88.
Not in our library10.1016/j.jpainsymman.2011.01.012
- 10.
Viganȯ A, Dorgan M, Bruera E, Suarez-Almazor ME. The Relative Accuracy of the Clinical Estimation of the Duration of Life for Patients with End of Life Cancer. Cancer. 1999;86(1):170-6.
Not in our library10.1002/(sici)1097-0142(19990701)86:1<170::aid-cncr23>3.0.co;2-s
- 11.
Glare P, Sinclair C, Downing M, Stone P, Maltoni M, Vigano A. Predicting survival in patients with advanced disease. 2008;4:2.
Not in our library10.1016/j.ejca.2008.02.030
- 12.
- 13.
Macmillan DO. Accuracy of prediction of survival by different professional groups in a hospice. Palliat Med. 1998;12:117-8.
Not in our library10.1191/026921698670033936
- 14.
Lau F, Downing GM, Lesperance M, Shaw J, Kuziemsky C. Use of Palliative Performance Scale in End-of-Life Prognostication. J Palliat Med. 2006;9(5):1066-75.
Not in our library10.1089/jpm.2006.9.1066
- 15.
Karnofsky DA, Abelmann WH, Craver LF, Burchenal JH. The use of the nitrogen mustards in the palliative treatment of carcinoma.With particular reference to bronchogenic carcinoma. Cancer. 1948;1(4):634-56.
Not in our library10.1002/1097-0142(194811)1:4<634::aid-cncr2820010410>3.0.co;2-l
- 16.
Pirovano M, Maltoni M, Nanni O, Marinari M, Indelli M, Zaninetta G, Petrella V, Barni S, Zecca E, Scarpi E, Labianca R, Amadori D, Luporini G. A New Palliative Prognostic Score: A First Step for the Staging of Terminally Ill Cancer Patients for the Italian Multicenter and Study Group on Palliative Care. J Pain Symptom Manag Pirovano et al. 1997;17(4):231–9.
Not in our library10.1016/s0885-3924(98)00145-6
- 17.
Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818-29.
Not in our library10.1097/00003246-198510000-00009
- 18.
Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, Sirio CA, Murphy DJ, Lotring T, Damiano A, Harrell FE. The APACHE III Prognostic System. Chest. 1991;100(6):1619-36.
Not in our library10.1378/chest.100.6.1619
- 19.
Le Gall J-R, Lemeshow S, Saulnier F. A New Simplified Acute Physiology Score (SAPS II) Based on a European/North American Multicenter Study. J Am Med Assoc. 1993;12(24):2957.
Not in our library10.1001/jama.1993.03510240069035
- 20.
Cardona-Morrell M, Hillman K. Development of a tool for defining and identifying the dying patient in hospital: Criteria for Screening and Triaging to Appropriate aLternative care (CriSTAL). BMJ Support Palliat Care. 2015;3(1):78-90.
Not in our library10.1136/bmjspcare-2014-000770
- 21.
Fischer SM, Gozansky WS, Sauaia A, Min S-J, Kutner JS, Kramer A. A Practical Tool to Identify Patients Who May Benefit from a Palliative Approach: The CARING Criteria. J Pain Symptom Manag. 2006;31(4):285-92.
Not in our library10.1016/j.jpainsymman.2005.08.012
- 22.
Richardson P, Greenslade J, Shanmugathasan S, Doucet K, Widdicombe N, Chu K, Brown A. PREDICT: a diagnostic accuracy study of a tool for predicting mortality within one year: Who should have an advance healthcare directive? Palliat Med. 2015;29(1):31-7.
Not in our library10.1177/0269216314540734
- 23.
Horne BD, May HT, Muhlestein JB, Ronnow BS, Lappė DL, Renlund DG, Kfoury AG, Carlquist JF, Fisher PW, Pearson RR, Bair TL, Anderson JL. Exceptional Mortality Prediction by Risk Scores from Common Laboratory Tests. AJM. 2009;122:550-8.
Not in our library10.1016/j.amjmed.2008.10.043
- 24.
Cowen ME, Strawderman RL, Czerwinski JL, Smith MJ, Halasyamani LK, Cowen ME. Mortality predictions on admission as a context for organizing care activities. J Hosp Med. 2013;5(5):229-35.
Not in our library10.1002/jhm.1998
- 25.
Meffert C, Ru̇cker G, Hatami I, Becker G. Identification of hospital patients in need of palliative care-a predictive score. BMC Palliat Care. 2016;15. 10.1186/s12904-016-0094-7.
Not in our library10.1186/s12904-016-0094-7
- 26.
Ramchandran KJ, Shega JW, Von Roenn J, Schumacher M, Szmuilowicz E, Rademaker A, Weitner BB, Loftus PD, Chu IM, Weitzman S. A predictive model to identify hospitalized cancer patients at risk for 30-day mortality based on admission criteria via the electronic medical record. Cancer. 2013;6(11):2074-80.
Not in our library10.1002/cncr.27974
- 27.
Amarasingham R, Moore BJ, Tabak YP, Drazner MH, Clark CA, Zhang S, Reed WG, Swanson TS, Ma Y, Halm EA. An Automated Model to Identify Heart Failure Patients at Risk for 30-Day Readmission or Death Using Electronic Medical Record Data. Med Care. 2010;48(11):981-8.
Not in our library10.1097/mlr.0b013e3181ef60d9
- 28.
Tabak YP, Johannes RS, Silber JH. Using Automated Clinical Data for Risk Adjustment. Medical Care. 2007;45(8):789-805.
Not in our library10.1097/mlr.0b013e31803d3b41
- 29.
Makar M, Ghassemi M, Cutler D, Obermeyer Z. Short-Term Mortality Prediction for Elderly Patients Using Medicare Claims Data. Int J Mach Learn Comput. 2015;5(3):192-7.
Not in our library10.7763/ijmlc.2015.v5.506
- 30.
Sim I, FH Z, SM T, SY H, N Z, Y H, M N. Two Ways of Knowing: Big Data and Evidence-Based Medicine. Ann Intern Med. 2016;164(8):562.
Not in our library10.7326/m15-2970
- 31.
Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016;9(13):1216-9.
Not in our library10.1056/nejmp1606181
- 32.
Rose S. Mortality Risk Score Prediction in an Elderly Population Using Machine Learning. Am J Epidemiol. 2013;3(5):443-52.
Not in our library10.1093/aje/kws241
- 33.
Wiemken TL, Furmanek SP, Mattingly WA, Guinn BE, Cavallazzi R, Fernandez-Botran R, Wolf LA, English CL, Ramirez JA. Predicting 30-day mortality in hospitalized patients with community-acquired pneumonia using statistical and machine learning approaches. J Respir Infect. 2017;1(3):5.
Not in our library
- 34.
Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, Andreini D, Budoff MJ, Cademartiri F, Callister TQ, Chang H-J, Chinnaiyan K, Chow BJ, Cury RC, Delago A, Gomez M, Gransar H, Hadamitzky M, Hausleiter J, Hindoyan N, Feuchtner G, Kaufmann PA, Kim Y-J, Leipsic J, Lin FY, Maffei E, Marques H, Pontone G, Raff G, Rubinshtein R, Shaw LJ, Stehli J, Villines TC, Dunning A, Min JK, Slomka PJ. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J. 2016;25(7):ehw188.
Not in our library10.1093/eurheartj/ehw188
- 35.
Cooper GF, Aliferis CF, Ambrosino R, Aronis J, Buchanon BG. An Evaluation of Machine-Learning Methods for Predicting Pneumonia Mortality. Artif Intell Med. 1997;9(2):107-38.
Not in our library10.1016/s0933-3657(96)00367-3
- 36.
Vomlel J, Kružík H, Tma P, Přeček J, Hutyra M. Machine Learning Methods for Mortality Prediction in Patients with ST Elevation Myocardial Infarction. In the Proceedings of The Nineth Workshop on Uncertainty Processing WUPES’12, Mariánské Lázně, Czech Republic. 2012. p. 204–213.
Not in our library
- 37.
- 38.
Lowe HJ, Ferris TA, Hernandez Nd PM, Weber SC. STRIDE – An Integrated Standards-Based Translational Research Informatics Platform. In: AMIA Annual Symposium Proceedings.2009. p. 391–5. Conference paper (AMIA Symposium).
Not in our library
- 39.
- 40.
Klambauer G, Unterthiner T, Mayr A, Hochreiter S Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, (eds).Self-normalizing neural networks; 2017. pp. 972–981. Curran Associates, Inc. Available: http://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf. Accessed 2 Nov 2018.
Not in our library
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