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Showing 1237 to 1248 of 1259 entries
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EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings.

AMIA ... Annual Symposium proceedings. AMIA Symposium

Rethmeier N, Serbetci NO, Möller S, Roller R.
PMID: 33936482
AMIA Annu Symp Proc. 2021 Jan 25;2020:1060-1069. eCollection 2020.

Recent medical prognostic models adapted from high data-resource fields like language processing have quickly grown in complexity and size. However, since medical data typically constitute low data-resource settings, performances on tasks like clinical prediction did not improve expectedly. Instead...

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Rethmeier N, Serbetci NO, Möller S, et al. EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings. AMIA Annu Symp Proc. 2021;2020:1060-1069
Rethmeier, N., Serbetci, N. O., Möller, S., & Roller, R. (2020). EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings. AMIA ... Annual Symposium proceedings. AMIA Symposium, 20201060-1069.
Rethmeier, Nils, et al. "EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings." AMIA ... Annual Symposium proceedings. AMIA Symposium vol. 2020 (2020): 1060-1069.
Rethmeier N, Serbetci NO, Möller S, Roller R. EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings. AMIA Annu Symp Proc. 2021 Jan 25;2020:1060-1069. eCollection 2020. PMID: 33936482; PMCID: PMC8075498.
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Magnetic resonance techniques to monitor disease evolution and treatment trial outcomes in multiple sclerosis.

Current opinion in neurology

Rovaris M, Filippi M.
PMID: 10499178
Curr Opin Neurol. 1999 Jun;12(3):337-44. doi: 10.1097/00019052-199906000-00012.

Although the correlations between magnetic resonance findings and long-term disease evolution range from poor to moderate, conventional precontrast and postcontrast magnetic resonance imaging provides sensitive and reliable measures to monitor multiple sclerosis activity over time. New pulse sequences with...

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Rovaris M, Filippi M. Magnetic resonance techniques to monitor disease evolution and treatment trial outcomes in multiple sclerosis. Curr Opin Neurol. 1999;12(3):337-44doi: 10.1097/00019052-199906000-00012.
Rovaris, M., & Filippi, M. (1999). Magnetic resonance techniques to monitor disease evolution and treatment trial outcomes in multiple sclerosis. Current opinion in neurology, 12(3), 337-44. https://doi.org/10.1097/00019052-199906000-00012
Rovaris, M, and Filippi, M. "Magnetic resonance techniques to monitor disease evolution and treatment trial outcomes in multiple sclerosis." Current opinion in neurology vol. 12,3 (1999): 337-44. doi: https://doi.org/10.1097/00019052-199906000-00012
Rovaris M, Filippi M. Magnetic resonance techniques to monitor disease evolution and treatment trial outcomes in multiple sclerosis. Curr Opin Neurol. 1999 Jun;12(3):337-44. doi: 10.1097/00019052-199906000-00012. PMID: 10499178.
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Hyperprogressive Disease: Main Features and Key Controversies.

International journal of molecular sciences

Arasanz H, Zuazo M, Bocanegra A, Chocarro L, Blanco E, Martínez M, Morilla I, Fernández G, Teijeira L, Morente P, Echaide M, Castro N, Fernández L, Garnica M, Ramos P, Escors D, Kochan G, Vera R.
PMID: 33916696
Int J Mol Sci. 2021 Apr 03;22(7). doi: 10.3390/ijms22073736.

Along with the positioning of immunotherapy as a preferential treatment for a wide variety of neoplasms, a new pattern of response consisting in a sudden acceleration of tumor growth has been described. This phenomenon has received the name of...

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Arasanz H, Zuazo M, Bocanegra A, et al. Hyperprogressive Disease: Main Features and Key Controversies. Int J Mol Sci. 2021;22(7)doi: 10.3390/ijms22073736.
Arasanz, H., Zuazo, M., Bocanegra, A., Chocarro, L., Blanco, E., Martínez, M., Morilla, I., Fernández, G., Teijeira, L., Morente, P., Echaide, M., Castro, N., Fernández, L., Garnica, M., Ramos, P., Escors, D., Kochan, G., & Vera, R. (2021). Hyperprogressive Disease: Main Features and Key Controversies. International journal of molecular sciences, 22(7), . https://doi.org/10.3390/ijms22073736
Arasanz, Hugo, et al. "Hyperprogressive Disease: Main Features and Key Controversies." International journal of molecular sciences vol. 22,7 (2021). doi: https://doi.org/10.3390/ijms22073736
Arasanz H, Zuazo M, Bocanegra A, Chocarro L, Blanco E, Martínez M, Morilla I, Fernández G, Teijeira L, Morente P, Echaide M, Castro N, Fernández L, Garnica M, Ramos P, Escors D, Kochan G, Vera R. Hyperprogressive Disease: Main Features and Key Controversies. Int J Mol Sci. 2021 Apr 03;22(7). doi: 10.3390/ijms22073736. PMID: 33916696; PMCID: PMC8038385.
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Cardiac resynchronization therapy with- or without defibrillator. Estimating the risk of arrhythmic death or assessing the likelihood of non-arrhythmic mortality?.

International journal of cardiology

Klein HU.
PMID: 33582197
Int J Cardiol. 2021 May 01;330:82-83. doi: 10.1016/j.ijcard.2021.02.024. Epub 2021 Feb 11.

No abstract available.

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Klein HU. Cardiac resynchronization therapy with- or without defibrillator. Estimating the risk of arrhythmic death or assessing the likelihood of non-arrhythmic mortality?. Int J Cardiol. 2021;330:82-83doi: 10.1016/j.ijcard.2021.02.024.
Klein, H. U. (2021). Cardiac resynchronization therapy with- or without defibrillator. Estimating the risk of arrhythmic death or assessing the likelihood of non-arrhythmic mortality?. International journal of cardiology, 33082-83. https://doi.org/10.1016/j.ijcard.2021.02.024
Klein, Helmut U. "Cardiac resynchronization therapy with- or without defibrillator. Estimating the risk of arrhythmic death or assessing the likelihood of non-arrhythmic mortality?." International journal of cardiology vol. 330 (2021): 82-83. doi: https://doi.org/10.1016/j.ijcard.2021.02.024
Klein HU. Cardiac resynchronization therapy with- or without defibrillator. Estimating the risk of arrhythmic death or assessing the likelihood of non-arrhythmic mortality?. Int J Cardiol. 2021 May 01;330:82-83. doi: 10.1016/j.ijcard.2021.02.024. Epub 2021 Feb 11. PMID: 33582197.
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Commentary: Better Prognostication, But to What End?.

Seminars in thoracic and cardiovascular surgery

Jordan S, Yendamuri S.
PMID: 33600975
Semin Thorac Cardiovasc Surg. 2021;33(2):579-580. doi: 10.1053/j.semtcvs.2020.12.020. Epub 2021 Feb 15.

No abstract available.

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Jordan S, Yendamuri S. Commentary: Better Prognostication, But to What End?. Semin Thorac Cardiovasc Surg. 2021;33(2):579-580doi: 10.1053/j.semtcvs.2020.12.020.
Jordan, S., & Yendamuri, S. (2021). Commentary: Better Prognostication, But to What End?. Seminars in thoracic and cardiovascular surgery, 33(2), 579-580. https://doi.org/10.1053/j.semtcvs.2020.12.020
Jordan, Sean, and Yendamuri, Sai. "Commentary: Better Prognostication, But to What End?." Seminars in thoracic and cardiovascular surgery vol. 33,2 (2021): 579-580. doi: https://doi.org/10.1053/j.semtcvs.2020.12.020
Jordan S, Yendamuri S. Commentary: Better Prognostication, But to What End?. Semin Thorac Cardiovasc Surg. 2021;33(2):579-580. doi: 10.1053/j.semtcvs.2020.12.020. Epub 2021 Feb 15. PMID: 33600975.
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Prognostic factors at admission on patients with cancer and COVID-19: Analysis of HOPE registry data.

Medicina clinica

Pérez-Segura P, Paz-Cabezas M, Núñez-Gil IJ, Arroyo-Espliguero R, Maroun Eid C, Romero R, Fernández Rozas I, Uribarri A, Becerra-Muñoz VM, García Aguado M, Huang J, Rondano E, Cerrato E, Rodríguez EA, Ortega-Armas ME, Raposeiras Roubin S, Pepe M, Feltes G, Gonzalez A, Cortese B, Buzón L, El-Battrawy I, Estrada V.
PMID: 34154809
Med Clin (Barc). 2021 Oct 08;157(7):318-324. doi: 10.1016/j.medcli.2021.02.021. Epub 2021 May 06.

BACKGROUND: Previous works seem to agree in the higher mortality of cancer patients with COVID-19. Identifying potential prognostic factors upon admission could help identify patients with a poor prognosis.METHODS: We aimed to explore the characteristics and evolution of COVID-19...

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Pérez-Segura P, Paz-Cabezas M, Núñez-Gil IJ, et al. Prognostic factors at admission on patients with cancer and COVID-19: Analysis of HOPE registry data. Med Clin (Barc). 2021;157(7):318-324doi: 10.1016/j.medcli.2021.02.021.
Pérez-Segura, P., Paz-Cabezas, M., Núñez-Gil, I. J., Arroyo-Espliguero, R., Maroun Eid, C., Romero, R., Fernández Rozas, I., Uribarri, A., Becerra-Muñoz, V. M., García Aguado, M., Huang, J., Rondano, E., Cerrato, E., Rodríguez, E. A., Ortega-Armas, M. E., Raposeiras Roubin, S., Pepe, M., Feltes, G., Gonzalez, A., Cortese, B., Buzón, L., El-Battrawy, I., & Estrada, V. (2021). Prognostic factors at admission on patients with cancer and COVID-19: Analysis of HOPE registry data. Medicina clinica, 157(7), 318-324. https://doi.org/10.1016/j.medcli.2021.02.021
Pérez-Segura, Pedro, et al. "Prognostic factors at admission on patients with cancer and COVID-19: Analysis of HOPE registry data." Medicina clinica vol. 157,7 (2021): 318-324. doi: https://doi.org/10.1016/j.medcli.2021.02.021
Pérez-Segura P, Paz-Cabezas M, Núñez-Gil IJ, Arroyo-Espliguero R, Maroun Eid C, Romero R, Fernández Rozas I, Uribarri A, Becerra-Muñoz VM, García Aguado M, Huang J, Rondano E, Cerrato E, Rodríguez EA, Ortega-Armas ME, Raposeiras Roubin S, Pepe M, Feltes G, Gonzalez A, Cortese B, Buzón L, El-Battrawy I, Estrada V. Prognostic factors at admission on patients with cancer and COVID-19: Analysis of HOPE registry data. Med Clin (Barc). 2021 Oct 08;157(7):318-324. doi: 10.1016/j.medcli.2021.02.021. Epub 2021 May 06. Spanish. PMID: 34154809; PMCID: PMC8101784.
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Colorectal cancer pulmonary oligometastases: pooled analysis and construction of a clinical lung metastasectomy prognostic model.

Annals of oncology : official journal of the European Society for Medical Oncology

Salah S, Watanabe K, Welter S, Park JS, Park JW, Zabaleta J, Ardissone F, Kim J, Riquet M, Nojiri K, Gisabella M, Kim SY, Tanaka K, Al-Haj Ali B.
PMID: 22547539
Ann Oncol. 2012 Oct;23(10):2649-2655. doi: 10.1093/annonc/mds100. Epub 2012 Apr 29.

BACKGROUND: Although resecting colorectal cancer (CRC) pulmonary metastasis is associated with long-term survival, identification of prognostic groups is needed for future randomized trials, and construction of a lung metastasectomy prognostic model (LMPM) is warranted.PATIENTS AND METHODS: We searched the...

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Salah S, Watanabe K, Welter S, et al. Colorectal cancer pulmonary oligometastases: pooled analysis and construction of a clinical lung metastasectomy prognostic model. Ann Oncol. 2012;23(10):2649-2655doi: 10.1093/annonc/mds100.
Salah, S., Watanabe, K., Welter, S., Park, J. S., Park, J. W., Zabaleta, J., Ardissone, F., Kim, J., Riquet, M., Nojiri, K., Gisabella, M., Kim, S. Y., Tanaka, K., & Al-Haj Ali, B. (2012). Colorectal cancer pulmonary oligometastases: pooled analysis and construction of a clinical lung metastasectomy prognostic model. Annals of oncology : official journal of the European Society for Medical Oncology, 23(10), 2649-2655. https://doi.org/10.1093/annonc/mds100
Salah, S, et al. "Colorectal cancer pulmonary oligometastases: pooled analysis and construction of a clinical lung metastasectomy prognostic model." Annals of oncology : official journal of the European Society for Medical Oncology vol. 23,10 (2012): 2649-2655. doi: https://doi.org/10.1093/annonc/mds100
Salah S, Watanabe K, Welter S, Park JS, Park JW, Zabaleta J, Ardissone F, Kim J, Riquet M, Nojiri K, Gisabella M, Kim SY, Tanaka K, Al-Haj Ali B. Colorectal cancer pulmonary oligometastases: pooled analysis and construction of a clinical lung metastasectomy prognostic model. Ann Oncol. 2012 Oct;23(10):2649-2655. doi: 10.1093/annonc/mds100. Epub 2012 Apr 29. PMID: 22547539.
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Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach.

Current research in translational medicine

Kocadagli O, Baygul A, Gokmen N, Incir S, Aktan C.
PMID: 34768217
Curr Res Transl Med. 2022 Jan;70(1):103319. doi: 10.1016/j.retram.2021.103319. Epub 2021 Oct 30.

This retrospective cohort study deals with evaluating severity of COVID-19 cases on the first symptoms and blood-test results of infected patients admitted to Emergency Department of Koc University Hospital (Istanbul, Turkey). To figure out remarkable hematological characteristics and risk...

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Kocadagli O, Baygul A, Gokmen N, et al. Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach. Curr Res Transl Med. 2021;70(1):103319doi: 10.1016/j.retram.2021.103319.
Kocadagli, O., Baygul, A., Gokmen, N., Incir, S., & Aktan, C. (2022). Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach. Current research in translational medicine, 70(1), 103319. https://doi.org/10.1016/j.retram.2021.103319
Kocadagli, Ozan, et al. "Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach." Current research in translational medicine vol. 70,1 (2022): 103319. doi: https://doi.org/10.1016/j.retram.2021.103319
Kocadagli O, Baygul A, Gokmen N, Incir S, Aktan C. Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach. Curr Res Transl Med. 2022 Jan;70(1):103319. doi: 10.1016/j.retram.2021.103319. Epub 2021 Oct 30. PMID: 34768217; PMCID: PMC8577545.
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A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning.

Kidney international

Zheng J, Yu H, Batur J, Shi Z, Tuerxun A, Abulajiang A, Lu S, Kong J, Huang L, Wu S, Wu Z, Qiu Y, Lin T, Zou X.
PMID: 34129883
Kidney Int. 2021 Oct;100(4):870-880. doi: 10.1016/j.kint.2021.05.031. Epub 2021 Jun 12.

Urolithiasis is a common urological disease, and treatment strategy options vary between different stone types. However, accurate detection of stone composition can only be performed in vitro. The management of infection stones is particularly challenging with yet no effective...

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Zheng J, Yu H, Batur J, et al. A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning. Kidney Int. 2021;100(4):870-880doi: 10.1016/j.kint.2021.05.031.
Zheng, J., Yu, H., Batur, J., Shi, Z., Tuerxun, A., Abulajiang, A., Lu, S., Kong, J., Huang, L., Wu, S., Wu, Z., Qiu, Y., Lin, T., & Zou, X. (2021). A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning. Kidney international, 100(4), 870-880. https://doi.org/10.1016/j.kint.2021.05.031
Zheng, Junjiong, et al. "A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning." Kidney international vol. 100,4 (2021): 870-880. doi: https://doi.org/10.1016/j.kint.2021.05.031
Zheng J, Yu H, Batur J, Shi Z, Tuerxun A, Abulajiang A, Lu S, Kong J, Huang L, Wu S, Wu Z, Qiu Y, Lin T, Zou X. A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning. Kidney Int. 2021 Oct;100(4):870-880. doi: 10.1016/j.kint.2021.05.031. Epub 2021 Jun 12. PMID: 34129883.
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Optimal resection rate for lung cancer in the UK: how high should we go?.

BMJ open respiratory research

Belcher E, Mitchell J, Stavroulias D, Di Chiara F, Rahman N.
PMID: 34326151
BMJ Open Respir Res. 2021 Jul;8(1). doi: 10.1136/bmjresp-2020-000771.

BACKGROUND: The optimal resection rate for institutions managing early-stage primary lung cancer is not known. Whether the prognosis of patients who do not proceed to operation is determined by their comorbidities for which they were deemed at prohibitively high-operative...

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Belcher E, Mitchell J, Stavroulias D, et al. Optimal resection rate for lung cancer in the UK: how high should we go?. BMJ Open Respir Res. 2021;8(1)doi: 10.1136/bmjresp-2020-000771.
Belcher, E., Mitchell, J., Stavroulias, D., Di Chiara, F., & Rahman, N. (2021). Optimal resection rate for lung cancer in the UK: how high should we go?. BMJ open respiratory research, 8(1), . https://doi.org/10.1136/bmjresp-2020-000771
Belcher, Elizabeth, et al. "Optimal resection rate for lung cancer in the UK: how high should we go?." BMJ open respiratory research vol. 8,1 (2021). doi: https://doi.org/10.1136/bmjresp-2020-000771
Belcher E, Mitchell J, Stavroulias D, Di Chiara F, Rahman N. Optimal resection rate for lung cancer in the UK: how high should we go?. BMJ Open Respir Res. 2021 Jul;8(1). doi: 10.1136/bmjresp-2020-000771. PMID: 34326151; PMCID: PMC8323385.
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Social determinants of COVID-19 incidence and outcomes: A rapid review.

PloS one

Upshaw TL, Brown C, Smith R, Perri M, Ziegler C, Pinto AD.
PMID: 33788848
PLoS One. 2021 Mar 31;16(3):e0248336. doi: 10.1371/journal.pone.0248336. eCollection 2021.

Early reports indicate that the social determinants of health are implicated in COVID-19 incidence and outcomes. To inform the ongoing response to the pandemic, we conducted a rapid review of peer-reviewed studies to examine the social determinants of COVID-19....

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Upshaw TL, Brown C, Smith R, et al. Social determinants of COVID-19 incidence and outcomes: A rapid review. PLoS One. 2021;16(3):e0248336doi: 10.1371/journal.pone.0248336.
Upshaw, T. L., Brown, C., Smith, R., Perri, M., Ziegler, C., & Pinto, A. D. (2021). Social determinants of COVID-19 incidence and outcomes: A rapid review. PloS one, 16(3), e0248336. https://doi.org/10.1371/journal.pone.0248336
Upshaw, Tara L, et al. "Social determinants of COVID-19 incidence and outcomes: A rapid review." PloS one vol. 16,3 (2021): e0248336. doi: https://doi.org/10.1371/journal.pone.0248336
Upshaw TL, Brown C, Smith R, Perri M, Ziegler C, Pinto AD. Social determinants of COVID-19 incidence and outcomes: A rapid review. PLoS One. 2021 Mar 31;16(3):e0248336. doi: 10.1371/journal.pone.0248336. eCollection 2021. PMID: 33788848; PMCID: PMC8011781.
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Authors' reply to Sabour and Ghajari "Clinical prediction models to predict the risk of multiple binary outcomes: Methodological issues".

Statistics in medicine

Martin GP, Sperrin M, Snell KIE, Buchan I, Riley RD.
PMID: 33687094
Stat Med. 2021 Mar 30;40(7):1861-1862. doi: 10.1002/sim.8872.

No abstract available.

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Martin GP, Sperrin M, Snell KIE, et al. Authors' reply to Sabour and Ghajari "Clinical prediction models to predict the risk of multiple binary outcomes: Methodological issues". Stat Med. 2021;40(7):1861-1862doi: 10.1002/sim.8872.
Martin, G. P., Sperrin, M., Snell, K. I. E., Buchan, I., & Riley, R. D. (2021). Authors' reply to Sabour and Ghajari "Clinical prediction models to predict the risk of multiple binary outcomes: Methodological issues". Statistics in medicine, 40(7), 1861-1862. https://doi.org/10.1002/sim.8872
Martin, Glen Philip, et al. "Authors' reply to Sabour and Ghajari "Clinical prediction models to predict the risk of multiple binary outcomes: Methodological issues"." Statistics in medicine vol. 40,7 (2021): 1861-1862. doi: https://doi.org/10.1002/sim.8872
Martin GP, Sperrin M, Snell KIE, Buchan I, Riley RD. Authors' reply to Sabour and Ghajari "Clinical prediction models to predict the risk of multiple binary outcomes: Methodological issues". Stat Med. 2021 Mar 30;40(7):1861-1862. doi: 10.1002/sim.8872. PMID: 33687094.
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Showing 1237 to 1248 of 1259 entries