Results 11 to 20 of about 1,072,367 (268)

Clinical prediction models [PDF]

British Journal of Surgery, 2016
Clinical prediction models (also known as prognostic models, risk scores) are mathematical equations that relate multiple predictors (risk factors, co-variates) to the probability of having a disease or condition (diagnostic) or the probability that an event will happen in the future (prognostic).
Ranstam, J, Cook, J, Collins, G
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Trends in Clinical Prediction Models of Stroke Outcomes Research: A Bibliometric Analysis

ASM Science Journal, 2021
Stroke is one of the leading causes of death and disability globally. Clinical models have been reported to predict stroke outcomes and thus, potentially guide clinical decisions.
Mustapha Mohammed, Hadzliana Zainal, Siew Chin Ong, Fatimatuzzahra’ Abd Aziz, Abubakar Sha'aban, Sagir Mustapha, Irene Looi, Balamurugan Tangiisuran   +7 more
doaj   +1 more source

Prediction models in urology [PDF]

Nature Clinical Practice Urology, 2007
Predicting risk is an inherent feature of clinical practice. How likely is a man with a given PSA level to have prostate cancer? Can lymph node dissection be safely avoided in a patient with testicular cancer? Should chemotherapy be given after cystectomy for bladder cancer?
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Calibration: the Achilles heel of predictive analytics

BMC Medicine, 2019
Background The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention.
Ben Van Calster, David J. McLernon, Maarten van Smeden, Laure Wynants, Ewout W. Steyerberg, On behalf of Topic Group ‘Evaluating diagnostic tests and prediction models’ of the STRATOS initiative   +5 more
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Predicting dry matter intake in mid-lactation Holstein cows using point-in-time data streams available on dairy farms

Journal of Dairy Science, 2022
: Quantifying dry matter intake (DMI) in lactating dairy cows is important for determining feed efficiency; however, there are no methods for economically quantifying individual cow DMI on dairy farms where cows are group-fed.
W.E. Brown, M.J. Caputo, C. Siberski, J.E. Koltes, F. Peñagaricano, K.A. Weigel, H.M. White   +6 more
doaj   +1 more source

Noninvasive Prediction of Elevated Wedge Pressure in Pulmonary Hypertension Patients Without Clear Signs of Left‐Sided Heart Disease: External Validation of the OPTICS Risk Score

Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, 2020
Background Although most newly presenting patients with pulmonary hypertension (PH) have elevated pulmonary artery wedge pressure, identification of so‐called postcapillary PH can be challenging.
Samara M. A. Jansen, Anna. E. Huis in ‘t Veld, Wouter Jacobs, Hans P. Grotjohan, Marc Waskowsky, Jan van der Maten, Arno van der Weerdt, Romke Hoekstra, Maria J. Overbeek, Sjoerd A. Mollema, Peter Hans C. G. Tolen, Lahssan H. Hassan El Bouazzaoui, Joris W. J. Vriend, J. Milena M. Roorda, Ramon de Nooijer, Ivo van der Lee, Bart (A. J.) Voogel, Kathinka Peels, Thomas Macken, Jacqueline M. Aerts, Anton Vonk Noordegraaf, M. Louis Handoko, Frances S. de Man, Harm Jan Bogaard   +23 more
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Logging-based prediction of organic geochemical parameters in oil shale during thermal evolution using the XGBoost algorithm [PDF]

Oil Shale
Oil shale in large basins undergoes multiple evolutionary stages, limiting the applicability of a single logging-based prediction model. This study focuses on the oil shale of the Qingshankou Formation in the Songliao Basin, using gamma ray (GR), deep ...
Lianxin Tao, Xin Liu, Zhisheng Luan, Ling Jiang, Hongliang Dang, Pingchang Sun   +5 more
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Predict the model of a camera

CoRR, 2020
In this work we address the problem of predicting the model of a camera based on the content of their photographs. We use two set of features, one set consist in properties extracted from a Discrete Wavelet Domain (DWD) obtained by applying a 4 level Fast Wavelet Decomposition of the images, and a second set are Local Binary Patterns (LBP) features ...
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Prediction Algorithm for State Prediction Model

Journal of Computers, 2012
Dynamic Bayesian network is the extension of Bayesian network in solving time series problems .It can be well dealt with the time-varying multivariable problem. A state model is given based on Dynamic Bayesian network. The model can more accurately describe the relationship between the system state and the influencing factors.
Zili Zhang, Hongwei Song, Yan Li, Hao Yang   +3 more
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Toward Advancing Long-Term Outcomes of Kidney Transplantation with Artificial Intelligence

Transplantology, 2021
After decades of pioneering advances and improvements, kidney transplantation is now the renal replacement therapy of choice for most patients with end-stage kidney disease (ESKD).
Raúl Castillo-Astorga, Camilo G. Sotomayor   +1 more
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