Zobrazeno 1 - 10
of 582
pro vyhledávání: '"Greiner, Russell."'
Autor:
Bushuiev, Roman, Bushuiev, Anton, de Jonge, Niek F., Young, Adamo, Kretschmer, Fleming, Samusevich, Raman, Heirman, Janne, Wang, Fei, Zhang, Luke, Dührkop, Kai, Ludwig, Marcus, Haupt, Nils A., Kalia, Apurva, Brungs, Corinna, Schmid, Robin, Greiner, Russell, Wang, Bo, Wishart, David S., Liu, Li-Ping, Rousu, Juho, Bittremieux, Wout, Rost, Hannes, Mak, Tytus D., Hassoun, Soha, Huber, Florian, van der Hooft, Justin J. J., Stravs, Michael A., Böcker, Sebastian, Sivic, Josef, Pluskal, Tomáš
The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular st
Externí odkaz:
http://arxiv.org/abs/2410.23326
Survival prediction often involves estimating the time-to-event distribution from censored datasets. Previous approaches have focused on enhancing discrimination and marginal calibration. In this paper, we highlight the significance of conditional ca
Externí odkaz:
http://arxiv.org/abs/2410.20579
Autor:
Lillelund, Christian Marius, Foomani, Ali Hossein Gharari, Sun, Weijie, Qi, Shi-ang, Greiner, Russell
Given an instance, a multi-event survival model predicts the time until that instance experiences each of several different events. These events are not mutually exclusive and there are often statistical dependencies between them. There are relativel
Externí odkaz:
http://arxiv.org/abs/2409.06525
Discrimination and calibration represent two important properties of survival analysis, with the former assessing the model's ability to accurately rank subjects and the latter evaluating the alignment of predicted outcomes with actual events. With t
Externí odkaz:
http://arxiv.org/abs/2405.07374
Autor:
Agrawal, Vikhyat, Kalmady, Sunil Vasu, Malipeddi, Venkataseetharam Manoj, Manthena, Manisimha Varma, Sun, Weijie, Islam, Saiful, Hindle, Abram, Kaul, Padma, Greiner, Russell
This research paper explores ways to apply Federated Learning (FL) and Differential Privacy (DP) techniques to population-scale Electrocardiogram (ECG) data. The study learns a multi-label ECG classification model using FL and DP based on 1,565,849 E
Externí odkaz:
http://arxiv.org/abs/2405.00725
Forecasting the occurrence and absence of novel disease outbreaks is essential for disease management. Here, we develop a general model, with no real-world training data, that accurately forecasts outbreaks and non-outbreaks. We propose a novel frame
Externí odkaz:
http://arxiv.org/abs/2404.08893
Autor:
Chakraborty, Amit K., Gao, Shan, Miry, Reza, Ramazi, Pouria, Greiner, Russell, Lewis, Mark A., Wang, Hao
The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse
Externí odkaz:
http://arxiv.org/abs/2403.16233
A survival dataset describes a set of instances (e.g. patients) and provides, for each, either the time until an event (e.g. death), or the censoring time (e.g. when lost to follow-up - which is a lower bound on the time until the event). We consider
Externí odkaz:
http://arxiv.org/abs/2306.11912
Autor:
Qi, Shi-ang, Kumar, Neeraj, Farrokh, Mahtab, Sun, Weijie, Kuan, Li-Hao, Ranganath, Rajesh, Henao, Ricardo, Greiner, Russell
One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) -- the average of the absolute difference between the time predicted by the model and the true event time, over all subjects. Unfortunately,
Externí odkaz:
http://arxiv.org/abs/2306.01196
Autor:
Weitz, Philippe, Valkonen, Masi, Solorzano, Leslie, Carr, Circe, Kartasalo, Kimmo, Boissin, Constance, Koivukoski, Sonja, Kuusela, Aino, Rasic, Dusan, Feng, Yanbo, Pouplier, Sandra Sinius, Sharma, Abhinav, Eriksson, Kajsa Ledesma, Robertson, Stephanie, Marzahl, Christian, Gatenbee, Chandler D., Anderson, Alexander R. A., Wodzinski, Marek, Jurgas, Artur, Marini, Niccolò, Atzori, Manfredo, Müller, Henning, Budelmann, Daniel, Weiss, Nick, Heldmann, Stefan, Lotz, Johannes, Wolterink, Jelmer M., De Santi, Bruno, Patil, Abhijeet, Sethi, Amit, Kondo, Satoshi, Kasai, Satoshi, Hirasawa, Kousuke, Farrokh, Mahtab, Kumar, Neeraj, Greiner, Russell, Latonen, Leena, Laenkholm, Anne-Vibeke, Hartman, Johan, Ruusuvuori, Pekka, Rantalainen, Mattias
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, th
Externí odkaz:
http://arxiv.org/abs/2305.18033