24 papers · ranked by Valyu relevance
Ewout W. Steyerberg, Karel G. M. Moons, Danielle A. van der Windt, Jill A. Hayden + 5 more
'Jill A. Hayden' 'Pablo Perel' 'Sara Schroter' 'Richard D. Riley' 'Harry Hemingway' 'Douglas G. Altman' ''] In this article, the third in the PROGRESS series on prognostic factor research, Sara Schroter and colleagues review how prognostic models are developed and validated, and then address how prognostic models are…
Daniel R Balcarcel, Sanjiv D Mehta, Celeste G Dixon, Charlotte Z Woods-Hill + 3 more
Prognostic models developed for use in the intensive care unit (ICU) can inform treatment decisions and improve patient care. However, despite extensive research, few models have contributed to improved patient-centred outcomes. A major limitation is that the influence of treatment interventions on patient outcomes…
Yu Uneno, Kei Taneishi, Masashi Kanai, Kazuya Okamoto + 15 more
First, we linked 1,730,535 laboratory variables to 57,581 time points and used these time-inclusive data. We aimed to develop a set of adaptable prognosis prediction models that could predict death events within n months (n = 1-6). Therefore, when constructing such model, laboratory variables monitored within n months…
Ying Fu, Ye Kwon Huh, Kaibo Liu
—Operating units often experience various failure modes in complex systems, leading to distinct degradation paths. Relying on a prognostic model trained on a single failure mode may lead to poor generalization performance across multiple failure modes. Therefore, accurately identifying the failure mode is of critical…
Aasthaa Bansal, Patrick J. Heagerty
Financial support for this study was provided in part by grants from the PhRMA Foundation, the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH), and by the National Center For Advancing Translational Sciences of the NIH. The funding agreement ensured the authors' independence in…
Mark T. Keegan, Marcio Soares
and risk-adjusted mortality O que todo intensivista deveria saber sobre os sistemas de escore prognóstico e mortalidade ajustada ao risco Authors: Mark T. Keegan, Marcio Soares Model performance should be assessed through the evaluation of discrimination and calibration. Discrimination quantifies the accuracy of a…
Andres Colubri, Adam C. Levine, Mathew Siakor, Vanessa Wolfman + 4 more
The 2014-2016 Ebola Virus Disease (EVD) outbreak highlighted the need for rigorous, rapid, and field-deployable tools to enable case management. We previously introduced an approach for EVD prognosis prediction, using models that can be implemented in the field and updated in light of new data. Here we enhance and…
Ehsan Taheri, Ilya Kolmanovsky, Oleg Gusikhin
It is not surprising that the idea of efficient maintenance algorithms (originally motivated by strict emission regulations, and now driven by safety issues, logistics and customer satisfaction) has culminated in the socalled condition-based maintenance program. Condition-based program/monitoring consists of two major…
Anni S. Halkola, Kaisa Joki, Tuomas Mirtti, Marko M. Mäkelä + 2 more
In many real-world applications, such as those based on patient electronic health records, prognostic prediction of patient survival is based on heterogeneous sets of clinical laboratory measurements. To address the trade-off between the predictive accuracy of a prognostic model and the costs related to its clinical…
Zarif L. Azher, Louis J. Vaickus, Lucas A. Salas, Brock C. Christensen + 1 more
Robust cancer prognostication can enable more effective patient care and management, which may potentially improve health outcomes. Deep learning has proven to be a powerful tool to extract meaningful information from cancer patient data. In recent years it has displayed promise in quantifying prognostication by…
Telma Pereira, Sofia Pires, Marta Gromicho, Susana Pinto + 2 more
'Mamede de Carvalho' 'Sara C. Madeira'] Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by a rapid motor decline, leading to respiratory failure and subsequently to death. In this context, researchers have sought for models to automatically predict disease progression to assisted…
Authors not listed
In medicine, prognostic scores or clinical prediction models are statistical models intended to estimate an individual's probability of experiencing a specific health outcome over a defined period, based on their clinical and non-clinical characteristics [1–[4]]. Outcomes can refer to clinical events such as disease…
Alejandro Schuler, John E. Walsh, Diana Hall, Jon Walsh + 1 more
'Charles G. Fisher'] Estimating causal effects from randomized experiments is central to clinical research. Reducing the statistical uncertainty in these analyses is an important objective for statisticians. Registries, prior trials, and health records constitute a growing compendium of historical data on patients…
Lara Pladet, Kim Luijken, Libera Fresiello, Dinis Dos Reis Miranda + 5 more
'Jeannine A Hermens' 'Maarten van Smeden' 'Olaf Cremer' 'Dirk W Donker' 'Christiaan L Meuwese'] Prognostic modelling techniques have rapidly evolved over the past decade and may greatly benefit patients supported with ExtraCorporeal Membrane Oxygenation (ECMO). Epidemiological and computational physiological approaches…
Xiaomei Li, Lin Liu, Jiuyong Li, Thuc D. Le
Predicting breast cancer prognosis helps improve the treatment and management of the disease. In the last decades, many prediction models have been developed for breast cancer prognosis based on transcriptomic data. A common assumption made by these models is that the test and training data follow the same…
Katie L. Spencer, Kate L. Absolom, Matthew J. Allsop, Samuel D. Relton + 10 more
PURPOSE This discussion paper outlines challenges and proposes solutions for successfully implementing prediction models that incorporate patient-reported outcomes (PROs) in cancer practice. METHODS We organized a full-day multidisciplinary meeting of people with expertise in cancer care delivery, PRO collection, PRO…
Jianwei Wang, Fei Deng, Fuqing Zeng, Andrew J. Shanahan + 1 more
Patients with prostate cancer more likely die of non-cancer cause of death (COD) than prostate cancer. It is thus important to accurately predict COD more precisely in these patients. Random forest, a model of machine learning, was useful for predicting binary cancer-specific deaths. However, its accuracy for…
Pietro Mascheroni, Symeon Savvopoulos, Juan Carlos López Alfonso, Michael Meyer-Hermann + 1 more
Biomedical problems are highly complex and multidimensional. Commonly, only a small subset of the relevant variables can be modeled by virtue of mathematical modeling due to lack of knowledge of the involved phenomena. Although these models are effective in analyzing the approximate dynamics of the system, their…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
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Machine learning holds significant promise for accelerating biomarker discovery in clinical proteomics, yet its real-world impact remains limited by widespread methodological pitfalls and unrealistic expectations. In this perspective, we critically examine the integration of machine learning into clinical proteomics…
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The use of hybrid models, combing mechanistic and machine learning (ML), has emerged as a promising approach, contributing to the development of Industry 4.0. This work presents a hybrid model that forecasts minibioreactor (MBR) production runs of mammalian cell culture recombinant for monoclonal antibodies (mAbs)…
Helle W. van den Maagdenberg, Martin Šícho, David Alencar Araripe, Sohvi Luukkonen + 9 more
Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous.…
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Integrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on…
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Kinetic modeling is essential for predicting changes in food quality during processing and storage. This study evaluates the application of physics-informed neural networks (PINN) for food kinetic modeling, integrating kinetic insights into neural network frameworks. Based on three case studies, namely seed drying…