23 papers · ranked by Valyu relevance
Guanlin Wu, Weiming Guo, Shuai Zhu, Gang Fan
Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer (RCC). The increasing incidence and poor prognosis of ccRCC after tumour metastasis makes the study of its pathogenesis extremely important. Traditional studies mostly focus on the regulation of ccRCC by single gene, while ignoring the…
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…
Bulat Zagidullin, Annukka Pasanen, Mikko Loukovaara, Ralf Bützow + 1 more
Endometrial carcinoma (EC) is one of the most common gynecological cancers in the world. In this work we apply Cox proportional hazards (CPH) and optimal survival tree (OST) algorithms to the retrospective prognostic modeling of disease-specific survival in 842 EC patients. We demonstrate that the linear CPH models are…
Yanbin Wang, Yuqi Wu, Hong Zhang, Xinyue Liu + 8 more
Hepatocellular carcinoma (HCC) is a highly aggressive tumor characterized by significant heterogeneity and invasiveness, leading to a lack of precise individualized treatment strategies and poor patient outcomes. This necessitates the urgent development of accurate patient stratification methods and targeted therapies…
Ainesh Sewak, Vanda Inácio, Joanne Wuu, Michael Benatar + 1 more
Identifying reliable biomarkers for predicting clinical events in longitudinal studies is important for accurate disease prognosis and for guiding development of new treatments. However, prognostic studies are often observational, making it difficult to account for patient heterogeneity. In amyotrophic lateral…
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…
Fergus Imrie, Bogdan Cebere, Eoin McKinney, Mihaela van der Schaar
Diagnostic and prognostic models are increasingly important in medicine and inform many clinical decisions. Recently, machine learning approaches have shown improvement over conventional modeling techniques by better capturing complex interactions between patient covariates in a data-driven manner. However, the use of…
Gary S Collins, Mae Chester-Jones, Stephen Gerry, Jie Ma + 4 more
Clinical prediction models are widely developed in the field of oncology, providing individualised risk estimates to aid diagnosis and prognosis. Machine learning methods are increasingly being used to develop prediction models, yet many suffer from methodological flaws limiting clinical implementation. This review…
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…
Yunfan Li, Arman Sabbaghi, Jonathan R. Walsh, Charles K. Fisher
Controlled Trials Authors: ['Yunfan Li' 'Arman Sabbaghi' 'Jonathan R. Walsh' 'Charles K. Fisher'] Randomized controlled trials (RCTs) with binary primary endpoints introduce novel challenges for inferring the causal effects of treatments. The most significant challenge is non-collapsibility, in which the conditional…
Qi Luo, Andrew E. Teschendorff
Most molecular classifications of cancer are based on bulk-tissue profiles that measure an average over many distinct cell-types. As such, cancer subtypes inferred from transcriptomic or epigenetic data are strongly influenced by cell-type composition and do not necessarily reflect subtypes defined by cell-type…
Igor Odrobina, Constantinos Bakogiannis, Michel Noutsias
This study attempts to identify and briefly describe the current directions in applied and theoretical clinical prediction research. Context-rich chronic heart failure syndrome (CHFS) telemedicine provides the medical foundation for this effort. In the chronic stage of heart failure, there are sudden exacerbations of…
Emilie Højbjerre‐Frandsen, Alejandro Schuler
Adjustment for "super" or "prognostic" composite covariates has become more popular in randomized trials recently. These prognostic covariates are often constructed from historical data by fitting a predictive model of the outcome on the raw covariates. A natural question that we have been asked by applied researchers…
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…
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…
Shan Gao, Elena Albu, Hein Putter, Pieter Stijnen + 6 more
prognostic outcome during admission in electronic health care records Authors: ['Shan Gao' 'Elena Albu' 'Hein Putter' 'Pieter Stijnen' 'Frank Rademakers' 'Veerle Cossey' 'Yves Debaveye' 'Christel Janssens' 'Ben Van Calster' 'Laure Wynants'] - 2 Department of Biomedical Data Sciences, Leiden University Medical Center…
Wouter A. C. van Amsterdam, Pim A. de Jong, Joost J.C. Verhoeff, Tim Leiner + 1 more
'Tim Leiner' 'Rajesh Ranganath'] In cancer research there is much interest in building and validating outcome predicting outcomes to support treatment decisions. However, because most outcome prediction models are developed and validated without regard to the causal aspects of treatment decision making, many published…
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…