Gillsmart8663

Finally, we explain how to arrive at a measure of variable importance using a universal, AUC-based method. We provide the full, structured code, as well as the complete glioblastoma survival database for the readers to download and execute in parallel to this section.Various available metrics to describe model performance in terms of discrimination (area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 Score) and calibration (slope, intercept, Brier score, expected/observed ratio, Estimated Calibration Index, Hosmer-Lemeshow goodness-of-fit) are presented. Recalibration is introduced, with Platt scaling and Isotonic regression as proposed methods. We also discuss considerations regarding the sample size required for optimal training of clinical prediction models-explaining why low sample sizes lead to unstable models, and offering the common rule of thumb of at least ten patients per class per input feature, as well as some more nuanced approaches. Missing data treatment and model-based imputation instead of mean, mode, or median imputation is also discussed. We explain how data standardization is important in pre-processing, and how it can be achieved using, e.g. centering and scaling. One-hot encoding is discussed-categorical features with more than two levels must be encoded as multiple features to avoid wrong assumptions. Regarding binary classification models, we discuss how to select a sensible predicted probability cutoff for binary classification using the closest-to-(0,1)-criterion based on AUC or based on the clinical question (rule-in or rule-out). Extrapolation is also discussed.We review the concept of overfitting, which is a well-known concern within the machine learning community, but less established in the clinical community. Overfitted models may lead to inadequate conclusions that may wrongly or even harmfully shape clinical decision-making. Overfitting can be defined as the difference among discriminatory training and testing performance, while it is normal that out-of-sample performance is equal to or ever so slightly worse than training performance for any adequately fitted model, a massively worse out-of-sample performance suggests relevant overfitting. We delve into resampling methods, specifically recommending k-fold cross-validation and bootstrapping to arrive at realistic estimates of out-of-sample error during training. Also, we encourage the use of regularization techniques such as L1 or L2 regularization, and to choose an appropriate level of algorithm complexity for the type of dataset used. Data leakage is addressed, and the importance of external validation to assess true out-of-sample performance and to-upon successful external validation-release the model into clinical practice is discussed. Finally, for highly dimensional datasets, the concepts of feature reduction using principal component analysis (PCA) as well as feature elimination using recursive feature elimination (RFE) are elucidated.We provide explanations on the general principles of machine learning, as well as analytical steps required for successful machine learning-based predictive modeling, which is the focus of this series. In particular, we define the terms machine learning, artificial intelligence, as well as supervised and unsupervised learning, continuing by introducing optimization, thus, the minimization of an objective error function as the central dogma of machine learning. In addition, we discuss why it is important to separate predictive and explanatory modeling, and most importantly state that a prediction model should not be used to make inferences. Lastly, we broadly describe a classical workflow for training a machine learning model, starting with data pre-processing and feature engineering and selection, continuing on with a training structure consisting of a resampling method, hyperparameter tuning, and model selection, and ending with evaluation of model discrimination and calibration as well as robust internal or external validation of the fully developed model. Methodological rigor and clarity as well as understanding of the underlying reasoning of the internal workings of a machine learning approach are required, otherwise predictive applications despite being strong analytical tools are not well accepted into the clinical routine.The democratization of machine learning (ML) through availability of open-source learning libraries, the availability of datasets in the "big data" era, increasing computing power even on mobile devices, and online training resources have both led to an explosion in applications and publications of ML in the clinical neurosciences, but has also enabled a dangerous amount of flawed analyses and cardinal methodological errors committed by benevolent authors. While powerful ML methods are nowadays available to almost anyone and can be applied after just few minutes of familiarizing oneself with these methods, that does not imply that one has mastered these techniques. This textbook for clinicians aims to demystify ML by illustrating its methodological foundations, as well as some specific applications throughout clinical neuroscience, and its limitations. While our mind can recognize, abstract, and deal with the many uncertainties in clinical practice, algorithms cannot. Algorithms must remain tools of our own mind, tools that we should be able to master, control, and apply to our advantage in an adjunctive manner. find more Our hope is that this book inspires and instructs physician-scientists to continue to develop the seeds that have been planted for machine intelligence in clinical neuroscience, not forgetting their inherent limitations.

We longitudinally evaluated the tumour growth and metabolic activity of three nasopharyngeal carcinoma (NPC) cell line models (C666-1, C17 and NPC43) and two xenograft models (Xeno76 and Xeno23) using a micropositron emission tomography and magnetic resonance (microPET/MR). With a better understanding of the interplay between tumour growth and metabolic characteristics of these NPC models, we aim to provide insights for the selection of appropriate NPC cell line/xenograft models to assist novel drug discovery and evaluation.

Mice were imaged by

F-deoxyglucose ([

F]FDG) microPET/MR twice a week for consecutive 3-7weeks. [

F]FDG uptake was quantified by standardized uptake value (SUV) and presented as SUVmean tumour-to-liver ratio (SUVRmean). Longitudinal tumour growth patterns and metabolic patterns were recorded. SUVRmean and histological characteristics were compared across the five NPC models. Cisplatin was administrated to one selected optimal tumour model, C17, to evaluate our imaging platform.

its use in novel drug discovery and evaluation for NPC.CD3-bispecific antibodies are a new class of immunotherapeutic drugs against cancer. The pharmacological activity of CD3-bispecifics is typically assessed through in vitro assays of cancer cell lines co-cultured with human peripheral blood mononuclear cells (PBMCs). Assay results depend on experimental conditions such as incubation time and the effector-to-target cell ratio, which can hinder robust quantification of pharmacological activity. In order to overcome these limitations, we developed a new, holistic approach for quantification of the in vitro dose-response relationship. Our experimental design integrates a time-independent analysis of the dose-response across different time points as an alternative to the static, "snap-shot" analysis based on a single time point commonly used in dose-response assays. We show that the potency values derived from static in vitro experiments depend on the incubation time, which leads to inconsistent results across multiple assays and compounds. We compared the potency values from the time-independent analysis with a model-based approach. We find comparably accurate potency estimates from the model-based and time-independent analyses and that the time-independent analysis provides a robust quantification of pharmacological activity. This approach may allow for an improved head-to-head comparison of different compounds and test systems and may prove useful for supporting first-in-human dose selection.

The purpose of this study is to describe ultra-processed food and drinks (UPFDs) consumption, and associations with intake of total sugar and dietary fibre, and high BMI in adults across Europe.

Using food consumption data collected by food records or 24-h dietary recalls availablefrom the European Food Safety Authority (EFSA) Comprehensive European Food Consumption Database, the foods consumed were classified by the level of processing using the NOVA classification. Diet quality was assessed by data linkage to the Dutch food composition tables (NEVO) and years lived with disability for high BMI from theGlobal Burden of Disease Study2019. Bivariate groupings were carried out to explore associations of UPFDs consumption with population intake of sugar and dietary fibre, and BMI burden, visualised by scatterplots.

The energy share from UPFDs varied markedly across the 22 European countries included, ranging from 14 to 44%, being the lowest in Italy and Romania, while the highest in the UK and Sweden. An overall modest decrease (2-15%) in UPFDs consumption is observed over time, except for Finland, Spain and the UK reporting increases (3-9%). Fine bakery wares and soft drinks were most frequently ranked as the main contributor. Countries with ahigher sugar intake reported also ahigher energy share from UPFDs, as most clearly observed for UPF (r = 0.57, p value = 0.032 for men; and r = 0.53, p value = 0.061 for women). No associations with fibre intake or high BMI were observed.

Population-level UPFDs consumption substantially varied across Europe, although main contributorsare similar. UPFDs consumption was not observed to be associated with country-level burden of high BMI, despite being related to ahigher total sugar intake.

Population-level UPFDs consumption substantially varied across Europe, although main contributors are similar. UPFDs consumption was not observed to be associated with country-level burden of high BMI, despite being related to a higher total sugar intake.

The range of medical apps is broad and diverse. The previous evaluations are inconsistent and limited to the respective areas of application.

The main objective of this work is to comprehensively present, organize, and evaluate the current range of urological apps with the help of asemi-automatic retrospective app store analysis (SARASA).

Application of an adaptable method based on filter processes according to predefined criteria by means of SARASA to characterize urological apps from various subject areas in the Apple App Store with subsequent manual filtering and evaluation.

From the original list of 34,830 apps in the "Medicine" category of the Apple App Store on 27September2021, 3556 apps remained after apps without aGerman-language store description were removed. 43subject-specific apps remained for further analysis and description. The number of reviews, rating, topicality, urological issues, technical support and richness of content were taken into account. The twomost relevant apps for each topic are presented in detail.