Validating and updating a risk model for pneumonia - a case study

Machine-intelligence for developing a potent signature to predict ovarian response to tailor assisted reproduction technology

The prediction of poor ovarian response (POR) for stratified interference is a critical clinical issue that has received an increasing amount of recent concern. Anthropogenic diagnostic modes remain too simple for the handling of actual clinical complexity. Therefore, this study conducted extensive selection using models that were derived from a variety of machine learning algorithms, including random forest (RF), decision trees, eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), and artificial neural networks (ANN) for the development of two models called the COS pre-launch model (CPLM) and the hCG pre-trigger model (HPTM) to assess POR based on different requirements. The results demonstrated that CPLM constructed using ANN achieved the highest AUC result of all the algorithms in COS pre-launch (AUC=0.859, C-index=0.87, good calibration), and HPTL constructed using random forest was found to be the most effective in hCG pre-trigger (AUC=0.903, C-index=0.90, good calibration). It is notable that CPLM and HPTM exhibited better performance than common clinical characteristics (0.895 [CPLM], and 0.903 [HPTM] in comparison to 0.824 [anti-Müllerian hormone (AMH)], and 0.799 [antral follicle count (AFC)]). Furthermore, variable importance figure elucidated the values of AMH, AFC, and E₂ level and follicle number on hCG day, which provides important theoretical guidance and experimental data for further application. Generally, the CPLM and HPTM can offer effective POR prediction for patients who are receiving assisted reproduction technology (ART), and has great potential for guiding the clinical treatment of infertility.

Morphogenetic Updating Algorithm and its Application in Updating Probabilistic Risk

The problem of risk update is one of the core problems of dynamic risk analysis. As for updating widely used probabilistic risk, it’s a kind of special problem of pattern recognition which means to transfer from old pattern to new pattern with new information. Aiming at updating probabilistic risk only with the new observations and without the original observations, this paper proposes the morphogenetic updating algorithm to update the probabilistic risk with only new observations and old risk values by reproducing the morphogenesis of new observations and old risk values based on morphogenetic estimation. The form rules hidden in the risk values and new observations are discovered by internal source in Write operation, and the projection of the input and the cosine similarity measure as the basis for defining the weight of updating are computed in Read operation. Based on Resconi theorem, the input is reproduced by its projection on the context which provides the same mathematical space for updating and stores the structural information hidden in the risk. By applying the algorithm to updating the probabilistic risk of typhoon rainstorms occurred in Guangdong Province, it proves that morphogenetic updating algorithm provides us a method to updating probabilistic risk fast and satisfactorily and it’s a sustainable updating method which can be used for dynamic risk analysis.

Adaptive prior weighting in generalized regression

The prior distribution is a key ingredient in Bayesian inference. Prior information on regression coefficients may come from different sources and may or may not be in conflict with the observed data. Various methods have been proposed to quantify a potential prior-data conflict, such as Box's p-value. However, there are no clear recommendations how to react to possible prior-data conflict in generalized regression models. To address this deficiency, we propose to adaptively weight a prespecified multivariate normal prior distribution on the regression coefficients. To this end, we relate empirical Bayes estimates of prior weight to Box's p-value and propose alternative fully Bayesian approaches. Prior weighting can be done for the joint prior distribution of the regression coefficients or-under prior independence-separately for prespecified blocks of regression coefficients. We outline how the proposed methodology can be implemented using integrated nested Laplace approximations (INLA) and illustrate the applicability with a Bayesian logistic regression model for data from a cross-sectional study. We also provide a simulation study that shows excellent performance of our approach in the case of prior misspecification in terms of root mean squared error and coverage. Supplementary Materials give details on software implementation and code and another application to binary longitudinal data from a randomized clinical trial using a Bayesian generalized linear mixed model.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.

Alternative performance measures for prediction models

As a performance measure for a prediction model, the area under the receiver operating characteristic curve (AUC) is insensitive to the addition of strong markers. A number of measures sensitive to performance change have recently been proposed; however, these relative-performance measures may lead to self-contradictory conclusions. This paper examines alternative performance measures for prediction models: the Lorenz curve-based Gini and Pietra indices, and a standardized version of the Brier score, the scaled Brier. Computer simulations are performed in order to study the sensitivity of these measures to performance change when a new marker is added to a baseline model. When the discrimination power of the added marker is concentrated in the gray zone of the baseline model, the AUC and the Gini show minimal performance improvements. The Pietra and the scaled Brier show more significant improvements in the same situation, comparatively. The Pietra and the scaled Brier indices are therefore recommended for prediction model performance measurement, in light of their ease of interpretation, clinical relevance and sensitivity to gray-zone resolving markers.