e19318 Background: ECOG PS is a prognostic indicator of outcomes, and scores of 0-1 (good ECOG PS) are often required for clinical trial enrollment. Patients treated in non-trial settings often lack ECOG PS scores limiting the ability of Real World Data from these patients to be used in external control arms (ECAs) or to provide optimal specificity for clinical effectiveness research. Machine Learning can be used to impute ECOG PS scores from other clinical data at various points during treatment. Methods: We developed a series of models using logistic regression (LR) or XGBoost (XGB) that impute ECOG PS at initial diagnosis, metastatic diagnosis and final evaluation using a curated Non-Small Cell Lung Cancer cohort of 31,425 patients with at least one ECOG PS score. Results: AUC-ROC values of up to 0.81 could be obtained for imputing a patient’s final ECOG PS, with lower AUC values when imputing ECOG PS at initial and metastatic diagnosis using large numbers (i.e. thousands) of features. We developed more interpretable models with 110 or 40 features with reduced but still satisfactory AUC, with accuracy of predicting good ECOG PS scores of around 80%. Key features were obtained from lab tests, physical exams, comorbidities, medications, age and metastatic status. The table below shows the results of several of these models. Where the models misclassify ECOG PS, the error was rarely greater than 1 grade. Conclusions: ECOG PS is subjective, suggesting that ML based cohort assignment will be sufficiently accurate to support their use in research. Further work will be required to assess if the ML predicted cohorts have different outcomes. [Table: see text]
Abstract. We consider the problem of analyzing influences in financial networks by studying correlations in stock price movements of companies in the S&P 500 index and measures of influence that can be attributed to each company. We demonstrate that under a novel and natural measure of influence involving cross-correlations of stock market returns and market capitalization, the resulting network of financial influences is Scale Free. This is further corroborated by the existence of an intuitive set of highly influential hub nodes in the network. Finally, it is also shown that companies that have been deleted from the S&P 500 index had low values of influence.
6556 Background: Survival prediction models for lung cancer patients could help guide their care and therapy decisions. The objectives of this study were to predict probability of survival beyond 90, 180 and 360 days from any point in a lung cancer patient’s journey. Methods: We developed a Gradient Boosting model (XGBoost) using data from 55k lung cancer patients in the ASCO CancerLinQ database that used 3958 unique variables including Dx and Rx codes, biomarkers, surgeries and lab tests from ≤1 year prior to the prediction point, which was chosen at random for each patient. We used 40% data for training, 25% for hyper-parameter tuning, 20% for testing and 15% for holdout validation. Death date available in the Electronic Health Record was cross checked by linkage to death registries. Results: The model was validated on the holdout set of 8,468 patients. The Area Under the Curve (AUC) for the model was 0.79. The precision and recall for predicting survival beyond the three time points were between 0.7-0.8 and 0.8-0.9 respectively (see table). This compares favourably to other lung cancer survival models created using different machine learning techniques (Jochems 2017, Dekker 2009). A Cox-PH model created using the top 20 variables also had a significantly lower performance (see table). Analysis of input variables yielded distinctive patterns for patient subgroups and time points. Tumor status, medications, lab values and functional status were found to be significant in patient sub cohorts. Conclusions: An AI model to predict survival of lung cancer patients built using a large real world dataset yielded high accuracy. This general model can further be used to predict survival of sub cohorts stratified by variables such as stage or various treatment effects. Such a model could be useful for assessing patient risk and treatment options, evaluating cost and quality of care or determining clinical trial eligibility. [Table: see text]
Lakshmi Narayanan, et al.: GC-MS and HPTLC Analysis of P. barbatumAn important medicinal plant, Polygonum barbatum was undertaken for gas chromatography-mass spectrometry analysis and high performance thin layer chromatography finger print profile to investigate the chemical constituent present in the hydroalcoholic extract of Polygonum barbatum leaves. Gas chromatography-mass spectrometry analysis shown that twenty two compounds were identified and named from the extract. Among these constituents, terpenoids are the major constituents present in this extract. Other major constituents were present in the extracts were carbohydrate and fatty acids. High performance thin layer chromatography fingerprint analysis of hydroalcoholic extract of Polygonum barbatum was carried out by using ethyl acetate-hexane (7:3 v/v) as a mobile phase. From the high performance thin layer
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.