Using survival prediction techniques to learn consumer-specific reservation price distributions.
Ontology highlight
ABSTRACT:
SUBMITTER: Jin P
PROVIDER: S-EPMC8084175 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundThe use of alternative modeling techniques for predicting patient survival is complicated by the fact that some alternative techniques cannot readily deal with censoring, which is essential for analyzing survival data. In the current study, we aimed to demonstrate that pseudo values enable statistically appropriate analyses of survival outcomes when used in seven alternative modeling techniques.MethodsIn this case study, we analyzed survival of 1282 Dutch patients with newly diagnosed Head and Neck Squamous Cell Carcinoma (HNSCC) with conventional Kaplan-Meier and Cox regression analysis. We subsequently calculated pseudo values to reflect the individual survival patterns. We used these pseudo values to compare recursive partitioning (RPART), neural nets (NNET), logistic regression (LR) general linear models (GLM) and three variants of support vector machines (SVM) with respect to dichotomous 60-month survival, and continuous pseudo values at 60 months or estimated survival time. We used the area under the ROC curve (AUC) and the root of the mean squared error (RMSE) to compare the performance of these models using bootstrap validation.ResultsOf a total of 1282 patients, 986 patients died during a median follow-up of 66 months (60-month survival: 52% [95% CI: 50%-55%]). The LR model had the highest optimism corrected AUC (0.791) to predict 60-month survival, followed by the SVM model with a linear kernel (AUC 0.787). The GLM model had the smallest optimism corrected RMSE when continuous pseudo values were considered for 60-month survival or the estimated survival time followed by SVM models with a linear kernel. The estimated importance of predictors varied substantially by the specific aspect of survival studied and modeling technique used.ConclusionsThe use of pseudo values makes it readily possible to apply alternative modeling techniques to survival problems, to compare their performance and to search further for promising alternative modeling techniques to analyze survival time.
| S-EPMC4065009 | biostudies-literature
Project description:The literature provides strong evidence that stock price values can be predicted from past price data. Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in a data set. This method is often used for dimensionality reduction and analysis of the data. In this paper, we develop a general method for stock price prediction using time-varying covariance information. To address the time-varying nature of financial time series, we assign exponential weights to the price data so that recent data points are weighted more heavily. Our proposed method involves a dimension-reduction operation constructed based on principle components. Projecting the noisy observation onto a principle subspace results in a well-conditioned problem. We illustrate our results based on historical daily price data for 150 companies from different market-capitalization categories. We compare the performance of our method to two other methods: Gauss-Bayes, which is numerically demanding, and moving average, a simple method often used by technical traders and researchers. We investigate the results based on mean squared error and directional change statistic of prediction, as measures of performance, and volatility of prediction as a measure of risk.
| S-EPMC7083277 | biostudies-literature
Project description: Not available
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Project description:In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients' survival outcomes. To assess dataset skewness and detect feature importance, we applied Pearson's second coefficient test of skewness and the Ordinary Least Squares method, respectively. Using two sampling strategies, holdout and five-fold cross-validation, we developed five machine learning (ML) models alongside a feed-forward deep neural network (DNN) for the multiclass classification and regression prediction of glioblastoma patient survival. After balancing the classification and regression datasets, we obtained 46,340 and 28,573 samples, respectively. Shapley additive explanations (SHAP) were then used to explain the decision-making process of the best model. In both classification and regression tasks, as well as across holdout and cross-validation sampling strategies, the DNN consistently outperformed the ML models. Notably, the accuracy were 90.25% and 90.22% for holdout and five-fold cross-validation, respectively, while the corresponding R2 values were 0.6565 and 0.6622. SHAP analysis revealed the importance of age at diagnosis as the most influential feature in the DNN's survival predictions. These findings suggest that the DNN holds promise as a practical auxiliary tool for clinicians, aiding them in optimal decision-making concerning the treatment and care trajectories for glioblastoma patients.
| S-EPMC10824760 | biostudies-literature
Project description:Deep generative models of molecules have grown immensely in popularity, trained on relevant datasets, these models are used to search through chemical space. The downstream utility of generative models for the inverse design of novel functional compounds, depends on their ability to learn a training distribution of molecules. The most simple example is a language model that takes the form of a recurrent neural network and generates molecules using a string representation. Since their initial use, subsequent work has shown that language models are very capable, in particular, recent research has demonstrated their utility in the low data regime. In this work, we investigate the capacity of simple language models to learn more complex distributions of molecules. For this purpose, we introduce several challenging generative modeling tasks by compiling larger, more complex distributions of molecules and we evaluate the ability of language models on each task. The results demonstrate that language models are powerful generative models, capable of adeptly learning complex molecular distributions. Language models can accurately generate: distributions of the highest scoring penalized LogP molecules in ZINC15, multi-modal molecular distributions as well as the largest molecules in PubChem. The results highlight the limitations of some of the most popular and recent graph generative models- many of which cannot scale to these molecular distributions.
| S-EPMC9174447 | biostudies-literature
Project description:ObjectiveTo determine how long it takes for neural impulses to travel along peripheral nerve fibers in living humans.MethodsA collision test was performed to measure the conduction velocity distribution of the ulnar nerve. Two stimuli at the distal and proximal sites were used to produce the collision. Compound muscle or nerve action potentials were recorded to perform the measurements on the motor or mixed nerve, respectively. Interstimulus interval was set at 1-5 ms. A quadri-pulse technique was used to measure the refractory period and calibrate the conduction time.ResultsCompound muscle action potential produced by the proximal stimulation started to emerge at the interstimulus interval of about 1.5 ms and increased with the increment in interstimulus interval. Two groups of motor nerve fibers with different conduction velocities were identified. The mixed nerve showed a wider conduction velocity distribution with identification of more subgroups of nerve fibers than the motor nerve.ConclusionsThe conduction velocity distributions in high resolution on a peripheral motor and mixed nerve are different and this can be measured with the collision test.SignificanceWe provided ground truth data to verify the neuroimaging pipelines for the measurements of latency connectome in the peripheral nervous system.
| S-EPMC8284026 | biostudies-literature
Project description:Recently it was shown that gene expression signatures generated from DNA microarray analyses have promise as biomarkers of clinical outcome and that the molecular characteristics of tumors could be elucidated. Through this study, we have determined a high-risk signature for recurrence as a prognostic biomarker using formalin-fixed HNSCC tumors and tested the results to an independent data set obtained from fresh frozen tumors as a comparison. Also, we have shown the genes that are involved in epithelial to mesenchymal transition and nuclear factor-ĸB signaling deregulation are the most prominent molecular characteristics of the high-risk tumors. Forty samples including 34 formalin-fixed tissues and 6 matched frozen tissues from 29 HNSCC patients were analyzed for gene expression. The formalin-fixed tumors were classified based on their gene expression by intrinsic analysis and the intrinsic gene list was tested on the classification of previously published 60 frozen HNSCC tumors which highly correlated. Based on the molecular classification, a 75-gene list that is predictive of high-risk for recurrence was determined by training on the formalin-fixed tumor set and tested on the independent frozen tumor set. The difference in recurrence-free survival (RFS) between the high-risk vs. low-risk groups in the training and test sets were statistically significant (Log-rank test, p=0.002 and p=0.03, respectively). Also, the gene expression data was interrogated using Gene Set Enrichment Analysis to determine functional significance. The most significant sets of genes that are enriched in the high-risk tumors were genes involving epithelial to mesenchymal transition (EMT), NF-ĸB activation and cell adhesion. In conclusion, global gene expression analysis is feasible using formalin-fixed tissue, and the data from different sample preparations and array platforms can be reliably combined for analyses. The 75-gene list can be utilized as a prognostic biomarker of recurrence and the molecular characteristics of EMT and NF-ĸB activation can be targeted as the novel therapy in the identified high-risk patients. Keywords: FFPE, survival analysis
2006-11-02 | GSE2837 | GEO
Project description:We formulate a test of the fungibility of money based on parallel shifts in the prices of different quality grades of a commodity. We embed the test in a discrete-choice model of product quality choice and estimate the model using panel microdata on gasoline purchases. We find that when gasoline prices rise consumers substitute to lower octane gasoline, to an extent that cannot be explained by income effects. Across a wide range of specifications, we consistently reject the null hypothesis that households treat "gas money" as fungible with other income. We compare the empirical fit of three psychological models of decision-making. A simple model of category budgeting fits the data well, with models of loss aversion and salience both capturing important features of the time series.
| S-EPMC4771414 | biostudies-literature
Project description:Accurate prognostic prediction is crucial for treatment decision-making in lung papillary adenocarcinoma (LPADC). The aim of this study was to predict cancer-specific survival in LPADC using ensemble machine learning and classical Cox regression models. Moreover, models were evaluated to provide recommendations based on quantitative data for personalized treatment of LPADC. Data of patients diagnosed with LPADC (2004-2018) were extracted from the Surveillance, Epidemiology, and End Results database. The set of samples was randomly divided into the training and validation sets at a ratio of 7:3. Three ensemble models were selected, namely gradient boosting survival (GBS), random survival forest (RSF), and extra survival trees (EST). In addition, Cox proportional hazards (CoxPH) regression was used to construct the prognostic models. The Harrell's concordance index (C-index), integrated Brier score (IBS), and area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used to evaluate the performance of the predictive models. A user-friendly web access panel was provided to easily evaluate the model for the prediction of survival and treatment recommendations. A total of 3615 patients were randomly divided into the training and validation cohorts (n = 2530 and 1085, respectively). The extra survival trees, RSF, GBS, and CoxPH models showed good discriminative ability and calibration in both the training and validation cohorts (mean of time-dependent AUC: > 0.84 and > 0.82; C-index: > 0.79 and > 0.77; IBS: < 0.16 and < 0.17, respectively). The RSF and GBS models were more consistent than the CoxPH model in predicting long-term survival. We implemented the developed models as web applications for deployment into clinical practice (accessible through https://shinyshine-820-lpaprediction-model-z3ubbu.streamlit.app/ ). All four prognostic models showed good discriminative ability and calibration. The RSF and GBS models exhibited the highest effectiveness among all models in predicting the long-term cancer-specific survival of patients with LPADC. This approach may facilitate the development of personalized treatment plans and prediction of prognosis for LPADC.
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Project description:Despite the recent proliferation of price transparency tools, consumer use and awareness of these tools is low. Better strategies to increase the use of price transparency tools are needed. To inform such efforts, we studied who is most likely to use a price transparency tool. We conducted a cross-sectional study of use of the Truven Treatment Cost Calculator among employees at 2 large companies for the 12 months following the introduction of the tool in 2011-2012. We examined frequency of sign-ons and used multivariate logistic regression to identify which demographic and health care factors were associated with greater use of the tool. Among the 70?408 families offered the tool, 7885 (11%) used it at least once and 854 (1%) used it at least 3 times in the study period. Greater use of the tool was associated with younger age, living in a higher income community, and having a higher deductible. Families with moderate annual out-of-pocket medical spending ($1000-$2779) were also more likely to use the tool. Consistent with prior work, we find use of this price transparency tool is low and not sustained over time. Employers and payers need to pursue strategies to increase interest in and engagement with health care price information, particularly among consumers with higher medical spending.
| S-EPMC5812034 | biostudies-literature