Reference Class Forecasting and Machine Learning for Improved Offshore Oil and Gas Megaproject Planning: Methods and Application

This article develops and describes rigorous oil and gas project forecasting methods. First, it builds a theoretical foundation by mapping megaproject performance literature to these projects. Second, it draws on heuristics and biases literature, using a questionnaire to demonstrate forecasting-related biases and principal-agent issues among industry project professionals. Third, it uses methodically collected project performance data to demonstrate that overrun distributions are non-normal and fat-tailed. Fourth, reference-class forecasting is demonstrated for cost and schedule uplifts. Finally, a predictive approach using machine learning (ML) considers project-specific factors to forecast the most likely cost and schedule overruns in a project.

Assessment of Speech Quality during Speech Rehabilitation Based on the Solution of the Classification Problem

The article considers an approach to the problem of assessing the quality of speech during speech rehabilitation as a classification problem. For this, a classifier is built on the basis of an LSTM neural network for dividing speech signals into two classes: before the operation and immediately after. At the same time, speech before the operation is the standard to which it is necessary to approach in the process of rehabilitation. The metric of belonging of the evaluated signal to the reference class acts as an assessment of speech. An experimental assessment of rehabilitation sessions and a comparison of the resulting assessments with expert assessments of phrasal intelligibility were carried out.

Personalizing Evidence for Shoulder Fracture Patients Using an Extended Instrumental Variable Causal Forest Algorithm

Objective: To assess the ability of an extended Instrumental Variable Causal Forest Algorithm (IV-CFA) to provide personalized evidence of early surgery effects on benefits and detriments for elderly shoulder fracture patients. Data Sources/Study Setting: Population of 72,751 fee-for-service Medicare beneficiaries with proximal humerus fractures (PHFs) in 2011 who survived a 60-day treatment window after an index PHF and were continuously Medicare fee-for-service eligible over the period 12 months prior to index to the minimum of 12 months after index or death. Study Design: IV-CFA estimated early surgery effects on both beneficial and detrimental outcomes for each patient in the study population. Classification and regression trees (CART) were applied to these estimates to create patient reference classes. Two-stage least squares (2SLS) estimators were applied to patients in each reference class to scrutinize the estimates relative to the known 2SLS properties. Principal Findings: This approach uncovered distinct reference classes of elderly PHF patients with respect to early surgery effects on benefit and detriment. Older, frailer patients with more comorbidities, and lower utilizers of healthcare were less likely to gain benefit and more likely to have detriment from early surgery. Reference classes were characterized by the appropriateness of early surgery rates with respect to benefit and detriment. Conclusions: Extended IV-CFA provides an illuminating method to uncover reference classes of patients based on treatment effects using observational data with a strong instrumental variable. This study isolated reference classes of new PHF patients in which changes in early surgery rates would improve patient outcomes. The inability to measure fracture complexity in Medicare claims means providers will need to discuss the appropriateness of these estimates to patients within a reference class in context of this missing information.

Personalizing Evidence for Shoulder Fracture Patients Using an Extended Instrumental Variable Causal Forest Algorithm

Objective: To assess the ability of an extended Instrumental Variable Causal Forest Algorithm (IV-CFA) to provide personalized evidence of early surgery effects on benefits and detriments for elderly shoulder fracture patients. Data Sources/Study Setting: Population of 72,751 fee-for-service Medicare beneficiaries with proximal humerus fractures (PHFs) in 2011 who survived a 60-day treatment window after an index PHF and were continuously Medicare fee-for-service eligible over the period 12 months prior to index to the minimum of 12 months after index or death. Study Design: IV-CFA estimated early surgery effects on both beneficial and detrimental outcomes for each patient in the study population. Classification and regression trees (CART) were applied to these estimates to create patient reference classes. Two-stage least squares (2SLS) estimators were applied to patients in each reference class to scrutinize the estimates relative to the known 2SLS properties. Principal Findings: This approach uncovered distinct reference classes of elderly PHF patients with respect to early surgery effects on benefit and detriment. Older, frailer patients with more comorbidities, and lower utilizers of healthcare were less likely to gain benefit and more likely to have detriment from early surgery. Reference classes were characterized by the appropriateness of early surgery rates with respect to benefit and detriment. Conclusions: Extended IV-CFA provides an illuminating method to uncover reference classes of patients based on treatment effects using observational data with a strong instrumental variable. This study isolated reference classes of new PHF patients in which changes in early surgery rates would improve patient outcomes. The inability to measure fracture complexity in Medicare claims means providers will need to discuss the appropriateness of these estimates to patients within a reference class in context of this missing information.

Relaxed Optimization: How Close Is a Consumer to Satisfying First-Order Conditions?

We propose relaxing the first-order conditions in optimization to approximate rational consumer choice. We assess the magnitude of departures with a new, axiomatically-founded measure that admits multiple interpretations. Standard inequality tests of rationality for any given reference class of preferences can be conveniently re-purposed to measure goodness-of-fit with that class. Another advantage of our approach is that it is applicable in any context where the first-order approach is meaningful (e.g., convex budget sets arising from progressive taxation). We apply these ideas to shed new light on existing portfolio-choice data.

Blood-based biomarkers and long-term risk of frailty – experience from the Swedish AMORIS cohort

Background Frailty is associated with reduced quality of life, poor health outcomes, and death. Past studies have investigated how specific biomarkers are associated with frailty but understanding biomarkers in concert with each other and the associated risk of frailty is critical for clinical application. Methods Using a sample aged ≥59 years at baseline from the Swedish AMORIS cohort (n=19341), with biomarkers measured at baseline (1985-1996), we conducted latent class analysis with 18 biomarkers and used Cox models to determine the association between class and frailty and all-cause mortality. Results Four classes were identified. Compared to the largest class, the Reference class (81.7%), all other classes were associated with increased risk of both frailty and mortality. The Anemia class (5.8%), characterized by comparatively lower iron markers and higher inflammatory markers, had HR=1.54, 95% CI 1.38, 1.73 for frailty and HR=1.76, 95% CI 1.65, 1.87 for mortality. The Diabetes class (6.5%) was characterized by higher glucose and fructosamine, and had HR=1.59, 95% CI 1.43, 1.77 for frailty and HR=1.74, 95% CI 1.64, 1.85 for mortality. Finally, the Liver class (6.0%), characterized by higher liver enzyme levels, had HR=1.15, 95% CI 1.01, 1.30 for frailty and HR=1.40, 95% CI 1.31, 1.50 for mortality. Sex-stratified analyses did not show any substantial differences between men and women. Conclusions Distinct sets of commonly available biomarkers were associated with development of frailty and monitoring these biomarkers in patients may allow for earlier detection and possible prevention of frailty, with the potential for improved quality of life.

Improved One-Class Modeling of High-Dimensional Metabolomics Data via Eigenvalue-Shrinkage

One-class modelling is a useful approach in metabolomics for the untargeted detection of abnormal metabolite profiles, when information from a set of reference observations is available to model “normal” or baseline metabolite profiles. Such outlying profiles are typically identified by comparing the distance between an observation and the reference class to a critical limit. Often, multivariate distance measures such as the Mahalanobis distance (MD) or principal component-based measures are used. These approaches, however, are either not applicable to untargeted metabolomics data, or their results are unreliable. In this paper, five distance measures for one-class modeling in untargeted metabolites are proposed. They are based on a combination of the MD and five so-called eigenvalue-shrinkage estimators of the covariance matrix of the reference class. A simple cross-validation procedure is proposed to set the critical limit for outlier detection. Simulation studies are used to identify which distance measure provides the best performance for one-class modeling, in terms of type I error and power to identify abnormal metabolite profiles. Empirical evidence demonstrates that this method has better type I error (false positive rate) and improved outlier detection power than the standard (principal component-based) one-class models. The method is illustrated by its application to liquid chromatography coupled to mass spectrometry (LC-MS) and nuclear magnetic response spectroscopy (NMR) untargeted metabolomics data from two studies on food safety assessment and diagnosis of rare diseases, respectively.

Comparison of Simple Averaging and Latent Class Modeling to Estimate the Area of Land Cover in the Presence of Reference Data Variability

Estimates of the area or percent area of the land cover classes within a study region are often based on the reference land cover class labels assigned by analysts interpreting satellite imagery and other ancillary spatial data. Different analysts interpreting the same spatial unit will not always agree on the land cover class label that should be assigned. Two approaches for accommodating interpreter variability when estimating the area are simple averaging (SA) and latent class modeling (LCM). This study compares agreement between area estimates obtained from SA and LCM using reference data obtained by seven trained, professional interpreters who independently interpreted an annual time series of land cover reference class labels for 300 sampled Landsat pixels. We also compare the variability of the LCM and SA area estimates over different numbers of interpreters and different subsets of interpreters within each interpreter group size, and examine area estimates of three land cover classes (forest, developed, and wetland) and three change types (forest gain, forest loss, and developed gain). Differences between the area estimates obtained from SA and LCM are most pronounced for the estimates of wetland and the three change types. The percent area estimates of these rare classes were usually greater for LCM compared to SA, with the differences between LCM and SA increasing as the number of interpreters providing the reference data increased. The LCM area estimates generally had larger standard deviations and greater ranges over different subsets of interpreters, indicating greater sensitivity to the selection of the individual interpreters who carried out the reference class labeling.