Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning.

A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected from crystals with random orientations. Inspired by the human decision-making process, a framework for automated crystal system classification from DPs with arbitrary orientations was developed. A convolutional neural network was trained using evidential deep learning, and the predictive uncertainties were quantified and leveraged to fuse multiview predictions. Using vector map representations of DPs, the framework achieves a testing accuracy of 0.94 in the examples considered, is robust to noise, and retains remarkable accuracy using experimental data. This work highlights the ability of ML to be used to accelerate experimental high-throughput materials data analytics.

Uncertainty-aware mixed-variable machine learning for materials design.

Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models' predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.

Pilot implementation of opioid stewardship measures using the national surgical quality improvement program-pediatric platform.

INTRODUCTION: Data surrounding optimal pediatric postoperative opioid prescribing are incomplete. The objective of this study was to leverage the American College of Surgeons (ACS) National Surgical Quality Improvement Program-Pediatric (NSQIP-P) and assess feasibility of added data collection surrounding pediatric perioperative pain management practices including opioid prescribing at discharge. METHODS: Nineteen (19) novel data elements were added to NSQIP-P data collection of selected patients, ages 5-18 years, who had undergone surgery at a single, free-standing children's hospital. Metrics around data abstraction and completion of variables were collected. Univariate analyses (using Chi-square or Wilcoxon Rank Sum tests) and multiple logistic regressions were performed to describe predictors of opioid prescribing at discharge and to monitor adherence to Food and Drug Administration (FDA) prescribing recommendations. RESULTS: Median abstraction time of the novel variables decreased from 12 to 5 min per patient over 13 months with 94% variable completion rate. Of 878 patients, 302 (36.4%) were prescribed opioids at discharge. Factors associated with an opioid prescription included older age (p < 0.001), white race (p < 0.05), undergoing an orthopedic surgery (p < 0.001), and receiving a regional block perioperatively (p < 0.001). All opioid prescriptions met FDA guidelines with no patients receiving codeine, and 98% of patients receiving opioid prescriptions < 50 morphine milli-equivalents per day. CONCLUSION: Collecting data on current pain management practices, opioid prescribing, and adherence to safety recommendations is feasible using the NSQIP-P with little added burden. Further expansion of data collection is needed to develop generalizable optimal prescribing practices for post-discharge pain management for children.

Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables.

Although Bayesian Optimization (BO) has been employed for accelerating materials design in computational materials engineering, existing works are restricted to problems with quantitative variables. However, real designs of materials systems involve both qualitative and quantitative design variables representing material compositions, microstructure morphology, and processing conditions. For mixed-variable problems, existing Bayesian Optimization (BO) approaches represent qualitative factors by dummy variables first and then fit a standard Gaussian process (GP) model with numerical variables as the surrogate model. This approach is restrictive theoretically and fails to capture complex correlations between qualitative levels. We present in this paper the integration of a novel latent-variable (LV) approach for mixed-variable GP modeling with the BO framework for materials design. LVGP is a fundamentally different approach that maps qualitative design variables to underlying numerical LV in GP, which has strong physical justification. It provides flexible parameterization and representation of qualitative factors and shows superior modeling accuracy compared to the existing methods. We demonstrate our approach through testing with numerical examples and materials design examples. The chosen materials design examples represent two different scenarios, one on concurrent materials selection and microstructure optimization for optimizing the light absorption of a quasi-random solar cell, and another on combinatorial search of material constitutes for optimal Hybrid Organic-Inorganic Perovskite (HOIP) design. It is found that in all test examples the mapped LVs provide intuitive visualization and substantial insight into the nature and effects of the qualitative factors. Though materials designs are used as examples, the method presented is generic and can be utilized for other mixed variable design optimization problems that involve expensive physics-based simulations.

Distinct Variation Pattern Discovery Using Alternating Nonlinear Principal Component Analysis.

Autoassociative neural networks (ANNs) have been proposed as a nonlinear extension of principal component analysis (PCA), which is commonly used to identify linear variation patterns in high-dimensional data. While principal component scores represent uncorrelated features, standard backpropagation methods for training ANNs provide no guarantee of producing distinct features, which is important for interpretability and for discovering the nature of the variation patterns in the data. Here, we present an alternating nonlinear PCA method, which encourages learning of distinct features in ANNs. A new measure motivated by the condition of orthogonal loadings in PCA is proposed for measuring the extent to which the nonlinear principal components represent distinct variation patterns. We demonstrate the effectiveness of our method using a simulated point cloud data set as well as a subset of the MNIST handwritten digits data. The results show that standard ANNs consistently mix the true variation sources in the low-dimensional representation learned by the model, whereas our alternating method produces solutions where the patterns are better separated in the low-dimensional space.

Fault Tree Analysis.

The purpose of this study was to use fault tree analysis to evaluate the adequacy of quality reporting programs in identifying root causes of postoperative bloodstream infection (BSI). A systematic review of the literature was used to construct a fault tree to evaluate 3 postoperative BSI reporting programs: National Surgical Quality Improvement Program (NSQIP), Centers for Medicare and Medicaid Services (CMS), and The Joint Commission (JC). The literature review revealed 699 eligible publications, 90 of which were used to create the fault tree containing 105 faults. A total of 14 identified faults are currently mandated for reporting to NSQIP, 5 to CMS, and 3 to JC; 2 or more programs require 4 identified faults. The fault tree identifies numerous contributing faults to postoperative BSI and reveals substantial variation in the requirements and ability of national quality data reporting programs to capture these potential faults. Efforts to prevent postoperative BSI require more comprehensive data collection to identify the root causes and develop high-reliability improvement strategies.

A design of experiments approach to validation sampling for logistic regression modeling with error-prone medical records.

BACKGROUND AND OBJECTIVE: Electronic medical record (EMR) databases offer significant potential for developing clinical hypotheses and identifying disease risk associations by fitting statistical models that capture the relationship between a binary response variable and a set of predictor variables that represent clinical, phenotypical, and demographic data for the patient. However, EMR response data may be error prone for a variety of reasons. Performing a manual chart review to validate data accuracy is time consuming, which limits the number of chart reviews in a large database. The authors' objective is to develop a new design-of-experiments-based systematic chart validation and review (DSCVR) approach that is more powerful than the random validation sampling used in existing approaches. METHODS: The DSCVR approach judiciously and efficiently selects the cases to validate (i.e., validate whether the response values are correct for those cases) for maximum information content, based only on their predictor variable values. The final predictive model will be fit using only the validation sample, ignoring the remainder of the unvalidated and unreliable error-prone data. A Fisher information based D-optimality criterion is used, and an algorithm for optimizing it is developed. RESULTS: The authors' method is tested in a simulation comparison that is based on a sudden cardiac arrest case study with 23 041 patients' records. This DSCVR approach, using the Fisher information based D-optimality criterion, results in a fitted model with much better predictive performance, as measured by the receiver operating characteristic curve and the accuracy in predicting whether a patient will experience the event, than a model fitted using a random validation sample. CONCLUSIONS: The simulation comparisons demonstrate that this DSCVR approach can produce predictive models that are significantly better than those produced from random validation sampling, especially when the event rate is low.

Tangent hyperplane kernel principal component analysis for denoising.

Kernel principal component analysis (KPCA) is a method widely used for denoising multivariate data. Using geometric arguments, we investigate why a projection operation inherent to all existing KPCA denoising algorithms can sometimes cause very poor denoising. Based on this, we propose a modification to the projection operation that remedies this problem and can be incorporated into any of the existing KPCA algorithms. Using toy examples and real datasets, we show that the proposed algorithm can substantially improve denoising performance and is more robust to misspecification of an important tuning parameter.