Yajun Mei
Yajun Mei
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Professor of Biostatistics
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Professional overview
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Yajun Mei is a Professor of Biostatistics at NYU/GPH, starting from July 1, 2024. He received the B.S. degree in Mathematics from Peking University, Beijing, China, in 1996, and the Ph.D. degree in Mathematics with a minor in Electrical Engineering from the California Institute of Technology, Pasadena, CA, USA, in 2003. He was a Postdoc in Biostatistics in the renowned Fred Hutch Cancer Center in Seattle, WA during 2003 and 2005. Prior to joining NYU, Dr. Mei was an Assistant/Associate/Full Professor in H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology, Atlanta, GA for 18 years from 2006 to 2024, and had been a co-director of Biostatistics, Epidemiology, and Study Design (BERD) of Georgia CTSA since 2018.
Dr. Mei’s research interests are statistics, machine learning, and data science, and their applications in biomedical science and public health, particularly, streaming data analysis, sequential decision/design, change-point problems, precision/personalized medicine, hot-spots detection for infectious diseases, longitudinal data analysis, bioinformatics, and clinical trials. His work has received several recognitions including Abraham Wald Prizes in Sequential Analysis in both 2009 and 2024, NSF CAREER Award in 2010, an elected Fellow of American Statistical Association (ASA) in 2023, and multiple best paper awards.
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Education
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BS, Mathematics, Peking UniversityPhD, Mathematics, California Institute of Technology
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Honors and awards
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Fellow of American Statistical Association (2023)Star Research Achievement Award, 2021 Virtual Critical Care Congress (2021)Best Paper Competition Award, Quality, Statistics & Reliability of INFORMS (2020)Bronze Snapshot Award, Society of Critical Care Medicine (2019)NSF Career AwardThank a Teacher Certificate, Center for Teaching and Learning (2011201220162020202120222023)Abraham Wald Prize (2009)Best Paper Award, 11th International Conference on Information Fusion (2008)New Researcher Fellow, Statistical and Applied Mathematical Sciences Institute (2005)Fred Hutchinson SPAC Travel Award to attend 2005 Joint Statistical Meetings, Minneapolis, MN (2005)Travel Award to 8th New Researchers Conference, Minneapolis, MN (2005)Travel Award to IEEE International Symposium on Information Theory, Chicago, IL (2004)Travel Award to IPAM workshop on inverse problem, UCLA, Los Angeles, CA (2003)Fred Hutchinson SPAC Course Scholarship (2003)Travel Award to the SAMSI workshop on inverse problem, Research Triangular Park, NC (2002)
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Publications
Publications
A boosting inspired personalized threshold method for sepsis screening
AbstractFeng, C., Feng, C., Griffin, P., Kethireddy, S., & Mei, Y. (n.d.).Publication year
2021Journal title
Journal of Applied StatisticsVolume
48Issue
1Page(s)
154-175AbstractSepsis is one of the biggest risks to patient safety, with a natural mortality rate between 25% and 50%. It is difficult to diagnose, and no validated standard for diagnosis currently exists. A commonly used scoring criteria is the quick sequential organ failure assessment (qSOFA). It demonstrates very low specificity in ICU populations, however. We develop a method to personalize thresholds in qSOFA that incorporates easily to measure patient baseline characteristics. We compare the personalized threshold method to qSOFA, five previously published methods that obtain an optimal constant threshold for a single biomarker, and to the machine learning algorithms based on logistic regression and AdaBoosting using patient data in the MIMIC-III database. The personalized threshold method achieves higher accuracy than qSOFA and the five published methods and has comparable performance to machine learning methods. Personalized thresholds, however, are much easier to adopt in real-life monitoring than machine learning methods as they are computed once for a patient and used in the same way as qSOFA, whereas the machine learning methods are hard to implement and interpret.A comparison of methods for determining HIV viral set point
AbstractMei, Y., Mei, Y., Wang, L., & Holte, S. E. (n.d.).Publication year
2008Journal title
Statistics in MedicineVolume
27Issue
1Page(s)
121-139AbstractDuring a course of human immunodeficiency virus (HIV-1) infection, the viral load usually increases sharply to a peak following infection and then drops rapidly to a steady state, where it remains until progression to AIDS. This steady state is often referred to as the viral set point. It is believed that the HIV viral set point results from an equilibrium between the HIV virus and immune response and is an important indicator of AIDS disease progression. In this paper, we analyze a real data set of viral loads measured before antiretroviral therapy is initiated, and propose two-phase regression models to utilize all available data to estimate the viral set point. The advantages of the proposed methods are illustrated by comparing them with two empirical methods, and the reason behind the improvement is also studied. Our results illustrate that for our data set, the viral load data are highly correlated and it is cost effective to estimate the viral set point based on one or two measurements obtained between 5 and 12 months after HIV infection. The utility and limitations of this recommendation will be discussed.A discussion on "Detection of intrusions in information systems by sequential change-point methods" by Tartakovsky, Rozovskii, Blažek, and Kim
AbstractMei, Y. (n.d.).Publication year
2006Journal title
Statistical MethodologyVolume
3Issue
3Page(s)
304-306Abstract~A multistage procedure for decentralized sequential multi-hypothesis testing problems
AbstractWang, Y., & Mei, Y. (n.d.).Publication year
2012Journal title
Sequential AnalysisVolume
31Issue
4Page(s)
505-527AbstractWe studied the problem of sequentially testing M ≥ 2 hypotheses with a decentralized sensor network system. In such a system, the local sensors observe raw data and then send quantized observations to a fusion center, which makes a final decision regarding hypothesis is true. Motivated by the two-stage tests in Wang and Mei (2011), we propose a multistage decentralized sequential test that provides multiple opportunities for the local sensors to adjust to the optimal local quantizers. It is demonstrated that when the hypothesis testing problem is asymmetric, the multistage test is second-order asymptotically optimal. Even though this result constitutes an interesting theoretical improvement over twostage tests that can enjoy only first-order asymptotic optimality, the corresponding practical merits seem to be only marginal. Indeed, performance gains over two-stage procedures with carefully selected thresholds are small.Active learning-based multistage sequential decision-making model with application on common bile duct stone evaluation
AbstractTian, H., Cohen, R. Z., Zhang, C., & Mei, Y. (n.d.).Publication year
2023Journal title
Journal of Applied StatisticsVolume
50Issue
14Page(s)
2951-2969AbstractMultistage sequential decision-making occurs in many real-world applications such as healthcare diagnosis and treatment. One concrete example is when the doctors need to decide to collect which kind of information from subjects so as to make the good medical decision cost-effectively. In this paper, an active learning-based method is developed to model the doctors' decision-making process that actively collects necessary information from each subject in a sequential manner. The effectiveness of the proposed model, especially its two-stage version, is validated on both simulation studies and a case study of common bile duct stone evaluation for pediatric patients.Active Quickest Detection When Monitoring Multi-streams with Two Affected Streams
AbstractAbstractWe study the multi-stream quickest detection problem under the active learning setup, It is assumed that there are p local streams in a system and s ≤ p unknown local streams are affected by an undesired event at some unknown time, but one is only able to take observations from r of these p local streams at each time instant. The objective is how to adaptively sample from these p local streams and how to use the observed data to raise a correct global alarm as quickly as possible. In this paper, we develop the first asymptotic optimality theory in the active quickest detection literature for the case when s = r = 2. To be more concrete, we propose to combine three ideas to develop efficient active quickest detection algorithms: (1) win-stay, lose-switch sampling strategy; (2) local CUSUM statistics for local monitoring; and (3) the SUM-Shrinkage technique to fuse local statistics into a global decision. We show that our proposed algorithms are asymptotically optimal in the sense of minimizing detection delay up to the second order subject to the false alarm constraint. Numerical studies are conducted to validate our theoretical results.Adaptive Online Monitoring of the Ising model
AbstractSuh, N., Zhang, R., & Mei, Y. (n.d.).Publication year
2019Page(s)
426-431AbstractIsing model is a general framework for capturing the dependency structure among random variables. It has many interesting real-world applications in the fields of medical imaging, genetics, disease surveillance, etc. Nonetheless, literature on the online change-point detection of the interaction parameter in the model is rather limited. This might be attributed to following two challenges: 1) the exact evaluation of the likelihood function with the given data is computationally infeasible due to the presence of partition function and 2) the post-change parameter usually is unknown. In this paper, we overcome these two challenges via our proposed adaptive pseudo-CUSUM procedure, which incorporates the notion of pseudo-likelihood function under the CUSUM framework. Asymptotic analysis, numerical simulation, and case study corroborate the statistical efficiency and the practicality of our proposed scheme.Adaptive Partially Observed Sequential Change Detection and Isolation
AbstractZhao, X., Hu, J., Mei, Y., & Yan1, H. (n.d.).Publication year
2022Journal title
TechnometricsVolume
64Issue
4Page(s)
502-512AbstractHigh-dimensional data has become popular due to the easy accessibility of sensors in modern industrial applications. However, one specific challenge is that it is often not easy to obtain complete measurements due to limited sensing powers and resource constraints. Furthermore, distinct failure patterns may exist in the systems, and it is necessary to identify the true failure pattern. This work focuses on the online adaptive monitoring of high-dimensional data in resource-constrained environments with multiple potential failure modes. To achieve this, we propose to apply the Shiryaev–Roberts procedure on the failure mode level and use the multi-arm bandit to balance the exploration and exploitation. We further discuss the theoretical property of the proposed algorithm to show that the proposed method can correctly isolate the failure mode. Finally, extensive simulations and two case studies demonstrate that the change point detection performance and the failure mode isolation accuracy can be greatly improved.Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection
AbstractHu, J., Mei, Y., Holte, S., & Yan, H. (n.d.).Publication year
2023Journal title
Journal of Applied StatisticsVolume
50Issue
14Page(s)
2889-2913AbstractIn this paper, we present an efficient statistical method (denoted as ‘Adaptive Resources Allocation CUSUM’) to robustly and efficiently detect the hotspot with limited sampling resources. Our main idea is to combine the multi-arm bandit (MAB) and change-point detection methods to balance the exploration and exploitation of resource allocation for hotspot detection. Further, a Bayesian weighted update is used to update the posterior distribution of the infection rate. Then, the upper confidence bound (UCB) is used for resource allocation and planning. Finally, CUSUM monitoring statistics to detect the change point as well as the change location. For performance evaluation, we compare the performance of the proposed method with several benchmark methods in the literature and showed the proposed algorithm is able to achieve a lower detection delay and higher detection precision. Finally, this method is applied to hotspot detection in a real case study of county-level daily positive COVID-19 cases in Washington State WA) and demonstrates the effectiveness with very limited distributed samples.An Adaptive Sampling Strategy for Online High-Dimensional Process Monitoring
AbstractLiu, K., Mei, Y., & Shi, J. (n.d.).Publication year
2015Journal title
TechnometricsVolume
57Issue
3Page(s)
305-319AbstractTemporally and spatially dense data-rich environments provide unprecedented opportunities and challenges for effective process control. In this article, we propose a systematic and scalable adaptive sampling strategy for online high-dimensional process monitoring in the context of limited resources with only partial information available at each acquisition time. The proposed adaptive sampling strategy includes a broad range of applications: (1) when only a limited number of sensors is available; (2) when only a limited number of sensors can be in "ON" state in a fully deployed sensor network; and (3) when only partial data streams can be analyzed at the fusion center due to limited transmission and processing capabilities even though the full data streams have been acquired remotely. A monitoring scheme of using the sum of top-r local CUSUM statistics is developed and named as "TRAS" (top-r based adaptive sampling), which is scalable and robust in detecting a wide range of possible mean shifts in all directions, when each data stream follows a univariate normal distribution. Two properties of this proposed method are also investigated. Case studies are performed on a hot-forming process and a real solar flare process to illustrate and evaluate the performance of the proposed method.Aneurysmal Subarachnoid Hemorrhage : Trends, Outcomes, and Predictions from a 15-Year Perspective of a Single Neurocritical Care Unit
AbstractSamuels, O. B., Sadan, O., Feng, C., Feng, C., Martin, K., Medani, K., Mei, Y., & Barrow, D. L. (n.d.).Publication year
2021Journal title
NeurosurgeryVolume
88Issue
3Page(s)
574-583AbstractBACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) is associated with disproportionally high mortality and long-term neurological sequelae. Management of patients with aSAH has changed markedly over the years, leading to improvements in outcome. OBJECTIVE: To describe trends in aSAH care and outcome in a high-volume single center 15-yr cohort. METHODS: All new admissions diagnosed with subarachnoid hemorrhage (SAH) to our tertiary neuro-intensive care unit between 2002 and 2016 were reviewed. Trend analysis was performed to assess temporal changes and a step-wise regression analysis was done to identify factors associated with outcomes. RESULTS: Out of 3970 admissions of patients with SAH, 2475 patients proved to have a ruptured intracranial aneurysm. Over the years of the study, patient acuity increased by Hunt & Hess (H&H) grade and related complications. Endovascular therapies became more prevalent over the years, and were correlated with better outcome. Functional outcome overall improved, yet the main effect was noted in the low- and intermediate-grade patients. Several parameters were associated with poor functional outcome, including long-term mechanical ventilation (odds ratio 11.99, CI 95% [7.15-20.63]), acute kidney injury (3.55 [1.64-8.24]), pneumonia (2.89 [1.89-4.42]), hydrocephalus (1.80 [1.24-2.63]) diabetes mellitus (1.71 [1.04-2.84]), seizures (1.69 [1.07-2.70], H&H (1.67 [1.45-1.94]), and age (1.06 [1.05-1.07]), while endovascular approach to treat the aneurysm, compared with clip-ligation, had a positive effect (0.35 [0.25-0.48]). CONCLUSION: This large, single referral center, retrospective analysis reveals important trends in the treatment of aSAH. It also demonstrates that despite improvement in functional outcome over the years, systemic complications remain a significant risk factor for poor prognosis. The historic H&H determination of outcome is less valid with today's improved care.Asymptotic optimality theory for active quickest detection with unknown postchange parameters
AbstractXu, Q., & Mei, Y. (n.d.).Publication year
2023Journal title
Sequential AnalysisVolume
42Issue
2Page(s)
150-181AbstractThe active quickest detection problem with unknown postchange parameters is studied under the sampling control constraint, where there are p local streams in a system but one is only able to take observations from one and only one of these p local streams at each time instant. The objective is to raise a correct alarm as quickly as possible once the change occurs subject to both false alarm and sampling control constraints. Here we assume that exactly one of the p local streams is affected, and the postchange distribution involves unknown parameters. In this context, we propose an efficient greedy cyclic sampling–based quickest detection algorithm and show that our proposed algorithm is asymptotically optimal in the sense of minimizing the detection delay under both false alarm and sampling control constraints. Numerical studies are conducted to show the effectiveness and applicability of the proposed algorithm.Asymptotic optimality theory for decentralized sequential hypothesis testing in sensor networks
AbstractMei, Y. (n.d.).Publication year
2008Journal title
IEEE Transactions on Information TheoryVolume
54Issue
5Page(s)
2072-2089AbstractThe decentralized sequential hypothesis testing problem is studied in sensor networks, where a set of sensors receive independent observations and send summary messages to the fusion center, which makes a final decision. In the scenario where the sensors have full access to their past observations, the first asymptotically Bayes sequential test is developed having the same asymptotic performance as the optimal centralized test that has access to all sensor observations. Next, in the scenario where the sensors do not have full access to their past observations, a simple but asymptotically Bayes sequential tests is developed, in which sensor message functions are what we call tandem quantizer, where each sensor only uses two different sensor quantizers with at most one switch between these two possibilities. Moreover, a new minimax formulation of optimal stationary sensor quantizers is proposed and is studied in detail in the case of additive Gaussian sensor noise. Finally, our results show that in the simplest models, feedback from the fusion center does not improve asymptotic performance in the scenario with full local memory, however, even a one-shot, one-bit feedback can significantly improve performance in the case of limited local memory.Asymptotic optimality theory for decentralized sequential multihypothesis testing problems
AbstractWang, Y., & Mei, Y. (n.d.).Publication year
2011Journal title
IEEE Transactions on Information TheoryVolume
57Issue
10Page(s)
7068-7083AbstractThe Bayesian formulation of sequentially testing M ≥ 3 hypotheses is studied in the context of a decentralized sensor network system. In such a system, local sensors observe raw observations and send quantized sensor messages to a fusion center which makes a final decision when stopping taking observations. Asymptotically optimal decentralized sequential tests are developed from a class of "two-stage" tests that allows the sensor network system to make a preliminary decision in the first stage and then optimize each local sensor quantizer accordingly in the second stage. It is shown that the optimal local quantizer at each local sensor in the second stage can be defined as a maximin quantizer which turns out to be a randomization of at most M-1 unambiguous likelihood quantizers (ULQ). We first present in detail our results for the system with a single sensor and binary sensor messages, and then extend to more general cases involving any finite alphabet sensor messages, multiple sensors, or composite hypotheses.Asymptotic statistical properties of communication-efficient quickest detection schemes in sensor networks
AbstractZhang, R., & Mei, Y. (n.d.).Publication year
2018Journal title
Sequential AnalysisVolume
37Issue
3Page(s)
375-396AbstractThe quickest change detection problem is studied in a general context of monitoring a large number K of data streams in sensor networks when the “trigger event” may affect different sensors differently. In particular, the occurring event might affect some unknown, but not necessarily all, sensors and also could have an immediate or delayed impact on those affected sensors. Motivated by censoring sensor networks, we develop scalable communication-efficient schemes based on the sum of those local cumulative sum (CUSUM) statistics that are “large” under either hard, soft, or order thresholding rules. Moreover, we provide the detection delay analysis of these communication-efficient schemes in the context of monitoring K independent data streams and establish their asymptotic statistical properties under two regimes: one is the classical asymptotic regime when the dimension K is fixed, and the other is the modern asymptotic regime when the dimension K goes to ∞ Our theoretical results illustrate the deep connections between communication efficiency and statistical efficiency.Asymptotic Theory of `1-Regularized PDE Identification from a Single Noisy Trajectory
AbstractHe, Y., Suh, N., Huo, X., Kang, S. H., & Mei, Y. (n.d.).Publication year
2022Journal title
SIAM-ASA Journal on Uncertainty QuantificationVolume
10Issue
3Page(s)
1012-1036AbstractWe provide a formal theoretical analysis on the PDE identification via the `1-regularized pseudo least square method from the statistical point of view. In this article, we assume that the differential equation governing the dynamic system can be represented as a linear combination of various linear and nonlinear differential terms. Under noisy observations, we employ local-polynomial fitting for estimating state variables and apply the `1 penalty for model selection. Our theory proves that the classical mutual incoherence condition on the feature matrix F and the β∗min-condition for the ground-truth signal β∗ are sufficient for the signed-support recovery of the `1-PsLS method. We run numerical experiments on two popular PDE models, the viscous Burgers and the Korteweg-de Vries (KdV) equations, and the results from the experiments corroborate our theoretical predictions.Author's responses
AbstractMei, Y. (n.d.).Publication year
2008Journal title
Sequential AnalysisVolume
27Issue
4Page(s)
414-419AbstractIn this rejoinder I briefly summarize my thoughts on appropriate measures of performance for evaluating change-point detection schemes, particularly the false alarm criterion. Then I address some specific issues in the light of the discussion pieces from eight experts in this field.Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control
AbstractZhang, W., & Mei, Y. (n.d.).Publication year
2023Journal title
TechnometricsVolume
65Issue
1Page(s)
33-43AbstractIn many real-world problems of real-time monitoring high-dimensional streaming data, one wants to detect an undesired event or change quickly once it occurs, but under the sampling control constraint in the sense that one might be able to only observe or use selected components data for decision-making per time step in the resource-constrained environments. In this article, we propose to incorporate multi-armed bandit approaches into sequential change-point detection to develop an efficient bandit change-point detection algorithm based on the limiting Bayesian approach to incorporate a prior knowledge of potential changes. Our proposed algorithm, termed Thompson-Sampling-Shiryaev-Roberts-Pollak (TSSRP), consists of two policies per time step: the adaptive sampling policy applies the Thompson Sampling algorithm to balance between exploration for acquiring long-term knowledge and exploitation for immediate reward gain, and the statistical decision policy fuses the local Shiryaev–Roberts–Pollak statistics to determine whether to raise a global alarm by sum shrinkage techniques. Extensive numerical simulations and case studies demonstrate the statistical and computational efficiency of our proposed TSSRP algorithm.Beyond Point Prediction : Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process
AbstractLi, Z., Xu, Q., Xu, Z., Mei, Y., Zhao, T., & Zha, H. (n.d.).Publication year
2024Journal title
Proceedings of Machine Learning ResearchVolume
235Page(s)
29096-29111AbstractSpatio-temporal point processes (STPPs) are potent mathematical tools for modeling and predicting events with both temporal and spatial features. Despite their versatility, most existing methods for learning STPPs either assume a restricted form of the spatio-temporal distribution, or suffer from inaccurate approximations of the intractable integral in the likelihood training objective. These issues typically arise from the normalization term of the probability density function. Moreover, existing works only provide point prediction for events without quantifying their uncertainty, such as confidence intervals for the event's arrival time and confidence regions for the event's location, which is crucial given the considerable randomness of the data. To tackle these challenges, we introduce SMASH: a Score MAtching-based pSeudolikeliHood estimator for learning marked STPPs. Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of marked STPPs through score-matching and predicts confidence intervals/regions for event time and location by generating samples through a score-based sampling algorithm. The superior performance of our proposed framework is demonstrated through extensive experiments on both point and confidence interval/region prediction of events.Cannabis Use Is Not Associated with Aneurysmal Subarachnoid Hemorrhage Complications or Outcomes
AbstractAkbik, F., Konan, H. D., Williams, K. P., Ermias, L. M., Shi, Y., Takieddin, O., Grossberg, J. A., Howard, B. M., Tong, F., Cawley, C. M., Mei, Y., Samuels, O. B., & Sadan, O. (n.d.).Publication year
2022Journal title
StrokeVolume
53Issue
8Page(s)
E375-E376Abstract~Comment on "Quantifying long-term scientific impact"
AbstractWang, J., Mei, Y., & Hicks, D. (n.d.).Publication year
2014Journal title
ScienceVolume
345Issue
6193Page(s)
149bAbstractWang et al. (Reports, 4 October 2013, p. 127) claimed high prediction power for their model of citation dynamics. We replicate their analysis but find discouraging results: 14.75% papers are estimated with unreasonably large μ (>5) and λ (>10) and correspondingly enormous prediction errors. The prediction power is even worse than simply using short-term citations to approximate long-term citations.Comments on "a note on optimal detection of a change in distribution," by benjamin Yakir
AbstractMei, Y. (n.d.).Publication year
2006Journal title
Annals of StatisticsVolume
34Issue
3Page(s)
1570-1576AbstractThe purpose of this note is to show that in a widely cited paper by Yakir [Ann. Statist. 25 (1997) 2117-2126], the proof that the so-called modified Shiryayev-Roberts procedure is exactly optimal is incorrect. We also clarify the issues involved by both mathematical arguments and a simulation study. The correctness of the theorem remains in doubt.Correlation-based dynamic sampling for online high dimensional process monitoring
AbstractNabhan, M., Mei, Y., & Shi, J. (n.d.).Publication year
2021Journal title
Journal of Quality TechnologyVolume
53Issue
3Page(s)
289-308AbstractEffective process monitoring of high-dimensional data streams with embedded spatial structures has been an arising challenge for environments with limited resources. Utilizing the spatial structure is key to improve monitoring performance. This article proposes a correlation-based dynamic sampling technique for change detection. Our method borrows the idea of Upper Confidence Bound algorithm and uses the correlation structure not only to calculate a global statistic, but also to infer unobserved sensors from partial observations. Simulation studies and two case studies on solar flare detection and carbon nanotubes (CNTs) buckypaper process monitoring are used to validate the effectiveness of our method.Cost-efficient fixed-width confidence intervals for the difference of two Bernoulli proportions
AbstractErazo, I., Goldsman, D., & Mei, Y. (n.d.).Publication year
2024Journal title
Journal of SimulationVolume
18Issue
5Page(s)
726-744AbstractWe study the properties of confidence intervals (CIs) for the difference of two Bernoulli distributions’ success parameters, (Formula presented.), in the case where the goal is to obtain a CI of a given half-width while minimising sampling costs when the observation costs may be different between the two distributions. We propose three different methods for constructing fixed-width CIs: (i) a two-stage sampling procedure, (ii) a sequential method that carries out sampling in batches, and (iii) an (Formula presented.) -stage “look-ahead” procedure. Under diverse scenarios, our proposed algorithms obtain significant cost savings versus their baseline counterparts. Furthermore, for the scenarios under study, our sequential-batches and (Formula presented.) -stage “look-ahead” procedures approximately obtain the nominal coverage while meeting the desired width requirement. Our sequential-batching method is more efficient than the “look-ahead” method computationally, with average running times an order-of-magnitude faster over the scenarios tested. We illustrate our procedures on a case study comparing generic and brand-name drugs.Creation of a Pediatric Choledocholithiasis Prediction Model
AbstractCohen, R. Z., Tian, H., Sauer, C. G., Willingham, F. F., Santore, M. T., Mei, Y., & Freeman, A. J. (n.d.).Publication year
2021Journal title
Journal of Pediatric Gastroenterology and NutritionVolume
73Issue
5Page(s)
636-641AbstractBackground:Definitive non-invasive detection of pediatric choledocholithiasis could allow more efficient identification of those patients who are most likely to benefit from therapeutic endoscopic retrograde cholangiopancreatography (ERCP) for stone extraction.Objective:To craft a pediatric choledocholithiasis prediction model using a combination of commonly available serum laboratory values and ultrasound results.Methods:A retrospective review of laboratory and imaging results from 316 pediatric patients who underwent intraoperative cholangiogram or ERCP due to suspicion of choledocholithiasis were collected and compared to presence of common bile duct stones on cholangiography. Multivariate logistic regression with supervised machine learning was used to create a predictive scoring model. Monte-Carlo cross-validation was used to validate the scoring model and a score threshold that would provide at least 90% specificity for choledocholithiasis was determined in an effort to minimize non-therapeutic ERCP.Results:Alanine aminotransferase (ALT), total bilirubin, alkaline phosphatase, and common bile duct diameter via ultrasound were found to be the key clinical variables to determine the likelihood of choledocholithiasis. The dictated specificity threshold of 90.3% yielded a sensitivity of 40.8% and overall accuracy of 71.5% in detecting choledocholithiasis. Positive predictive value was 71.4% and negative predictive value was 72.1%.Conclusion:Our novel pediatric choledocholithiasis predictive model is a highly specific tool to suggest ERCP in the setting of likely choledocholithiasis.