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5 results on '"Shen, Xinpeng"'

1. A novel method for Causal Structure Discovery from EHR data, a demonstration on type-2 diabetes mellitus

Author

Shen, Xinpeng, Ma, Sisi, Vemuri, Prashanthi, Castro, M. Regina, Caraballo, Pedro J., and Simon, Gyorgy J.

Subjects
Computer Science - Machine Learning
Abstract

Introduction: The discovery of causal mechanisms underlying diseases enables better diagnosis, prognosis and treatment selection. Clinical trials have been the gold standard for determining causality, but they are resource intensive, sometimes infeasible or unethical. Electronic Health Records (EHR) contain a wealth of real-world data that holds promise for the discovery of disease mechanisms, yet the existing causal structure discovery (CSD) methods fall short on leveraging them due to the special characteristics of the EHR data. We propose a new data transformation method and a novel CSD algorithm to overcome the challenges posed by these characteristics. Materials and methods: We demonstrated the proposed methods on an application to type-2 diabetes mellitus. We used a large EHR data set from Mayo Clinic to internally evaluate the proposed transformation and CSD methods and used another large data set from an independent health system, Fairview Health Services, as external validation. We compared the performance of our proposed method to Fast Greedy Equivalence Search (FGES), a state-of-the-art CSD method in terms of correctness, stability and completeness. We tested the generalizability of the proposed algorithm through external validation. Results and conclusions: The proposed method improved over the existing methods by successfully incorporating study design considerations, was robust in face of unreliable EHR timestamps and inferred causal effect directions more correctly and reliably. The proposed data transformation successfully improved the clinical correctness of the discovered graph and the consistency of edge orientation across bootstrap samples. It resulted in superior accuracy, stability, and completeness., Comment: 20 pages, 2 figures

Published
2020

2. Challenges and Opportunities with Causal Discovery Algorithms: Application to Alzheimer's Pathophysiology.

Author

Shen, Xinpeng, Ma, Sisi, Vemuri, Prashanthi, Simon, Gyorgy, and Alzheimer’s Disease Neuroimaging Initiative

Subjects
Alzheimer’s Disease Neuroimaging Initiative, Brain, Humans, Alzheimer Disease, Apolipoproteins E, tau Proteins, Positron-Emission Tomography, Magnetic Resonance Imaging, Data Interpretation, Statistical, Longitudinal Studies, Algorithms, Models, Neurological, Aged, Aged, 80 and over, Female, Male, Apolipoprotein E4, Amyloid beta-Peptides, Neuroimaging, Datasets as Topic, Biomarkers, Observational Studies as Topic, Latent Class Analysis, Data Interpretation, Statistical, Models, Neurological, and over
Abstract

Causal Structure Discovery (CSD) is the problem of identifying causal relationships from large quantities of data through computational methods. With the limited ability of traditional association-based computational methods to discover causal relationships, CSD methodologies are gaining popularity. The goal of the study was to systematically examine whether (i) CSD methods can discover the known causal relationships from observational clinical data and (ii) to offer guidance to accurately discover known causal relationships. We used Alzheimer's disease (AD), a complex progressive disease, as a model because the well-established evidence provides a "gold-standard" causal graph for evaluation. We evaluated two CSD methods, Fast Causal Inference (FCI) and Fast Greedy Equivalence Search (FGES) in their ability to discover this structure from data collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used structural equation models (which is not designed for CSD) as control. We applied these methods under three scenarios defined by increasing amounts of background knowledge provided to the methods. The methods were evaluated by comparing the resulting causal relationships with the "gold standard" graph that was constructed from literature. Dedicated CSD methods managed to discover graphs that nearly coincided with the gold standard. For best results, CSD algorithms should be used with longitudinal data providing as much prior knowledge as possible.

Published
2020

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