Erich Kummerfeld
YOU?
Author Swipe
View article: Examining Causal Pathways to Suicidal Ideation and Nonsuicidal Self‐Injury in the Adolescent Brain Cognitive Development Study
Examining Causal Pathways to Suicidal Ideation and Nonsuicidal Self‐Injury in the Adolescent Brain Cognitive Development Study Open
Introduction Suicide is the second leading cause of death in adolescents in the United States. There is an urgent need to advance understanding of risk mechanisms in adolescents to guide early interventions. While prior research has implic…
View article: Ecological Momentary Assessment of Voice & Psychological Factors: Group & Individual Mechanisms
Ecological Momentary Assessment of Voice & Psychological Factors: Group & Individual Mechanisms Open
Objectives Cross‐sectional associations between voice and psychological factors are known, but changes over time offer opportunities to refine our understanding of their interactions and consider customized treatment options. Study objecti…
View article: Artifacts in spatial transcriptomics data: their detection, importance, prevalence, and prevention
Artifacts in spatial transcriptomics data: their detection, importance, prevalence, and prevention Open
Data artifacts may induce errors in findings from any spatial transcriptomics platform. To provide protection from these errors, we have developed Border, Location, and edge Artifact DEtection (BLADE). BLADE is a novel collection of automa…
View article: Correlational and causal modeling of alcohol‐related symptoms and internalizing disorder status: Further elucidation of a harm paradox
Correlational and causal modeling of alcohol‐related symptoms and internalizing disorder status: Further elucidation of a harm paradox Open
Background Individuals with internalizing (anxiety and depressive) disorder (INTD) suffer from an alcohol‐related “harm paradox”; that is, they experience more alcohol‐related symptoms in aggregate than do others who drink at the same leve…
View article: Delineating empirically plausible causal pathways to suicidality among people at clinical high risk for psychosis.
Delineating empirically plausible causal pathways to suicidality among people at clinical high risk for psychosis. Open
Suicidality is common among people at clinical high risk (CHR) for psychosis. Delineating causal pathways to suicidality and identifying its determinants would inform tailored intervention efforts for these individuals. To this end, we ana…
View article: Integrating causal discovery and clinically-relevant insights to explore directional relationships between autistic features, sex at birth, and cognitive abilities
Integrating causal discovery and clinically-relevant insights to explore directional relationships between autistic features, sex at birth, and cognitive abilities Open
Background Access to “big data” is a boon for researchers, fostering collaboration and resource-sharing to accelerate advancements across fields. Yet, disentangling complex datasets has been hindered by methodological limitations, calling …
View article: Causal discovery analysis: A promising tool in advancing precision medicine for eating disorders
Causal discovery analysis: A promising tool in advancing precision medicine for eating disorders Open
Objective Precision medicine (i.e., individually tailored treatments) represents an optimal goal for treating complex psychiatric disorders, including eating disorders. Within the eating disorders field, most treatment development efforts …
View article: One data set, many analysts: Implications for practicing scientists
One data set, many analysts: Implications for practicing scientists Open
Researchers routinely face choices throughout the data analysis process. It is often opaque to readers how these choices are made, how they affect the findings, and whether or not data analysis results are unduly influenced by subjective d…
View article: Quantifying heterogeneity in mood–alcohol relationships with idiographic causal models
Quantifying heterogeneity in mood–alcohol relationships with idiographic causal models Open
Background Ecological momentary assessment (EMA) studies have provided conflicting evidence for the mood regulation tenet that people drink in response to positive and negative moods. The current study examined mood‐to‐alcohol relationship…
View article: Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study
Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study Open
AI-based tools have not yet reached full diagnostic potential for COVID-19 and underperform compared with radiologist prediction.Keywords: Diagnosis, Classification, Application Domain, Infection, Lung Supplemental material is av…
View article: The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks
The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks Open
View article: A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals
A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals Open
Importance An artificial intelligence (AI)-based model to predict COVID-19 likelihood from chest x-ray (CXR) findings can serve as an important adjunct to accelerate immediate clinical decision making and improve clinical decision making. …
View article: A simple interpretation of undirected edges in essential graphs is wrong
A simple interpretation of undirected edges in essential graphs is wrong Open
Artificial intelligence for causal discovery frequently uses Markov equivalence classes of directed acyclic graphs, graphically represented as essential graphs , as a way of representing uncertainty in causal directionality. There has been…
View article: An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis
An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis Open
Alcohol use disorder (AUD) has high prevalence and adverse societal impacts, but our understanding of the factors driving AUD is hampered by a lack of studies that describe the complex neurobehavioral mechanisms driving AUD. We analyzed ca…
View article: Assessing the collective utility of multiple analyses on clinical alcohol use disorder data
Assessing the collective utility of multiple analyses on clinical alcohol use disorder data Open
Objective The objective of this study was to assess the potential of combining graph learning methods with latent variable estimation methods for mining clinically useful information from observational clinical data sets. Materials and Met…
View article: Causal Clustering for 1-Factor Measurement Models
Causal Clustering for 1-Factor Measurement Models Open
Many scientific research programs aim to learn the causal structure of real world phenomena. This learning problem is made more difficult when the target of study cannot be directly observed. One strategy commonly used by social scientists…