Benjamin Kompa
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View article: Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy
Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy Open
Neuropathic corneal pain (NCP) is an underdiagnosed ocular disorder caused by aberrant nociception and hypersensitivity of corneal nerves, often resulting in chronic pain and discomfort even in the absence of noxious stimuli. Recently, mic…
View article: The diagnostic and triage accuracy of the GPT-3 artificial intelligence model: an observational study
The diagnostic and triage accuracy of the GPT-3 artificial intelligence model: an observational study Open
The National Heart, Lung, and Blood Institute.
View article: Correction to: Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review
Correction to: Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review Open
View article: The Diagnostic and Triage Accuracy of the GPT-3 Artificial Intelligence Model
The Diagnostic and Triage Accuracy of the GPT-3 Artificial Intelligence Model Open
Importance Artificial intelligence (AI) applications in health care have been effective in many areas of medicine, but they are often trained for a single task using labeled data, making deployment and generalizability challenging. Whether…
View article: Deep Learning Methods for Proximal Inference via Maximum Moment Restriction
Deep Learning Methods for Proximal Inference via Maximum Moment Restriction Open
The No Unmeasured Confounding Assumption is widely used to identify causal effects in observational studies. Recent work on proximal inference has provided alternative identification results that succeed even in the presence of unobserved …
View article: Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review
Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review Open
View article: Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures Open
Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings. Currently, the quality of a model’s uncertainty is evaluated using point-predict…
View article: Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures Open
Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings. Currently, the quality of a model’s uncertainty is evaluated using point-p…
View article: Second opinion needed: communicating uncertainty in medical machine learning
Second opinion needed: communicating uncertainty in medical machine learning Open
View article: Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data Open
Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of embeddings for medical concepts learned using an extremely …
View article: Learning a Latent Space of Highly Multidimensional Cancer Data
Learning a Latent Space of Highly Multidimensional Cancer Data Open
We introduce a Unified Disentanglement Network (UFDN) trained on The Cancer Genome Atlas (TCGA), which we refer to as UFDN-TCGA. We demonstrate that UFDN-TCGA learns a biologically relevant, low-dimensional latent space of high-dimensional…
View article: Learning a Generative Model of Cancer Metastasis
Learning a Generative Model of Cancer Metastasis Open
We introduce a Unified Disentanglement Network (UFDN) trained on The Cancer Genome Atlas (TCGA). We demonstrate that the UFDN learns a biologically relevant latent space of gene expression data by applying our network to two classification…
View article: Clinical Concept Embeddings Learned from Massive Sources of Multimodal\n Medical Data
Clinical Concept Embeddings Learned from Massive Sources of Multimodal\n Medical Data Open
Word embeddings are a popular approach to unsupervised learning of word\nrelationships that are widely used in natural language processing. In this\narticle, we present a new set of embeddings for medical concepts learned using\nan extreme…
View article: Clinical Concept Embeddings Learned from Massive Sources of Medical Data.
Clinical Concept Embeddings Learned from Massive Sources of Medical Data. Open
Word embeddings have emerged as a popular approach to unsupervised learning of word relationships in machine learning and natural language processing. In this article, we benchmark two of the most popular algorithms, GloVe and word2vec, to…
View article: Intronic SNP in ESR1 encoding human estrogen receptor alpha is associated with brain ESR1 mRNA isoform expression and behavioral traits
Intronic SNP in ESR1 encoding human estrogen receptor alpha is associated with brain ESR1 mRNA isoform expression and behavioral traits Open
Genetic variants of ESR1 have been implicated in multiple diseases, including behavioral disorders, but causative variants remain uncertain. We have searched for regulatory variants affecting ESR1 expression in human brain, measuring allel…