Sayera Dhaubhadel
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View article: Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population
Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population Open
This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study year. We used a DenseNet121 mo…
View article: Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population
Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population Open
Objectives: This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study year. Materials an…
View article: High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning
High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning Open
We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide…
View article: Deep sequential neural network models improve stratification of suicide attempt risk among US veterans
Deep sequential neural network models improve stratification of suicide attempt risk among US veterans Open
Objective To apply deep neural networks (DNNs) to longitudinal EHR data in order to predict suicide attempt risk among veterans. Local explainability techniques were used to provide explanations for each prediction with the goal of ultimat…
View article: High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning
High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning Open
We present an ensemble transfer learning model to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverseset of base models was trained to predict a binary outcome constructed from reported suicide, suicide a…
View article: Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans
Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans Open
Importance Suicide is a leading cause of death; however, the molecular genetic basis of suicidal thoughts and behaviors (SITB) remains unknown. Objective To identify novel, replicable genomic risk loci for SITB. Design, Setting, and Partic…
View article: MACE prediction using high-dimensional machine learning and mechanistic interpretation: A longitudinal cohort study in US veterans
MACE prediction using high-dimensional machine learning and mechanistic interpretation: A longitudinal cohort study in US veterans Open
High dimensional predictive models of Major Adverse Cardiac Events (MACE), which includes heart attack (AMI), stroke, and death caused by cardiovascular disease (CVD), were built using four longitudinal cohorts of Veterans Administration (…
View article: An Effective Baseline for Robustness to Distributional Shift
An Effective Baseline for Robustness to Distributional Shift Open
Refraining from confidently predicting when faced with categories of inputs different from those seen during training is an important requirement for the safe deployment of deep learning systems. While simple to state, this has been a part…
View article: Why I'm not Answering: Understanding Determinants of Classification of\n an Abstaining Classifier for Cancer Pathology Reports
Why I'm not Answering: Understanding Determinants of Classification of\n an Abstaining Classifier for Cancer Pathology Reports Open
Safe deployment of deep learning systems in critical real world applications\nrequires models to make very few mistakes, and only under predictable\ncircumstances. In this work, we address this problem using an abstaining\nclassifier that …
View article: Why I'm not Answering
Why I'm not Answering Open
Safe deployment of deep learning systems in critical real world applications requires models to make few mistakes, and only under predictable circumstances. Development of such a model is not yet possible, in general. In this work, we addr…
View article: Why I'm not Answering: An Abstention-Based Approach to Classify Cancer Pathology Reports
Why I'm not Answering: An Abstention-Based Approach to Classify Cancer Pathology Reports Open
Safe deployment of deep learning systems in critical real world applications
requires models to make few mistakes, and only under predictable circumstances.
Development of such a model is not yet possible, in general. In this work, we
addr…
View article: Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports
Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports Open
Safe deployment of deep learning systems in critical real world applications requires models to make very few mistakes, and only under predictable circumstances. In this work, we address this problem using an abstaining classifier that is …