Search Results - "Dhaubhadel, Sayera"

Model selection applied to 750 outpatient ICD-9 codes identifies hazards important for all-cause cancer mortality in 2 million veterans with 14 years of follow-up by McMahon, Benjamin, Dhaubhadel, Sayera, Hengartner, Nicolas, Danciu, Ioana, Janet, Tate, Justice, Amy

“…Abstract only 2053 Background: Cost-benefit analysis before undergoing cancer treatments can involve a broad array of factors, yet existing statistical…”
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F81. ELUCIDATING GENETIC AND ENVIRONMENTAL RISK FACTORS FOR ANTIPSYCHOTIC-INDUCED METABOLIC ADVERSE EFFECTS USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN THE MILLION VETERAN PROGRAM by Fanous, Ayman, Bacanu, Silviu-Alin, Mcmahon, Benjamin, Harvey, Philip, Kreyenbuhl, Julie, Marder, Stephen, Agarwal, Khushbu, Abbaszadegan, Hamed, Choudhury, Sutanay, Colby, Ham, Dhaubhadel, Sayera, Kolade, Beauty

“…Antipsychotic drugs are widely used to treat psychiatric disorders such as schizophrenia and bipolar disorder. However, they are known to have important…”
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54. PREDICTION OF ANTIPSYCHOTIC-INDUCED METABOLIC ADVERSE EFFECTS USING MULTIMODAL ARTIFICIAL INTELLIGENCE by Agarwal, Khushbu, Bacanu, Silviu-Alin, Choudhury, Sutanay, Madduri, Ravi, Ham, Colby, Zuniga, Alejandro, Dhaubhadel, Sayera, Harvey, Philip, Kreyenbuhl, Julie, Marder, Stephen, Abbaszadegan, Hamed, Kolade, Beauty, Mcmahon, Benjamin, Fanous, Ayman

“…Antipsychotic medications are a mainstay of pharmacotherapy of psychiatric illnesses but have been shown to cause considerable metabolic adverse effects such…”
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Deep sequential neural network models improve stratification of suicide attempt risk among US veterans by Martinez, Carianne, Levin, Drew, Jones, Jessica, Finley, Patrick D, McMahon, Benjamin, Dhaubhadel, Sayera, Cohn, Judith, Oslin, David W, Kimbrel, Nathan A, Beckham, Jean C

“…Abstract Objective To apply deep neural networks (DNNs) to longitudinal EHR data in order to predict suicide attempt risk among veterans. Local explainability…”
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High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning by Dhaubhadel, Sayera, Ganguly, Kumkum, Ribeiro, Ruy M., Cohn, Judith D., Hyman, James M., Hengartner, Nicolas W., Kolade, Beauty, Singley, Anna, Bhattacharya, Tanmoy, Finley, Patrick, Levin, Drew, Thelen, Haedi, Cho, Kelly, Costa, Lauren, Ho, Yuk-Lam, Justice, Amy C., Pestian, John, Santel, Daniel, Zamora-Resendiz, Rafael, Crivelli, Silvia, Tamang, Suzanne, Martins, Susana, Trafton, Jodie, Oslin, David W., Beckham, Jean C., Kimbrel, Nathan A., McMahon, Benjamin H.

“…We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models…”
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F81. ELUCIDATING GENETIC AND ENVIRONMENTAL RISK FACTORS FOR ANTIPSYCHOTIC-INDUCED METABOLIC ADVERSE EFFECTS USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN THE MILLION VETERAN PROGRAM by Fanous, Ayman, Bacanu, Silviu-Alin, Mcmahon, Benjamin, Harvey, Philip, Kreyenbuhl, Julie, Marder, Stephen, Agarwal, Khushbu, Abbaszadegan, Hamed, Choudhury, Sutanay, Colby, Ham, Dhaubhadel, Sayera, Kolade, Beauty

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An Effective Baseline for Robustness to Distributional Shift by Thulasidasan, Sunil, Thapa, Sushil, Dhaubhadel, Sayera, Chennupati, Gopinath, Bhattacharya, Tanmoy, Bilmes, Jeff

“…Refraining from confidently predicting when faced with categories of inputs different from those seen during training is an important requirement for the safe…”
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An Effective Baseline for Robustness to Distributional Shift by Thulasidasan, Sunil, Thapa, Sushil, Dhaubhadel, Sayera, Chennupati, Gopinath, Bhattacharya, Tanmoy, Bilmes, Jeff

“…Refraining from confidently predicting when faced with categories of inputs different from those seen during training is an important requirement for the safe…”
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Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population by Chandrashekar, Mayanka, Goethert, Ian, Haque, Md Inzamam Ul, McMahon, Benjamin, Dhaubhadel, Sayera, Knight, Kathryn, Erdos, Joseph, Reagan, Donna, Taylor, Caroline, Kuzmak, Peter, Gaziano, John Michael, McAllister, Eileen, Costa, Lauren, Ho, Yuk-Lam, Cho, Kelly, Tamang, Suzanne, Fodeh-Jarad, Samah, Ovchinnikova, Olga S, Justice, Amy C, Hinkle, Jacob, Danciu, Ioana

“…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…”
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Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports by Dhaubhadel, Sayera, Mohd-Yusof, Jamaludin, Ganguly, Kumkum, Chennupati, Gopinath, Thulasidasan, Sunil, Hengartner, Nicolas W, Mumphrey, Brent J, Durbin, Eric B, Doherty, Jennifer A, Lemieux, Mireille, Schaefferkoetter, Noah, Tourassi, Georgia, Coyle, Linda, Penberthy, Lynne, McMahon, Benjamin H, Bhattacharya, Tanmoy

“…Safe deployment of deep learning systems in critical real world applications requires models to make very few mistakes, and only under predictable…”
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