Adler J. Perotte - researchr alias

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Viewing Publication 1 - 44 from 44

2023

2021

A conceptual framework for external validityAmelia J. Averitt, Patrick B. Ryan, Chunhua Weng, Adler J. Perotte. jbi, 121:103870, 2021. [doi]

Multi-Channel LSTM for Modeling Irregularly Sampled Time SeriesShreyas Bhave, Adler J. Perotte. amia 2021: [doi]

Point Processes for Competing Observations with Recurrent Networks (POPCORN): A Generative Model of EHR DataShreyas Bhave, Adler J. Perotte. mlhc 2021: 770-789 [doi]

LVHNet: Detecting Cardiac Structural Abnormalities with Chest X-RaysShreyas Bhave, Pierre Elias, Victor Alfonso Rodriguez, Timothy Poterucha, Simi Mutasa, Jay Leb, Nir Uriel, Adler J. Perotte. amia 2021: [doi]

Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration eventsLi-heng Fu, Chris Knaplund, Kenrick Cato, Adler J. Perotte, Min-Jeoung Kang, Patricia C. Dykes, David J. Albers, Sarah Collins Rossetti. jamia, 28(9):1955-1963, 2021. [doi]

Inverse-Weighted Survival GamesXintian Han, Mark Goldstein, Aahlad Manas Puli, Thomas Wies, Adler J. Perotte, Rajesh Ranganath. nips 2021: 2160-2172 [doi]

Clinically relevant pretraining is all you needOliver J. Bear Don't Walk IV, Tony Y. Sun, Adler J. Perotte, Noémie Elhadad. jamia, 28(9):1970-1976, 2021. [doi]

UMLS-based data augmentation for natural language processing of clinical research literatureTian Kang, Adler J. Perotte, Youlan Tang, Casey N. Ta, Chunhua Weng. jamia, 28(4):812-823, 2021. [doi]

A neuro-symbolic method for understanding free-text medical evidenceTian Kang, Ali Turfah, Jaehyun Kim, Adler J. Perotte, Chunhua Weng. jamia, 28(8):1703-1711, 2021. [doi]

CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasksChao Pang, Xinzhuo Jiang, Krishna S. Kalluri, Matthew E. Spotnitz, RuiJun Chen, Adler J. Perotte, Karthik Natarajan. ml4h 2021: 239-260 [doi]

Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patientsVictor Alfonso Rodriguez, Shreyas Bhave, RuiJun Chen, Chao Pang, George Hripcsak, Soumitra Sengupta, Noemie Elhadad, Robert A. Green, Jason S. Adelman, Katherine Schlosser Metitiri, Pierre Elias, Holden Groves, Sumit Mohan, Karthik Natarajan, Adler J. Perotte. jamia, 28(7):1480-1488, 2021. [doi]

2020

Zero-Shot Clinical Acronym Expansion via Latent Meaning CellsGriffin Adams, Mert Ketenci, Shreyas Bhave, Adler J. Perotte, Noémie Elhadad. nips 2020: 12-40 [doi]

Adversarially-Learned Balancing Weights for Causal InferenceAmelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte. amia 2020: [doi]

The Counterfactual χ-GAN: Finding comparable cohorts in observational health dataAmelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte. jbi, 109:103515, 2020. [doi]

Translating evidence into practice: eligibility criteria fail to eliminate clinically significant differences between real-world and study populationsAmelia J. Averitt, Chunhua Weng, Patrick B. Ryan, Adler J. Perotte. npjdm, 3, 2020. [doi]

Deep Survival Analysis: The Impact of Feature MissingnessShreyas Bhave, Xintian Han, Rajesh Ranganath, Adler J. Perotte. amia 2020: [doi]

X-CAL: Explicit Calibration for Survival AnalysisMark Goldstein, Xintian Han, Aahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath. nips 2020: [doi]

Causal Estimation with Functional ConfoundersAahlad Manas Puli, Adler J. Perotte, Rajesh Ranganath. nips 2020: [doi]

2019

2018

Noisy-Or Risk Allocation Model for Causal InferenceAmelia J. Averitt, Adler J. Perotte. amia 2018: [doi]

Deep Survival Analysis: Nonparametrics and MissingnessXenia Miscouridou, Adler J. Perotte, Noemie Elhadad, Rajesh Ranganath. mlhc 2018: 244-256 [doi]

Development of a Machine Learning Model for Prediction of Successful Extubation and User-Friendly Implementation for Real-World UseJoongheum Park, Hao Shi, Adler J. Perotte. amia 2018: [doi]

2017

Clinical Trial Eligibility Criteria Fail to Meet Burden of GeneralizabilityAmelia J. Averitt, Chunhua Weng, Adler J. Perotte. amia 2017: [doi]

Lessons Learned from the Conversion of MIMIC3 to the OHDSI Common Data ModelJoongheum Park, Liana Tascau, Adler J. Perotte. amia 2017: [doi]

2016

Approaches for using temporal and other filters for next generation phenotype discoveryDavid J. Albers, Adler J. Perotte, George Hripcsak. amia 2016: [doi]

Standardization of FDA Adverse Event Reporting System to the OHDSI Common Data ModelAmelia J. Averitt, Adler J. Perotte. amia 2016: [doi]

A probabilistic model for learning relationships between diagnosis codes and clinical free textAdler J. Perotte, Noemie Elhadad. amia 2016: [doi]

Quality of Race and Ethnicity Data in Electronic Health RecordsFernanda Polubriaginof, Mary Regina Boland, Adler J. Perotte, David K. Vawdrey. cri 2016: [doi]

Patient-provided Data Improves Race and Ethnicity Data Quality in Electronic Health RecordsFernanda Polubriaginof, Hojjat Salmasian, Andrea W. Shapiro, Jennifer E. Prey, George Hripcsak, Adler J. Perotte, Nicholas P. Tatonetti, David K. Vawdrey. amia 2016: [doi]

Deep Survival AnalysisRajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei. mlhc 2016: 101-114 [doi]

2015

Feasibility of Converting the Medicare Synthetic Public Use Data Into a Standardized Data Model for Clinical Research InformaticsMark Danese, Erica A. Voss, Jennifer Duryea, Michelle Gleeson, Ryan Duryea, Amy Matcho, Donald O'Hara, William E. Stephens, Adler J. Perotte, Lee Evans, Christian G. Reich. amia 2015: [doi]

Parameterizing time in electronic health record studiesGeorge Hripcsak, David J. Albers, Adler J. Perotte. jamia, 22(4):794-804, 2015. [doi]

Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysisAdler J. Perotte, Rajesh Ranganath, Jamie S. Hirsch, David M. Blei, Noémie Elhadad. jamia, 22(4):872-880, 2015. [doi]

Learning probabilistic phenotypes from heterogeneous EHR dataRimma Pivovarov, Adler J. Perotte, Edouard Grave, John Angiolillo, Chris H. Wiggins, Noémie Elhadad. jbi, 58:156-165, 2015. [doi]

The Survival Filter: Joint Survival Analysis with a Latent Time SeriesRajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei. uai 2015: 742-751

2014

Diagnosis code assignment: models and evaluation metricsAdler J. Perotte, Rimma Pivovarov, Karthik Natarajan, Nicole Weiskopf, Frank Wood, Noemie Elhadad. jamia, 21(2):231-237, 2014. [doi]

2013

Temporal Properties of Diagnosis Code Time Series in AggregateAdler J. Perotte, George Hripcsak. titb, 17(2):477-483, 2013. [doi]

2011

Exploiting time in electronic health record correlationsGeorge Hripcsak, David J. Albers, Adler J. Perotte. jamia, 18(Supplement):109-115, 2011. [doi]

Hierarchically Supervised Latent Dirichlet AllocationAdler J. Perotte, Frank Wood, Noemie Elhadad, Nicholas Bartlett. nips 2011: 2609-2617 [doi]

2009

A Bayesian Analysis of Dynamics in Free RecallRichard Socher, Samuel Gershman, Adler J. Perotte, Per B. Sederberg, David M. Blei, Kenneth A. Norman. nips 2009: 1714-1722 [doi]

2005

Methods for reducing interference in the Complementary Learning Systems model: Oscillating inhibition and autonomous memory rehearsalKenneth A. Norman, Ehren L. Newman, Adler J. Perotte. NN, 18(9):1212-1228, 2005. [doi]

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