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ORCIDNigam H. Shah
Nigam Shah – N. H. Shah 0001
Person information
- affiliation: Stanford University, CA, USA
- affiliation (former): Pennsylvania State University, University Park, PA, USA
Other persons with the same name
- N. H. Shah 0002 — Coventry University, UK
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2020 – today
- 2023
[j89]Karandeep Singh, Nigam H. Shah
, Andrew J. Vickers:
Assessing the net benefit of machine learning models in the presence of resource constraints. J. Am. Medical Informatics Assoc. 30(4): 668-673 (2023)- [j88]Diana Cagliero, Natalie Deuitch, Nigam Shah, Chris Feudtner, Danton Char:
A framework to identify ethical concerns with ML-guided care workflows: a case study of mortality prediction to guide advance care planning. J. Am. Medical Informatics Assoc. 30(5): 819-827 (2023) - [j87]Yizhe Xu, Agata Foryciarz, Ethan Steinberg, Nigam H. Shah:
Clinical utility gains from incorporating comorbidity and geographic location information into risk estimation equations for atherosclerotic cardiovascular disease. J. Am. Medical Informatics Assoc. 30(5): 878-887 (2023) - [j86]Michael Wornow, Elsie Gyang Ross, Alison Callahan, Nigam H. Shah:
APLUS: A Python library for usefulness simulations of machine learning models in healthcare. J. Biomed. Informatics 139: 104319 (2023) - [i27]Ethan Steinberg, Yizhe Xu, Jason A. Fries, Nigam Shah:
Self-Supervised Time-to-Event Modeling with Structured Medical Records. CoRR abs/2301.03150 (2023) - [i26]Conor K. Corbin, Rob Maclay, Aakash Acharya, Sreedevi Mony, Soumya Punnathanam, Rahul Thapa, Nikesh Kotecha, Nigam H. Shah, Jonathan H. Chen:
DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record. CoRR abs/2303.06269 (2023) - [i25]Michael Wornow, Yizhe Xu, Rahul Thapa, Birju S. Patel, Ethan Steinberg, Scott L. Fleming, Michael A. Pfeffer, Jason A. Fries, Nigam H. Shah:
The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs. CoRR abs/2303.12961 (2023) - [i24]Debadutta Dash, Rahul Thapa, Juan M. Banda, Akshay Swaminathan, Morgan Cheatham, Mehr Kashyap, Nikesh Kotecha, Jonathan H. Chen, Saurabh Gombar, Lance Downing, Rachel Pedreira, Ethan Goh, Angel Arnaout, Garret Kenn Morris, Honor Magon, Matthew P. Lungren, Eric Horvitz, Nigam H. Shah:
Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery. CoRR abs/2304.13714 (2023) - [i23]Alexey Youssef, Michael Pencina, Anshul Thakur, Tingting Zhu, David A. Clifton, Nigam H. Shah:
All models are local: time to replace external validation with recurrent local validation. CoRR abs/2305.03219 (2023) - 2022
- [j85]Jonathan Lu, Amelia Sattler, Samantha Wang, Ali Raza Khaki, Alison Callahan, Scott L. Fleming, Rebecca Fong, Benjamin Ehlert, Ron C. Li, Lisa Shieh, Kavitha Ramchandran, Michael Gensheimer, Sarah Chobot, Stephen Pfohl, Siyun Li, Kenny Shum, Nitin Parikh, Priya Desai, Briththa Seevaratnam, Melanie Hanson, Margaret Smith, Yizhe Xu, Arjun Gokhale, Steven Lin, Michael A. Pfeffer, Winifred Teuteberg, Nigam H. Shah:
Considerations in the reliability and fairness audits of predictive models for advance care planning. Frontiers Digit. Health 4 (2022) - [c72]Katelyn K. Bechler, Nigam Shah:
Predicting patients who are likely to develop Lupus Nephritis of those newly diagnosed with Systemic Lupus Erythematosus. AMIA 2022 - [c71]Stephen Pfohl, Yizhe Xu, Agata Foryciarz, Nikolaos Ignatiadis, Julian Genkins, Nigam Shah:
Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare. FAccT 2022: 1039-1052 - [c70]Daniel Lopez Martinez, Alex Yakubovich, Martin Seneviratne, Ádám D. Lelkes, Akshit Tyagi, Jonas Kemp, Ethan Steinberg, N. Lance Downing, Ron C. Li, Keith E. Morse, Nigam H. Shah, Ming-Jun Chen:
Instability in clinical risk stratification models using deep learning. ML4H@NeurIPS 2022: 552-565 - [i22]Stephen R. Pfohl, Yizhe Xu, Agata Foryciarz, Nikolaos Ignatiadis, Julian Genkins, Nigam H. Shah:
Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare. CoRR abs/2202.01906 (2022) - [i21]Daniel Lopez Martinez, Alex Yakubovich, Martin Seneviratne, Ádám D. Lelkes, Akshit Tyagi, Jonas Kemp, Ethan Steinberg, N. Lance Downing, Ron C. Li, Keith E. Morse, Nigam H. Shah, Ming-Jun Chen:
Instability in clinical risk stratification models using deep learning. CoRR abs/2211.10828 (2022) - 2021
- [j84]Lin Lawrence Guo, Stephen R. Pfohl, Jason Alan Fries, José D. Posada, Scott L. Fleming, Catherine Aftandilian, Nigam Shah, Lillian Sung:
Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine. Appl. Clin. Inform. 12(4): 808-815 (2021) - [j83]Michael Francis Gensheimer, Sonya Aggarwal, Kathryn R. K. Benson, Justin N. Carter, Solomon Henry, Douglas J. Wood, Scott G. Soltys, Steven Hancock, Erqi Pollom, Nigam H. Shah, Daniel T. Chang:
Automated model versus treating physician for predicting survival time of patients with metastatic cancer. J. Am. Medical Informatics Assoc. 28(6): 1108-1116 (2021) - [j82]Kenneth Jung, Sehj Kashyap, Anand Avati, Stephanie Harman, Heather Shaw, Ron C. Li, Margaret Smith, Kenny Shum, Jacob Javitz, Yohan Vetteth, Tina Seto, Steven C. Bagley, Nigam H. Shah:
A framework for making predictive models useful in practice. J. Am. Medical Informatics Assoc. 28(6): 1149-1158 (2021) - [j81]Tina Hernandez-Boussard, Matthew P. Lundgren, Nigam Shah:
Conflicting information from the Food and Drug Administration: Missed opportunity to lead standards for safe and effective medical artificial intelligence solutions. J. Am. Medical Informatics Assoc. 28(6): 1353-1355 (2021) - [j80]Alison Callahan, Vladimir Polony, José D. Posada, Juan M. Banda, Saurabh Gombar, Nigam H. Shah:
ACE: the Advanced Cohort Engine for searching longitudinal patient records. J. Am. Medical Informatics Assoc. 28(7): 1468-1479 (2021) - [j79]Tina Hernandez-Boussard, Matthew P. Lungren, Nigam Shah:
Corrigendum: Conflicting information from the Food and Drug Administration: Missed opportunity to lead standards for safe and effective medical artificial intelligence solutions. J. Am. Medical Informatics Assoc. 28(7): 1600 (2021) - [j78]Birju S. Patel, Ethan Steinberg, Stephen R. Pfohl, Nigam H. Shah:
Learning decision thresholds for risk stratification models from aggregate clinician behavior. J. Am. Medical Informatics Assoc. 28(10): 2258-2264 (2021) - [j77]Lawrence Bai, Madeleine K. D. Scott, Ethan Steinberg, Laurynas Kalesinskas, Aida Habtezion, Nigam H. Shah, Purvesh Khatri:
Computational drug repositioning of atorvastatin for ulcerative colitis. J. Am. Medical Informatics Assoc. 28(11): 2325-2335 (2021) - [j76]Sehj Kashyap, Keith E. Morse, Birju S. Patel, Nigam H. Shah:
A survey of extant organizational and computational setups for deploying predictive models in health systems. J. Am. Medical Informatics Assoc. 28(11): 2445-2450 (2021) - [j75]Stephen R. Pfohl, Agata Foryciarz, Nigam H. Shah:
An empirical characterization of fair machine learning for clinical risk prediction. J. Biomed. Informatics 113: 103621 (2021) - [j74]Ethan Steinberg, Kenneth Jung, Jason A. Fries, Conor K. Corbin, Stephen R. Pfohl, Nigam H. Shah:
Language models are an effective representation learning technique for electronic health record data. J. Biomed. Informatics 113: 103637 (2021) - [j73]Michael Ko, Emma Chen, Ashwin Agrawal, Pranav Rajpurkar, Anand Avati, Andrew Yan-Tak Ng, Sanjay Basu, Nigam H. Shah:
Improving hospital readmission prediction using individualized utility analysis. J. Biomed. Informatics 119: 103826 (2021) - [j72]Alvina G. Lai, Wai Hoong Chang, Constantinos A. Parisinos, Michail Katsoulis, Ruth M. Blackburn, Anoop D. Shah, Vincent Nguyen, Spiros C. Denaxas, George Davey Smith, Tom R. Gaunt, Krishnarajah Nirantharakumar, Murray P. Cox, Donall Forde, Folkert W. Asselbergs, Steve K. Harris, Sylvia Richardson, Reecha Sofat, Richard J. B. Dobson, Aroon D. Hingorani, Riyaz Patel, Jonathan Sterne, Amitava Banerjee, Alastair K. Denniston, Simon Ball, Neil J. Sebire, Nigam H. Shah, Graham R. Foster, Bryan Williams, Harry Hemingway:
An informatics consult approach for generating clinical evidence for treatment decisions. BMC Medical Informatics Decis. Mak. 21(1): 281 (2021) - [j71]Nigam Shah:
Summarizing Patients Like Mine via an On-demand Consultation Service. Proc. VLDB Endow. 14(13): 3417 (2021) - [c69]Yuan Luo, Fei Wang, Benjamin S. Glicksberg, Jessilyn Dunn, Nigam Shah:
Multi-Modal Data Science for Healthcare: State of the Art, Challenges, and Opportunities. AMIA 2021 - [i20]Stephen R. Pfohl, Haoran Zhang, Yizhe Xu, Agata Foryciarz, Marzyeh Ghassemi, Nigam H. Shah:
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations. CoRR abs/2108.12250 (2021) - [i19]Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries, Alaa Youssef, Timothy J. Amrhein, Marcello Chang, Imon Banerjee, Daniel L. Rubin, Lei Xing, Nigam Shah, Matthew P. Lungren:
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR. CoRR abs/2111.11665 (2021) - 2020
- [j70]Mehr Kashyap, Martin G. Seneviratne, Juan M. Banda, Thomas Falconer, Borim Ryu, Sooyoung Yoo, George Hripcsak, Nigam H. Shah:
Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network. J. Am. Medical Informatics Assoc. 27(6): 877-883 (2020) - [j69]Sehj Kashyap, Saurabh Gombar, Steve Yadlowsky, Alison Callahan, Jason A. Fries, Benjamin A. Pinsky, Nigam H. Shah:
Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening. J. Am. Medical Informatics Assoc. 27(7): 1026-1131 (2020) - [j68]Tina Hernandez-Boussard, Selen Bozkurt, John P. A. Ioannidis, Nigam H. Shah:
MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care. J. Am. Medical Informatics Assoc. 27(12): 2011-2015 (2020) - [j67]Chunhua Weng, Nigam H. Shah, George Hripcsak:
Deep phenotyping: Embracing complexity and temporality - Towards scalability, portability, and interoperability. J. Biomed. Informatics 105: 103433 (2020) - [j66]Alison Callahan, Ethan Steinberg, Jason A. Fries, Saurabh Gombar, Birju S. Patel, Conor K. Corbin, Nigam H. Shah:
Estimating the efficacy of symptom-based screening for COVID-19. npj Digit. Medicine 3 (2020) - [j65]Ron C. Li, Steven M. Asch, Nigam H. Shah:
Developing a delivery science for artificial intelligence in healthcare. npj Digit. Medicine 3 (2020) - [j64]Adam S. Miner, Albert Haque, Jason A. Fries, Scott L. Fleming, Denise E. Wilfley, G. Terence Wilson, Arnold Milstein, Dan Jurafsky, Bruce A. Arnow, W. Stewart Agras, Li Fei-Fei, Nigam H. Shah:
Assessing the accuracy of automatic speech recognition for psychotherapy. npj Digit. Medicine 3 (2020) - [c68]Vojtech Huser, Clair Blacketer, Karthik Natarajan, Robert T. Miller, Andrew Williams, Selva Muthu Kumaran Sathappan, José D. Posada, Nigam Shah:
Data Quality Assessment of Laboratory Data. AMIA 2020 - [c67]Xu Zuo, Jianfu Li, Bo Zhao, Yujia Zhou, Xiao Dong, Jon D. Duke, Karthik Natarajan, George Hripcsak, Nigam Shah, Juan M. Banda, Ruth M. Reeves, Hua Xu:
Normalizing Clinical Document Titles to LOINC Document Ontology: an Initial Study. AMIA 2020 - [i18]Ethan Steinberg, Kenneth Jung, Jason A. Fries, Conor K. Corbin, Stephen R. Pfohl, Nigam H. Shah:
Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data. CoRR abs/2001.05295 (2020) - [i17]Somalee Datta, José D. Posada, Garrick Olson, Wencheng Li, Ciaran O'Reilly, Deepa Balraj, Joseph Mesterhazy, Joseph Pallas, Priyamvada Desai, Nigam Shah:
A new paradigm for accelerating clinical data science at Stanford Medicine. CoRR abs/2003.10534 (2020) - [i16]Stephen R. Pfohl, Agata Foryciarz, Nigam H. Shah:
An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction. CoRR abs/2007.10306 (2020) - [i15]Jason A. Fries, Ethan Steinberg, Saelig Khattar, Scott L. Fleming, José D. Posada, Alison Callahan, Nigam H. Shah:
Trove: Ontology-driven weak supervision for medical entity classification. CoRR abs/2008.01972 (2020)
2010 – 2019
- 2019
- [j63]Vincent X. Liu, David W. Bates, Jenna Wiens, Nigam H. Shah:
The number needed to benefit: estimating the value of predictive analytics in healthcare. J. Am. Medical Informatics Assoc. 26(12): 1655-1659 (2019) - [j62]Kenneth Jung, Sylvia E. K. Sudat, Nicole Kwon, Walter F. Stewart, Nigam H. Shah:
Predicting need for advanced illness or palliative care in a primary care population using electronic health record data. J. Biomed. Informatics 92 (2019) - [j61]Juan M. Banda, Ashish Sarraju, Fahim Abbasi, Justin Parizo, Mitchel Pariani, Hannah Ison, Elinor Briskin, Hannah Wand, Sébastien Dubois, Kenneth Jung, Seth A. Myers, Daniel J. Rader, Joseph B. Leader, Michael F. Murray, Kelly D. Myers, Katherine Wilemon, Nigam H. Shah, Joshua W. Knowles:
Finding missed cases of familial hypercholesterolemia in health systems using machine learning. npj Digit. Medicine 2 (2019) - [j60]Alison Callahan, Jason A. Fries, Christopher Ré, James I Huddleston III, Nicholas J. Giori, Scott L. Delp, Nigam H. Shah:
Medical device surveillance with electronic health records. npj Digit. Medicine 2 (2019) - [j59]Saurabh Gombar, Alison Callahan, Robert M. Califf, Robert A. Harrington, Nigam H. Shah:
It is time to learn from patients like mine. npj Digit. Medicine 2 (2019) - [c66]Stephen Pfohl, Ben J. Marafino, Adrien Coulet, Fatima Rodriguez, Latha Palaniappan, Nigam H. Shah:
Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk. AIES 2019: 271-278 - [c65]Stephen R. Pfohl, Tony Duan, Daisy Yi Ding, Nigam H. Shah:
Counterfactual Reasoning for Fair Clinical Risk Prediction. MLHC 2019: 325-358 - [c64]Daisy Yi Ding, Chloé Simpson, Stephen Pfohl, Dave C. Kale, Kenneth Jung, Nigam H. Shah:
The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data. PSB 2019: 18-29 - [c63]Anand Avati, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam H. Shah, Andrew Y. Ng:
Countdown Regression: Sharp and Calibrated Survival Predictions. UAI 2019: 145-155 - [i14]Albee Y. Ling, Allison W. Kurian, Jennifer L. Caswell-Jin, George W. Sledge Jr., Nigam H. Shah, Suzanne R. Tamang:
A Semi-Supervised Machine Learning Approach to Detecting Recurrent Metastatic Breast Cancer Cases Using Linked Cancer Registry and Electronic Medical Record Data. CoRR abs/1901.05958 (2019) - [i13]Alison Callahan, Jason A. Fries, Christopher Ré, James I Huddleston III, Nicholas J. Giori, Scott L. Delp, Nigam H. Shah:
Medical device surveillance with electronic health records. CoRR abs/1904.07640 (2019) - [i12]Stephen Pfohl, Tony Duan, Daisy Yi Ding, Nigam H. Shah:
Counterfactual Reasoning for Fair Clinical Risk Prediction. CoRR abs/1907.06260 (2019) - [i11]Scott L. Fleming, Kuhan Jeyapragasan, Tony Duan, Daisy Yi Ding, Saurabh Gombar, Nigam Shah, Emma Brunskill:
Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcare. CoRR abs/1911.07084 (2019) - [i10]Anitha Kannan, Jason Alan Fries, Eric Kramer, Jen Jen Chen, Nigam Shah, Xavier Amatriain:
The accuracy vs. coverage trade-off in patient-facing diagnosis models. CoRR abs/1912.08041 (2019) - 2018
- [j58]Jason K. Wang, Jason Hom, Santhosh Balasubramanian, Alejandro Schuler, Nigam H. Shah, Mary K. Goldstein, Michael T. M. Baiocchi, Jonathan H. Chen:
An evaluation of clinical order patterns machine-learned from clinician cohorts stratified by patient mortality outcomes. J. Biomed. Informatics 86: 109-119 (2018) - [j57]Chunhua Weng, Nigam Shah, George Hripcsak:
Call for papers: Deep phenotyping for Precision Medicine. J. Biomed. Informatics 87: 66-67 (2018) - [j56]Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Y. Ng, Nigam H. Shah:
Improving palliative care with deep learning. BMC Medical Informatics Decis. Mak. 18(S-4): 55-64 (2018) - [j55]Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Michaela Hardt, Peter J. Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Yi Zhang, Gerardo Flores, Gavin E. Duggan, Jamie Irvine, Quoc Le, Kurt Litsch, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Gregory S. Corrado, Jeffrey Dean:
Scalable and accurate deep learning with electronic health records. npj Digit. Medicine 1 (2018) - [c62]Ruijun Chen, Patrick B. Ryan, Karthik Natarajan, Thomas Falconer, Christian G. Reich, Rohit Vashisht, Nigam Shah, George Hripcsak:
Treatment Pathways in Patients with Cancer Using a Large-scale Observational Data Network. AMIA 2018 - [c61]Stephen R. Pfohl, Nigam Shah:
Transfer learning to adapt predictive models for pediatric patients in the EHR. AMIA 2018 - [c60]Martin G. Seneviratne, Juan M. Banda, James D. Brooks, Nigam Shah, Tina Hernandez-Boussard:
Identifying cases of metastatic prostate cancer using machine learning on electronic health records. AMIA 2018 - [c59]Philip R. O. Payne, Nigam H. Shah, Jessica D. Tenenbaum, Lara M. Mangravite:
Session introduction. PSB 2018: 240-246 - [c58]Sarah Poole, Nigam Shah:
Addressing vital sign alarm fatigue using personalized alarm thresholds. PSB 2018: 472-483 - [r2]Nigam Shah:
Biomedical Data/Content Acquisition, Curation. Encyclopedia of Database Systems (2nd ed.) 2018 - [i9]Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Peter J. Liu, Xiaobing Liu, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Gavin E. Duggan, Gerardo Flores, Michaela Hardt, Jamie Irvine, Quoc V. Le, Kurt Litsch, Jake Marcus, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Greg Corrado, Jeff Dean:
Scalable and accurate deep learning for electronic health records. CoRR abs/1801.07860 (2018) - [i8]Alejandro Schuler, Nigam Shah:
General-purpose validation and model selection when estimating individual treatment effects. CoRR abs/1804.05146 (2018) - [i7]Anand Avati, Tony Duan, Kenneth Jung, Nigam H. Shah, Andrew Y. Ng:
Countdown Regression: Sharp and Calibrated Survival Predictions. CoRR abs/1806.08324 (2018) - [i6]Daisy Yi Ding, Chloé Simpson, Stephen Pfohl, Dave C. Kale, Kenneth Jung, Nigam H. Shah:
The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data. CoRR abs/1808.03331 (2018) - [i5]Stephen Pfohl, Ben J. Marafino, Adrien Coulet, Fatima Rodriguez, Latha Palaniappan, Nigam H. Shah:
Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk. CoRR abs/1809.04663 (2018) - [i4]Anand Avati, Stephen Pfohl, Chris Lin, Thao Nguyen, Meng Zhang, Philip Hwang, Jessica Wetstone, Kenneth Jung, Andrew Y. Ng, Nigam H. Shah:
Predicting Inpatient Discharge Prioritization With Electronic Health Records. CoRR abs/1812.00371 (2018) - 2017
- [j54]Yen S. Low, Aaron C. Daugherty, Elizabeth A. Schroeder, William Chen, Tina Seto, Susan C. Weber, Michael Lim, Trevor Hastie, Maya Mathur, Manisha Desai, Carl Farrington, Andrew A. Radin, Marina Sirota, Pragati Kenkare, Caroline A. Thompson, Peter P. Yu, Scarlett L. Gomez, George W. Sledge, Allison W. Kurian, Nigam H. Shah:
Synergistic drug combinations from electronic health records and gene expression. J. Am. Medical Informatics Assoc. 24(3): 565-576 (2017) - [j53]Rave Harpaz, William DuMouchel, Martijn J. Schuemie, Olivier Bodenreider, Carol Friedman, Eric Horvitz, Anna Ripple, Alfred Sorbello, Ryen W. White, Rainer Winnenburg, Nigam H. Shah:
Toward multimodal signal detection of adverse drug reactions. J. Biomed. Informatics 76: 41-49 (2017) - [c57]Jon D. Duke, George Hripcsak, Patrick B. Ryan, Nigam Shah:
From Large-Scale Network Analytics to Clinical Solutions in OHDSI. AMIA 2017 - [c56]Jason K. Wang, Alejandro Schuler, Nigam Shah, Jonathan H. Chen:
Impact of Clinician Experience on Machine Learned Clinical Order Patterns. AMIA 2017 - [c55]Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Y. Ng, Nigam H. Shah:
Improving palliative care with deep learning. BIBM 2017: 311-316 - [c54]Vibhu Agarwal, Mathew Smuck, Nigam Shah:
Quantifying the relative change in physical activity after Total Knee Arthroplasty using accelerometer based measurements. CRI 2017 - [c53]Juan M. Banda, Yoni Halpern, David A. Sontag, Nigam Shah:
Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network. CRI 2017 - [c52]Alejandro Schuler, David A. Wulf, Yun Lu, Jennifer M. Baker, Gabriel J. Escobar, Nigam Shah, Vincent X. Liu:
A novel propensity modeling approach to estimate the causal impact of acute organ dysfunction on long-term survival in sepsis. CRI 2017 - [c51]Sébastien Dubois, Nathanael Romano, Kenneth Jung, Nigam Shah, David C. Kale:
The Effectiveness of Transfer Learning in Electronic Health Records Data. ICLR (Workshop) 2017 - [c50]Vibhu Agarwal, Nigam H. Shah:
Learning Attributes of Disease Progression from Trajectories of Sparse Lab Values. PSB 2017: 184-194 - [c49]Philip R. O. Payne, Kun Huang, Nigam H. Shah, Jessica D. Tenenbaum:
Open Data for Discovery Science. PSB 2017: 649-652 - [i3]Sébastien Dubois, Nathanael Romano, Nigam Shah, Kenneth Jung:
Learning Effective Representations from Clinical Notes. CoRR abs/1705.07025 (2017) - [i2]Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Y. Ng, Nigam H. Shah:
Improving Palliative Care with Deep Learning. CoRR abs/1711.06402 (2017) - 2016
- [j52]Anika Oellrich, Nigel Collier, Tudor Groza, Dietrich Rebholz-Schuhmann, Nigam Shah, Olivier Bodenreider, Mary Regina Boland, Ivo I. Georgiev, Hongfang Liu, Kevin M. Livingston, Augustin Luna, Ann-Marie Mallon, Prashanti Manda, Peter N. Robinson, Gabriella Rustici, Michelle Simon, Liqin Wang, Rainer Winnenburg, Michel Dumontier:
The digital revolution in phenotyping. Briefings Bioinform. 17(5): 819-830 (2016) - [j51]Alison Callahan, Saminda Abeyruwan, Hassan Al-Ali, Kunie Sakurai, Adam R. Ferguson, Phillip G. Popovich, Nigam H. Shah, Ubbo Visser, John L. Bixby, Vance P. Lemmon:
RegenBase: a knowledge base of spinal cord injury biology for translational research. Database J. Biol. Databases Curation 2016 (2016) - [j50]Karin Verspoor, Anika Oellrich, Nigel Collier, Tudor Groza, Philippe Rocca-Serra, Larisa N. Soldatova, Michel Dumontier, Nigam Shah:
Thematic issue of the Second combined Bio-ontologies and Phenotypes Workshop. J. Biomed. Semant. 7: 66:1-66:4 (2016) - [j49]Rainer Winnenburg, Nigam H. Shah:
Generalized enrichment analysis improves the detection of adverse drug events from the biomedical literature. BMC Bioinform. 17: 250 (2016) - [j48]Laura K. Wiley, Peter Tarczy-Hornoch, Joshua C. Denny, Robert R. Freimuth, Casey L. Overby, Nigam Shah, Ross D. Martin, Indra Neil Sarkar:
Harnessing next-generation informatics for personalizing medicine: a report from AMIA's 2014 Health Policy Invitational Meeting. J. Am. Medical Informatics Assoc. 23(2): 413-419 (2016) - [j47]Vibhu Agarwal, Tanya Podchiyska, Juan M. Banda, Veena Goel, Tiffany I. Leung, Evan P. Minty, Timothy E. Sweeney, Elsie Gyang, Nigam H. Shah:
Learning statistical models of phenotypes using noisy labeled training data. J. Am. Medical Informatics Assoc. 23(6): 1166-1173 (2016) - [j46]Sarah Poole, Lee Frederick Schroeder, Nigam Shah:
An unsupervised learning method to identify reference intervals from a clinical database. J. Biomed. Informatics 59: 276-284 (2016) - [c48]Jon D. Duke, Nigam H. Shah, George Hripcsak, Patrick B. Ryan:
Ensuring Reproducibility in Observational Research: Building and Sharing Knowledge Resources in the OHDSI Network. AMIA 2016 - [c47]Jianying Hu, Nigam H. Shah, Bradley A. Malin, Patrick B. Ryan, Anil Jain:
Big Data for Healthcare and Life Sciences: Learning Useful Insights from Imperfect Data. AMIA 2016 - [c46]Rohit Vashisht, Kenneth Jung, Nigam Shah:
Learning Effective Treatment Pathways for Type-2 Diabetes from a clinical data warehouse. AMIA 2016 - [c45]Vibhu Agarwal, Lichy Han, Isaac Madan, Shaurya Saluja, Aaditya Shidham, Nigam Shah:
Predicting hospital visits from geo-tagged Internet search logs. CRI 2016 - [c44]Jon D. Duke, George Hripcsak, Nigam Shah, Patrick B. Ryan, Vojtech Huser:
Observational Health Data Sciences and Informatics (OHDSI): A Rapidly Growing International Network for Open Science and Data Analytics in Healthcare. CRI 2016 - [c43]Justin Norden, Timothy E. Sweeney, Aleksandra Pavlovic, Euan A. Ashley, Nigam Shah, Cindy Kin, Alexander Morgan:
A Pilot Study of the Integration of a Quantified-self Wearable Device with EMR data in the Acute Postoperative Setting. CRI 2016 - [c42]Sarah F. Poole, Shaun J. Grannis, Nigam Shah:
Predicting Emergency Department Visits. CRI 2016 - [c41]Kelly Regan, Zachary B. Abrams, Michael F. Sharpnack, Arunima Srivastava, Kun Huang, Nigam Shah, Philip R. O. Payne:
Session Introduction. PSB 2016: 1-8 - [c40]Alejandro Schuler, Vincent X. Liu, Joe Wan, Alison Callahan, Madeleine Udell, David E. Stark, Nigam H. Shah:
Discovering Patient Phenotypes Using Generalized Low Rank Models. PSB 2016: 144-155 - 2015
- [j45]Larisa N. Soldatova,