Search Results for author: Nigam H. Shah

Found 20 papers, 8 papers with code

The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs

no code implementations22 Mar 2023 Michael Wornow, Yizhe Xu, Rahul Thapa, Birju Patel, Ethan Steinberg, Scott Fleming, Michael A. Pfeffer, Jason Fries, Nigam H. Shah

The successes of foundation models such as ChatGPT and AlphaFold have spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations.

Instability in clinical risk stratification models using deep learning

no code implementations20 Nov 2022 Daniel Lopez-Martinez, Alex Yakubovich, Martin Seneviratne, Adam D. Lelkes, Akshit Tyagi, Jonas Kemp, Ethan Steinberg, N. Lance Downing, Ron C. Li, Keith E. Morse, Nigam H. Shah, Ming-Jun Chen

While it has been well known in the ML community that deep learning models suffer from instability, the consequences for healthcare deployments are under characterised.

Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare

1 code implementation3 Feb 2022 Stephen R. Pfohl, Yizhe Xu, Agata Foryciarz, Nikolaos Ignatiadis, Julian Genkins, Nigam H. Shah

A growing body of work uses the paradigm of algorithmic fairness to frame the development of techniques to anticipate and proactively mitigate the introduction or exacerbation of health inequities that may follow from the use of model-guided decision-making.

Decision Making Fairness

A comparison of approaches to improve worst-case predictive model performance over patient subpopulations

1 code implementation27 Aug 2021 Stephen R. Pfohl, Haoran Zhang, Yizhe Xu, Agata Foryciarz, Marzyeh Ghassemi, Nigam H. Shah

Predictive models for clinical outcomes that are accurate on average in a patient population may underperform drastically for some subpopulations, potentially introducing or reinforcing inequities in care access and quality.

Ontology-driven weak supervision for clinical entity classification in electronic health records

1 code implementation5 Aug 2020 Jason A. Fries, Ethan Steinberg, Saelig Khattar, Scott L. Fleming, Jose Posada, Alison Callahan, Nigam H. Shah

In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e. g. the order of an event relative to a time index) can inform many important analyses.

General Classification Named Entity Recognition (NER) +3

An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction

1 code implementation20 Jul 2020 Stephen R. Pfohl, Agata Foryciarz, Nigam H. Shah

The use of machine learning to guide clinical decision making has the potential to worsen existing health disparities.

BIG-bench Machine Learning Decision Making +1

Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data

1 code implementation6 Jan 2020 Ethan Steinberg, Ken Jung, Jason A. Fries, Conor K. Corbin, Stephen R. Pfohl, Nigam H. Shah

Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes.

Representation Learning

Counterfactual Reasoning for Fair Clinical Risk Prediction

no code implementations14 Jul 2019 Stephen Pfohl, Tony Duan, Daisy Yi Ding, Nigam H. Shah

We investigate the extent to which the augmented counterfactual fairness criteria may be applied to develop fair models for prolonged inpatient length of stay and mortality with observational electronic health records data.

Counterfactual Inference Decision Making +1

Medical device surveillance with electronic health records

1 code implementation3 Apr 2019 Alison Callahan, Jason A. Fries, Christopher Ré, James I Huddleston III, Nicholas J Giori, Scott Delp, Nigam H. Shah

Using hip replacements as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96. 3% precision, 98. 5% recall, and 97. 4% F1, improved classification performance by 12. 7- 53. 0% over rule-based methods, and detected over 6 times as many complication events compared to using structured data alone.

Reading Comprehension

Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk

no code implementations12 Sep 2018 Stephen Pfohl, Ben Marafino, Adrien Coulet, Fatima Rodriguez, Latha Palaniappan, Nigam H. Shah

Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cholesterol-lowering and other therapies.

Fairness Management

The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data

no code implementations9 Aug 2018 Daisy Yi Ding, Chloé Simpson, Stephen Pfohl, Dave C. Kale, Kenneth Jung, Nigam H. Shah

We present experiments that elucidate when multitask learning with neural nets improves performance for phenotyping using EHR data relative to neural nets trained for a single phenotype and to well-tuned logistic regression baselines.

Countdown Regression: Sharp and Calibrated Survival Predictions

1 code implementation21 Jun 2018 Anand Avati, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam H. Shah, Andrew Ng

Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance.

Decision Making Mortality Prediction +2

Improving Palliative Care with Deep Learning

no code implementations17 Nov 2017 Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Ng, Nigam H. Shah

The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care.

Some methods for heterogeneous treatment effect estimation in high-dimensions

1 code implementation1 Jul 2017 Scott Powers, Junyang Qian, Kenneth Jung, Alejandro Schuler, Nigam H. Shah, Trevor Hastie, Robert Tibshirani

When devising a course of treatment for a patient, doctors often have little quantitative evidence on which to base their decisions, beyond their medical education and published clinical trials.

Vocal Bursts Intensity Prediction

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