2.7k citations · 3.1k across the 21 of their papers we have counts for
24 papers
Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects
Steve Yadlowsky, Scott Fleming, Nigam Shah +2
There are a number of available methods for selecting whom to prioritize for treatment, including ones based on treatment effect estimation, risk scoring, and hand-crafted rules. W…
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations
Stephen R. Pfohl, Haoran Zhang, Yizhe Xu +3
Predictive models for clinical outcomes that are accurate on average in a patient population may underperform drastically for some subpopulations, potentially introducing or reinfo…
Ontology-driven weak supervision for clinical entity classification in electronic health records
Jason A. Fries, Ethan Steinberg, Saelig Khattar +4
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…
An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction
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. Several recent works frame the problem as that of algorithmic…
Using public clinical trial reports to evaluate observational study methods
Ethan Steinberg, Nikolaos Ignatiadis, Steve Yadlowsky +2
Observational studies are valuable for estimating the effects of various medical interventions, but are notoriously difficult to evaluate because the methods used in observational…
A new paradigm for accelerating clinical data science at Stanford Medicine
Somalee Datta, Jose Posada, Garrick Olson +7
Stanford Medicine is building a new data platform for our academic research community to do better clinical data science. Hospitals have a large amount of patient data and research…
Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data
Ethan Steinberg, Ken Jung, Jason A. Fries +3
Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes. This process…
The accuracy vs. coverage trade-off in patient-facing diagnosis models
Anitha Kannan, Jason Alan Fries, Eric Kramer +3
A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process. These tools are powered by diagnosis…
Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcare
Scott L. Fleming, Kuhan Jeyapragasan, Tony Duan +4
There is an emerging trend in the reinforcement learning for healthcare literature. In order to prepare longitudinal, irregularly sampled, clinical datasets for reinforcement learn…
Counterfactual Reasoning for Fair Clinical Risk Prediction
Stephen Pfohl, Tony Duan, Daisy Yi Ding +1
The use of machine learning systems to support decision making in healthcare raises questions as to what extent these systems may introduce or exacerbate disparities in care for hi…
Medical device surveillance with electronic health records
Alison Callahan, Jason A Fries, Christopher Ré +4
Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real…
A Semi-Supervised Machine Learning Approach to Detecting Recurrent Metastatic Breast Cancer Cases Using Linked Cancer Registry and Electronic Medical Record Data
Albee Y. Ling, Allison W. Kurian, Jennifer L. Caswell-Jin +3
Objectives: Most cancer data sources lack information on metastatic recurrence. Electronic medical records (EMRs) and population-based cancer registries contain complementary infor…
Predicting Inpatient Discharge Prioritization With Electronic Health Records
Anand Avati, Stephen Pfohl, Chris Lin +7
The paper develops machine‑learning models using eight years of Stanford Hospital electronic health records to predict which inpatients will be discharged within the next 24 hours,…
Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk
Stephen Pfohl, Ben Marafino, Adrien Coulet +3
Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cho…
The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data
Daisy Yi Ding, Chloé Simpson, Stephen Pfohl +3
Electronic phenotyping is the task of ascertaining whether an individual has a medical condition of interest by analyzing their medical record and is foundational in clinical infor…
Countdown Regression: Sharp and Calibrated Survival Predictions
Anand Avati, Tony Duan, Sharon Zhou +3
Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance. The field of meteorology, w…
A comparison of methods for model selection when estimating individual treatment effects
Alejandro Schuler, Michael Baiocchi, Robert Tibshirani +1
Practitioners in medicine, business, political science, and other fields are increasingly aware that decisions should be personalized to each patient, customer, or voter. A given t…
Monitoring physical function in patients with knee osteoarthritis using data from wearable activity monitors
Vibhu Agarwal, Matthew Smuck, Nigam H Shah
Currently used clinical assessments for physical function do not objectively quantify daily activities in routine living. Wearable activity monitors enable objective measurement of…
Scalable and accurate deep learning for electronic health records
Alvin Rajkomar, Eyal Oren, Kai Chen +31
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical model…
Improving Palliative Care with Deep Learning
Anand Avati, Kenneth Jung, Stephanie Harman +3
Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses,…
Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset
Alejandro Schuler, Ken Jung, Robert Tibshirani +2
Many decisions in healthcare, business, and other policy domains are made without the support of rigorous evidence due to the cost and complexity of performing randomized experimen…
Some methods for heterogeneous treatment effect estimation in high-dimensions
Scott Powers, Junyang Qian, Kenneth Jung +4
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 cl…
Effective Representations of Clinical Notes
Sebastien Dubois, Nathanael Romano, David C. Kale +2
Clinical notes are a rich source of information about patient state. However, using them to predict clinical events with machine learning models is challenging. They are very high…
Provenance-Centered Dataset of Drug-Drug Interactions
Juan M. Banda, Tobias Kuhn, Nigam H. Shah +1
Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (MEDLINE), electronic health records,…