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Found 9 papers, 0 papers with code
The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data
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.
Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk
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.
Predicting Inpatient Discharge Prioritization With Electronic Health Records
Identifying patients who will be discharged within 24 hours can improve hospital resource management and quality of care.
Counterfactual Reasoning for Fair Clinical Risk Prediction
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.
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021.
Large Language Models Encode Clinical Knowledge
To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars.
Ranked #4 on
Multiple Choice Question Answering (MCQA)
on MedMCQA
(Dev Set (Acc-%) metric)
Towards Expert-Level Medical Question Answering with Large Language Models
Large language models (LLMs) have catalyzed significant progress in medical question answering; Med-PaLM was the first model to exceed a "passing" score in US Medical Licensing Examination (USMLE) style questions with a score of 67. 2% on the MedQA dataset.
Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking
However, even pretrain reward ensembles do not eliminate reward hacking: we show several qualitative reward hacking phenomena that are not mitigated by ensembling because all reward models in the ensemble exhibit similar error patterns.
The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa
Whereas experts generally expressed a shared view about the relevance of colonial history for the development and implementation of AI technologies in Africa, the majority of the general population participants surveyed did not think there was a direct link between AI and colonialism.
