Algorithmic fairness in artificial intelligence for medicine and healthcare
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
Electronic health records and polygenic risk scores for predicting disease risk
Accurate prediction of disease risk based on the genetic make-up of an individual is
essential for effective prevention and personalized treatment. Nevertheless, to date …
essential for effective prevention and personalized treatment. Nevertheless, to date …
Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm
Objectives We propose a one-shot, privacy-preserving distributed algorithm to perform
logistic regression (ODAL) across multiple clinical sites. Materials and Methods ODAL …
logistic regression (ODAL) across multiple clinical sites. Materials and Methods ODAL …
Algorithm fairness in ai for medicine and healthcare
In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …
Learning from local to global: An efficient distributed algorithm for modeling time-to-event data
Objective We developed and evaluated a privacy-preserving One-shot Distributed Algorithm
to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level …
to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level …
Targeting underrepresented populations in precision medicine: A federated transfer learning approach
Targeting underrepresented populations in precision medicine: A federated transfer learning
approach Page 1 The Annals of Applied Statistics 2023, Vol. 17, No. 4, 2970–2992 …
approach Page 1 The Annals of Applied Statistics 2023, Vol. 17, No. 4, 2970–2992 …
Heterogeneity-aware and communication-efficient distributed statistical inference
In multicentre research, individual-level data are often protected against sharing across
sites. To overcome the barrier of data sharing, many distributed algorithms, which only …
sites. To overcome the barrier of data sharing, many distributed algorithms, which only …
Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites
Integrating real-world data (RWD) from several clinical sites offers great opportunities to
improve estimation with a more general population compared to analyses based on a single …
improve estimation with a more general population compared to analyses based on a single …
Federated and distributed learning applications for electronic health records and structured medical data: a sco** review
Objectives Federated learning (FL) has gained popularity in clinical research in recent years
to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent …
to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent …
Use of electronic health record data for drug safety signal identification: a sco** review
Introduction Pharmacovigilance programs protect patient health and safety by identifying
adverse event signals through postmarketing surveillance of claims data and spontaneous …
adverse event signals through postmarketing surveillance of claims data and spontaneous …