Dr. Sudip Gupta – Algorithmic Bias and Causal Fairness in Healthcare Evidence from MIMIC database
Dr. Gupta discussed causal fairness principles to mitigate bias and promote equity in healthcare using MIMIC 3 data.
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Causal Fairness in Healthcare
Causal fairness in healthcare refers to an ethical and methodological approach aimed at addressing disparities and ensuring equity in healthcare outcomes by focusing on the underlying causal relationships between interventions and health outcomes.
Dr. Tim Mackey – Leveraging Blockchain Technology to Enable Indigenous Data Sovereignty of Genomic Data
Dr. Mackey discussed a project funded by the Robert Wood Johnson Foundation in partnership with The Native Biodata Consortium to develop a blockchain-based governance system for managing Indigenous genomic data.
Indigenous Data Sovereignty and Blockchain
Indigenous data sovereignty (IDS) is defined as the right of an Indigenous nation to govern the collection, ownership, and application of data generated by its members.
Learning and Implementing Code Tests
Learning and Implementing Code Tests An Intern’s Perspective Nada Haboudal January 18, 2024 Nada Haboudal | January 18, 2024 Welcome to my blog post, where I showcase my work with pytest, a super handy tool for testing Python code. Introduction…
Precision Medicine
Precision Medicine James Tabery December 8, 2023 James Tabery | December 8, 2023 Every month, ETAI will be sharing a term or concept of the month that is related to ethical issues in biomedical research. The term of the month is part…
Dr. Xiaoqian Jiang – Sensitive Data Detection with High-Throughput Machine Learning Models in Electronic Health Records
Dr. Jiang explored how a groundbreaking discovery was utilized to generate 30 metadata-based features through machine learning for the automatic detection of PHI fields in structured Electronic Health Record (EHR) data.
Dr. Babak Salimi – Certifying Fair Predictive Models in the Face of Selection Bias
Dr. Salimi offered insights for data management, machine learning, and responsible data science, emphasizing the significance of handling selection bias in algorithmic decision-making.
Precision PHI Screening
This term reflects the paper's emphasis on using high-throughput machine learning models to precisely detect sensitive data, specifically Protected Health Information (PHI), in electronic health records.
Data Quality and Algorithmic Fairness
In today's digital age, algorithmic decision-making systems play a crucial role in fields like credit scoring and medical diagnoses. While they are often praised for being 'objective,' these systems can exhibit biases, mostly stemming from the data they rely on.