Vishwali Mhasawade|Yuan Zhao|Rumi Chunara
Machine learning and algorithmic fairness in public and population health
New York University|New York University|New York University
much work machine learning health focused processes inside hospital clinic however represents narrow set tasks challenges related health greater potential impact leveraging machine learning health tasks broadly perspective aim highlight potential opportunities challenges machine learning within holistic view health influences build research population public health focuses mechanisms different cultural social environmental factors effect health individuals communities present brief introduction research fields data sources types tasks use identify settings machine learning relevant contribute new knowledge given key foci health equity disparities within public population health juxtapose topics machine learning subfield algorithmic fairness highlight specific opportunities machine learning public population health may synergize achieve health equity algorithmic solutions improve treatment starting transform health care mhasawade colleagues discuss perspective machine learning applications population public health extend beyond clinical practice working general health data comes challenges notably ensuring algorithmic fairness face existing health disparities area provides new kinds data questions machine learning community