Project in Reinforcement learning
Budget: $30 – $250 USD
Automated medical diagnosis
Medical diagnoses are essentially an exercise in mapping patient information (such as history and current symptoms) to the correct disease profile. While this may sound relatively simple, it can, in clinical terms, be an incredibly complex task representing an enormous burden (in both time and cognitive energy required) for busy clinicians.
There are already outlined costs of mistaken diagnoses according to some blogs Along with being attributed to around 10 percent of U.S. patient deaths, misdiagnosed patients have been paid nearly $40B in compensation over the past 25 years. That’s why ML algorithms to improve diagnosis are so vital to the health-care industry and its patients. But RL techniques hold particular promise because most ML diagnosis solutions require large amounts of annotated data for training purposes. RL agents, by contrast, require smaller amounts of labeled data.
Medical diagnoses are essentially an exercise in mapping patient information (such as history and current symptoms) to the correct disease profile. While this may sound relatively simple, it can, in clinical terms, be an incredibly complex task representing an enormous burden (in both time and cognitive energy required) for busy clinicians.
There are already outlined costs of mistaken diagnoses according to some blogs Along with being attributed to around 10 percent of U.S. patient deaths, misdiagnosed patients have been paid nearly $40B in compensation over the past 25 years. That’s why ML algorithms to improve diagnosis are so vital to the health-care industry and its patients. But RL techniques hold particular promise because most ML diagnosis solutions require large amounts of annotated data for training purposes. RL agents, by contrast, require smaller amounts of labeled data.