摘要
Cystic fibrosis patients often develop lung infections because of the presence of thick and sticky mucus that fills their airways. The presence of this thick mucus prevents the lungs from filtering out certain dominant bacterial types, making patients highly susceptible to infections that can range anywhere in severity from mild to life-threatening. These infections can cause great distress for patients as it becomes harder for patients to breathe and increases the chance of mortality by respiratory failure. It is important to be able to track the progression or regression of cystic fibrosis to determine the best course of treatment. Thus, this project focuses on the use of an AI model to examine the microbiology of cystic fibrosis patients and predict the condition or stage of lung function in the future, as a way to guide doctors with their treatment plan. Due to the limited amounts of publicly available patient data, we used all of the data in the training and testing of our machine learning algorithms initially and then tried a 50% training, 10% validation, and 40% testing split. Our results show that with relatively simple models (cubic polynomials), we can predict FEV1 from statistically significant bacteria sequences within 98% accuracy when training on sufficiently large samples.
Cystic fibrosis patients often develop lung infections because of the presence of thick and sticky mucus that fills their airways. The presence of this thick mucus prevents the lungs from filtering out certain dominant bacterial types, making patients highly susceptible to infections that can range anywhere in severity from mild to life-threatening. These infections can cause great distress for patients as it becomes harder for patients to breathe and increases the chance of mortality by respiratory failure. It is important to be able to track the progression or regression of cystic fibrosis to determine the best course of treatment. Thus, this project focuses on the use of an AI model to examine the microbiology of cystic fibrosis patients and predict the condition or stage of lung function in the future, as a way to guide doctors with their treatment plan. Due to the limited amounts of publicly available patient data, we used all of the data in the training and testing of our machine learning algorithms initially and then tried a 50% training, 10% validation, and 40% testing split. Our results show that with relatively simple models (cubic polynomials), we can predict FEV1 from statistically significant bacteria sequences within 98% accuracy when training on sufficiently large samples.
作者
Manasvi Pinnaka
Eric Cheek
Manasvi Pinnaka;Eric Cheek(Basis Independent Silicon Valley, San Jose, USA;Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, USA)
