Data scientist, applying machine learning to healthcare | Wearable computing. USC EE PhD. https://speakerdeck.com/elleros 2018-12-24 16:44:08 -0500 Medical institutions, universities and software giants like Google and Microsoft are dedicating increasing resources to machine learning for healthcare. This is a very exciting but relatively young field. However, best practices for methods and reporting of results are not yet fully established. I have 2.5 years of experience as data scientist at a national cancer center working on clinical data, evaluating external vendors and peer reviewing machine learning in healthcare papers. The talk gives an overview of best practices in prototyping machine learning models on data from the patient electronic health record (EHR). The topics addressed are: 1. Introduction to the EHR 2. Overview of machine learning applications to the EHR 3. Cohort definition for survival problems 4. Data cleaning 5. Performance metrics. Excerpts of papers from renowned institutions will be critically reviewed. The material is intended to be useful not only to machine learning for healthcare professionals, but to practitioners dealing with very unbalanced datasets in the temporal domain. For example, customer churn prediction can be modeled as survival problem. Medical institutions, universities and software giants like Google and Microsoft are dedicating increasing resources to machine learning for healthcare. This is a very exciting but relatively young field. However, best practices for methods and reporting of results are not yet fully established. I have 2.5 years of experience as data scientist at a national cancer center working on clinical data, evaluating external vendors and peer reviewing machine learning in healthcare papers. The talk gives an overview of best practices in prototyping machine learning models on data from the patient electronic health record (EHR). The topics addressed are: 1. Introduction to the EHR 2. Overview of machine learning applications to the EHR 3. Cohort definition for survival problems 4. Data cleaning 5. Performance metrics. Excerpts of papers from renowned institutions will be critically reviewed. The material is intended to be useful not only to machine learning for healthcare professionals, but to practitioners dealing with very unbalanced datasets in the temporal domain. For example, customer churn prediction can be modeled as survival problem. Sat, 17 Aug 2019 00:00:00 -0400 https://speakerdeck.com/elleros/best-practices-for-prototyping-ml-models-for-healthcare https://speakerdeck.com/elleros/best-practices-for-prototyping-ml-models-for-healthcare There is a fast growing interest in the application of machine learning to the field of healthcare. Medical institutions, universities and software giants like Google and Amazon are dedicating increasing resources to this field. Medical informaticians are likely to be exposed to presentations of machine learning applications by researchers, external vendors, or just by browsing the scientific literature. This is a very exciting but relatively young field. However, best practices for methods and reporting of results are not yet fully established. This presentation gives an overview of keys aspects of machine learning methods, along with pros and cons of the most important error metrics. The goal is to provide simple guidelines on what to look for and which questions to ask to assess the validity of claims of oral presentations and papers. There is a fast growing interest in the application of machine learning to the field of healthcare. Medical institutions, universities and software giants like Google and Amazon are dedicating increasing resources to this field. Medical informaticians are likely to be exposed to presentations of machine learning applications by researchers, external vendors, or just by browsing the scientific literature. This is a very exciting but relatively young field. However, best practices for methods and reporting of results are not yet fully established. This presentation gives an overview of keys aspects of machine learning methods, along with pros and cons of the most important error metrics. The goal is to provide simple guidelines on what to look for and which questions to ask to assess the validity of claims of oral presentations and papers. Wed, 03 Oct 2018 00:00:00 -0400 https://speakerdeck.com/elleros/practical-tips-for-reviewing-presentations-of-machine-learning-healthcare-applications https://speakerdeck.com/elleros/practical-tips-for-reviewing-presentations-of-machine-learning-healthcare-applications