Protecting user health data in ML applications

Jan 15, 2020 ... It is possible to risk-stratify patients' risk of utilization without interacting with the patient or collecting information beyond the ... Oct 12, 2021 ... The application of machine learning is best thought of as search problem for the best mapping of inputs to outputs given the knowledge and resources available ... Mar 22, 2021 ... Some application areas where semi-supervised learning is used include machine translation, fraud detection, labeling data and text ... May 26, 2020 ... For now, the important points to remember are: linearly dependent vectors contain redundant information, whereas linearly independent vectors do ... What is the average amount of information we expect from the output of S? H(S) is the entropy of S. • Note that this depends only on the probability ... HomeInformationalMachine Learning in Risk Management: Strategies & Impact. February 26, ... One big bank, for example, applied machine learning to enhance credit risk assessment. METHOD: We applied machine learning, specifically a combination of mutual information-based feature selection and supervised learning classifiers on ... We are seeking to fund machine learning research on the following topics in information security: ... Applied Scientist, Artificial General Intelligence. US, MA, ... ... computer science and applied machine learning. Hence, the technical results needs to be summarized in few remarks. Lastly, a lot of time has been spent with ... ... Applied Machine Learning, Intermediate Special Topics in Informatics. Job ... Library and Information Science; Linguistics; Machine Learning; Mathematics.