Lecture

Data Science in Personalized and Global Health: Privacy-Enhancing Technologies

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In course

HUM-388: Personalized health: biomedical & social challenge

La médecine personnalisée, préventive, prédictive et participative bouleverse les connaissances médicales établies, ainsi que les configurations sociales et politiques de la médecine. Ce cours analyse ces processus à travers une approche interdisciplinaire ...

Description

This lecture explores the field of data science in personalized and global health, focusing on advanced privacy-enhancing technologies. Dr. Jean Louis Raisaro discusses the multidisciplinary nature of data science, the importance of health data modalities, and the applications of artificial intelligence in healthcare. The lecture covers topics such as data preparation, model training, and the implementation of AI in medicine. It also addresses key issues like data sharing, privacy, and the use of advanced privacy-enhancing technologies to protect aggregate data output. The presentation concludes with a discussion on the legal compliance and the future of privacy-preserving AI in medicine.

Instructor

Luca Chiapperino

Expertise Social Studies of Science; Science and Technology Studies; Public Health Ethics; Applied Philosophy; Sustainability Transitions Teaching & PhD Courses Health A: Innovation, Equity, Sustainability HUM-125(a) The course provides an interdisciplinary examination of key global health challenges, focusing on biomedical innovations, pharmaceuticals, sustainability, mental health, and health policies. It offer a critical skillset to engineering students approaching these complex health ch

Official source

Related concepts (32)

Data mining

Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.

Data preparation

Data preparation is the act of manipulating (or pre-processing) raw data (which may come from disparate data sources) into a form that can readily and accurately be analysed, e.g. for business purposes. Data preparation is the first step in data analytics projects and can include many discrete tasks such as loading data or data ingestion, data fusion, data cleaning, data augmentation, and data delivery.

Data sharing

Data sharing is the practice of making data used for scholarly research available to other investigators. Many funding agencies, institutions, and publication venues have policies regarding data sharing because transparency and openness are considered by many to be part of the scientific method. A number of funding agencies and science journals require authors of peer-reviewed papers to share any supplemental information (raw data, statistical methods or source code) necessary to understand, develop or reproduce published research.

Data Preprocessing

Data preprocessing can refer to manipulation or dropping of data before it is used in order to ensure or enhance performance, and is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues. Analyzing data that has not been carefully screened for such problems can produce misleading results.

Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

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