Leveraging Data Science for Global Health

1. Front Matter

   Pages i-xii

   PDF

2. Building a Data Science Ecosystem for Healthcare

   1. Front Matter

      Pages 1-1

      PDF

   2. Health Information Technology as Premise for Data Science in Global Health: A Discussion of Opportunities and Challenges

      • Louis Agha-Mir-Salim, Raymond Francis Sarmiento

      Pages 3-15Open Access

   3. An Introduction to Design Thinking and an Application to the Challenges of Frail, Older Adults

      • Tony Gallanis

      Pages 17-33Open Access

   4. Developing Local Innovation Capacity to Drive Global Health Improvements

      • Christopher Moses

      Pages 35-54Open Access

   5. Building Electronic Health Record Databases for Research

      • Lucas Bulgarelli, Antonio Núñez-Reiz, Rodrigo Octavio Deliberato

      Pages 55-64Open Access

   6. Funding Global Health Projects

      • Katharine Morley, Michael Morley, Andrea Beratarrechea

      Pages 65-75Open Access

   7. From Causal Loop Diagrams to System Dynamics Models in a Data-Rich Ecosystem

      • Gary Lin, Michele Palopoli, Viva Dadwal

      Pages 77-98Open Access

   8. Workshop on Blockchain Use Cases in Digital Health

      • Philip Christian C. Zuniga, Rose Ann C. Zuniga, Marie Jo-anne Mendoza, Ada Angeli Cariaga, Raymond Francis Sarmiento, Alvin B. Marcelo

      Pages 99-107Open Access

3. Health Data Science Workshops

   1. Front Matter

      Pages 109-109

      PDF

   2. Applied Statistical Learning in Python

      • Calvin J. Chiew

      Pages 111-128Open Access

   3. Machine Learning for Patient Stratification and Classification Part 1: Data Preparation and Analysis

      • Cátia M. Salgado, Susana M. Vieira

      Pages 129-150Open Access

   4. Machine Learning for Patient Stratification and Classification Part 2: Unsupervised Learning with Clustering

      • Cátia M. Salgado, Susana M. Vieira

      Pages 151-168Open Access

   5. Machine Learning for Patient Stratification and Classification Part 3: Supervised Learning

      • Cátia M. Salgado, Susana M. Vieira

      Pages 169-198Open Access

   6. Machine Learning for Clinical Predictive Analytics

      • Wei-Hung Weng

      Pages 199-217Open Access

   7. Robust Predictive Models in Clinical Data—Random Forest and Support Vector Machines

      • Siqi Liu, Hao Du, Mengling Feng

      Pages 219-228Open Access

   8. Introduction to Clinical Natural Language Processing with Python

      • Leo Anthony Celi, Christina Chen, Daniel Gruhl, Chaitanya Shivade, Joy Tzung-Yu Wu

      Pages 229-250Open Access

   9. Introduction to Digital Phenotyping for Global Health

      • Olivia Mae Waring, Maiamuna S. Majumder

      Pages 251-261Open Access

   10. Medical Image Recognition: An Explanation and Hands-On Example of Convolutional Networks

       • Dianwen Ng, Mengling Feng

       Pages 263-284Open Access

   11. Biomedical Signal Processing: An ECG Application

       • Chen Xie

       Pages 285-303Open Access

This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.

• Open Access
• Big Data
• Machine Learning
• Artificial Intelligence
• Health Informatics
• Digital Disease Surveillance
• Health Mapping
• Health Records for Non-Communicable Diseases
• HealthMap
• Tools for Clinical Trials

“This book seems to empower the reader to gradually embark on the development of medical applications incorporating data science. … This book is well structured, written with a good level of linguistic guts, and could be recommended to data science students rather than researchers or health professionals.” (Thierry Edoh, Computing Reviews, March 24, 2022)

• Massachusetts Institute of Technology, Cambridge, USA

  Leo Anthony Celi

• Boston Children’s Hospital, Harvard Medical School, Boston, USA

  Maimuna S. Majumder

• University of Puerto Rico Río Piedras, San Juan, USA

  Patricia Ordóñez

• ScienteLab, Department of Global Health, University of Washington, Seattle, USA

  Juan Sebastian Osorio

• Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, USA

  Kenneth E. Paik

• Imperial College London, London, UK

  Melek Somai

Leo Anthony Celi, M.D., M.S., M.P.H., has practiced medicine in three continents, giving him broad perspectives in healthcare delivery. As clinical research director and principal research scientist at the MIT Laboratory for Computational Physiology (LCP) and as an attending physician at the Beth Israel Deaconess Medical Center (BIDMC), he brings together clinicians and data scientists to support research using data routinely collected in the process of care. Leo also founded and co-directs Sana, a cross-disciplinary organization based at the Institute for Medical Engineering and Science at MIT, whose objective is to leverage information technology to improve health outcomes in low- and middle-income countries. He is one of the course directors for global health informatics to improve quality of care, and collaborative data science in medicine, both at MIT. He is an editor of the textbook for each course, both released under an open access license. Leo has spoken in 25 countries about the value of data in improving health outcomes.