Appendix F — Further Reading
Appendix D: Further Reading
A curated guide to essential resources for deepening your knowledge of AI in public health.
Online Courses
Foundational Machine Learning
Stanford CS229: Machine Learning - Instructor: Andrew Ng - Platform: Coursera / Stanford Online - Level: Intermediate - Duration: 11 weeks - Topics: Supervised learning, unsupervised learning, deep learning, best practices - Why take it: Gold standard ML course, mathematical rigor with practical applications - Link: https://www.coursera.org/learn/machine-learning
Fast.ai: Practical Deep Learning for Coders - Instructors: Jeremy Howard, Rachel Thomas - Platform: Fast.ai - Level: Beginner to advanced - Duration: Self-paced - Topics: Deep learning, computer vision, NLP, ethics - Why take it: Top-down approach, code-first, healthcare examples - Link: https://course.fast.ai/
MIT 6.S191: Introduction to Deep Learning - Platform: MIT OpenCourseWare - Level: Intermediate - Duration: 1 week intensive - Topics: Neural networks, CNNs, RNNs, GANs, reinforcement learning - Why take it: Comprehensive, includes labs - Link: http://introtodeeplearning.com/
Healthcare-Specific AI
AI in Healthcare Specialization (Stanford) - Platform: Coursera - Level: Intermediate - Duration: 3 months - Topics: Medical imaging, EHR data, clinical trials, deployment - Why take it: Healthcare-focused, taught by Stanford faculty - Link: https://www.coursera.org/specializations/ai-healthcare
AI for Medicine Specialization (deeplearning.ai) - Instructor: Andrew Ng et al. - Platform: Coursera - Level: Intermediate - Duration: 3 months - Courses: 1. AI for Medical Diagnosis 2. AI for Medical Prognosis 3. AI for Medical Treatment - Link: https://www.coursera.org/specializations/ai-for-medicine
MIT Critical Data: Secondary Analysis of Electronic Health Records - Platform: MIT OpenCourseWare - Level: Advanced - Duration: Self-paced - Topics: EHR data, MIMIC database, clinical prediction - Link: https://ocw.mit.edu/
Public Health and Epidemiology
Epidemiology in Public Health Practice (Johns Hopkins) - Platform: Coursera - Level: Beginner - Duration: 4 weeks - Topics: Study design, measures of disease, screening, causation - Why take it: Essential epidemiology foundation for AI applications - Link: https://www.coursera.org/specializations/epidemiology
Data Science for Public Health (Imperial College London) - Platform: Coursera - Level: Intermediate - Duration: 6 months - Topics: Data analysis, visualization, statistical modeling, machine learning in public health - Link: https://www.coursera.org/specializations/data-science-public-health
Ethics and Fairness
Data Science Ethics (University of Michigan) - Platform: Coursera - Level: Beginner to intermediate - Duration: 4 weeks - Topics: Privacy, fairness, transparency, accountability - Why take it: Critical thinking about AI ethics - Link: https://www.coursera.org/learn/data-science-ethics
Fairness in Machine Learning (MIT) - Platform: MIT OpenCourseWare - Level: Advanced - Duration: Self-paced - Topics: Fairness definitions, bias mitigation, fairness-aware ML - Link: https://stellar.mit.edu/
Books
Technical Foundations
“Pattern Recognition and Machine Learning” by Christopher Bishop - Level: Advanced - Topics: Bayesian methods, neural networks, graphical models - Best for: Mathematical foundations, reference text - Why read it: Comprehensive, rigorous, standard graduate text
“Deep Learning” by Goodfellow, Bengio, and Courville - Level: Intermediate to advanced - Topics: Neural networks, optimization, CNNs, RNNs, regularization - Best for: Deep learning theory and practice - Why read it: Authoritative, free online - Link: https://www.deeplearningbook.org/
“Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron - Level: Beginner to intermediate - Topics: Practical ML, neural networks, implementation - Best for: Learning by doing, practical projects - Why read it: Code-heavy, excellent examples
“Probabilistic Machine Learning” by Kevin Murphy - Level: Advanced - Topics: Probabilistic graphical models, Bayesian methods, deep learning - Best for: Rigorous treatment of ML from probabilistic perspective - Link: https://probml.github.io/pml-book/
Healthcare AI
“Machine Learning for Healthcare” edited by Ranganath, Perotte, Zitnik - Level: Advanced - Topics: Clinical prediction, imaging, EHR analysis, interpretability - Best for: Cutting-edge research in healthcare ML - Why read it: Comprehensive coverage of healthcare AI research
“Artificial Intelligence in Medicine” by Markus Wenzel - Level: Intermediate - Topics: Medical imaging, diagnosis, treatment planning, drug discovery - Best for: Overview of AI applications in medicine
“Clinical Prediction Models” by Ewout Steyerberg - Level: Intermediate to advanced - Topics: Risk prediction, model development, validation, updating - Best for: Rigorous approach to clinical prediction - Why read it: Gold standard for clinical prediction methodology
Ethics and Society
“Weapons of Math Destruction” by Cathy O’Neil - Level: General audience - Topics: Algorithmic bias, societal impact, fairness - Best for: Understanding societal implications - Why read it: Accessible, compelling examples, critical perspective
“Automating Inequality” by Virginia Eubanks - Level: General audience - Topics: Algorithms in social services, child welfare, healthcare - Best for: Case studies of algorithmic harm - Why read it: Real-world impact on vulnerable populations
“The Ethical Algorithm” by Kearns and Roth - Level: Intermediate - Topics: Fairness, privacy, game theory, algorithmic design - Best for: Technical approaches to ethical AI - Why read it: Bridges theory and practice
“Race After Technology” by Ruha Benjamin - Level: General audience - Topics: Race, technology, algorithmic bias, social justice - Best for: Critical race perspective on AI - Why read it: Essential perspective on tech and inequality
Public Health
“Modern Epidemiology” by Rothman, Greenland, Lash - Level: Advanced - Topics: Causal inference, study design, bias, confounding - Best for: Rigorous epidemiologic foundation - Why read it: Standard graduate text, essential for healthcare AI
“Infectious Disease Epidemiology” by Nelson and Williams - Level: Intermediate - Topics: Disease transmission, outbreak investigation, surveillance - Best for: Understanding infectious disease dynamics
Academic Journals
AI and Machine Learning
Top-Tier General ML: - NeurIPS (Conference on Neural Information Processing Systems) - ICML (International Conference on Machine Learning) - ICLR (International Conference on Learning Representations) - JMLR (Journal of Machine Learning Research) - Machine Learning (Springer)
Access: Many papers available on arXiv.org preprints
Healthcare AI
Clinical Journals Publishing AI Research: - The Lancet Digital Health 🎯 - Focus: Digital health technologies, AI applications - Open access - Link: https://www.thelancet.com/journals/landig
- npj Digital Medicine (Nature) 🎯
- Focus: Digital health, AI, sensors, apps
- Open access
- Link: https://www.nature.com/npjdigitalmed/
- Journal of the American Medical Informatics Association (JAMIA) 🎯
- Focus: Health informatics, clinical decision support, AI
- Link: https://academic.oup.com/jamia
- NEJM AI (New England Journal of Medicine) 🎯
- Focus: AI in medicine, launched 2024
- High-impact clinical AI research
- Link: https://ai.nejm.org/
Specialized Healthcare AI: - Radiology: Artificial Intelligence - Focus: AI in medical imaging - Journal of Medical Internet Research - Focus: Digital health, eHealth - IEEE Journal of Biomedical and Health Informatics - Focus: Biomedical informatics, signals
Public Health
- American Journal of Public Health
- American Journal of Epidemiology
- Epidemiology
- International Journal of Epidemiology
- BMC Public Health
Ethics and Fairness
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) 🎯
- Premier venue for algorithmic fairness
- Interdisciplinary: CS, law, social science
- Proceedings open access
- AI and Ethics (Springer)
- Ethics and Information Technology
Datasets and Data Repositories
Clinical Datasets
MIMIC-III / MIMIC-IV 🎯 - Content: ICU patient data (60,000+ admissions) - Includes: Vitals, labs, medications, notes, outcomes - Access: Free after credentialing - Use cases: Clinical prediction, NLP, time-series analysis - Link: https://mimic.mit.edu/
eICU Collaborative Research Database - Content: Multi-center ICU data (200,000+ admissions) - Access: Free after credentialing - Link: https://eicu-crd.mit.edu/
SEER (Surveillance, Epidemiology, and End Results) - Content: Cancer registry data - Access: Free, public - Link: https://seer.cancer.gov/
Medical Imaging
ChestX-ray14 🎯 - Content: 112,000 chest X-rays with labels - Labels: 14 pathologies - Access: Free - Link: https://nihcc.app.box.com/v/ChestXray-NIHCC
CheXpert - Content: 224,000 chest X-rays - Labels: 14 conditions - Access: Free - Link: https://stanfordmlgroup.github.io/competitions/chexpert/
RSNA Pneumonia Detection Challenge - Content: 30,000 chest X-rays - Labels: Pneumonia bounding boxes - Access: Kaggle - Link: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge
The Cancer Imaging Archive (TCIA) - Content: Cancer imaging across modalities - Access: Free - Link: https://www.cancerimagingarchive.net/
Public Health / Surveillance
CDC Data - FluView: Influenza surveillance - COVID Data Tracker: COVID-19 data - WONDER: Mortality, natality data - Link: https://data.cdc.gov/
WHO Global Health Observatory - Content: Global health statistics - Topics: Mortality, disease burden, risk factors - Link: https://www.who.int/data/gho
Johns Hopkins COVID-19 Data - Content: Global COVID-19 cases, deaths, vaccinations - Updated: Daily - Link: https://github.com/CSSEGISandData/COVID-19
Synthetic / Benchmark
Synthea 🎯 - Content: Synthetic patient generator - Creates: Realistic EHR data - Use: Development, testing, education - Link: https://synthetichealth.github.io/synthea/
Tools and Software
Machine Learning Frameworks
Scikit-learn 🎯 - Language: Python - Focus: Classical ML algorithms - Best for: Tabular data, rapid prototyping - Docs: https://scikit-learn.org/
PyTorch 🎯 - Language: Python - Focus: Deep learning - Best for: Research, flexibility - Docs: https://pytorch.org/
TensorFlow / Keras 🎯 - Language: Python - Focus: Deep learning - Best for: Production deployment - Docs: https://www.tensorflow.org/
XGBoost 🎯 - Language: Python, R, others - Focus: Gradient boosting - Best for: Tabular data, competitions - Docs: https://xgboost.readthedocs.io/
Healthcare-Specific Libraries
lifelines - Focus: Survival analysis - Link: https://lifelines.readthedocs.io/
scikit-survival - Focus: Survival analysis with ML - Link: https://scikit-survival.readthedocs.io/
MNE-Python - Focus: EEG/MEG analysis - Link: https://mne.tools/
nibabel - Focus: Neuroimaging data (fMRI, MRI) - Link: https://nipy.org/nibabel/
Fairness and Explainability
Fairlearn (Microsoft) 🎯 - Focus: Fairness assessment and mitigation - Link: https://fairlearn.org/
AI Fairness 360 (IBM) 🎯 - Focus: Fairness metrics and algorithms - Link: https://aif360.mybluemix.net/
SHAP 🎯 - Focus: Model explanations - Link: https://shap.readthedocs.io/
LIME - Focus: Local explanations - Link: https://github.com/marcotcr/lime
InterpretML (Microsoft) - Focus: Interpretable models (EBM) - Link: https://interpret.ml/
Deployment and MLOps
MLflow - Focus: ML lifecycle management - Link: https://mlflow.org/
Weights & Biases - Focus: Experiment tracking, collaboration - Link: https://wandb.ai/
BentoML - Focus: Model serving - Link: https://bentoml.com/
Communities and Forums
Online Communities
Reddit: - r/MachineLearning - ML research and news - r/datascience - Data science practice - r/healthinformatics - Healthcare informatics - r/publichealth - Public health discussions
Stack Overflow: - Tags: machine-learning, healthcare, scikit-learn, tensorflow
Cross Validated (stats.stackexchange.com): - Statistical questions, study design, interpretation
Professional Organizations
American Medical Informatics Association (AMIA) - Focus: Health informatics, clinical informatics - Benefits: Conferences, networking, journals - Link: https://amia.org/
Healthcare Information and Management Systems Society (HIMSS) - Focus: Health IT, digital health - Link: https://www.himss.org/
American Public Health Association (APHA) - Focus: Public health practice and research - Link: https://www.apha.org/
Society for Medical Decision Making (SMDM) - Focus: Clinical decision making, modeling - Link: https://smdm.org/
Conferences
AI/ML in Healthcare: - MLHC (Machine Learning for Healthcare) - August - CHIL (Conference on Health, Inference, and Learning) - April - AMIA Annual Symposium - November
General ML: - NeurIPS - December - ICML - July - ICLR - May
Public Health: - APHA Annual Meeting - November - Society for Epidemiologic Research - June
Staying Current
Blogs
Distill.pub 🎯 - Beautiful, interactive ML explanations - High quality, peer-reviewed - Link: https://distill.pub/
Towards Data Science - Medium publication on data science - Practical tutorials and case studies
Google AI Blog - Google AI research updates - Link: https://ai.googleblog.com/
OpenAI Blog - OpenAI research and developments - Link: https://openai.com/blog/
DeepMind Blog - DeepMind research highlights - Link: https://www.deepmind.com/blog
Podcasts
The TWIML AI Podcast - Interviews with AI researchers and practitioners - Link: https://twimlai.com/
Data Skeptic - Data science, statistics, ML topics - Link: https://dataskeptic.com/
Linear Digressions - Machine learning explained accessibly - Link: https://lineardigressions.com/
Practical Skill Development
Kaggle
Healthcare Competitions: - RSNA Pneumonia Detection - Diabetic Retinopathy Detection - SIIM-ACR Pneumothorax Segmentation
Learn: Kaggle Learn micro-courses on ML, deep learning, ethics
DataCamp / Coursera Projects
Guided Projects: - Clinical data analysis - Medical image classification - Survival analysis - Time series forecasting
Advanced Topics
Causal Inference
“The Book of Why” by Judea Pearl - Accessible introduction to causality - Why correlation ≠ causation matters for AI
“Causal Inference: What If” by Hernán and Robins - Rigorous causal inference methods - Free online: https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
Course: Introduction to Causal Inference (Brady Neal) - Free online course - Link: https://www.bradyneal.com/causal-inference-course
Survival Analysis
“Survival Analysis: A Self-Learning Text” by Kleinbaum and Klein - Accessible introduction - Clinical focus
“Modeling Survival Data” by Therneau and Grambsch - Advanced methods, R focus
Time Series
“Forecasting: Principles and Practice” by Hyndman and Athanasopoulos - Comprehensive forecasting methods - Free online: https://otexts.com/fpp3/
“Deep Learning for Time Series Forecasting” by Jason Brownlee - Practical guide to DL for time series
Privacy-Preserving ML
Differential Privacy Book (Dwork and Roth) - Foundational text on differential privacy - Link: https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf
Federated Learning Tutorial - Distributed ML preserving privacy - Link: https://federated.withgoogle.com/
Career Development
Certifications
Google Professional ML Engineer - Cloud-based ML deployment - Link: https://cloud.google.com/certification/machine-learning-engineer
AWS Certified Machine Learning - Specialty - ML on AWS platform - Link: https://aws.amazon.com/certification/certified-machine-learning-specialty/
Clinical Informatics Board Certification (ABPM) - For physicians interested in informatics - Link: https://www.abpm.org/
Job Boards
- AI/ML Healthcare Jobs: https://www.aijobs.com/
- Health Informatics Jobs: https://www.himss.org/resources/jobmine
- Academic Positions: https://academicjobsonline.org/
Conclusion
The field of AI in public health is rapidly evolving. Stay current by: - ✅ Following 2-3 key journals in your area - ✅ Subscribing to 1-2 newsletters - ✅ Attending 1-2 conferences annually - ✅ Practicing on real datasets - ✅ Engaging with online communities - ✅ Contributing to open-source projects
Remember: The best way to learn is by doing. Pick a project, get your hands dirty with code, and learn iteratively!
Ready to dive deeper? Start with one resource from each category that matches your current level and interests.