Predictive Diagnostics & Risk Scoring
Develop machine learning models to predict disease onset, patient risk, and clinical outcomes using electronic health records (EHR), lab results, and other structured clinical data.
56 courses
Learn foundational data science, statistical analysis, and machine learning techniques by building a practical health prediction model using Python.
Learn to build and evaluate predictive machine learning models to forecast patient health risks and survival rates using clinical data.
Understand how machine learning algorithms analyze clinical data, predict patient outcomes, and transform healthcare delivery, even if you have no prior coding experience.
Prepare raw datasets for predictive modeling by mastering data cleaning, transformation, and feature extraction techniques using MATLAB.
Apply machine learning to a realistic patient journey, learning to analyze clinical data, handle de-identification, and model healthcare outcomes through written guides.
Learn to analyze epidemiological data and build predictive models that forecast disease spread using real-world datasets.
Learn to predict future trends and analyze time-to-event data using modern machine learning techniques and statistical verification.
Build, validate, and interpret predictive machine learning models using MATLAB to solve real-world data challenges, even with minimal programming experience.
Learn to analyze clinical reasoning, identify learner difficulties, and apply structured feedback models to guide students and residents in healthcare settings.
Master the business fundamentals of deploying artificial intelligence and machine learning in healthcare to drive operational efficiency and improve patient outcomes.
Learn to analyze and predict complex trends by modeling multiple variables simultaneously using real-world epidemiological data.
Master the techniques used to assess, compare, and refine regression and classification models to ensure reliable predictions in real-world scenarios.
A practical guide for healthcare professionals to understand and apply deep learning models using clinical data.
Bridge the gap between medicine and computer science by learning how to analyze clinical data and apply foundational machine learning models to real-world healthcare.
Learn to extract valuable clinical insights from unstructured medical records, clinical notes, and healthcare reports to drive better medical and administrative decisions.
Master the fundamentals of healthcare data modeling to guide preventive care and individualized treatment strategies.
Learn to query, extract, and analyze clinical data from Electronic Health Record databases using SQL to uncover valuable healthcare insights.
Learn to build transparent and trustworthy deep learning models for clinical decision support using state-of-the-art interpretability techniques.
Learn to analyze medical data, clean healthcare datasets, and build foundational predictive models using Python to support clinical decision-making.
Understand Bayesian and Markov networks to model uncertainty and make informed decisions in risk assessment, diagnosis, and predictive systems.
Showing 20 of 56 courses