This course covers the basics of the coding language, Python. It starts with syntax for beginners and covers a variety of practical topics needed for the language.

This class covers the basic usage and installation of RDKit (an open-source cheminformatics software (https://www.rdkit.org/). This tutorial includes loading molecules, drawing molecules, molecular fingerprint analysis, similarity metrics, and more.

This course covers the basics of the coding language, R (Posit). It starts with syntax for beginners and covers a variety of practical topics needed to use the language for medical informatics purposes. This class also covers a basic understanding of Linear Regression models.

This lecture will provide an overview of the “Tidyverse” approach to data analysis. We will explore basic functions from the R packages dplyr and ggplot2 and using the “tidy data” concept.

Structured Query Language (SQL) is used to create and query relational databases. This lecture teaches you how to write and read SQL statements. Practicing clinical informaticians use this skill more often than any other.

Master the essential Unix/Linux commands and tools that drive high-performance and cloud computing. This hands-on class will equip you with the skills to efficiently navigate, organize, and process data.

This class builds upon Introduction to Unix/Linux Programming, with more commands useful for navigating large Unix based servers via command line. Also includes demonstrations of regular expressions to manipulate text using https://regex101.com/.

This workshop will introduce participants to the relationship between health information technology (HIT) and health literacy, i.e. the consumers’/patients’ crucial ability to find, understand, and use health information.

This workshop will introduce participants to the concept of Big Data analytics and will teach attendees to apply critical thinking skills to make important clinical decisions that will improve quality, lower cost, and improve health outcomes.

This course on “human factors” focuses on how people interact with products, systems, and processes. In our case, we examine how we use and design EHRs, medical devices, and the visualization of medical data. 

Cybersecurity – How we seek to protect systems, networks, and programs from digital attack with a focus on the protection of patient data. Qualitative Methods – Review the many methods of observations, interviews, focus groups, and unobtrusive measures that allow us to better understand behaviors and attitudes of patients and providers.

Clinical decision support is how we implement clinical guidelines into the practice of medicine. In this talk we explore several types of CDS implementations and their impact on clinical care. Learners will understand how to specify CDS rules and codify them consistently.

Modeling and HL7 teaches methods for doing object oriented modeling and provides learners with tools to represent systems formally.  We also discuss how to use HL7 FHIR to query, modify and delete data, which can be helpful in linking externally developed CDS to an EHR.

Medical Terminology and standards are used to create interoperability that can help to ensure that data transferred retains its computable meaning. This talk will ensure that you know relevant terminological standards and representative tooling.

This class provides a gentle introduction to different sorts of knowledge representation systems and the reasoning methods that are suitable for them, i.e. propositional logic, description logics and first order logic. Snomed CT and the Basic Formal Ontology are used as specific examples.

Students will learn the pragmatic principles used in the OBO Foundry and the BioPortal ontologies and how they differ from the realism-based principles underlying the Basic Formal Ontology and the Ontology for General Medical Science (OGMS). They will also learn that distinct concept-based ontologies rarely use the same set of principles so that they promote data-silos rather than avoiding them.

This class teaches students how to use Referent Tracing as a methodology to design high-quality realism-based ontologies, to identify shortcomings in information systems and to perform ontology-based quality control on databases.

This course covers some of the basics of the SPARQL triple-store query language. It starts with background about ontologies and triple stores and demonstrates a range of SPARQL queries of increasing complexity.

This course will be a brief introduction to using natural language processing for doing information extraction for biomedical science.  Topics covered will include a brief overview of NLP techniques such as parts-of-speech and named entity identification and how such techniques can be used to perform biomedical informatics tasks.

The first of two introductory sessions on machine and deep learning topics. In this class we will cover general background on ML/DL and walkthrough a few machine learning examples using decision trees and leverage a publicly available dataset. This class will involve hands-on programming with Python using Jupyter notebooks.

In part two of this introduction to machine learning, we will explore and implement other types of models including random forest and logistic regression. We will also introduce neural networks and further explore topics in machine and deep learning such as hyperparameters, under/overfitting, and performance metrics.

The CANDO platform uses advanced computational methods to look at how thousands of potential drugs interact with a wide range of biological targets at multiple scales, all at once.  Researchers are able to quickly identify, repurpose, and design promising compounds for further study and development by analyzing this data using advanced AI methods.

The CANDO platform uses advanced computational methods to look at how thousands of potential drugs interact with a wide range of biological targets at multiple scales, all at once.  Researchers are able to quickly identify, repurpose, and design promising compounds for further study and development by analyzing this data using advanced AI methods.

This lecture will introduce the fundamentals of Explainable AI (XAI) and its importance in biomedical informatics. Through real data analysis examples, we will explore how XAI methods can be applied to biomedical research.

Learn how clinical genomic data is analyzed, interpreted, and integrated with electronic health records (EHR) using advanced bioinformatics tools. This course covers key concepts in genomic data processing, variant analysis, EHR integration, and their applications in precision medicine, with a focus on clinical genomic diagnosis.

This talk will define digitized biomedical image data, image processing, feature extraction, image classification, object detection, image segmentation, and image generation. Attendees will get a brief overview of both traditional and AI-centric approaches to these applications, with demonstrations in digital and computational pathology.

Explore key pathology systems, data sources, and the integration of LIS, Middleware, and EHR in laboratory workflows. Learn how informatics and digital pathology enhance disease diagnosis and research by analyzing laboratory data and anatomic pathology images, empowering biomedical practitioners and researchers to better understand illnesses.