Hasso-Plattner-Institut20 Jahre HPI
Hasso-Plattner-Institut20 Jahre HPI
  
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Digital Health Research Lab: In Depth Use Cases with EHRs (Wintersemester 2019/2020)

Lecturer: Prof. Dr. Erwin Böttinger (Digital Health - Personalized Medicine) , Dr. Hanna Drimalla (Digital Health - Personalized Medicine) , Dr. Claudia Schurmann (Digital Health - Personalized Medicine) , Jan-Philipp Sachs (Digital Health - Personalized Medicine) , Ariane Morassi Sasso (Digital Health - Personalized Medicine) , Suparno Datta (Digital Health - Personalized Medicine) , PhD Riccardo Miotto (Digital Health - Personalized Medicine) , Dr. Girish Nadkarni (Digital Health - Personalized Medicine) , Benjamin Glicksberg (Digital Health - Personalized Medicine) , Sedigheh Eslami

General Information

  • Weekly Hours: 4
  • Credits: 6
  • Graded: yes
  • Enrolment Deadline: 01.10.-30.10.2019
  • Teaching Form: Seminar
  • Enrolment Type: Compulsory Elective Module
  • Course Language: English
  • Maximum number of participants: 20

Programs & Modules

Digital Health MA

Description

The Digital Health Research Lab (DHRL) is a preparatory course for the master project of the research group of Professor Dr. Erwin Böttinger “Machine Learning on Real-World Health Data with Cloud-based In-Memory Database Computing” - but the course is also open to students not participating in this or another master project but interested in EHR and Mount Sinai Date Warehouse! 

In the master project, students will apply machine learning methods using electronic health record (EHR) data of the Mount Sinai Data Warehouse (MSDW). To prepare you for this task, you will learn the basics about data access with FIBER (a python library for accessing MSHS Data Warehouse) and data analysis with Python by hands-on tasks on a small EHR data set.

The master project is structured around three use cases: heart disease, mental health, and back pain.
In the DHRL we will provide you with background knowledge about these topics, discuss current research questions in these fields and lay the foundation for the master project.

Last but not least, we will also teach you the fundamentals of clinical human research including ethical guidelines.

 

In Depth description for Block 3 (Nov. 15/16)

Background: Large-scale electronic health record (EHR) data have demonstrated the potential to completely transform the process of scientific discovery in precision medicine. Simply put, EHR data is any and all data that is collected during routine interactions with a hospital system, including clinical (e.g., diagnoses) and administrative (e.g., billing) information among many others. The ‘real world data’ contained within EHRs provide a tremendous amount of useful biomedical information that go beyond traditional experimental collection processes. Statistical and machine learning approaches applied to EHR data have led to important and clinically-relevant discoveries across many medical domains.

Problem: There are many roadblocks that come with working with EHR data, including infrastructure, quality control procedures, and addressing systemic and local biases. It is imperative that researchers interested in utilizing EHR data take into account the various limitations of the data in order to design effective and robust experiments.

Goal: The purpose of this course is to introduce students to the world of clinical informatics, with a particular emphasis on best practices for working with EHR data for high-impact projects. We will delve into the following topics:

  • What data are contained in EHR?
  • What are limitations to EHR data?
  • What biases exist in such data and what are strategies to address them?
  • How can other -omics data effectively be tied to EHR in an extensible mult-modal framework?
  • What are common data models like OMOP and FHIR and why are they so important for EHR research?
  • How to design a robust EHR-based studies and ask important questions?
  • What are some state-of-the-art machine learning applications on EHR data?
  • How can we move beyond manuscripts to translate findings from EHR data into the real world, such as the generation of real world evidence (RWE)?

This workshop will consist of lectures, interactive discussions, and simple exercises

Requirements

If you want to join the class, please contact asap  Ariane Sasso for applying for access to the Mount Sinai Data Warehouse

Dates

18-OCT

Lecturer Topic

09:15 - 10:45

Hanna Drimalla

Understanding the Project and its Data - What and Why?

Background of EHR Research & Research Question

11:00 - 12:30

Hanna Drimalla

Theory to the 3 use cases I (Mental Health)

13:30 - 15:00

Ariane Morassi Sasso

Theory to the 3 use cases II (Hypertension) + ICD

15:15 - 16:45

TBD

IRB & Ethics

19-OCT

   

09:00 - 11:30

Jan Philipp Sachs

Smartsheet & Biased Data and Guidelines

11:30 - 14:00

Jan Philipp Sachs

Theory to the 3 use cases III (Back pain)

25 - OCT

   

09:15 - 10:45

Suparno Datta

Data Access & MSDW structure (Hands-On)

11:00 - 12:30

Suparno Datta

FIBER

13:30 - 15:00

Ariane Morassi Sasso

Phenotyping

15:15 - 16:45

Hanna Drimalla & Jan Philipp Sachs

Clinical Notes (NLP)

26 - OCT

   

09:00 - 11:30

Hanna Drimalla

Machine Learning with Python I

11:30 - 14:00

Suparno Datta

Machine Learning with Python II

Nov 15/16

Ben Glicksberg (HPI-MS)/Riccardo Miotto (HPI-MS)

Data mining in electronic health records

Jan 10/11

Girish Nadkarni (HPI-MS)/Claudia Schurmann

EHR-linked biobanking for genomic discovery

 

Place
G1 E 14/15 

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