Centre for Data Intensive Sciences and Applications

Webinarium om hälsodata och EU-projektet Health Data Sweden


  • När? 27 feb 2023 kl 12-13
  • Var? Online

Forskargruppen eHälsa och hälsodata i samverkan vid Uppsala universitet bjuder in till webinarium om hälsodata. På progammet finns bland annat:

– Vad innebär egentligen hälsodata och varför är det viktigt?
– Introduktion av EU-projektet Health Data Sweden (HDS)
– Sebastiaan Meijer (KTH) koordinator för HDS
– Maria Hägglund (UU) ansvarig för Uppsalas aktiviteter i HDS
– Stort utrymme för frågor och diskussion

Anmäl dig här  så kommer mer information ungefär en vecka innan webinariet.
Vi ses!

För frågor kontakta: Maria Hägglund (maria.hagglund@kbh.uu.se) eller Sara Riggare (sara.riggare@kbh.uu.se), Forskargruppen eHälsa och hälsodata i samverkan


Welcome to our first PhD-seminar November 4th


  • When? November 4th 14-16
  • Where? D1140 – Växjö (link will provided for those who wants to attend online)
  • Registration: We would like to know how many that will attend onsite/online in order to get some fika for those onsite. So please register by November 2nd https://forms.gle/ZwwgoQ4JK4e41BBR6


14.00-14.10     Welcome and practical information from Welf Löwe

14.10-14.55     Presentation and discussion: Visual Analytics for Explainable Machine Learning in a Nutshell – Angelos Chatzimparmpas

14.55 – 15.05  Coffee break

15.05 – 15.50  Presentation and discussion: Getting the most out of health data, combing the best of two worlds – Olle Björneld

15.50 -16.00    Sum up and plan for our next seminar on January 13th


Visual Analytics for Explainable Machine Learning in a Nutshell – Angelos Chatzimparmpas

Machine learning (ML) research has recently gained much attention, with various models proposed to understand and predict patterns and trends in data originating from various domains. Unfortunately, users find it harder to evaluate and trust the results of these models as they become more complex because most of their internal workings are kept in secret black boxes.

One possible solution to this problem is the explanation of ML models with visual analytics (VA) since it enables human experts to analyze large and complex information spaces such as data and model spaces. By doing so, evidence has shown an improvement in predictions and an increase in the reliability of the results.

This talk aims to provide an overview of the state-of-the-art in explainable and trustworthy ML with the use of visualizations, as well as the development of VA systems for each stage of a typical ML pipeline. Furthermore, we will briefly introduce some of these tools and discuss how such VA techniques can help us not only understand ML models but also do this in a human-centered and steerable way.

Getting the most out of health data, combing the best of two worlds – Olle Björneld

Machine learning driven knowledge discovery on real world data based on domain knowledge. Real world data does not comply with machine learning models very well and prediction models perform suboptimal if pre-processing of data is deficient.

Based on experience from medical registry studies using electronic health data (EHR) performed in collaboration with domain experts, data analyst and statistician an automatic feature engineering framework and method have been developed. The framework is called automatic Knowledge Driven Feature Engineering (aKDFE) and have been evaluated by machine learning pipeline.

Experiment shows that prediction models performs better if aKDFE is used without losing explainability, but more experiments need to be performed in other domains to fully quantify the results. The key aspect is how to concentrate and mine inherent knowledge in transaction data to optimal machine learning driven prediction models.

A warm welcome,

Welf & Diana

Summer/winter schools available through EUniWell


Here is a overview of Summer/Winter schools at Leiden University (in particular at Leiden Medical Center) that is available to us through EUniWell, might be of interest for some of you.

  • Data Science in Health and Disease – Leiden-Brazil Summer School (June 2021, online)
  • Population Health Management – LUMC Summer School (June 2021, online)
  • TechMed – LUMC Summer School (July 2021, online)
  • Artificial Intelligence & Value Based Healthcare – LUMC Summer School (August 2021, online)
  • Regenerative Medicine – Leiden Summer School (October 2021)
  • Translational Research on Neuromuscular Diseases – LUMC / ERN EURO-NMD / TREAT-NMD Winter School (December 2021)

More information and registration:


PhD course: eHealth – improved data to and from patients, 3 credits


In April 2019 we will give a new course for PhD-students in eHealth. The course will give an introduction to eHealth and health informatics including benefits and challenges with eHealth, examples of applications in use, register based epidemiology, decision support systems, overview and examples of research within the interdisciplinary field of health informatics.

Teaching in this course will be lectures online (via mymoodle) as well as 2 seminars where students will present and discuss papers from this field of research.

This course will be given in collaboration with  the eHealth Institute. We welcome PhD-students from DISA as well as other PhD-students at Linnaeus University who are interested in eHealth and health informatics.

  • Pace: Half time, distance learning with approximately 2 meetings on campus in Kalmar
  • Language: English
  • When: April 2019 (preliminary 1/4 – 28/4)
  • Contact: If you are interested in this course, please send an e-mail to Tora Hammar, tora.hammar@lnu.se

The eHealth Institute, Department of medicine and optometry, Linnaeus University will be responsible for the course.


Stort grattis till eHälsoområdet inom DISA!


Området eHälsa får nu ytterligare förstärkning under tre år med medel för att skapa en strategisk plattform för forskning och som samverkar med det omgivande samhället. Här finns alltså möjligheter att knyta ihop tvärvetenskaplig forskning vid olika fakulteterna med forskningen inom DISA

En strategisk plattform ska utmärkas av att den bedriver forskning inom ett ämnesområde med viss bredd och samverkar med aktörer i samhället. En plattform ska involvera medarbetare från minst tre fakulteter, ha strategisk angelägenhetsgrad och även utvecklingspotential. Ledningen har också prioriterat områden med potential för extern finansiering.

För mer information kontakta Göran Petersson, Professor i hälsoinformatik inriktning läkemedelsvetenskap, verksamhetsledare för eHälsoinstitutet och vår forskningskoordinator för eHälsomorådet inom DISA