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aiHealthLab - The Laboratory of Artificial Intelligence in Health

Our vision is to develop integrated intelligence for diagnostics and therapeutics.

Our group's overall research focuses in the emerging field of artificial intelligence applied to health. Artificial intelligence represents methods to process large amounts of data and recognize the patterns within. Health data is generated from a variety of sources and comprises molecular profiles, medical records, digital biomarkers and social media activity.

The mission of our group is to apply artificial intelligence in order to set standards, understand mechanisms and guide decisions in healthcare. Health data is generated in-house or is obtained through collaborations with pharmaceutical companies and hospitals. Our group uses analytics for personalized medicine, drug discovery and development, and support systems in clinics.

Research

The applied projects and research of the aiHealthLab are structured around questions regarding biomedical data and analytics.

biomeddata.jpgBiomedical data 

Produced in-house and accessed through collaborations with hospitals and pharmaceutical industries.

MEDICT (data pharma)

Digital ubiquity has transformed the way biopharmaceutical companies conduct clinical trials.

to MEDICT (data pharma)

Personalis – Personalized medical platform for patients with autoimmune diseases (data hospital)

Autoimmune diseases (AID) such as systemic lupus erythematosus and multiple sclerosis are stably increasing in particular in Western countries. Currently, ...

to Personalis – Personalized medical platform for patients with autoimmune diseases (data hospital)

Prediction of an antigen-specific antibody sub-repertoire (data in-house)

Immunotherapeutic drugs are important biologics and blockbusters against infectious diseases and cancer.

to Prediction of an antigen-specific antibody sub-repertoire (data in-house)

Analytics.jpgAnalytics

Informatics and data science are used for:

  • pre-processing, benchmarking of bioinformatic tools and reproducibility analysis,
  • development of algorithms and analysis using graph analysis, machine learning and deep learning,
  • integrated software development for real-world data and metadata, clinical decision systems and drug discovery.

Pre-processing

Benchmarking immunoinformatic tools

Antibody repertoires reveal insights into the biology of the adaptive immune system and empower diagnostics and therapeutics.

to Benchmarking immunoinformatic tools

Digital biomarkers in healthcare

The identification and application of biomarkers in the clinical and medical fields has an enormous impact on society.

to Digital biomarkers in healthcare

Algorithms

Predicting personal immune scenarios

Antibodies neutralize pathogens and are important therapeutics and diagnostics.

to Predicting personal immune scenarios

Machine learning for the automated interpretation of mass spectrometry data

Mass spectrometry (MS) is an analytical technique that measures the mass-to-charge ratio of ions.

to Machine learning for the automated interpretation of mass spectrometry data

Integration

Quality of real-world data

As the new front of Digital Biomarkers quickly expands, Real-World Data (RWD) is the largest domain of application, be it for data collection, hypothesis ...

to Quality of real-world data

Digital persona

Today we live digitally: a recorded life in an interconnected world. Each of us is a physical and a digital persona. A digital persona is the collection of ...

to Digital persona

Institute for Medical Engineering and Medical Informatics

FHNW University of Applied Sciences and Arts Northwestern Switzerland School of Life Sciences, Institute for Medical Engineering and Medical Informatics Hofackerstrasse 30 CH - 4132 Muttenz
More information about the location
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