Citizen-Controlled

Citizen-controlled Data Science for Multiple Sclerosis Research

Started
April 1, 2018
Status
Completed
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Abstract

Multiple Sclerosis (MS) is a complex chronic disease whose manifestation depends on clinical, environmental and individual factors and for which prediction of individual progression is poor and often treatment decisions are hampered by the lack of objective parameters (e.g., related to fatigue).

MS data was employed as the use case within the MIDATA project which aims at developing an ethically fair and secure data infrastructure that permits collection, integration and analysis of diverse types of data under the control of the citizen/patient.

The task of SDSC within this project was to extract data from the doctor's reports collected and stored with the hospital software kisim. A doctor's report is a semi-structured text of a few to several dozen lines where each line is associated with a topic such as diagnosis, current state, history, MRI or medication. The neurology clinic at the university hospital of Zurich USZ has developed and is maintaining the database seantis to store MS patients records in a structured manner. So far, the seantis database has been filled manually by transcribing information from the doctor's reports to the corresponding fields.

People

Collaborators

SDSC Team:
Fernando Perez-Cruz
Lilian Gasser
Luis Salamanca

PI | Partners:

Institute of Molecular Systems Biology:

  • Dr. Ernst Hafen

More info

Department of Computer Science:

  • Prof. Dr. Gunnar Rätsch
  • Prof. Dr. Christian Holz
  • Dr. Cristobal Esteban Aizpiri
  • Liliana Barrios

More info

Klinik für Neurologie:

  • Dr. med. Andreas Lutterotti
  • Marc Hilty
  • Dr. med. Roland Martin

More info

Institute for Medical Informatics:

  • Dr. François von Kaenel

More info

Scientific IT Services

  • Bräunlich Gerhard

More info

description

Goal:

Semi-automatic update of the MS database seantis using the doctor's reports.

Approach:

  • Build embedding of doctor's reports using Doc2Vec where one text line corresponds to one document.
  • Multi-class classification of text lines using embedding vectors as features and manually assigned labels as targets. This intermediate step allows to predict text line labels for new  unseen doctor's reports.
  • For specific parts of seantis (MS diagnosis, MRI information, ...), tailored classification procedures were developed to predict columns of interest, e.g. MS diagnosis type, type of MRI (spinal or cranial) and whether new and/or contrast medium enhancing lesions were detected.

Impact:

Facilitate the update of the seantis database by providing predictions for fields of interest based on extracted information from doctor's reports.

Gallery

Figure 1: General overview
Figure 2: Applied methodology

Annexe

Additionnal resources

Bibliography

Publications

Paitz, Patrick; Chmiel, Ma\lgorzata; Husmann, Lena; Volpi, Michele; Kamper, Francois; Walter, Fabian"Generic seismic mass-movement detection leveraging unsupervised statistical learning methods"IUGG23-07422023

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