Research

Health and clinical text

Language technology for hospitals and health research: clinical notes, drug safety and patient privacy.

Much of what is known about patients is written as free text: discharge letters, clinical notes, reports of side effects. We build methods that help clinicians and researchers use this text, while protecting patient privacy and working in the languages spoken in Swiss hospitals.

Recent work includes drafting discharge summaries in Italian together with clinicians, finding adverse drug reactions to support drug safety, and removing personal information from clinical records.

Projects

AutoDischarge

Semi-automated generation of discharge summaries

Helps doctors write discharge letters faster by drafting them from the patient record, for the doctor to check and complete.

Applied 2025–now · Innosuisse

M2P2

Medical, multilingual and privacy-preserving NLP in the clinical domain

Makes modern language technology usable in Swiss hospitals: across the country's languages and without exposing patient data.

Research 2025–2026 · Swiss National Science Foundation (SNF)

QUADRATIC

NLP in support of pharmacovigilance: Quality Adverse Drug Reaction Active Control

Finds reports of adverse drug reactions in clinical text, to support drug-safety monitoring.

Research 2024–2025 · Swiss National Science Foundation (SNF)

WRSD

Identifying the risk of, and preventing, work-related stress disorders

Explored how text and data can help identify the risk of work-related stress.

Applied 2022–2024

MisInfoCOV

Misinformation in social media during COVID-19

Studied how to identify misinformation on social media, and public stance towards health policies, during the pandemic.

Research 2021–2022

COVID-19 Twitter Monitor

Social media mining for health

Combined ten methods for analysing COVID-19 tweets into one public platform with interactive visualisations.

Research 2020–2021

Patient insights from social media

Mining patient insights in social media conversations

With Roche, extracted information from patient conversations online to support patient-centred drug development.

Applied 2019–2021 · Industry

SwissMADE

Automated detection of adverse drug events from older inpatients' records

Used NLP and data mining on electronic medical records from five Swiss hospitals to detect adverse drug events in older patients. Part of the National Research Programme NRP 74 'Smarter Health Care'.

Research 2017–2021 · Swiss National Science Foundation (NRP 74)

People

Publications