Our research integrates large-scale data from the Acutelines research biobank including multi-omics profiles, waveform signals (ECG/PPG), clinical records (laboratory parameters, vital signs, EHRs), medical imaging, and population cohorts. By combining advanced data science with clinical insight, we aim to identify actionable biomarkers, mechanistic pathways, and predictive models that support real-time decision-making in emergency and critical care.
Acute diseases such as sepsis remain a major challenge in emergency and critical care due to their rapid onset, complex mechanisms, and high variability between patients. To address this complexity, the Acutelines Data Science Group develops explainable multimodal AI models that integrate heterogeneous data from molecular, physiological, and clinical domains. By combining multi-omics profiles, vital signs, laboratory parameters, waveform signals, and patient facial photos, we aim to build interpretable models that can support early recognition, risk stratification, and personalized interventions. Our current research projects include:
Through these projects, we aim to transform multimodal data into clinically actionable insights and develop explainable AI systems that can be trusted and implemented in real-world healthcare.
Acute diseases such as sepsis require rapid and accurate clinical decisions, yet current tools often fail to capture the complexity of patient physiology and disease progression. By integrating molecular, physiological, and clinical data, our research provides a comprehensive and real-time view of patient status, improving clinicians’ ability to anticipate and respond to critical changes.
The explainable multimodal AI systems developed by our group aim to support early recognition, optimize triage, and guide personalized interventions in emergency and critical care. These models help clinicians interpret complex data, predict deterioration before it occurs, and tailor treatments to individual patients.
Ultimately, our work contributes to a more precise, data-driven healthcare system, improving patient outcomes while reducing diagnostic uncertainty and the burden on healthcare providers.
