Multimodal deep learning
Models that learn jointly from text, image, tabular, and time-series patient data.
Research Assistant • Data Scientist • PhD Student
Md Faisal Kabir
Intelligent Systems Group, Kiel University • Christian-Albrechts-Universität zu Kiel • Kiel, Germany
My PhD research centres on developing a recommendation system for diagnosis support in emergency medicine. To get there I work on AI-supported integration of heterogeneous clinical data — multimodal deep learning across text, image, tabular, and time-series sources, together with synthetic data generation and missing-value imputation.
Kiel, Germany
Research focus
From heterogeneous clinical data to decision support
Synthetic data & imputation
Generating realistic clinical data and recovering missing values so models stay robust on incomplete records.
Diagnosis support
Recommendation systems that rank likely diagnoses, treatments, and next measurements in emergency care.
About
Data science for time-critical healthcare
My PhD at Christian-Albrechts-Universität zu Kiel is a recommendation system for diagnosis support in emergency medicine, built on AI-supported integration of multimodal clinical data. I work in the Intelligent Systems Group at Kiel University on machine learning, deep learning, and clinical data quality.
Scientific work
Latest publication
Diagnosis and Treatment Support as an AI-Driven Assistant System for TeleNotary Emergency Care
A deep multimodal model over time-series, tabular, and free-text patient data that ranks likely diagnoses, treatments, and next measurements for pre-hospital care.
Portfolio
Latest project
KIT2
KIT2 — AI assistant for TeleNotArzt emergency care
Completed collaboration with RWTH Aachen University. A deep multimodal model that ranks likely diagnoses, treatments, and next measurements for pre-hospital emergency care, with supporting work on missing-value imputation and synthetic data generation.
All projectsGet in touch
Contact
Online
ORCID • Scholar • ResearchGate • LinkedIn • GitHub