Research across clinical prediction, multimodal learning, and translational systems that hold up in practice.
The work spans renal outcomes, multimodal post-training, clinical NLP, robotics vision, and registry-scale clinical research. The common thread is systems that remain useful after contact with real data, real workflows, and real deployment constraints.
On this page
Programs
Three current programs with the strongest research weight.
Doctoral focus
Renal outcome prediction with clinically usable lead time
Doctoral work on prediction of renal outcomes with an emphasis on fairness, interpretability, and decisions that can be acted on in practice.
Methods
temporal modeling / fairness evaluation / interpretability analysis
Metrics
AUROC / calibration / lead-time utility
Datasets
nephrology cohorts / longitudinal clinical records
Post-training
MMLM post-training for clinical reasoning
Post-training multimodal models with attention to grounded reasoning, evaluation quality, and deployment-minded reliability.
Methods
post-training / evaluation design / multimodal alignment
Metrics
task accuracy / groundedness / failure analysis
Datasets
multimodal medical corpora / instruction datasets
Registry-scale work
Liver transplant registry modeling at EU scale
Research infrastructure and modeling on the largest single-institute liver transplant registry in Europe, built for rigorous translational analysis.
Methods
registry curation / outcome modeling / cohort design
Metrics
cohort completeness / discrimination / clinical interpretability
Datasets
liver transplant registry / linked clinical records
Publications and preprints
Publications
Peer-reviewed journal articles and accepted manuscripts.
Forecasting ICU Acute Kidney Injury with Actionable Lead Time Using Interpretable Machine Learning: Development and Multi-Center Validation
Extends kidney injury prediction toward real lead-time utility instead of retrospective signal mining alone.
Summary
Interpretable machine-learning models are developed to forecast ICU acute kidney injury with actionable lead time, including multi-center validation to test generalizability.
Contributors Abdulla Zahi Hourani; Zuzanna Jakubowska; Jolanta Malyzsko
Prognostic Value of Different Iron Status Definitions in Congestive Heart Failure: A Retrospective MIMIC-IV Analysis of Risk Stratification and Mortality
Shows how more precise phenotyping can materially change downstream prognostic separation.
Summary
Retrospective MIMIC-IV analysis compares multiple iron-status definitions for one-year mortality risk stratification in congestive heart failure.
Contributors Hourani A; Surmeli A; Devarapalli S
Clinical Picture and Outcomes in Patients Diagnosed with Brain Abscess
Pairs clinically grounded infection research with a stronger systems view of hospitalization trajectories.
Summary
Retrospective cohort study characterizes the presentation and outcomes of patients with brain abscess, highlighting factors linked to hospital course and prognosis.
Contributors Furman-Dlubala A; Bednarska A; Radkowski M; Paciorek M; Kolodziejska J; Laskus T; Bursa D; Porowski D; Makowiecki M; Hourani A et al.
The Impact of COVID-19 Non-Pharmaceutical Interventions on Notifiable Infectious Diseases in Poland: A Comprehensive Analysis from 2014-2022
Connects model thinking to surveillance, policy shifts, and long-horizon epidemiologic interpretation.
Summary
A population-level analysis of notifiable infectious disease trends in Poland from 2014 to 2022, covering the period shaped by COVID-19 interventions.
Contributors Abdulla Zahi Hourani; Abdelrahman Abdelsalam; Arman David Surmeli
Impact
Outputs, recognition, and infrastructure.
Awards
Repeated placements in medicine, cardiology, and plenary sessions
Conference work has already produced first- and third-place results, suggesting strong translational framing as well as technical depth.
Doctoral focus
Fair and transparent renal outcome prediction
Current PhD work centers on interpretable risk models for nephrology with attention to equity-aware evaluation and clinically faithful stratification.
Infrastructure
Project Quintessence and research infrastructure
Current work extends beyond papers into research infrastructure, registry-scale collaboration, multimodal systems, robotics, and open technical builds.
Methods stack
- Interpretable machine learning
- Temporal forecasting and lead-time evaluation
- MMLM post-training and multimodal evaluation
- Clinical NLP and LLM-assisted extraction
- Registry-scale cohort design and data curation
- Calibration, external validation, and failure analysis
- Computer vision and agentic workflow design
- Human-in-the-loop review for safety-sensitive outputs
Institutions and partners
- Medical University of Warsaw
- European Renal Association
- Khalifa University
- Project Quintessence collaborators
- Clinical and industry research partners
Contact
Research collaborations work best when the clinical question and the technical execution are both taken seriously.
If the overlap is prediction, post-training, registry science, clinical NLP, or translational evaluation, that is likely a good fit.
On this page