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Profile
I am Professor of Clinical AI and Machine Learning at Queen Mary University of London, where I lead the Osmani Lab within Digital Environment Research Institute (DERI).
My interdisciplinary research focuses on analysis of large-scale, longitudinal health records, including biomarkers, imaging, multi-omics, and routine care data to optimise treatment strategies, improve patient care and mitigate health inequities. Apart from clinical data, I also work on incorporating human behaviour data, such as those generated from wearable devices and smartphones, with a particular focus on mental health.
Methodological aspects of my research include generative architectures, such as GANs VAEs, and Diffusion Models, for synthetic (artificial) patient data, explainable AI methods and sample complexity.
The overarching objective of my research is to integrate predictive modelling at the bedside and bring the acquired evidence back, in a continuously improving feedback loop, consequently establishing a learning digital health system.
My research is funded by UKRI’s Medical Research Council (MRC), Engineering and Physical Sciences Research Council (EPSRC), National Institute for Health and Care Research (NIHR), British Heart Foundation (BHF), and the European Commission (from FP7 to Horizon Europe). I have established collaborations with the leading clinical and research institutions worldwide to translate research into clinical practice.
More information can be found in https://venetosmani.com
Research
Publications
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Malaguti MC, Gios L, Giometto B et al. (publicationYear). Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study. nameOfConference
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Sheikhalishahi S, Bhattacharyya A, Celi LA et al. (2023). An interpretable deep learning model for time-series electronic health records: Case study of delirium prediction in critical care. nameOfConference
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Mamandipoor B, Wernly S, Semmler G et al. (2023). Machine learning models predict liver steatosis but not liver fibrosis in a prospective cohort study. nameOfConference
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Koköfer A, Mamandipoor B, Flamm M et al. (publicationYear). The impact of ethnic background on ICU care and outcome in sepsis and septic shock – A retrospective multicenter analysis on 17,949 patients. nameOfConference
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Rezar R, Mamandipoor B, Seelmaier C et al. (2023). Hyperlactatemia and altered lactate kinetics are associated with excess mortality in sepsis. nameOfConference
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Carrington AM, Manuel DG, Fieguth PW et al. (2023). Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation. nameOfConference
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Chierici M, Puica N, Pozzi M et al. (publicationYear). Automatically detecting Crohn’s disease and Ulcerative Colitis from endoscopic imaging. nameOfConference
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Mamandipoor B, Bruno RR, Wernly B et al. (publicationYear). COVID-19 machine learning model predicts outcomes in older patients from various European countries, between pandemic waves, and in a cohort of Asian, African, and American patients. nameOfConference
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Muñoz S, Iglesias CÁ, Mayora O et al. (2022). Prediction of stress levels in the workplace using surrounding stress. nameOfConference
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Bhattacharyya A, Sheikhalishahi S, Torbic H et al. (2022). Delirium prediction in the ICU: designing a screening tool for preventive interventions. nameOfConference
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