Dr Venet Osmani
PhD (SETU, Ireland)
Information School
Senior Lecturer in Data Science


+44 114 222 2676
Full contact details
Information School
Room 212
Regent Court (IS)
211 Portobello
Sheffield
S1 4DP
- Profile
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Before joining the University of Sheffield, I was a tenured Senior Researcher in Digital Health Centre at Fondazione Bruno Kessler research institute, Italy where I led a group of researchers and students in tackling challenges in medicine using machine learning methods. I was also a lecturer in the department of Psychology and Cognitive Science at University of Trento, Italy.
- Research interests
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My research interests are in developing machine learning methods, to address some of the fundamental questions in medicine. These include:
- predictive modelling
- explainable AI
- generative adversarial approaches (GAN)
- causal inference
- health inequality and bias
My work focuses on analysis of large-scale, longitudinal health records, including:
- biomarkers
- imaging
- multi-omics
- routine health care data
The main aims are to optimise treatment strategies, improve patient care, and provide novel insights to health institutions.
Apart from clinical data, I also work on incorporating human behaviour data, such as those generated from wearable devices, with a particular focus on mental health.
The overarching objective of my research is to integrate predictive modelling in the bedside and bring the acquired evidence back, in a continuously improving feedback loop, consequently establishing a learning health system.
Through my interdisciplinary research I have established and maintain close collaborations with some of the leading research and US clinical institutions, including Massachusetts Institute of Technology, Cleveland Clinic, Mayo Clinic, and Harvard School of Public Health as well as leading European institutions. These and many other international collaborations are reflected in over a hundred peer-reviewed journal and conference publications.
- Publications
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Books
Journal articles
- Hyperlactatemia and altered lactate kinetics are associated with excess mortality in sepsis. Wiener klinische Wochenschrift.
- 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. PLOS Digital Health, 1(11). View this article in WRRO
- Prediction of stress levels in the workplace using surrounding stress. Information Processing and Management, 59(6).
- Machine Learning Models predict liver steatosis. Zeitschrift für Gastroenterologie, 60(08), e689-e689.
- Delirium prediction in the ICU: designing a screening tool for preventive interventions. JAMIA Open, 5(2). View this article in WRRO
- Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation.. JMIR Med Inform, 10(3), e32949.
- Management of intoxicated patients – a descriptive outcome analysis of 4,267 ICU patients. BMC Emergency Medicine, 22(1).
- Red Cell Distribution Width Is Independently Associated with Mortality in Sepsis. Medical Principles and Practice, 31(2), 187-194.
- Deep ROC analysis and AUC as balanced average accuracy, for improved classifier selection, audit and explanation. IEEE Transactions on Pattern Analysis and Machine Intelligence. View this article in WRRO
- Underweight but not overweight is associated with excess mortality in septic ICU patients. Wiener klinische Wochenschrift, 134(3-4), 139-147.
- Prediction of blood lactate values in critically ill patients: a retrospective multi-center cohort study. Journal of Clinical Monitoring and Computing, 36(4), 1087-1097.
- Failure of lactate clearance predicts the outcome of critically ill septic patients. Diagnostics, 10(12). View this article in WRRO
- Machine learning models cannot replace screening colonoscopy for the prediction of advanced colorectal adenoma. Journal of Personalized Medicine, 11(10), 981-981. View this article in WRRO
- ICU-mortality in old and very old patients suffering from sepsis and septic shock. Frontiers in Medicine, 8.
- Use of eHealth platforms and apps to support monitoring and management of home-quarantined patients with COVID-19 in the province of Trento, Italy: app development and implementation. JMIR Formative Research, 5(5).
- Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation. BMC Medical Informatics and Decision Making, 21(1). View this article in WRRO
- The evolution of personalized healthcare and the pivotal role of European regions in its implementation. Personalized Medicine, 18(3), 283-294.
- Propensity-Adjusted Comparison of Mortality of Elderly Versus Very Elderly Ventilated Patients. Respiratory Care, 66(5), 814-821.
- Machine learning predicts mortality in septic patients using only routinely available ABG variables: a multi-centre evaluation. International Journal of Medical Informatics, 145.
- Sex-specific outcomes and management in critically ill septic patients. European Journal of Internal Medicine, 83, 74-77.
- Unobtrusive Stress Assessment Using Smartphones. IEEE Transactions on Mobile Computing, 20(6), 2313-2325.
- Benchmarking machine learning models on multi-centre eICU critical care dataset. PLoS ONE, 15(7). View this article in WRRO
- Blood lactate concentration prediction in critical care. Studies in Health Technology and Informatics, 270, 73-77.
- Monitoring and detecting faults in wastewater treatment plants using deep learning. Environmental Monitoring and Assessment, 192(2).
- 400: PREDICTING DELIRIUM RISK FOR THE FOLLOWING 24 HOURS IN CRITICALLY ILL PATIENTS USING DEEP LEARNING. Critical Care Medicine, 48(1), 182-182.
- Detecting dressing failures using temporal–relational visual grammars. Journal of Ambient Intelligence and Humanized Computing, 10(7), 2757-2770.
- Classification of bipolar disorder episodes based on analysis of voice and motor activity of patients. Pervasive and Mobile Computing, 31, 50-66.
- Stress modelling and prediction in presence of scarce data. Journal of Biomedical Informatics, 63, 344-356.
- Automatic Stress Detection in Working Environments From Smartphones’ Accelerometer Data: A First Step. IEEE Journal of Biomedical and Health Informatics, 20(4), 1053-1060.
- Smartphone app usage as a predictor of perceived stress levels at workplace. Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare.
- Smartphones in Mental Health: Detecting Depressive and Manic Episodes. IEEE Pervasive Computing, 14(3), 10-13.
- Mobile phones as medical devices in mental disorder treatment: an overview. Personal and Ubiquitous Computing, 19(2), 335-353.
- Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients. IEEE Journal of Biomedical and Health Informatics, 19(1), 140-148.
- An analysis of distance estimation to detect proximity in social interactions. Journal of Ambient Intelligence and Humanized Computing, 5(3), 297-306.
- Trade-offs in monitoring social interactions. IEEE Communications Magazine, 51(7), 114-121.
- Automatic Sensing of Speech Activity and Correlation with Mood Changes, 195-205.
- Mental health and the impact of ubiquitous technologies. Personal and Ubiquitous Computing, 17(2), 211-213.
- Monitoring Dressing Activity Failures through RFID and Video. Methods of Information in Medicine, 51(01), 45-54.
- Analysis of Social Interactions Through Mobile Phones. Mobile Networks and Applications, 17(6), 808-819.
- Technologies to monitor cognitive decline - A preliminary case study. Proceedings of the 3d International ICST Conference on Pervasive Computing Technologies for Healthcare.
- Human activity recognition in pervasive health-care: Supporting efficient remote collaboration. Journal of Network and Computer Applications, 31(4), 628-655.
- Automatically detecting Crohn’s disease and Ulcerative Colitis from endoscopic imaging. BMC Medical Informatics and Decision Making, 22(S6).
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review. JMIR Medical Informatics, 7(2), e12239-e12239.
Chapters
Conference proceedings papers
- Participants’ Experience and Adherence in Repeated Measurement Studies Among Office-Based Workers. Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers
- Gastroenterologist against the machine - opportunities and limitations of machine learning models for prediction of advanced adenoma. Zeitschrift für Gastroenterologie, 9 September 2021 - 11 September 2021.
- Data-Driven Analysis of Parkinson's Disease and its Detection at an Early Stage. Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare
- Policy Paper on Healthy Ageing – BFHA2020 Conference. Liječnički vjesnik, Vol. 142(supp 1)
- Enabling prescription-based health apps. Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare
- Investigating correlation between verbal interactions and perceived stress. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 25 August 2015 - 29 August 2015.
- Tell me your apps and I will tell you your mood. Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments
- Session details: Data modeling and information management for pervasive assistive environments. Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments
- Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients. Proceedings of the 5th Augmented Human International Conference
- Correlation of significant places with self-reported state of bipolar disorder patients. Proceedings of the 4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies", 3 November 2014 - 5 November 2014.
- Monitoring activity of patients with bipolar disorder using smart phones. Proceedings of International Conference on Advances in Mobile Computing & Multimedia - MoMM '13, 2 December 2013 - 4 December 2013.
- Preface. Proceedings of the 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, PervasiveHealth 2013
- Welcome Message from the Chairs. 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation, 14 November 2012 - 16 November 2012.
- Speech activity detection using accelerometer. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 28 August 2012 - 1 September 2012.
- Multi-Modal Mobile Sensing of Social Interactions. Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare, 21 May 2012 - 24 May 2012.
- Investigation of indoor localization with ambient FM radio stations. 2012 IEEE International Conference on Pervasive Computing and Communications, 19 March 2012 - 23 March 2012.
- Smart Phone Sensing to Examine Effects of Social Interactions and Non-sedentary Work Time on Mood Changes (pp 200-213)
- AID-ME: Automatic identification of dressing failures through monitoring of patients and activity Evaluation. Proceedings of the 4th International ICST Conference on Pervasive Computing Technologies for Healthcare, 22 March 2010 - 25 March 2010.
- Tuning to your position: FM radio based indoor localization with spontaneous recalibration. 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom), 29 March 2010 - 2 April 2010.
- Self-organising object networks using context zones for distributed activity recognition. Proceedings of the Second International Conference on Body Area Networks BodyNets, 11 June 2007 - 13 June 2007.
Theses / Dissertations
Other
Preprints
- Vital signs as a source of racial bias, Cold Spring Harbor Laboratory.
- Investigating Presence of Ethnoracial Bias in Clinical Data using Machine Learning, Cold Spring Harbor Laboratory.
- Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation (Preprint), JMIR Publications Inc..
- Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation, arXiv.
- Use of eHealth Platforms and Apps to Support Monitoring and Management of Home-Quarantined Patients With COVID-19 in the Province of Trento, Italy: App Development and Implementation (Preprint), JMIR Publications Inc..
- Information extraction from clinical notes: A systematic review for Chronic Diseases (Preprint), JMIR Publications Inc..
- Hyperlactatemia and altered lactate kinetics are associated with excess mortality in sepsis. Wiener klinische Wochenschrift.
- Professional activities and memberships
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I am an invited expert for the following governmental funding bodies:
- UK's Medical Research Council (MRC)
- UK's National Institute for Health Research (NIHR)
- European Commission (EC) in the following programmes:
- Horizon Europe / Horizon 2020
- EIC Pathfinder / Future and Emerging Technologies (FET)
- Main Health Programme
- Marie Curie Actions programmes
- Swiss National Science Foundation (SNSF)
- Dutch Research Council (NWO)
Furthermore, I am on the Editorial Board of BMC Medical Informatics and Decision Making journal as well as an Associate Editor of Frontiers’ Mobile and Ubiquitous Computing Journal.