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Dr Dionisio Acosta Mena Acosta Mena
222 Euston Road
Tel: +44 (0)20 3549 5289
Fax: +44 (0)20 7679 8002
Dr Dionisio Acosta Mena Acosta Mena profile picture
  • Honorary Associate Professor
  • Institute of Health Informatics
  • Faculty of Pop Health Sciences

I received a BSc in Computer Science Engineering (1992; Non-uniform parallel random variate generation using Gibbs Sampling) from Simon Bolivar University, Venezuela, under the supervision of Dr. Luis R. Pericci. I was awarded a PhD in Biomedical Engineering (2002; Statistical Classification of Magnetic Resonance Imaging Data) from the University of Sussex, UK, under the supervision on Dr. Des Watson and Dr. Adrian Thomas.

Research Themes
Research Summary

My research interests are in the application of machine learning, signal/image processing and computational models for the implementation of systems to improve clinical practice and healthcare service delivery. The following are examples of the research I have conducted and supervised:

Clinical Decision Support Systems

Machine Learning in Healthcare & Biomedicine

  • GSK-UCL Collaboration: Machine Learning and Computational Models of Disease Trajectories from Electronic Health Record Data for Drug Discovery (J. P. Casas, C. Dale, D. Acosta (PI), £512K, ongoing).
  • Predicting Cardiovascular Outcomes using Proteomic Data in the British Women’s Health and Heart Study using Machine Learning Methods (J. P. Casas (PI), D. Acosta, ongoing).
  • Supervised Classification of Parkinson Disease Pharmacotherapy Management Scenarios Using Wearables Data (V. Nguyen, MSc Project, 2017).
  • Predicting Early Pregnancy Outcomes using Urine MALDI-ToF Mass Spectrometry (R. Zmuidinaite, MSc Project, 2017).
  • Automatic Classification of 1H MR Spectroscopy of Brain Masses Using Machine Learning Methods (W. Shi, MSc Project, 2017).
  • Automatic Verification of Computerised Clinical Pathways (S. Satwilkar, MSc Project, 2016).
  • Prediction of Hospital Admission using A&E Attendance Data: A Comparison of Classical and Machine Learning Methods on 388,910 attendances. (R. Shackleton, MSc Project, 2016).
  • Predicting Number of Emergency Hospital Admissions using Time Series Models (I. Odera, MSc Project, 2014).
  • Predicting Pulmonary Embolism in Post-Operative Orthopaedic Patients (P. Kesara, MSc Project, 2014).
  • Matching A&E Supply To Demand using Computational Scheduling Algorithms (R. Wortham, MSc Project,2014).

Data-Driven Clinical Trials

  • EHR4CR Project Electronic Health Records for Clinical Research (D. Kalra (PI), D. Acosta (co-I) UCL, Euros 16MM, 2016, InSite Platform)
  • Automatically identifying patients for clinical trials in the Cardiovascular Foundation of Colombia (FCV, Bucaramanga). (J.P. Casas (PI), N. Romero, D. Acosta, ongoing)
  • Clinical Trial Recruitment using OpenEyes Electronic Patient Record (MSc Project M. Clemo)

PhD Students

  • Cancer Early Diagnosis using Disease Trajectories Machine Learning Models from Electronic Health Record Data of the National Guard Health Affairs, Saudi Arabia (A. AlFayez., ongoing).
  • Re-use of EHR Data to Support Clinical Trials Design and Execution (J. W Choi, ongoing)
  • Integration of Electronic health Records with Genomic data for Drug discovery (K. Khan, ongoing)
  • Diagnostic and Treatment Action Selection in Chest Pain Rapid Access Clinics (K. Zacharias, ongoing)
  • Quality framework for semantic interoperability in health informatics: definition and implementation (A. Moreno-Conde, PhD, 2016).

Keywords:  machine learning, clinical decision support systems, statistical decision theory, pattern recognition, medical imaging.

Teaching Summary

I am the Director of the Health Informatics Programme. I lecture:

  • Decision Supports Systems
  • Electronic Health Records
  • Machine Learning in Healthcare & Biomedicine
Academic Background
2002   Doctor of Philosophy University of Sussex
1992   Bachelor of Science Universidad de Simon Bolivar
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