Development of a decision support tool to improve the diagnosis and classification of myocardial infarction using signal processing and statistical ma
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Molecular. Genetics & Pop Health
Abstract
Project title: Development of a decision support tool to improve the diagnosis and classification of myocardial infarction using signal processing and statistical machine learning
Project summary
The aim of the project is to develop a new clinical decision support tool that will provide accurate individual probabilities of the diagnosis and classification of myocardial infarction for the evaluation of patients with acute chest pain in the Emergency Department. We will use our supervised learning approach in consecutive patients with suspected acute coronary syndrome from the HighSTEACS trial (n=54,000, clinicaltrials.gov NCT:01852523) as the training set to determine informative and non-informative variables. Validation and testing will be performed in patients from the HiSTORIC trial (n=34,000 split into two datasets, clinicaltrials.gov NCT03005158) to determine model calibration and generalization in new, unseen data during the training process of the machine learning algorithm. In both trials the diagnostic classification (type 1-5 myocardial infarction) was performed by a team of physicians using an established adjudication portal and web-interface. The project will develop new data mining tools to process the electrocardiograms and fuse multi-modal information from additional clinical tests to classify MI types.
Training outcomes:
- Practical understanding of the problems at the interface of clinical practice and data analytics, including the language barrier with niche terminology on both ends
- Developing expertise in time-series analysis, signal processing, and statistical machine learning in order to tackle large-scale challenging problems
- Programming skills: transforming algorithmic concepts to software tools, and developing interfaces which can be used by experts
Project summary
The aim of the project is to develop a new clinical decision support tool that will provide accurate individual probabilities of the diagnosis and classification of myocardial infarction for the evaluation of patients with acute chest pain in the Emergency Department. We will use our supervised learning approach in consecutive patients with suspected acute coronary syndrome from the HighSTEACS trial (n=54,000, clinicaltrials.gov NCT:01852523) as the training set to determine informative and non-informative variables. Validation and testing will be performed in patients from the HiSTORIC trial (n=34,000 split into two datasets, clinicaltrials.gov NCT03005158) to determine model calibration and generalization in new, unseen data during the training process of the machine learning algorithm. In both trials the diagnostic classification (type 1-5 myocardial infarction) was performed by a team of physicians using an established adjudication portal and web-interface. The project will develop new data mining tools to process the electrocardiograms and fuse multi-modal information from additional clinical tests to classify MI types.
Training outcomes:
- Practical understanding of the problems at the interface of clinical practice and data analytics, including the language barrier with niche terminology on both ends
- Developing expertise in time-series analysis, signal processing, and statistical machine learning in order to tackle large-scale challenging problems
- Programming skills: transforming algorithmic concepts to software tools, and developing interfaces which can be used by experts
Organisations
People |
ORCID iD |
| Nicholas Mills (Primary Supervisor) | |
| Dimitrios Doudesis (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| MR/N013166/1 | 01/10/2016 | 30/09/2025 | |||
| 2104455 | Studentship | MR/N013166/1 | 01/09/2018 | 31/05/2022 | Dimitrios Doudesis |