CV Technology Innovation Unit



We are working on applying machine learning techniques to vascular medicine clinical applications. In several of these applications we are trying to compare machine learning to regression and to traditional clinical methods. We are looking at topics such as: dosage prediction, outcomes prediction, behavior prediction, and risk stratification. Within these studies we are trying to answer questions like: what are the reduced clinical features which can be used to accurately predict an outcome? Can prediction be tuned based on financial considerations? Can we apply machine learning to define new measures of risk which are improved predictors of outcomes? In addition, we are assessing the limitations of applying machine learning to vascular medicine applications.​

Publications

Journals

  • Hussain A. Isma'eel, George E. Sakr, Mustapha Serhan, Nader Lamaa, Ayman Hakim, Paul C. Cremer, Wael A. Jaber, Torkom Garabedian, Imad Elhajj, Antoine Abchee, “Artificial Neural Network based model enhances risk stratification and reduces non-invasive cardiac stress imaging compared to Diamond Forrester and Morise risk assessment models: a prospective study," Journal of Nuclear Cardiology, pp. 1-9, First Online 21 February 2017. doi:10.1007/s12350-017-0823-1
  • Hussain A. Isma'eel, Paul C. Cremer, Shaden Khalaf, Mohamad M. Almedawar, Imad H. Elhajj, George E. Sakr, Wael A. Jaber, “Artificial neural network modeling enhances risk stratification and can reduce downstream testing for patients with suspected acute coronary syndromes, negative cardiac biomarkers, and normal ECGs," The International Journal of Cardiovascular Imaging, pp 1-10, December, 2015. DOI 10.1007/s10554-015-0821-9
  • Hussain A. Isma'eel, Mustapha Serhan, George E. Sakr, Nader Lamaa, Torkom Garabedian*, Imad Elhajj, Hadi Skouri, Antoine Abchee, “Diamond-Forrester and Morise risk models perform poorly in predicting obstractive coronary disease in Middle Eastern Cohort," International Journal of Cardiology, Volume 203, 15 January 2016, Pages 803–805.http://dx.doi.org/10.1016/j.ijcard.2015.11.011 (Correspondence)
  • Mohamad Almedawar, Hussain Isma'eel, George Sakr, Jihan Fathallah, Torkom Garabedian, Savo Bou Zein Eddine, Lara Nasreddine, Imad Elhajj, “Artificial Neural Network modeling using clinical and knowledge independent variables predicts salt intake reduction behavior," Cardiovascular Diagnosis and Therapy, Vol 5, No 3, 219-228, June 2015. doi: 10.3978/j.issn.2223-3652.2015.04.10
  • Isma'eel A Hussain, George Sakr, Robert Habib, Mohamad Musbah Almedawar, Nathalie Zgheib, Imad H. Elhajj1, “Improved Accuracy of Anticoagulant Dose Prediction using a Pharmacogenetic and Artificial Neural Network Based Method," European Journal of Clinical Pharmacology, Vol. 70, Issue 3, pp. 265-73, Springer, March 2014. doi:10.1007/s00228-013-1617-2.​

Invited Article

Imad Elhajj, Hussain Isma'eel, “Artificial Intelligence for Improving Health," Human and Health, pp. 30-31, Issue 44, Summer 2018.​

PO086 Machine Learning Approaches Improve Noninvasive Prediction of Ischemic From Non-Ischemic Cardiomyopathies
M Baydoun, L Safatly, J Walsh, OA Hassan, W Jaroudi, A El Hajj, ...
Global Heart 13 (4), 404

MS05. 2 ECG From Paper-based Back to Digital Format
M Baydoun, L Safatly, A El Bizri, A El Hajj, H Ghaziri, H Isma'eel
Global Heart 13 (4), 379

Posters

  • Hussain A. Isma'eel, George E. Sakr, Mustapha Serhan, Nader Lamaa, Torkom Garabedian, Imad Elhajj, Antoine Abchee, “Artificial Neural Network Based Model can Better Risk Stratify Patients Undergoing Stress Echocardiography or Nuclear Stress Test and Reduce Studies by >50%,"  European Society of Cardiology Congress, Rome, Italy, 27-31 August 2016.
  • Isma'eel H., Sakr G., Fathallah J., Almedawar MM., Bou Zein Eddine S., Al-Shaar L., Al Harith D., Kayrouz S., Nasreddine L., Elhajj I., “Artificial Neural Network Modeling using Clinical and Knowledge Features Predicts Salt Reduction Behavior," European Society of Cardiology Congress, Barcelona, Spain, 30 August - 3 September 2015.
  • Isma'eel H., Sakr G., Fathallah J., Almedawar MM., Bou Zein Eddine S., Al-Shaar L., Al Harith D., Kayrouz S., Nasreddine L., Elhajj I., “Artificial Neural Network Modeling using Clinical and Knowledge Features Predicts Salt Reduction Behavior," Fourth Annual AUB Basic Biomedical Research Day, Beirut, Lebanon, 15 February, 2014.​

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​Inv​estigators

  • Imad Elhajj, PhD​
  • Hussain Isma'eel, MD
  • Georges Sakr, PhD
  • Elie Shammas, PhD
  • Ali Al Hajj, PhD