Publications

  1. Improved accuracy of anticoagulant dose prediction using a pharmacogenetic and artificial neural network-based method.

    Isma'eel HA, Sakr GE, Habib RH, Almedawar MM, Zgheib NK, Elhajj IH. Eur J Clin Pharmacol. 2014 Mar;70(3):265-73. doi: 10.1007/s00228-013-1617-2. Epub 2013 Dec 3. PMID: 24297344

  2. Artificial neural network modeling using clinical and knowledge independent variables predicts salt intake reduction behavior.

    Isma'eel HA, Sakr GE, Almedawar MM, Fathallah J, Garabedian T, Eddine SB, Nasreddine L, Elhajj IH. Cardiovasc Diagn Ther. 2015 Jun;5(3):219-28. doi: 10.3978/j.issn.2223-3652.2015.04.10. PMID: 26090333 Free PMC article.

  3. 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.

    Isma'eel HA, Cremer PC, Khalaf S, Almedawar MM, Elhajj IH, Sakr GE, Jaber WA. Int J Cardiovasc Imaging. 2016 Apr;32(4):687-96. doi: 10.1007/s10554-015-0821-9. Epub 2015 Dec 1. PMID: 26626458

  4. 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.

    Isma'eel HA, Sakr GE, Serhan M, Lamaa N, Hakim A, Cremer PC, Jaber WA, Garabedian T, Elhajj I, Abchee AB. J Nucl Cardiol. 2018 Oct;25(5):1601-1609. doi: 10.1007/s12350-017-0823-1. Epub 2017 Feb 21. PMID: 28224450

  5. A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type.

    Walsh JL, AlJaroudi WA, Lamaa N, Abou Hassan OK, Jalkh K, Elhajj IH, Sakr G, Isma'eel H. Scand Cardiovasc J. 2020 Apr;54(2):92-99. doi: 10.1080/14017431.2019.1678764. Epub 2019 Oct 18. PMID: 31623474

  6. High Precision Digitization of Paper-Based ECG Records: A Step Toward Machine Learning.

    Baydoun M, Safatly L, Abou Hassan OK, Ghaziri H, El Hajj A, Isma'eel H. IEEE J Transl Eng Health Med. 2019 Nov 7;7:1900808. doi: 10.1109/JTEHM.2019.2949784. eCollection 2019. PMID: 32166049 Free PMC article.

  7. A comparison of artificial intelligence-based algorithms for the identification of patients with depressed right ventricular function from 2-dimentional echocardiography parameters and clinical features.

    Ahmad A, Ibrahim Z, Sakr G, El-Bizri A, Masri L, Elhajj IH, El-Hachem N, Isma'eel H. Cardiovasc Diagn Ther. 2020 Aug;10(4):859-868. doi: 10.21037/cdt-20-471. PMID: 32968641 Free PMC article.

  8. Advances in telemedicine for the management of the elderly cardiac patient.

    Jamal NE, Abi-Saleh B, Isma'eel H. J Geriatr Cardiol. 2021 Sep 28;18(9):759-767. doi: 10.11909/j.issn.1671-5411.2021.09.004. PMID: 34659382 Free PMC article.

  9. Using artificial intelligence to uncover ssociation of left atrial strain with the framingham risk score for atrial fibrillation development

    Ali Ahmad Shareef Mansour Ali Zgheib Lise Safatly Ali El Hajj Mohamad Baydoun Hassan Ghaziri Hussam Aridi Hussain Ismaeel J Am Coll Cardiol. 2020 Mar, 75 (11_Supplement_1) 455.

  10. Ischemia Prediction

    Machine learning allows for improved accuracy in predicting the need for coronary angiography for the diagnosis of ischemic cardiomyopathy. A cohort of 204 consecutive patients with reduced ejection fraction (EF less than 50% on echocardiography) with clinical, ECG and echocardiography (including speckle tracking) variables. We tested several classification algorithms and determined the most relevant variables. These included regional wall motion, the right bundle branch block, in addition to the post-systolic shortening and its deceleration time. The incorporation of these tools in electronic systems as well as others will facilitate patient care. Developed a web-based application to predict ischemia, available at: Ischemia Prediction