This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2388) Clinical Information and Decision Making (1314) Electronic Health Records (961) Natural Language Processing (636) Theme Issue: Medical Informatics and COVID-19 (103) Infectious Diseases (non-STD/STI) (1478) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1481)

Published on in Vol 22, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20773, first published .
Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic

Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic

Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic

Authors of this article:

Antoine Neuraz1, 2, 3 Author Orcid Image ;   Ivan Lerner1, 2 Author Orcid Image ;   William Digan2, 4 Author Orcid Image ;   Nicolas Paris5 Author Orcid Image ;   Rosy Tsopra2, 4 Author Orcid Image ;   Alice Rogier2, 4 Author Orcid Image ;   David Baudoin2, 4 Author Orcid Image ;   Kevin Bretonnel Cohen6 Author Orcid Image ;   Anita Burgun1, 2, 4 Author Orcid Image ;   Nicolas Garcelon2, 7 Author Orcid Image ;   Bastien Rance2, 4 Author Orcid Image ;   AP-HP/Universities/INSERM COVID-19 Research Collaboration; AP-HP COVID CDR Initiative8
Article Authors Cited by (54) Tweetations (14) Metrics

Journals

  1. Catelli R, Gargiulo F, Casola V, De Pietro G, Fujita H, Esposito M. Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set. Applied Soft Computing 2020;97:106779 View
  2. Chouchana L, Beeker N, Garcelon N, Rance B, Paris N, Salamanca E, Polard E, Burgun A, Treluyer J, Neuraz A, Ancel P, Bauchet A, Benoit V, Bernaux M, Bellamine A, Bey R, Bourmaud A, Breant S, Carrat F, Caucheteux C, Champ J, Cormont S, Daniel C, Dubiel J, Ducloas C, Esteve L, Frank M, Gramfort A, Griffon N, Grisel O, Guilbaud M, Hassen-Khodja C, Hemery F, Hilka M, Jannot A, Lambert J, Layese R, Leblanc J, Lebouter L, Lemaitre G, Leprovost D, Lerner I, Levi Sallah K, Maire A, Mamzer M, Martel P, Mensch A, Moreau T, Orlova N, Ravera H, Rozes A, Sandrin A, Serre P, Tannier X, Van Gysel D, Varoquaux G, Vie J, Wack M, Wajsburt P, Wassermann D, Zapletal E. Association of Antihypertensive Agents with the Risk of In-Hospital Death in Patients with Covid-19. Cardiovascular Drugs and Therapy 2022;36(3):483 View
  3. Ahmed U, Mukhiya S, Srivastava G, Lamo Y, Lin J. Attention-Based Deep Entropy Active Learning Using Lexical Algorithm for Mental Health Treatment. Frontiers in Psychology 2021;12 View
  4. Safdari R, Rezayi S, Saeedi S, Tanhapour M, Gholamzadeh M. Using data mining techniques to fight and control epidemics: A scoping review. Health and Technology 2021;11(4):759 View
  5. Guo Y, Zhang Y, Lyu T, Prosperi M, Wang F, Xu H, Bian J. The application of artificial intelligence and data integration in COVID-19 studies: a scoping review. Journal of the American Medical Informatics Association 2021;28(9):2050 View
  6. Hoertel N, Sánchez‐Rico M, Gulbins E, Kornhuber J, Carpinteiro A, Lenze E, Reiersen A, Abellán M, de la Muela P, Vernet R, Blanco C, Cougoule C, Beeker N, Neuraz A, Gorwood P, Alvarado J, Meneton P, Limosin F. Association Between FIASMAs and Reduced Risk of Intubation or Death in Individuals Hospitalized for Severe COVID‐19: An Observational Multicenter Study. Clinical Pharmacology & Therapeutics 2021;110(6):1498 View
  7. Jannot A, Countouris H, Van Straaten A, Burgun A, Katsahian S, Rance B. Low-income neighbourhood was a key determinant of severe COVID-19 incidence during the first wave of the epidemic in Paris. Journal of Epidemiology and Community Health 2021;75(12):1143 View
  8. Peng C, Wang H, Guo Y, Qi G, Zhang C, Chen T, He J, Jin Z. Calcium channel blockers improve prognosis of patients with coronavirus disease 2019 and hypertension. Chinese Medical Journal 2021;134(13):1602 View
  9. Zhang H, Feng T. Network-Based Data Analysis Reveals Ion Channel-Related Gene Features in COVID-19: A Bioinformatic Approach. Biochemical Genetics 2023;61(2):471 View
  10. Solaimanzadeh I. Why Pulmonary Vasodilation May Be Part of a Key Strategy to Improve Survival in COVID-19. Cureus 2021 View
  11. Choksi T, Zhang H, Chen T, Malhotra N. Outcomes of Hospitalized COVID-19 Patients Receiving Renin Angiotensin System Blockers and Calcium Channel Blockers. American Journal of Nephrology 2021;52(3):250 View
  12. Ahmed U, Lin J, Srivastava G. Hyper-Graph Attention Based Federated Learning Methods for Use in Mental Health Detection. IEEE Journal of Biomedical and Health Informatics 2023;27(2):768 View
  13. Ahmed U, Lin J, Srivastava G. Deep Hierarchical Attention Active Learning for Mental Disorder Unlabeled Data in AIoMT. ACM Transactions on Sensor Networks 2023;19(3):1 View
  14. Chammas J, Delaney D, Chabaytah N, Abdulkarim S, Schwertani A. COVID-19 and the cardiovascular system: insights into effects and treatments. Canadian Journal of Physiology and Pharmacology 2021;99(11):1119 View
  15. Ahmed U, Lin J, Srivastava G. Fuzzy Contrast Set Based Deep Attention Network for Lexical Analysis and Mental Health Treatment. ACM Transactions on Asian and Low-Resource Language Information Processing 2022;21(5):1 View
  16. Sánchez-Rico M, Limosin F, Hoertel N. Is a Diagnosis of Schizophrenia Spectrum Disorder Associated With Increased Mortality in Patients With COVID-19?. American Journal of Psychiatry 2022;179(1):71 View
  17. Demidova T, Lobanova K, Perekhodov S, Antsiferov M, Oynotkinova O. Retrospective analysis of clinical outcomes of patients with COVID-19 depending on receiving antihypertensive, lipid-lowering and antihypertensive therapy. Terapevticheskii arkhiv 2021;93(10):1193 View
  18. Reeves J, Pageler N, Wick E, Melton G, Tan Y, Clay B, Longhurst C. The Clinical Information Systems Response to the COVID-19 Pandemic. Yearbook of Medical Informatics 2021;30(01):105 View
  19. Ahmed U, Srivastava G, Yun U, Lin J. EANDC: An explainable attention network based deep adaptive clustering model for mental health treatment. Future Generation Computer Systems 2022;130:106 View
  20. Grabar N, Grouin C. Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing. Yearbook of Medical Informatics 2021;30(01):257 View
  21. Ahmed U, Jhaveri R, Srivastava G, Lin J. Explainable Deep Attention Active Learning for Sentimental Analytics of Mental Disorder. ACM Transactions on Asian and Low-Resource Language Information Processing 2022 View
  22. Al-Garadi M, Yang Y, Sarker A. The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges. Healthcare 2022;10(11):2270 View
  23. Verspoor K. The Evolution of Clinical Knowledge During COVID-19: Towards a Global Learning Health System. Yearbook of Medical Informatics 2021;30(01):176 View
  24. Brown J, Bhatnagar M, Gordon H, Goodner J, Cobb J, Lutrick K. An Electronic Data Capture Tool for Data Collection During Public Health Emergencies: Development and Usability Study. JMIR Human Factors 2022;9(2):e35032 View
  25. Fatima R, Samad Shaikh N, Riaz A, Ahmad S, El-Affendi M, Alyamani K, Nabeel M, Ali Khan J, Yasin A, Latif R, Javed A. A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques. Computational Intelligence and Neuroscience 2022;2022:1 View
  26. Kow C, Ramachandram D, Hasan S. Clinical outcomes of hypertensive patients with COVID-19 receiving calcium channel blockers: a systematic review and meta-analysis. Hypertension Research 2022;45(2):360 View
  27. Ahmed U, Lin J, Srivastava G. Graph Attention Network for Text Classification and Detection of Mental Disorder. ACM Transactions on the Web 2023;17(3):1 View
  28. Sadeghpopur S, Ghasemnejad-Berenji H, Pashapour S, Ghasemnejad-Berenji M. Using of calcium channel blockers in patients with COVID-19: a magic bullet or a double-edged sword?. Journal of Basic and Clinical Physiology and Pharmacology 2022;33(1):117 View
  29. Bataille P, Layese R, Claudepierre P, Paris N, Dubiel J, Amiot A, Sbidian E. Paradoxical reactions and biologic agents: a French cohort study of 9303 patients. British Journal of Dermatology 2022;187(5):676 View
  30. Ahmed U, Lin J, Srivastava G. Hyper-graph-based attention curriculum learning using a lexical algorithm for mental health. Pattern Recognition Letters 2022;157:135 View
  31. Kow C, Ramachandram D, Hasan S. Use of Calcium Channel Blockers and the Risk of All-cause Mortality and Severe Illness in Patients With COVID-19: A Systematic Review and Meta-analysis. Journal of Cardiovascular Pharmacology 2022;79(2):199 View
  32. Lerner I, Serret-Larmande A, Rance B, Garcelon N, Burgun A, Chouchana L, Neuraz A. Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS). JMIR Medical Informatics 2022;10(3):e35190 View
  33. Pauletto P, Delgado C, da Rocha J. Acid sphingomyelinase (ASM) and COVID‐19: A review of the potential use of ASM inhibitors against SARS‐CoV‐2. Cell Biochemistry and Function 2023;41(3):284 View
  34. Alshahrani S, Khan N. COVID-19 advising application development for Apple devices (iOS). PeerJ Computer Science 2023;9:e1274 View
  35. Tyagi N, Bhushan B. Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions. Wireless Personal Communications 2023;130(2):857 View
  36. Ahmed U, Lin J, Srivastava G. Multi-Aspect Deep Active Attention Network for Healthcare Explainable Adoption. IEEE Journal of Biomedical and Health Informatics 2023;27(4):1709 View
  37. Kim P, Nadarajan V, Ahmed M, Furman K, Gurm Z, Kale P, Khoury Z, Koussa S, LaBuda D, Mekjian M, Polamarasetti P, Simo L, Thill C, Wittenberg S, Dhar S, Komnenov D. The Associations of Antihypertensive Medications, Steroids, Beta Blockers, Statins and Comorbidities with COVID-19 Outcomes in Patients with and without Chronic Kidney Disease: A Retrospective Study. COVID 2023;3(5):682 View
  38. MacMahon M, Hwang W, Yim S, MacMahon E, Abraham A, Barton J, Tharmakulasingam M, Bilokon P, Gaddi V, Han N. An in silico drug repurposing pipeline to identify drugs with the potential to inhibit SARS-CoV-2 replication. Informatics in Medicine Unlocked 2023;43:101387 View
  39. Hoertel N, Rezaei K, Sánchez-Rico M, Delgado-Álvarez A, Kornhuber J, Gulbins E, Olfson M, Ouazana-Vedrines C, Carpinteiro A, Cougoule C, Becker K, Alvarado J, Limosin F. Medications Modulating the Acid Sphingomyelinase/Ceramide System and 28-Day Mortality among Patients with SARS-CoV-2: An Observational Study. Pharmaceuticals 2023;16(8):1107 View
  40. Michalski A, Lis K, Stankiewicz J, Kloska S, Sycz A, Dudziński M, Muras-Szwedziak K, Nowicki M, Bazan-Socha S, Dabrowski M, Basak G. Supporting the Diagnosis of Fabry Disease Using a Natural Language Processing-Based Approach. Journal of Clinical Medicine 2023;12(10):3599 View
  41. Loscertales J, Abrisqueta-Costa P, Gutierrez A, Hernández-Rivas J, Andreu-Lapiedra R, Mora A, Leiva-Farré C, López-Roda M, Callejo-Mellén Á, Álvarez-García E, García-Marco J. Real-World Evidence on the Clinical Characteristics and Management of Patients with Chronic Lymphocytic Leukemia in Spain Using Natural Language Processing: The SRealCLL Study. Cancers 2023;15(16):4047 View
  42. Bazoge A, Morin E, Daille B, Gourraud P. Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review. JMIR Medical Informatics 2023;11:e42477 View
  43. Ortiz A, Portoles J, Pino-Pino M, Barea J, López M, de Sequera P, Quiroga B, Echarri R, Prieto Velasco M, Díaz R, Gómez Marqués G, Sanchez Perez P, Torregrosa V, Rodriguez M. Clinical Characteristics and Management of Patients with Secondary Hyperparathyroidism Undergoing Hemodialysis: A Feasibility Analysis of Electronic Health Records Using Natural Language Processing. Kidney Diseases 2023;9(3):187 View
  44. Ahmed U, Lin J, Srivastava G. Social Media Multiaspect Detection by Using Unsupervised Deep Active Attention. IEEE Transactions on Computational Social Systems 2023;10(4):2137 View
  45. Sánchez-Rico M, Edán-Sánchez A, Olfson M, Alvarado J, Airagnes G, Rezaei K, Delcuze A, Peyre H, Limosin F, Hoertel N. Antipsychotic use and 28-day mortality in patients hospitalized with COVID-19: A multicenter observational retrospective study. European Neuropsychopharmacology 2023;75:93 View
  46. Corpechot C, Verdoux M, Frank‐Soltysiak M, Duclos‐Vallée J, Grimaldi L. Exploring the impact of ursodeoxycholic acid therapy on COVID‐19 in a real‐world setting. Journal of Medical Virology 2024;96(1) View
  47. Faviez C, Vincent M, Garcelon N, Boyer O, Knebelmann B, Heidet L, Saunier S, Chen X, Burgun A. Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity. Orphanet Journal of Rare Diseases 2024;19(1) View
  48. de Sequera P, Arias J, Quiroga B, Benavent M, Procaccini F, Romero I, López G, Diez J, Ortiz A. Cardiovascular risk assessment: Missing albuminuria contributing to gender inequality. Nefrología 2024 View
  49. Ahmed U, Lin J, Srivastava G. Graph Attention-Based Curriculum Learning for Mental Healthcare Classification. IEEE Journal of Biomedical and Health Informatics 2024;28(5):2581 View
  50. Faviez C, Chen X, Garcelon N, Zaidan M, Billot K, Petzold F, Faour H, Douillet M, Rozet J, Cormier-Daire V, Attié-Bitach T, Lyonnet S, Saunier S, Burgun A. Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies. BMC Medical Informatics and Decision Making 2024;24(1) View

Books/Policy Documents

  1. Dey M, Islam M, Rana T. Handbook of Big Data and Analytics in Accounting and Auditing. View
  2. Ahmed U, Lin J, Srivastava G. Advances in Knowledge Discovery and Data Mining. View
  3. França R, Monteiro A, Arthur R, Iano Y. Cognitive Intelligence and Big Data in Healthcare. View