Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing

Journals

1. Ancochea J, Izquierdo J, Soriano J. Evidence of Gender Differences in the Diagnosis and Management of Coronavirus Disease 2019 Patients: An Analysis of Electronic Health Records Using Natural Language Processing and Machine Learning. Journal of Women's Health 2021;30(3):393 View
2. Sánchez-Montañés M, Rodríguez-Belenguer P, Serrano-López A, Soria-Olivas E, Alakhdar-Mohmara Y. Machine Learning for Mortality Analysis in Patients with COVID-19. International Journal of Environmental Research and Public Health 2020;17(22):8386 View
3. Hariyanto T, Putri C, Frinka P, Louisa J, Lugito N, Kurniawan A. Human Immunodeficiency Virus (HIV) and outcomes from coronavirus disease 2019 (COVID-19) pneumonia: A Meta-Analysis and Meta-Regression. AIDS Research and Human Retroviruses 2021 View
4. Jimenez-Solem E, Petersen T, Hansen C, Hansen C, Lioma C, Igel C, Boomsma W, Krause O, Lorenzen S, Selvan R, Petersen J, Nyeland M, Ankarfeldt M, Virenfeldt G, Winther-Jensen M, Linneberg A, Ghazi M, Detlefsen N, Lauritzen A, Smith A, de Bruijne M, Ibragimov B, Petersen J, Lillholm M, Middleton J, Mogensen S, Thorsen-Meyer H, Perner A, Helleberg M, Kaas-Hansen B, Bonde M, Bonde A, Pai A, Nielsen M, Sillesen M. Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients. Scientific Reports 2021;11(1) View
5. Chung H, Ko H, Kang W, Kim K, Lee H, Park C, Song H, Choi T, Seo J, Lee J. Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation. Journal of Medical Internet Research 2021;23(4):e27060 View
6. Lybarger K, Ostendorf M, Thompson M, Yetisgen M. Extracting COVID-19 diagnoses and symptoms from clinical text: A new annotated corpus and neural event extraction framework. Journal of Biomedical Informatics 2021;117:103761 View
7. Hariyanto T, Kurniawan A. Obstructive sleep apnea (OSA) and outcomes from coronavirus disease 2019 (COVID-19) pneumonia: a systematic review and meta-analysis. Sleep Medicine 2021;82:47 View
8. Alballa N, Al-Turaiki I. Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review. Informatics in Medicine Unlocked 2021;24:100564 View
9. Alsunaidi S, Almuhaideb A, Ibrahim N, Shaikh F, Alqudaihi K, Alhaidari F, Khan I, Aslam N, Alshahrani M. Applications of Big Data Analytics to Control COVID-19 Pandemic. Sensors 2021;21(7):2282 View
10. Martos Pérez F, Gomez Huelgas R, Martín Escalante M, Casas Rojo J. Minimizing Selection and Classification Biases. Comment on “Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing”. Journal of Medical Internet Research 2021;23(5):e27142 View
11. Izquierdo J, Soriano J. Authors’ Reply to: Minimizing Selection and Classification Biases Comment on “Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing”. Journal of Medical Internet Research 2021;23(5):e29405 View
12. Kalra R, Dhanjal J, Meena A, Kalel V, Dahiya S, Singh B, Dewanjee S, Kandimalla R. COVID-19, Neuropathology, and Aging: SARS-CoV-2 Neurological Infection, Mechanism, and Associated Complications. Frontiers in Aging Neuroscience 2021;13 View
13. Syrowatka A, Kuznetsova M, Alsubai A, Beckman A, Bain P, Craig K, Hu J, Jackson G, Rhee K, Bates D. Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases. npj Digital Medicine 2021;4(1) View
14. Riswantini D, Nugraheni E, Arisal A, Khotimah P, Munandar D, Suwarningsih W. Big Data Research in Fighting COVID-19: Contributions and Techniques. Big Data and Cognitive Computing 2021;5(3):30 View
15. Canales L, Menke S, Marchesseau S, D’Agostino A, del Rio-Bermudez C, Taberna M, Tello J. Assessing the Performance of Clinical Natural Language Processing Systems: Development of an Evaluation Methodology. JMIR Medical Informatics 2021;9(7):e20492 View
16. Purja S, Shin H, Lee J, Kim E. Is loss of smell an early predictor of COVID-19 severity: a systematic review and meta-analysis. Archives of Pharmacal Research 2021;44(7):725 View
17. Iannella G, Vicini C, Lechien J, Ravaglia C, Poletti V, di Cesare S, Amicarelli E, Gardelli L, Grosso C, Patacca A, Magistrelli E, De Benedetto M, Toraldo D, Arigliani M, Cammaroto G, Meccariello G, De Vito A, Magliulo G, Greco A, de Vincentiis M, Ralli M, Pace A, Montincone V, Maniaci A, Cocuzza S, Seligardi M, di Giacinto I, Corso R. Association Between Severity of COVID-19 Respiratory Disease and Risk of Obstructive Sleep Apnea. Ear, Nose & Throat Journal 2024;103(1):NP10 View
18. 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
19. Segura T, Medrano I, Collazo S, Maté C, Sguera C, Del Rio-Bermudez C, Casero H, Salcedo I, García-García J, Alcahut-Rodríguez C, Aquino J, Casadevall D, Donaire D, Marin-Corral J, Menke S, Polo N, Taberna M. Symptoms timeline and outcomes in amyotrophic lateral sclerosis using artificial intelligence. Scientific Reports 2023;13(1) View
20. González-Juanatey C, Anguita-Sá́nchez M, Barrios V, Núñez-Gil I, Gómez-Doblas J, García-Moll X, Lafuente-Gormaz C, Rollán-Gómez M, Peral-Disdie V, Martínez-Dolz L, Rodríguez-Santamarta M, Viñolas-Prat X, Soriano-Colomé T, Muñoz-Aguilera R, Plaza I, Curcio-Ruigómez A, Orts-Soler E, Segovia J, Maté C, Cequier Á, Pizzi C. Assessment of medical management in Coronary Type 2 Diabetic patients with previous percutaneous coronary intervention in Spain: A retrospective analysis of electronic health records using Natural Language Processing. PLOS ONE 2022;17(2):e0263277 View
21. Garcia-Gutiérrez S, Esteban-Aizpiri C, Lafuente I, Barrio I, Quiros R, Quintana J, Uranga A, García-Gutiérrez S, Quintana J, Orive M, Gonzalez N, Anton A, Villanueva A, Muñoz C, Legarreta M, Quirós R, Yandiola P, Egurrola M, Aramburu A, Artaraz A, Chasco L, Bronte O, García P, Jodar A, Fernandez V, Esteban C, Mas N, Pulido E, Bengoetxea I, Martínez A, Bilbao A, Gorostiza I, Arriaga I, Zapiarain J, Parraza N, Iriberri M, Zalacain R, Ruiz L, Serrano L, Couto A, Ateka O, Cano A, Ibarra M, Millan E, Bacigalupe M, Letona J, Arcelay A, Berraondo I, Castells X, Posso M, Perestelo L, Acosta G, Gonzñalez C, Redondo M, Padilla M, Muñoz A, de Madariaga R. Machine learning-based model for prediction of clinical deterioration in hospitalized patients by COVID 19. Scientific Reports 2022;12(1) View
22. Rögnvaldsson K, Eyþórsson E, Emilsson Ö, Eysteinsdóttir B, Pálsson R, Gottfreðsson M, Guðmundsson G, Steingrímsson V. Obstructive sleep apnea is an independent risk factor for severe COVID-19: a population-based study. Sleep 2022;45(3) View
23. Ramadori G. SARS-CoV-2-Infection (COVID-19): Clinical Course, Viral Acute Respiratory Distress Syndrome (ARDS) and Cause(s) of Death. Medical Sciences 2022;10(4):58 View
24. Oyelade T, Alqahtani J, Hjazi A, Li A, Kamila A, Raya R. Global and Regional Prevalence and Outcomes of COVID-19 in People Living with HIV: A Systematic Review and Meta-Analysis. Tropical Medicine and Infectious Disease 2022;7(2):22 View
25. Miller J, Tada M, Goto M, Chen H, Dang E, Mohr N, Lee S. Prediction models for severe manifestations and mortality due to COVID‐19: A systematic review. Academic Emergency Medicine 2022;29(2):206 View
26. Ramón A, Torres A, Milara J, Cascón J, Blasco P, Mateo J. eXtreme Gradient Boosting-based method to classify patients with COVID-19. Journal of Investigative Medicine 2022;70(7):1472 View
27. González-Juanatey C, Anguita-Sánchez M, Barrios V, Núñez-Gil I, Gómez-Doblas J, García-Moll X, Lafuente-Gormaz C, Rollán-Gómez M, Peral-Disdier V, Martínez-Dolz L, Rodríguez-Santamarta M, Viñolas-Prat X, Soriano-Colomé T, Muñoz-Aguilera R, Plaza I, Curcio-Ruigómez A, Orts-Soler E, Segovia J, Fanjul V, Cequier Á. Major Adverse Cardiovascular Events in Coronary Type 2 Diabetic Patients: Identification of Associated Factors Using Electronic Health Records and Natural Language Processing. Journal of Clinical Medicine 2022;11(20):6004 View
28. Lumbreras S. Inteligencia Artificial y medicina: la necesidad de modelos interpretables. TECHNO REVIEW. International Technology, Science and Society Review /Revista Internacional De Tecnología, Ciencia Y Sociedad 2021;9(2):97 View
29. Gomollón F, Gisbert J, Guerra I, Plaza R, Pajares Villarroya R, Moreno Almazán L, López Martín M, Domínguez Antonaya M, Vera Mendoza M, Aparicio J, Martínez V, Tagarro I, Fernández-Nistal A, Lumbreras S, Maté C, Montoto C. Clinical characteristics and prognostic factors for Crohn’s disease relapses using natural language processing and machine learning: a pilot study. European Journal of Gastroenterology & Hepatology 2022;34(4):389 View
30. Calleja Panero J, de la Poza G, Hidalgo L, Aguilera Sancho-Tello M, Torras X, Santos de Lamadrid R, Maté C, Sánchez Antolín G. Patient journey of individuals tested for HCV in Spain: LiverTAI, a retrospective analysis of EHRs through natural language processing. Gastroenterología y Hepatología 2023;46(7):491 View
31. Saadatmand S, Salimifard K, Mohammadi R, Kuiper A, Marzban M, Farhadi A. Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients. Annals of Operations Research 2023;328(1):1043 View
32. Sîrbu A, Barbieri G, Faita F, Ferragina P, Gargani L, Ghiadoni L, Priami C. Early outcome detection for COVID-19 patients. Scientific Reports 2021;11(1) View
33. 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
34. 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
35. Valdés Sanz N, García-Layana A, Colas T, Moriche M, Montero Moreno M, Ciprandi G. Clinical Characterization of Inpatients with Acute Conjunctivitis: A Retrospective Analysis by Natural Language Processing and Machine Learning. Applied Sciences 2022;12(23):12352 View
36. Jung C, Mamandipoor B, Fjølner J, Bruno R, Wernly B, Artigas A, Bollen Pinto B, Schefold J, Wolff G, Kelm M, Beil M, Sviri S, van Heerden P, Szczeklik W, Czuczwar M, Elhadi M, Joannidis M, Oeyen S, Zafeiridis T, Marsh B, Andersen F, Moreno R, Cecconi M, Leaver S, De Lange D, Guidet B, Flaatten H, Osmani V. Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation. JMIR Medical Informatics 2022;10(3):e32949 View
37. 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
38. Hoekstra O, Hurst W, Tummers J. Healthcare related event prediction from textual data with machine learning: A Systematic Literature Review. Healthcare Analytics 2022;2:100107 View
39. Montoto C, Gisbert J, Guerra I, Plaza R, Pajares Villarroya R, Moreno Almazán L, López Martín M, Domínguez Antonaya M, Vera Mendoza I, Aparicio J, Martínez V, Tagarro I, Fernandez-Nistal A, Canales L, Menke S, Gomollón F. Evaluation of Natural Language Processing for the Identification of Crohn Disease–Related Variables in Spanish Electronic Health Records: A Validation Study for the PREMONITION-CD Project. JMIR Medical Informatics 2022;10(2):e30345 View
40. Chang Y, Chiu Y, Chuang T. Linguistic Pattern–Infused Dual-Channel Bidirectional Long Short-term Memory With Attention for Dengue Case Summary Generation From the Program for Monitoring Emerging Diseases–Mail Database: Algorithm Development Study. JMIR Public Health and Surveillance 2022;8(7):e34583 View
41. Islam M, Hossain M, Molla M, Sharif M, Hasan P, Hossain F, Sikder A, Uddin M, Amin M. A 2‐month post‐COVID‐19 follow‐up study on patients with dyspnea. Health Science Reports 2021;4(4) View
42. Caskey J, McConnell I, Oguss M, Dligach D, Kulikoff R, Grogan B, Gibson C, Wimmer E, DeSalvo T, Nyakoe-Nyasani E, Churpek M, Afshar M. Identifying COVID-19 Outbreaks From Contact-Tracing Interview Forms for Public Health Departments: Development of a Natural Language Processing Pipeline. JMIR Public Health and Surveillance 2022;8(3):e36119 View
43. Alabbad D, Almuhaideb A, Alsunaidi S, Alqudaihi K, Alamoudi F, Alhobaishi M, Alaqeel N, Alshahrani M. Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia. Informatics in Medicine Unlocked 2022;30:100937 View
44. Izquierdo J, Soriano J, González Y, Lumbreras S, Ancochea J, Echeverry C, Rodríguez J. Use of N-Acetylcysteine at high doses as an oral treatment for patients hospitalized with COVID-19. Science Progress 2022;105(1) View
45. Al Mutair A, Elhazmi A, Alhumaid S, Ahmad G, Rabaan A, Alghadeer M, Chagla H, Tirupathi R, Sharma A, Dhama K, Alsalman K, Alalawi Z, Aljofan Z, Al Mutairi A, Alomari M, Awad M, Al-Omari A. Examining the Clinical Prognosis of Critically Ill Patients with COVID-19 Admitted to Intensive Care Units: A Nationwide Saudi Study. Medicina 2021;57(9):878 View
46. Pereira D, Silveira L, Guimarães M, Polanczyk C, Nunes A, Costa A, Farace B, Cimini C, Carvalho C, Ponce D, Roesch E, Manenti E, Lucas F, Rodrigues F, Anschau F, Aranha F, Bartolazzi F, Vietta G, Nascimento G, Duani H, Vianna H, Guimarães H, Costa J, Batista J, Alvarenga J, Chatkin J, Morais J, Machado-Rugolo J, Ruschel K, Pinheiro L, Menezes L, Couto L, Kopittke L, Castro L, Nasi L, Cabral M, Floriani M, Souza M, Carneiro M, Bicalho M, Godoy M, Nogueira M, Guimarães Júnior M, Sampaio N, Oliveira N, Assaf P, Finger R, Campos R, Menezes R, Francisco S, Alvarenga S, Guimarães S, Araújo S, Oliveira T, Diniz T, Ramires Y, Cenci E, Oliveira T, Schwarzbold A, Ziegelmann P, Pozza R, Carvalho C, Pires M, Marcolino M. Hypothyroidism does not lead to worse prognosis in COVID-19: findings from the Brazilian COVID-19 registry. International Journal of Infectious Diseases 2022;116:319 View
47. Izquierdo J, Oeste C, Hernández Medrano I. Artificial Intelligence in Pneumology: Diagnostic and Prognostic Utilities. Archivos de Bronconeumología 2023;59(2):67 View
48. BuHamra S, Almutairi A, Buhamrah A, Almadani S, Alibrahim Y. An NLP tool for data extraction from electronic health records: COVID-19 mortalities and comorbidities. Frontiers in Public Health 2022;10 View
49. Rosario B, Zhang A, Patel M, Rajmane A, Xie N, Weeraratne D, Alterovitz G. Characterizing Thrombotic Complication Risk Factors Associated With COVID-19 via Heterogeneous Patient Data: Retrospective Observational Study. Journal of Medical Internet Research 2022;24(10):e35860 View
50. Shishimorov I, Magnitskaya O, Ponomareva Y. GENETIC PREDICTORS OF SEVERITY AND EFFICACY OF COVID-19 PHARMACOTHERAPY. Pharmacy & Pharmacology 2021;9(3):174 View
51. Fan S, Lin J, Wu S, Mu X, Guo J, Chen T. Random forest model can predict the prognosis of hospital-acquired Klebsiella pneumoniae infection as well as traditional logistic regression model. PLOS ONE 2022;17(11):e0278123 View
52. Stanevich O, Bakin E, Korshunova A, Gudkova A, Afanasev A, Shlyk I, Lioznov D, Polushin Y, Kulikov A. Informativeness estimation for the main clinical and laboratory parameters in patients with severe COVID-19. Terapevticheskii arkhiv 2022;94(11):1225 View
53. Lewek P, Banaś I, Witkowski K, Lewek J, Kardas P. The prevalence of symptoms and its correlation with sex in polish COVID-19 adult patients: Cross-sectional online open survey. Frontiers in Medicine 2023;10 View
54. Mao Z, Liu C, Li Q, Cui Y, Zhou F. Intelligent Intensive Care Unit: Current and Future Trends. Intensive Care Research 2023;3(2):182 View
55. Kumar R, Maheshwari S, Sharma A, Linda S, Kumar S, Chatterjee I. Ensemble learning-based early detection of influenza disease. Multimedia Tools and Applications 2024;83(2):5723 View
56. González-Juanatey C, Anguita-Sánchez M, Barrios V, Núñez-Gil I, Gómez-Doblas J, García-Moll X, Lafuente-Gormaz C, Rollán-Gómez M, Peral-Disdier V, Martínez-Dolz L, Rodríguez-Santamarta M, Viñolas-Prat X, Soriano-Colomé T, Muñoz-Aguilera R, Plaza I, Curcio-Ruigómez A, Orts-Soler E, Segovia-Cubero J, Fanjul V, Marín-Corral J, Cequier Á, SAVANA Research Group . Impact of Advanced Age on the Incidence of Major Adverse Cardiovascular Events in Patients with Type 2 Diabetes Mellitus and Stable Coronary Artery Disease in a Real-World Setting in Spain. Journal of Clinical Medicine 2023;12(16):5218 View
57. Calleja Panero J, de la Poza G, Hidalgo L, Aguilera Sancho-Tello M, Torras X, Santos de Lamadrid R, Maté C, Sánchez Antolín G. Patient journey of individuals tested for HCV in Spain: LiverTAI, a retrospective analysis of EHRs through natural language processing. Gastroenterología y Hepatología (English Edition) 2023;46(7):491 View
58. 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
59. Casas-Rojo J, Ventura P, Antón Santos J, de Latierro A, Arévalo-Lorido J, Mauri M, Rubio-Rivas M, González-Vega R, Giner-Galvañ V, Otero Perpiñá B, Fonseca-Aizpuru E, Muiño A, Del Corral-Beamonte E, Gómez-Huelgas R, Arnalich-Fernández F, Llorente Barrio M, Sancha-Lloret A, Rábago Lorite I, Loureiro-Amigo J, Pintos-Martínez S, García-Sardón E, Montaño-Martínez A, Rojano-Rivero M, Ramos-Rincón J, López-Escobar A. Improving prediction of COVID-19 mortality using machine learning in the Spanish SEMI-COVID-19 registry. Internal and Emergency Medicine 2023;18(6):1711 View
60. Klimchuk A, Beloglazov V, Yatskov I, Agzamova Y, Kamshii A, Zayaeva A. Post-vaccination and post-infectious immune response against new coronavirus infection on the background of obesity and overweight. Obesity and metabolism 2023;20(1):60 View
61. Muñoz A, Souto J, Lecumberri R, Obispo B, Sanchez A, Aparicio J, Aguayo C, Gutierrez D, Palomo A, Fanjul V, del Rio-Bermudez C, Viñuela-Benéitez M, Hernández-Presa M. Development of a predictive model of venous thromboembolism recurrence in anticoagulated cancer patients using machine learning. Thrombosis Research 2023;228:181 View
62. Calleja-Panero J, Esteban Mur R, Jarque I, Romero-Gómez M, Group S, García Labrador L, González Calvo J. Chronic liver disease-associated severe thrombocytopenia in Spain: Results from a retrospective study using machine learning and natural language processing. Gastroenterología y Hepatología 2024;47(3):236 View
63. Pavlopoulos J, Romell A, Curman J, Steinert O, Lindgren T, Borg M, Randl K. Automotive fault nowcasting with machine learning and natural language processing. Machine Learning 2024;113(2):843 View
64. Dipaola F, Gatti M, Giaj Levra A, Menè R, Shiffer D, Faccincani R, Raouf Z, Secchi A, Rovere Querini P, Voza A, Badalamenti S, Solbiati M, Costantino G, Savevski V, Furlan R. Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study. Scientific Reports 2023;13(1) View
65. Boydston J, Biryukov J, Yeager J, Zimmerman H, Williams G, Green B, Reese A, Beck K, Bohannon J, Miller D, Freeburger D, Graham A, Wahl V, Hevey M, Dabisch P. Aerosol Particle Size Influences the Infectious Dose and Disease Severity in a Golden Syrian Hamster Model of Inhalational COVID-19. Journal of Aerosol Medicine and Pulmonary Drug Delivery 2023;36(5):235 View
66. Li P, Parvej M, Zhang C, Guo S, Zhang J. Advances in the Development of Representation Learning and Its Innovations against COVID-19. COVID 2023;3(9):1389 View
67. Morena D, Campos C, Castillo M, Alonso M, Benavent M, Izquierdo J. Impact of the COVID-19 Pandemic on the Epidemiological Situation of Pulmonary Tuberculosis–Using Natural Language Processing. Journal of Personalized Medicine 2023;13(12):1629 View
68. Benavent D, Muñoz-Fernández S, De la Morena I, Fernández-Nebro A, Marín-Corral J, Castillo Rosa E, Taberna M, Sanabra C, Sastre C. Using natural language processing to explore characteristics and management of patients with axial spondyloarthritis and psoriatic arthritis treated under real-world conditions in Spain: SpAINET study. Therapeutic Advances in Musculoskeletal Disease 2023;15 View
69. Beloglazov V, Yatskov I, Kamshiy A, Agzamova Y. Role of Toll-like receptor gene polymorphism in pathogenesis of new coronavirus infection. Medical Immunology (Russia) 2023;25(6):1299 View
70. Román Ivorra J, Trallero-Araguas E, Lopez Lasanta M, Cebrián L, Lojo L, López-Muñíz B, Fernández-Melon J, Núñez B, Silva-Fernández L, Veiga Cabello R, Ahijado P, De la Morena Barrio I, Costas Torrijo N, Safont B, Ornilla E, Restrepo J, Campo A, Andreu J, Díez E, López Robles A, Bollo E, Benavent D, Vilanova D, Luján Valdés S, Castellanos-Moreira R. Prevalence and clinical characteristics of patients with rheumatoid arthritis with interstitial lung disease using unstructured healthcare data and machine learning. RMD Open 2024;10(1):e003353 View
71. Queipo M, Barbado J, Torres A, Mateo J. Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection. Biomedicines 2024;12(2):409 View
72. Sung S, Kim Y, Kim S, Jung H. Identification of Predictors for Clinical Deterioration in Patients With COVID-19 via Electronic Nursing Records: Retrospective Observational Study. Journal of Medical Internet Research 2024;26:e53343 View
73. Badiola-Zabala G, Lopez-Guede J, Estevez J, Graña M. Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022. Electronics 2024;13(6):1005 View
74. Ahmed A, Zengul F, Khan S, Hearld K, Feldman S, Hall A, Orewa G, Willig J, Kennedy K. Developing a decision model to early predict ICU admission for COVID-19 patients: A machine learning approach. Intelligence-Based Medicine 2024;9:100136 View
75. Kim G, Ju C, Seok H, Lee D. Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients. Applied Sciences 2024;14(7):2715 View
76. Ashchina L, Baranova N, Bolgova A, Levashova O, Lesina O. Prognostic significance of <I>TLR3</I> and <I>TLR9</I> gene polymorphism in assessing the severity of COVID-19. Journal Infectology 2024;16(1):47 View
77. Calleja-Panero J, Esteban Mur R, Jarque I, Romero-Gómez M, Group S, García Labrador L, González Calvo J. Chronic liver disease-associated severe thrombocytopenia in Spain: Results from a retrospective study using machine learning and natural language processing. Gastroenterología y Hepatología (English Edition) 2024;47(3):236 View
78. Nemet M, Vukoja M. Obstructive Sleep Apnea and Acute Lower Respiratory Tract Infections: A Narrative Literature Review. Antibiotics 2024;13(6):532 View

Books/Policy Documents

1. Duarte R, Lopes J, Guimarães T, Ferreira S, Santos M. AI-assisted Solutions for COVID-19 and Biomedical Applications in Smart Cities. View
2. Yousuff M, Babu R, Anusha R, Matheen M. The Role of AI, IoT and Blockchain in Mitigating the Impact of COVID-19. View
3. Poberezhets V, Kasteleyn M, Aardoom J. Digital Respiratory Healthcare. View