Estado del arte, ventajas y limitaciones de la inteligencia artificial en epidemiología y salud pública
DOI:
https://doi.org/10.56050/01205498.1647Palabras clave:
Inteligencia artificial, Salud pública, EpidemiologíaResumen
Objetivo: Realizar una revisión narrativa del estado actual de la aplicación de las técnicas de inteligencia artificial (IA) en las áreas de la epidemiología y salud pública, así como de sus limitaciones y oportunidades. Metodología: Se realizó una revisión estructurada de la literatura para lo cual se desarrolló una estrategia de búsqueda genérica compuesta por vocabulario controlado explotado como términos (MeSH (Medical Subject Headings), DeCS (Descriptores en Ciencias de la Salud) y Emtree (Embase Subject Headings) y por lenguaje libre se llevó a cabo una búsqueda de la literatura en las siguientes bases de datos MEDLINE, Embase, Epistemonikos y LILACS, se establecieron los criterios de elegibilidad de los artículos, los cuales se utilizaron para los procesos de tamización y selección de los mismos, los que se realizaron por duplicado. Finalmente se llevó a cabo el proceso de extracción de los datos utilizando una herramienta estructurada, la síntesis de la evidencia se realizó de manera cuantitativa por medio de tablas de síntesis. Se realizó un análisis descriptivo univariado basado en el cálculo de frecuencias absolutas y relativas de las variables cualitativas y se calcularon medidas de tendencia central (media y mediana) y medidas de dispersión junto con los valores máximo y mínimo parea las variables cuantitativas. Resultados: El 18,4% de los artículos publicados provenían de Estados Unidos seguido por el 13,1% de China. En relación con las publicaciones, 11% fueron latinoamericanas. En cuanto al modelo de inteligencia artificial utilizado el 36% correspondió a modelos de Machine learning y árboles de decisión, seguido por redes neuronales en el 30%; el 58,5% de los algoritmos fueron modelos supervisados, el 43% de los modelos no fue objeto de validación, el 51,9% de los modelos se utilizaron para diagnóstico de enfermedades, el 21% para tamizaje y el 11% para evaluar el tratamiento. En relación con el propósito en salud pública, el 49,3% se utilizaron para protección de la salud, el 36% para promoción y el 14% para mejorar la eficiencia en la prestación de los servicios de salud. Respecto a su utilización en epidemiología, el 53% pretendían determinar factores de riesgo o exposición a enfermedades, el área predominante de desarrollo de los modelos fue infectología en el 61% de las publicaciones encontradas. Conclusiones: La IA se presenta como una herramienta útil en áreas como la epidemiología y en la toma de decisiones en salud pública al desarrollar algoritmos a partir de datos complejos que permiten predecir una variedad de desenlaces. Sin embargo, es necesario estandarizar los métodos en aspectos, tales como la calidad de los datos utilizados en estos algoritmos, en los métodos de validación utilizados, lo cual permitiría su aplicación en el contexto clínico.
Biografía del autor/a
Adriana Beltrán-Ostos, AIpocrates
Médico Internista-Reumatóloga, PhD(c) epidemiología clínica, miembro de AIpócrates.
Ana María Urdaneta, AIpocrates
Médico Especialista en epidemiología clínica, miembro de AIpócrates.
Jaime Alberto González, AIpocrates
Médico Internista-Hematoncólogo, MSc Oncología Molecular, miembro de AIpócrates.
Referencias bibliográficas
1. Flouris AD, Duffy J. Applications of artificial intelligence systems in the analysis of epidemiological data. Eur J Epidemiol. 2006;21(3):167-70. doi: 10.1007/s10654-006-0005-y.
2. Salcedo J, Rosales M, Kim JS, Nuno D, Suen S, Chang AH. Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study. PLOS ONE. 2021 Jul 21;16(7).
3. Benke K, Benke G. Artificial Intelligence and Big Data in Public Health. Int J Environ Res Public Health. 2018 Dec 10;15(12):2796. doi: 10.3390/ijerph15122796.
4. Gómez Zuleta MA, Cano Rosales DF, Bravo Higuera DF, Ruano Balseca JA, Romero Castro E. Detección automática de pólipos colorrectales con técnicas de inteligencia artificial. Revista colombiana de Gastroenterología. 2021 Mar 8;36(1).
5. Puerta Barrera JF, Amaya Hurtado D, Jiménez Moreno R. Prediction system of erythemas for phototypes I and II, using deep-learning. Revista Vitae. 2015 May 1;22(3).
6. Sprockel JJ, Alzate W. Aplicación de la computación evolutiva en el diagnóstico del infarto agudo del miocardio: Hospital de San José, Bogotá DC, Colombia. 2012. Revista Repertorio de Medicina y Cirugía. 2014 Sep 1;23(3).
7. Effland T, Lawson A, Balter S, Devinney K, Reddy V, Waechter H, et al. Discovering foodborne illness in online restaurant reviews. J Am Med Inform Assoc. 2018 Dec 1;25(12):1586-1592. doi: 10.1093/jamia/ocx093. PMID: 29329402; PMCID: PMC7647154.
8. Chakraborty M, Sarkar S, Mukherjee A, Shamsudduha M, Ahmed KM, Bhattacharya A, et al. Modeling regionalscale groundwater arsenic hazard in the transboundary Ganges River Delta, India and Bangladesh: Infusing physically-based model with machine learning. Sci Total Environ. 2020 Dec 15;748:141107. doi: 10.1016/j.scitotenv.2020.141107. Epub 2020 Jul 25.
9. Ochoa Reyes A, Orellana García A, Sanchez Corales Y, Davila Hernández F. Componente web para el análisis de información clínica usando la técnica de Minería de Datos por agrupamiento. Revista Cubana de Informática Médica. 2014;6(1):5–16.
10. Kuiava VA, Kuiava EL, Chielle EO, Syllos R. Desenvolvimento de sistema estruturado com inteligência artificial para apoio no diagnóstico de patologias oftalmológicas mais relevantes. Clinical & Biomedical Research. 2021 Aug 30;
11. Nieuwlaat R, Eikelboom JW, Schulman S, van Spall HG, Schulze KM, Connolly BJ, et al. Cluster randomized controlled trial of a simple warfarin maintenance dosing algorithm versus usual care among primary care practices. J Thromb Thrombolysis. 2014 May;37(4):435-42. doi: 10.1007/s11239-013-0969-x.
12. Labovitz DL, Shafner L, Reyes Gil M, Virmani D, Hanina A. Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy. Stroke. 2017 May;48(5):1416-1419. doi: 10.1161/ STROKEAHA.116.016281. Epub 2017 Apr 6.
13. INFANT Collaborative Group. Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. Lancet. 2017 Apr 29;389(10080):1719-1729. doi: 10.1016/S0140-6736(17)30568-8. Epub 2017 Mar 21.
14. Finkelstein SM, Lindgren BR, Robiner W, Lindquist R, Hertz M, Carlin BP, et al. A randomized controlled trial comparing health and quality of life of lung transplant recipients following nurse and computer-based triage utilizing home spirometry monitoring. Telemed J E Health. 2013 Dec;19(12):897-903. doi: 10.1089/tmj.2013.0049. Epub 2013 Oct 1.
15. Caballero-Ruiz E, García-Sáez G, Rigla M, Villaplana M, Pons B, Hernando ME. A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs. Int J Med Inform. 2017 Jun;102:35-49. doi: 10.1016/j.ijmedinf.2017.02.014. Epub 2017 Mar 6.
16. Rim TH, Soh ZD, Tham YC, Yang HHS, Lee G, Kim Y, et al. Deep Learning for Automated Sorting of Retinal Photographs. Ophthalmol Retina. 2020 Aug;4(8):793-800. doi: 10.1016/j.oret.2020.03.007. Epub 2020 Mar 17.
17. Sabanayagam C, Xu D, Ting DSW, Nusinovici S, Banu R, Hamzah H, Lim C, et al. A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations. Lancet Digit Health. 2020 Jun;2(6):e295-e302. doi: 10.1016/S2589- 7500(20)30063-7. Epub 2020 May 12.
18. SIOP 2016 Scientific Programme+Index. Pediatric Blood & Cancer. 2016 Nov;63.
19. Kolanu N, Brown AS, Beech A, Center JR, White CP. Natural language processing of radiology reports for the identification of patients with fracture. Arch Osteoporos. 2021 Jan 6;16(1):6. doi: 10.1007/s11657-020-00859-5.
20. Lim HK, Ha HI, Park SY, Han J. Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study. PLoS One. 2021 Mar 4;16(3):e0247330. doi: 10.1371/journal.pone.0247330.
21. Monlezun DJ, Dart L, Vanbeber A, Smith-Barbaro P, Costilla V, Samuel C, et al. Machine Learning-Augmented Propensity Score-Adjusted Multilevel Mixed Effects Panel Analysis of Hands-On Cooking and Nutrition Education versus Traditional Curriculum for Medical Students as Preventive Cardiology: Multisite Cohort Study of 3,248 Trainees over 5 Years. BioMed Research International. 2018.
22. Adeyinka DA, Olakunde BO, Muhajarine N. Evidence of health inequity in child survival: spatial and Bayesian network analyses of stillbirth rates in 194 countries. Sci Rep. 2019 Dec 24;9(1):19755. doi: 10.1038/s41598- 019-56326-w.
23. Carrasco-Hernandez L, Jódar-Sánchez F, Núñez-Benjumea F, Moreno Conde J, Mesa González M, Civit-Balcells A, et al. A Mobile Health Solution Complementing Psychopharmacology-Supported Smoking Cessation: Randomized Controlled Trial. JMIR Mhealth Uhealth. 2020 Apr 27;8(4):e17530. doi: 10.2196/17530.
24. Salod Z, Singh Y. Comparison of the performance of machine learning algorithms in breast cancer screening and detection: A protocol. J Public Health Res. 2019 Dec 4;8(3):1677. doi: 10.4081/jphr.2019.1677.
25. Pergialiotis V, Pouliakis A, Parthenis C, Damaskou V, Chrelias C, Papantoniou N, et al. The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women. Public Health. 2018 Nov;164:1-6. doi: 10.1016/j.puhe.2018.07.012. Epub 2018 Aug 24.
26. Wang TH, Zhou XF, Ni Y, Pan ZG. Health information needs regarding diabetes mellitus in China: an internet-based analysis. BMC Public Health. 2020 Jun 23;20(1):990. doi: 10.1186/s12889-020-09132-3.
27. Bonakdari H, Pelletier JP, Martel-Pelletier J. A continuous data driven translational model to evaluate effectiveness of population-level health interventions: case study, smoking ban in public places on hospital admissions for acute coronary events. J Transl Med. 2020 Dec 9;18(1):466. doi: 10.1186/s12967-020-02628-x.
28. Davagdorj K, Pham VH, Theera-Umpon N, Ryu KH. XGBoost-Based Framework for Smoking-Induced Noncommunicable Disease Prediction. Int J Environ Res Public Health. 2020 Sep 7;17(18):6513. doi: 10.3390/ ijerph17186513.
29. Adhikary R., Krishnakumar A., Kolwankar S., Shah A., Sanghavi S., Mattoo V. Effectiveness of digital therapeutics to improve blood pressure control among patients with hypertension and diabetes in india. J Am Coll Cardiol. 2020 Mar, 75 (11_Supplement_1) 3583.
30. Chary M, Genes N, McKenzie A, Manini AF. Leveraging social networks for toxicovigilance. J Med Toxicol. 2013 Jun;9(2):184-91. doi: 10.1007/s13181-013-0299-6.
31. Tang N, Huang G, Li M, Xu F. Artificial intelligence plays an important role in containing public health emergencies. Infect Control Hosp Epidemiol. 2020 Jul;41(7):869- 870. doi: 10.1017/ice.2020.103. Epub 2020 Apr 2.
32. Sinčak P, Ondo J, Kaposztasova D, Virčikova M, Vranayova Z, Sabol J. Artificial intelligence in public health prevention of legionelosis in drinking water systems. Int J Environ Res Public Health. 2014 Aug 21;11(8):8597-611. doi: 10.3390/ijerph110808597.
33. Xiong Y, Ba X, Hou A, Zhang K, Chen L, Li T. Automatic detection of mycobacterium tuberculosis using artificial intelligence. J Thorac Dis. 2018 Mar;10(3):1936-1940. doi: 10.21037/jtd.2018.01.91.
34. Wang S, Ramkrishna D. On the Spread of Coronavirus Infection. A Mechanistic Model to Rate Strategies for Disease Management. US; 2020.
35. Delen D, Eryarsoy E, Davazdahemami B. No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic. JMIR Public Health Surveill. 2020 May 28;6(2):e19862. doi: 10.2196/19862.
36. Payedimarri AB, Concina D, Portinale L, Canonico M, Seys D, Vanhaecht K, et al. Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review. Int J Environ Res Public Health. 2021 Apr 23;18(9):4499. doi: 10.3390/ijerph18094499.
37. Scardoni A, Balzarini F, Signorelli C, Cabitza F, Odone A. Artificial intelligence-based tools to control healthcare associated infections: A systematic review of the literature. J Infect Public Health. 2020 Aug;13(8):1061-1077. doi: 10.1016/j.jiph.2020.06.006. Epub 2020 Jun 16.
38. Wakamiya S, Kawai Y, Aramaki E. Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study. JMIR Public Health Surveill. 2018 Sep 25;4(3):e65. doi: 10.2196/publichealth.8627.
39. Gao Y, Cai GY, Fang W, Li HY, Wang SY, Chen L, et al. Machine learning based early warning system enables accurate mortality risk prediction for COVID-19. Nat Commun. 2020 Oct 6;11(1):5033. doi: 10.1038/s41467- 020-18684-2.
40. Doyle OM, Leavitt N, Rigg JA. Finding undiagnosed patients with hepatitis C infection: an application of artificial intelligence to patient claims data. Sci Rep. 2020 Jun 29;10(1):10521. doi: 10.1038/s41598-020-67013-6.
41. Kolozsvári LR, Bérczes T, Hajdu A, Gesztelyi R, Tiba A, Varga I, et al. Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves. Inform Med Unlocked. 2021;25:100691. doi: 10.1016/j.imu.2021.100691. Epub 2021 Aug 8.
42. Sinčak P, Ondo J, Kaposztasova D, Virčikova M, Vranayova Z, Sabol J. Artificial intelligence in public health prevention of legionelosis in drinking water systems. Int J Environ Res Public Health. 2014 Aug 21;11(8):8597- 611. doi: 10.3390/ijerph110808597.
43. Dande P, Samant P. Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review. Tuberculosis (Edinb). 2018 Jan;108:1-9. doi: 10.1016/j.tube.2017.09.006. Epub 2017 Sep 20.
44. Qiu Y, Chen X, Shi W. Impacts of Social and Economic Factors on the Transmission of Coronavirus Disease 2019 (COVID-19) in China. medRxiv [Preprint]. 2020 Mar 17:2020.03.13.20035238. doi: 10.1101/2020.03.13.20035238. Update in: J Popul Econ. 2020 May 9:1-46.
45. Alessa A, Faezipour M. Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study. JMIR Public Health Surveill. 2019 Jun 25;5(2):e12383. doi: 10.2196/12383.
46. Adikari A, Nawaratne R, De Silva D, Ranasinghe S, Alahakoon O, Alahakoon D. Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence. J Med Internet Res. 2021 Apr 30;23(4):e27341. doi: 10.2196/27341.
47. Gao Y, Cai GY, Fang W, Li HY, Wang SY, Chen L, et al. Machine learning based early warning system enables accurate mortality risk prediction for COVID-19. Nat Commun. 2020 Oct 6;11(1):5033. doi: 10.1038/s41467- 020-18684-2.
48. Cresswell K, Tahir A, Sheikh Z, Hussain Z, Domínguez Hernández A, Harrison E, et al. Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence-Enabled Social Media Analysis. J Med Internet Res. 2021 May 17;23(5):e26618. doi: 10.2196/26618.
49. Doyle OM, Leavitt N, Rigg JA. Finding undiagnosed patients with hepatitis C infection: an application of artificial intelligence to patient claims data. Sci Rep. 2020 Jun 29;10(1):10521. doi: 10.1038/s41598-020-67013-6.
50. Yu CS, Chang SS, Chang TH, Wu JL, Lin YJ, Chien HF, et al. A COVID-19 Pandemic Artificial IntelligenceBased System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study. J Med Internet Res. 2021 May 20;23(5):e27806. doi: 10.2196/27806. Erratum in: J Med Internet Res. 2021 Jul 9;23(7):e31085.
51. Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med. 2020 Jun;121:103792. doi: 10.1016/j. compbiomed.2020.103792. Epub 2020 Apr 28.
52. Nayarisseri A, Khandelwal R, Madhavi M, Selvaraj C, Panwar U, Sharma K, et al. Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation. Curr Top Med Chem. 2020;20(24):2146-2167. doi: 10.2174/1568026620666200704135327.
53. Rahman F, Meyer R, Kriak J, Goldblatt S, Slepian MJ. Big Data Analytics + Virtual Clinical Semantic Network (vCSN): An Approach to Addressing the Increasing Clinical Nuances and Organ Involvement of COVID-19. ASAIO J. 2021 Jan 1;67(1):18-24. doi: 10.1097/ MAT.0000000000001275.
54. Alakus TB, Turkoglu I. A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning. Interdiscip Sci. 2021 Mar;13(1):44-60. doi: 10.1007/s12539-020-00405-4. Epub 2021 Jan 12.
55. Gil-Jardiné C, Chenais G, Pradeau C, Tentillier E, Revel P, Combes X, et al. Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification. Scand J Trauma Resusc Emerg Med. 2021 Mar 31;29(1):55. doi: 10.1186/s13049-021-00862-w.
56. Tran TT, Fang TY, Pham VT, Lin C, Wang PC, Lo MT. Development of an Automatic Diagnostic Algorithm for Pediatric Otitis Media. Otol Neurotol. 2018 Sep;39(8):1060- 1065. doi: 10.1097/MAO.0000000000001897.
57. Wieczorek M, Siłka J, Połap D, Woźniak M, Damaševičius R. Real-time neural network based predictor for cov19 virus spread. PLoS One. 2020 Dec 17;15(12):e0243189. doi: 10.1371/journal.pone.0243189.
58. Popa IV, Diculescu M, Mihai C, Cijevschi-Prelipcean C, Burlacu A. COVID-19 and Inflammatory Bowel Diseases: Risk Assessment, Shared Molecular Pathways, and Therapeutic Challenges. Gastroenterology Research and Practice. 2020 Jul 10;2020.
59. Nkamgang OT, Tchiotsop D, Tchinda BS, Fotsin HB. A neuro-fuzzy system for automated detection and classification of human intestinal parasites. Informatics in Medicine Unlocked. 2018;13.
60. Kolitz S, Kim J, Zhang J, Cha Y, Battula S, Kusko R, et al. 477 Deep learning to drive COVID-19 rapid drug repurposing. Journal for ImmunoTherapy of Cancer. 2020 Nov 9;8(Suppl 3).
61. Izquierdo JL, Ancochea J; Savana COVID-19 Research Group, Soriano JB. Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing. J Med Internet Res. 2020 Oct 28;22(10):e21801. doi: 10.2196/21801.
62. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017 Jun 28;546(7660):686. doi: 10.1038/nature22985. Erratum for: Nature. 2017 Feb 2;542(7639):115-118.
63. Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018 Aug 1;29(8):1836-1842. doi: 10.1093/annonc/mdy166.
64. Yang J, Sun X, Liang J, Rosin PL. Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2018.
65. Maron RC, Weichenthal M, Utikal JS, Hekler A, Berking C, Hauschild A, et al. Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. Eur J Cancer. 2019 Sep;119:57-65. doi: 10.1016/j.ejca.2019.06.013. Epub 2019 Aug 14.
66. Haggenmüller S, Maron RC, Hekler A, Utikal JS, Barata C, Barnhill RL, et al. Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts. Eur J Cancer. 2021 Oct;156:202-216. doi: 10.1016/j.ejca.2021.06.049. Epub 2021 Sep 8.
67. Fujisawa Y, Otomo Y, Ogata Y, Nakamura Y, Fujita R, Ishitsuka Y, et al. Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis. Br J Dermatol. 2019 Feb;180(2):373- 381. doi: 10.1111/bjd.16924. Epub 2018 Sep 19.
68. Brinker TJ, Hekler A, Enk AH, Berking C, Haferkamp S, Hauschild A, et al. Deep neural networks are superior to dermatologists in melanoma image classification. Eur J Cancer. 2019 Sep;119:11-17. doi: 10.1016/j. ejca.2019.05.023. Epub 2019 Aug 8.
69. Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C, et al. A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. Eur J Cancer. 2019 Apr;111:148-154. doi: 10.1016/j.ejca.2019.02.005. Epub 2019 Mar 8.
70. Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C, Set al. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur J Cancer. 2019 May;113:47-54. doi: 10.1016/j.ejca.2019.04.001. Epub 2019 Apr 10.
71. Alheejawi S, Berendt R, Jha N, Maity SP, Mandal M. Automated proliferation index calculation for skin melanoma biopsy images using machine learning. Comput Med Imaging Graph. 2021 Apr;89:101893. doi: 10.1016/j. compmedimag.2021.101893. Epub 2021 Mar 5.
72. 6th European Conference on Schizophrenia Research: Advancing Research—Promoting Recovery. European Archives of Psychiatry and Clinical Neuroscience. 2017 Sep 8;267(S1).
73. Adamou M, Antoniou G, Greasidou E, Lagani V, Charonyktakis P, Tsamardinos I, et al. Toward Automatic Risk Assessment to Support Suicide Prevention. Crisis. 2019 Jul;40(4):249-256. doi: 10.1027/0227-5910/a000561. Epub 2018 Nov 26.
74. Sibbritt D, Gibberd R. The effective use of a summary table and decision tree methodology to analyze very large healthcare datasets. Health Care Manag Sci. 2004 Aug;7(3):163-71. doi: 10.1023/b:hcms.0000039379.329 63.9e.
75. Delen D, Walker G, Kadam A. Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med. 2005 Jun;34(2):113-27. doi: 10.1016/j.artmed.2004.07.002.
76. Pittet D. Infection control and quality health care in the new millennium. Am J Infect Control. 2005 Jun;33(5):258- 67. doi: 10.1016/j.ajic.2004.11.004.
77. Stead WW. Clinical Implications and Challenges of Artificial Intelligence and Deep Learning. JAMA. 2018 Sep 18;320(11):1107-1108. doi: 10.1001/jama.2018.11029.
78. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Intern Med. 2018 Nov 1;178(11):1544-1547. doi: 10.1001/jamainternmed.2018.3763.
79. Park SH, Choi J, Byeon JS. Key Principles of Clinical Validation, Device Approval, and Insurance Coverage Decisions of Artificial Intelligence. Korean J Radiol. 2021 Mar;22(3):442-453. doi: 10.3348/kjr.2021.0048.
80. Qiu Y, Chen X, Shi W. Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China. J Popul Econ. 2020 May 9:1-46. doi: 10.1007/s00148-020-00778-2. Epub ahead of print.
2. Salcedo J, Rosales M, Kim JS, Nuno D, Suen S, Chang AH. Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study. PLOS ONE. 2021 Jul 21;16(7).
3. Benke K, Benke G. Artificial Intelligence and Big Data in Public Health. Int J Environ Res Public Health. 2018 Dec 10;15(12):2796. doi: 10.3390/ijerph15122796.
4. Gómez Zuleta MA, Cano Rosales DF, Bravo Higuera DF, Ruano Balseca JA, Romero Castro E. Detección automática de pólipos colorrectales con técnicas de inteligencia artificial. Revista colombiana de Gastroenterología. 2021 Mar 8;36(1).
5. Puerta Barrera JF, Amaya Hurtado D, Jiménez Moreno R. Prediction system of erythemas for phototypes I and II, using deep-learning. Revista Vitae. 2015 May 1;22(3).
6. Sprockel JJ, Alzate W. Aplicación de la computación evolutiva en el diagnóstico del infarto agudo del miocardio: Hospital de San José, Bogotá DC, Colombia. 2012. Revista Repertorio de Medicina y Cirugía. 2014 Sep 1;23(3).
7. Effland T, Lawson A, Balter S, Devinney K, Reddy V, Waechter H, et al. Discovering foodborne illness in online restaurant reviews. J Am Med Inform Assoc. 2018 Dec 1;25(12):1586-1592. doi: 10.1093/jamia/ocx093. PMID: 29329402; PMCID: PMC7647154.
8. Chakraborty M, Sarkar S, Mukherjee A, Shamsudduha M, Ahmed KM, Bhattacharya A, et al. Modeling regionalscale groundwater arsenic hazard in the transboundary Ganges River Delta, India and Bangladesh: Infusing physically-based model with machine learning. Sci Total Environ. 2020 Dec 15;748:141107. doi: 10.1016/j.scitotenv.2020.141107. Epub 2020 Jul 25.
9. Ochoa Reyes A, Orellana García A, Sanchez Corales Y, Davila Hernández F. Componente web para el análisis de información clínica usando la técnica de Minería de Datos por agrupamiento. Revista Cubana de Informática Médica. 2014;6(1):5–16.
10. Kuiava VA, Kuiava EL, Chielle EO, Syllos R. Desenvolvimento de sistema estruturado com inteligência artificial para apoio no diagnóstico de patologias oftalmológicas mais relevantes. Clinical & Biomedical Research. 2021 Aug 30;
11. Nieuwlaat R, Eikelboom JW, Schulman S, van Spall HG, Schulze KM, Connolly BJ, et al. Cluster randomized controlled trial of a simple warfarin maintenance dosing algorithm versus usual care among primary care practices. J Thromb Thrombolysis. 2014 May;37(4):435-42. doi: 10.1007/s11239-013-0969-x.
12. Labovitz DL, Shafner L, Reyes Gil M, Virmani D, Hanina A. Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy. Stroke. 2017 May;48(5):1416-1419. doi: 10.1161/ STROKEAHA.116.016281. Epub 2017 Apr 6.
13. INFANT Collaborative Group. Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. Lancet. 2017 Apr 29;389(10080):1719-1729. doi: 10.1016/S0140-6736(17)30568-8. Epub 2017 Mar 21.
14. Finkelstein SM, Lindgren BR, Robiner W, Lindquist R, Hertz M, Carlin BP, et al. A randomized controlled trial comparing health and quality of life of lung transplant recipients following nurse and computer-based triage utilizing home spirometry monitoring. Telemed J E Health. 2013 Dec;19(12):897-903. doi: 10.1089/tmj.2013.0049. Epub 2013 Oct 1.
15. Caballero-Ruiz E, García-Sáez G, Rigla M, Villaplana M, Pons B, Hernando ME. A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs. Int J Med Inform. 2017 Jun;102:35-49. doi: 10.1016/j.ijmedinf.2017.02.014. Epub 2017 Mar 6.
16. Rim TH, Soh ZD, Tham YC, Yang HHS, Lee G, Kim Y, et al. Deep Learning for Automated Sorting of Retinal Photographs. Ophthalmol Retina. 2020 Aug;4(8):793-800. doi: 10.1016/j.oret.2020.03.007. Epub 2020 Mar 17.
17. Sabanayagam C, Xu D, Ting DSW, Nusinovici S, Banu R, Hamzah H, Lim C, et al. A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations. Lancet Digit Health. 2020 Jun;2(6):e295-e302. doi: 10.1016/S2589- 7500(20)30063-7. Epub 2020 May 12.
18. SIOP 2016 Scientific Programme+Index. Pediatric Blood & Cancer. 2016 Nov;63.
19. Kolanu N, Brown AS, Beech A, Center JR, White CP. Natural language processing of radiology reports for the identification of patients with fracture. Arch Osteoporos. 2021 Jan 6;16(1):6. doi: 10.1007/s11657-020-00859-5.
20. Lim HK, Ha HI, Park SY, Han J. Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study. PLoS One. 2021 Mar 4;16(3):e0247330. doi: 10.1371/journal.pone.0247330.
21. Monlezun DJ, Dart L, Vanbeber A, Smith-Barbaro P, Costilla V, Samuel C, et al. Machine Learning-Augmented Propensity Score-Adjusted Multilevel Mixed Effects Panel Analysis of Hands-On Cooking and Nutrition Education versus Traditional Curriculum for Medical Students as Preventive Cardiology: Multisite Cohort Study of 3,248 Trainees over 5 Years. BioMed Research International. 2018.
22. Adeyinka DA, Olakunde BO, Muhajarine N. Evidence of health inequity in child survival: spatial and Bayesian network analyses of stillbirth rates in 194 countries. Sci Rep. 2019 Dec 24;9(1):19755. doi: 10.1038/s41598- 019-56326-w.
23. Carrasco-Hernandez L, Jódar-Sánchez F, Núñez-Benjumea F, Moreno Conde J, Mesa González M, Civit-Balcells A, et al. A Mobile Health Solution Complementing Psychopharmacology-Supported Smoking Cessation: Randomized Controlled Trial. JMIR Mhealth Uhealth. 2020 Apr 27;8(4):e17530. doi: 10.2196/17530.
24. Salod Z, Singh Y. Comparison of the performance of machine learning algorithms in breast cancer screening and detection: A protocol. J Public Health Res. 2019 Dec 4;8(3):1677. doi: 10.4081/jphr.2019.1677.
25. Pergialiotis V, Pouliakis A, Parthenis C, Damaskou V, Chrelias C, Papantoniou N, et al. The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women. Public Health. 2018 Nov;164:1-6. doi: 10.1016/j.puhe.2018.07.012. Epub 2018 Aug 24.
26. Wang TH, Zhou XF, Ni Y, Pan ZG. Health information needs regarding diabetes mellitus in China: an internet-based analysis. BMC Public Health. 2020 Jun 23;20(1):990. doi: 10.1186/s12889-020-09132-3.
27. Bonakdari H, Pelletier JP, Martel-Pelletier J. A continuous data driven translational model to evaluate effectiveness of population-level health interventions: case study, smoking ban in public places on hospital admissions for acute coronary events. J Transl Med. 2020 Dec 9;18(1):466. doi: 10.1186/s12967-020-02628-x.
28. Davagdorj K, Pham VH, Theera-Umpon N, Ryu KH. XGBoost-Based Framework for Smoking-Induced Noncommunicable Disease Prediction. Int J Environ Res Public Health. 2020 Sep 7;17(18):6513. doi: 10.3390/ ijerph17186513.
29. Adhikary R., Krishnakumar A., Kolwankar S., Shah A., Sanghavi S., Mattoo V. Effectiveness of digital therapeutics to improve blood pressure control among patients with hypertension and diabetes in india. J Am Coll Cardiol. 2020 Mar, 75 (11_Supplement_1) 3583.
30. Chary M, Genes N, McKenzie A, Manini AF. Leveraging social networks for toxicovigilance. J Med Toxicol. 2013 Jun;9(2):184-91. doi: 10.1007/s13181-013-0299-6.
31. Tang N, Huang G, Li M, Xu F. Artificial intelligence plays an important role in containing public health emergencies. Infect Control Hosp Epidemiol. 2020 Jul;41(7):869- 870. doi: 10.1017/ice.2020.103. Epub 2020 Apr 2.
32. Sinčak P, Ondo J, Kaposztasova D, Virčikova M, Vranayova Z, Sabol J. Artificial intelligence in public health prevention of legionelosis in drinking water systems. Int J Environ Res Public Health. 2014 Aug 21;11(8):8597-611. doi: 10.3390/ijerph110808597.
33. Xiong Y, Ba X, Hou A, Zhang K, Chen L, Li T. Automatic detection of mycobacterium tuberculosis using artificial intelligence. J Thorac Dis. 2018 Mar;10(3):1936-1940. doi: 10.21037/jtd.2018.01.91.
34. Wang S, Ramkrishna D. On the Spread of Coronavirus Infection. A Mechanistic Model to Rate Strategies for Disease Management. US; 2020.
35. Delen D, Eryarsoy E, Davazdahemami B. No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic. JMIR Public Health Surveill. 2020 May 28;6(2):e19862. doi: 10.2196/19862.
36. Payedimarri AB, Concina D, Portinale L, Canonico M, Seys D, Vanhaecht K, et al. Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review. Int J Environ Res Public Health. 2021 Apr 23;18(9):4499. doi: 10.3390/ijerph18094499.
37. Scardoni A, Balzarini F, Signorelli C, Cabitza F, Odone A. Artificial intelligence-based tools to control healthcare associated infections: A systematic review of the literature. J Infect Public Health. 2020 Aug;13(8):1061-1077. doi: 10.1016/j.jiph.2020.06.006. Epub 2020 Jun 16.
38. Wakamiya S, Kawai Y, Aramaki E. Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study. JMIR Public Health Surveill. 2018 Sep 25;4(3):e65. doi: 10.2196/publichealth.8627.
39. Gao Y, Cai GY, Fang W, Li HY, Wang SY, Chen L, et al. Machine learning based early warning system enables accurate mortality risk prediction for COVID-19. Nat Commun. 2020 Oct 6;11(1):5033. doi: 10.1038/s41467- 020-18684-2.
40. Doyle OM, Leavitt N, Rigg JA. Finding undiagnosed patients with hepatitis C infection: an application of artificial intelligence to patient claims data. Sci Rep. 2020 Jun 29;10(1):10521. doi: 10.1038/s41598-020-67013-6.
41. Kolozsvári LR, Bérczes T, Hajdu A, Gesztelyi R, Tiba A, Varga I, et al. Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves. Inform Med Unlocked. 2021;25:100691. doi: 10.1016/j.imu.2021.100691. Epub 2021 Aug 8.
42. Sinčak P, Ondo J, Kaposztasova D, Virčikova M, Vranayova Z, Sabol J. Artificial intelligence in public health prevention of legionelosis in drinking water systems. Int J Environ Res Public Health. 2014 Aug 21;11(8):8597- 611. doi: 10.3390/ijerph110808597.
43. Dande P, Samant P. Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review. Tuberculosis (Edinb). 2018 Jan;108:1-9. doi: 10.1016/j.tube.2017.09.006. Epub 2017 Sep 20.
44. Qiu Y, Chen X, Shi W. Impacts of Social and Economic Factors on the Transmission of Coronavirus Disease 2019 (COVID-19) in China. medRxiv [Preprint]. 2020 Mar 17:2020.03.13.20035238. doi: 10.1101/2020.03.13.20035238. Update in: J Popul Econ. 2020 May 9:1-46.
45. Alessa A, Faezipour M. Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study. JMIR Public Health Surveill. 2019 Jun 25;5(2):e12383. doi: 10.2196/12383.
46. Adikari A, Nawaratne R, De Silva D, Ranasinghe S, Alahakoon O, Alahakoon D. Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence. J Med Internet Res. 2021 Apr 30;23(4):e27341. doi: 10.2196/27341.
47. Gao Y, Cai GY, Fang W, Li HY, Wang SY, Chen L, et al. Machine learning based early warning system enables accurate mortality risk prediction for COVID-19. Nat Commun. 2020 Oct 6;11(1):5033. doi: 10.1038/s41467- 020-18684-2.
48. Cresswell K, Tahir A, Sheikh Z, Hussain Z, Domínguez Hernández A, Harrison E, et al. Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence-Enabled Social Media Analysis. J Med Internet Res. 2021 May 17;23(5):e26618. doi: 10.2196/26618.
49. Doyle OM, Leavitt N, Rigg JA. Finding undiagnosed patients with hepatitis C infection: an application of artificial intelligence to patient claims data. Sci Rep. 2020 Jun 29;10(1):10521. doi: 10.1038/s41598-020-67013-6.
50. Yu CS, Chang SS, Chang TH, Wu JL, Lin YJ, Chien HF, et al. A COVID-19 Pandemic Artificial IntelligenceBased System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study. J Med Internet Res. 2021 May 20;23(5):e27806. doi: 10.2196/27806. Erratum in: J Med Internet Res. 2021 Jul 9;23(7):e31085.
51. Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med. 2020 Jun;121:103792. doi: 10.1016/j. compbiomed.2020.103792. Epub 2020 Apr 28.
52. Nayarisseri A, Khandelwal R, Madhavi M, Selvaraj C, Panwar U, Sharma K, et al. Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation. Curr Top Med Chem. 2020;20(24):2146-2167. doi: 10.2174/1568026620666200704135327.
53. Rahman F, Meyer R, Kriak J, Goldblatt S, Slepian MJ. Big Data Analytics + Virtual Clinical Semantic Network (vCSN): An Approach to Addressing the Increasing Clinical Nuances and Organ Involvement of COVID-19. ASAIO J. 2021 Jan 1;67(1):18-24. doi: 10.1097/ MAT.0000000000001275.
54. Alakus TB, Turkoglu I. A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning. Interdiscip Sci. 2021 Mar;13(1):44-60. doi: 10.1007/s12539-020-00405-4. Epub 2021 Jan 12.
55. Gil-Jardiné C, Chenais G, Pradeau C, Tentillier E, Revel P, Combes X, et al. Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification. Scand J Trauma Resusc Emerg Med. 2021 Mar 31;29(1):55. doi: 10.1186/s13049-021-00862-w.
56. Tran TT, Fang TY, Pham VT, Lin C, Wang PC, Lo MT. Development of an Automatic Diagnostic Algorithm for Pediatric Otitis Media. Otol Neurotol. 2018 Sep;39(8):1060- 1065. doi: 10.1097/MAO.0000000000001897.
57. Wieczorek M, Siłka J, Połap D, Woźniak M, Damaševičius R. Real-time neural network based predictor for cov19 virus spread. PLoS One. 2020 Dec 17;15(12):e0243189. doi: 10.1371/journal.pone.0243189.
58. Popa IV, Diculescu M, Mihai C, Cijevschi-Prelipcean C, Burlacu A. COVID-19 and Inflammatory Bowel Diseases: Risk Assessment, Shared Molecular Pathways, and Therapeutic Challenges. Gastroenterology Research and Practice. 2020 Jul 10;2020.
59. Nkamgang OT, Tchiotsop D, Tchinda BS, Fotsin HB. A neuro-fuzzy system for automated detection and classification of human intestinal parasites. Informatics in Medicine Unlocked. 2018;13.
60. Kolitz S, Kim J, Zhang J, Cha Y, Battula S, Kusko R, et al. 477 Deep learning to drive COVID-19 rapid drug repurposing. Journal for ImmunoTherapy of Cancer. 2020 Nov 9;8(Suppl 3).
61. Izquierdo JL, Ancochea J; Savana COVID-19 Research Group, Soriano JB. Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing. J Med Internet Res. 2020 Oct 28;22(10):e21801. doi: 10.2196/21801.
62. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017 Jun 28;546(7660):686. doi: 10.1038/nature22985. Erratum for: Nature. 2017 Feb 2;542(7639):115-118.
63. Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018 Aug 1;29(8):1836-1842. doi: 10.1093/annonc/mdy166.
64. Yang J, Sun X, Liang J, Rosin PL. Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2018.
65. Maron RC, Weichenthal M, Utikal JS, Hekler A, Berking C, Hauschild A, et al. Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. Eur J Cancer. 2019 Sep;119:57-65. doi: 10.1016/j.ejca.2019.06.013. Epub 2019 Aug 14.
66. Haggenmüller S, Maron RC, Hekler A, Utikal JS, Barata C, Barnhill RL, et al. Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts. Eur J Cancer. 2021 Oct;156:202-216. doi: 10.1016/j.ejca.2021.06.049. Epub 2021 Sep 8.
67. Fujisawa Y, Otomo Y, Ogata Y, Nakamura Y, Fujita R, Ishitsuka Y, et al. Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis. Br J Dermatol. 2019 Feb;180(2):373- 381. doi: 10.1111/bjd.16924. Epub 2018 Sep 19.
68. Brinker TJ, Hekler A, Enk AH, Berking C, Haferkamp S, Hauschild A, et al. Deep neural networks are superior to dermatologists in melanoma image classification. Eur J Cancer. 2019 Sep;119:11-17. doi: 10.1016/j. ejca.2019.05.023. Epub 2019 Aug 8.
69. Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C, et al. A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. Eur J Cancer. 2019 Apr;111:148-154. doi: 10.1016/j.ejca.2019.02.005. Epub 2019 Mar 8.
70. Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C, Set al. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur J Cancer. 2019 May;113:47-54. doi: 10.1016/j.ejca.2019.04.001. Epub 2019 Apr 10.
71. Alheejawi S, Berendt R, Jha N, Maity SP, Mandal M. Automated proliferation index calculation for skin melanoma biopsy images using machine learning. Comput Med Imaging Graph. 2021 Apr;89:101893. doi: 10.1016/j. compmedimag.2021.101893. Epub 2021 Mar 5.
72. 6th European Conference on Schizophrenia Research: Advancing Research—Promoting Recovery. European Archives of Psychiatry and Clinical Neuroscience. 2017 Sep 8;267(S1).
73. Adamou M, Antoniou G, Greasidou E, Lagani V, Charonyktakis P, Tsamardinos I, et al. Toward Automatic Risk Assessment to Support Suicide Prevention. Crisis. 2019 Jul;40(4):249-256. doi: 10.1027/0227-5910/a000561. Epub 2018 Nov 26.
74. Sibbritt D, Gibberd R. The effective use of a summary table and decision tree methodology to analyze very large healthcare datasets. Health Care Manag Sci. 2004 Aug;7(3):163-71. doi: 10.1023/b:hcms.0000039379.329 63.9e.
75. Delen D, Walker G, Kadam A. Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med. 2005 Jun;34(2):113-27. doi: 10.1016/j.artmed.2004.07.002.
76. Pittet D. Infection control and quality health care in the new millennium. Am J Infect Control. 2005 Jun;33(5):258- 67. doi: 10.1016/j.ajic.2004.11.004.
77. Stead WW. Clinical Implications and Challenges of Artificial Intelligence and Deep Learning. JAMA. 2018 Sep 18;320(11):1107-1108. doi: 10.1001/jama.2018.11029.
78. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Intern Med. 2018 Nov 1;178(11):1544-1547. doi: 10.1001/jamainternmed.2018.3763.
79. Park SH, Choi J, Byeon JS. Key Principles of Clinical Validation, Device Approval, and Insurance Coverage Decisions of Artificial Intelligence. Korean J Radiol. 2021 Mar;22(3):442-453. doi: 10.3348/kjr.2021.0048.
80. Qiu Y, Chen X, Shi W. Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China. J Popul Econ. 2020 May 9:1-46. doi: 10.1007/s00148-020-00778-2. Epub ahead of print.
Cómo citar
[1]
Beltrán-Ostos, A. et al. 2022. Estado del arte, ventajas y limitaciones de la inteligencia artificial en epidemiología y salud pública. Medicina. 43, 4 (ene. 2022), 582–593. DOI:https://doi.org/10.56050/01205498.1647.
Descargas
Los datos de descargas todavía no están disponibles.
Publicado
2022-01-18
Número
Sección
Artículos de Revisión
Licencia
Derechos de autor 2022 Medicina
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Copyright
ANM de Colombia
Los autores deben declarar revisión, validación y aprobación para publicación del manuscrito, además de la cesión de los derechos patrimoniales de publicación, mediante un documento que debe ser enviado antes de la aparición del escrito. Puede solicitar el formato a través del correo revistamedicina@anmdecolombia.org.co o descargarlo directamente Documento Garantías y cesión de derechos.docx
Copyright
ANM de Colombia
Authors must state that they reviewed, validated and approved the manuscript's publication. Moreover, they must sign a model release that should be sent.
