Bhai SF, Vissing J (2023) Diagnosis and management of metabolic myopathies. Muscle Nerve. https://doi.org/10.1002/mus.27840
Natesan V (2022) Therapeutics in Metabolic Diseases. In: Advances in experimental medicine and biology. pp 255–273
Song BG, Choi SC, Goh MJ et al (2023) Metabolic dysfunction-associated fatty liver disease and the risk of hepatocellular carcinoma. JHEP Rep. https://doi.org/10.1016/j.jhepr.2023.100810
Article PubMed PubMed Central Google Scholar
Park JG (2023) Unraveling metabolic dysfunction-Associated fatty liver disease: refining sub-phenotypes for resolving its heterogeneity. Gut Liver 17:489–490. https://doi.org/10.5009/gnl230222
Article PubMed PubMed Central Google Scholar
Marschner RA, Roginski AC, Ribeiro RT et al (2023) Uncovering actions of type 3 deiodinase in the metabolic dysfunction-Associated fatty liver Disease (MAFLD). https://doi.org/10.3390/cells12071022. Cells 12:
Tiivoja E, Reinson K, Muru K et al (2022) The prevalence of inherited metabolic disorders in Estonian population over 30 years: a significant increase during study period. JIMD Rep. https://doi.org/10.1002/jmd2.12325
Article PubMed PubMed Central Google Scholar
Zakir F, Mohapatra S, Farooq U, et al (2022) Introduction to metabolic disorders. In: Dureja H, Murty SN, Wich PR, Dua K (eds) Drug Delivery Systems for Metabolic Disorders. Elsevier, pp 1–20
Batool A, Zaman S, Ayub A, Prevalence of Clinical Spectrum of Inherited Metabolic Disorders in Infants and Children at a Tertiary Care Hospital in Rawalpindi, Pakistan (2020) Pakistan Armed Forces Med J. https://doi.org/10.51253/pafmj.v70i6.2417
Miotto R, Li L, Kidd BA, Dudley JT (2016) Deep patient: an unsupervised representation to predict the future of patients from the Electronic Health Records. Sci Rep. https://doi.org/10.1038/srep26094
Article PubMed PubMed Central Google Scholar
Poalelungi DG, Musat CL, Fulga A et al (2023) Advancing patient care: how Artificial Intelligence is transforming Healthcare. J Pers Med 13:1214. https://doi.org/10.3390/jpm13081214
Article PubMed PubMed Central Google Scholar
TURING AM, Mind LIX (1950) I.—Computing Machinery and Intelligence. https://doi.org/10.1093/mind/LIX.236.433
Salto-Tellez M, Maxwell P, Hamilton P (2019) Artificial intelligence—the third revolution in pathology. Histopathology 74:372–376. https://doi.org/10.1111/his.13760
Kaplan A, Haenlein M (2019) Siri, Siri, in my hand: who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz 62:15–25. https://doi.org/10.1016/j.bushor.2018.08.004
Morandín-Ahuerma F (2022) What is Artificial Intelligence? Int J Res Publication Reviews 03:1947–1951. https://doi.org/10.55248/gengpi.2022.31261
Gaur N, Dharwadkar R, Thomas J (2022) Personalized Therapy Using Deep Learning Advances. In: Malviya R, Ghinea G, Dhanaraj RK, et al (eds) Deep Learning for Targeted Treatments. Wiley, pp 171–197
Sangro P, de la Torre Aláez M, Sangro B, D’Avola D (2023) Metabolic dysfunction–associated fatty liver disease (MAFLD): an update of the recent advances in pharmacological treatment. J Physiol Biochem 79:869–879. https://doi.org/10.1007/s13105-023-00954-4
Article PubMed PubMed Central Google Scholar
Kamini RS (2023) Artificial Intelligence and Machine Learning Models for Diagnosing Neurodegenerative disorders. In: Koundal D, Jain DK, Guo Y et al (eds) Data Analysis for neurodegenerative disorders. Springer Nature Singapore, Singapore, pp 15–48
Pike A, Benkli B, Gilani SO, Hirani S (2023) Chapter 9 - Artificial intelligence and machine learning. In: Kaye AD, Urman RD, Cornett EM, Edinoff AN (eds) Substance Use and Addiction Research. Academic Press, pp 99–106
Shen D, Wu G, Suk H, Il (2017) Deep learning in Medical Image Analysis. Annu Rev Biomed Eng. https://doi.org/10.1146/annurev-bioeng-071516-044442
Castañeda WAC, Filho PB (2023) Towards an Artificial Intelligence Based Chronic Disease Management. Preprints (Basel). https://doi.org/10.20944/preprints202304.0491.v1
Nigar N, Jaleel A, Islam S et al (2023) IoMT Meets Machine Learning: From Edge to Cloud Chronic Diseases Diagnosis System. J Healthc Eng 2023:. https://doi.org/10.1155/2023/9995292
Aishwarya S (2023) Artificial Intelligence Driving Diabetes Care. J Int Med Graduates. https://doi.org/10.56570/jimgs.v2i1.92. 2:
Knights V, Kolak M, Markovikj G, Gajdoš Kljusurić J (2023) Modeling and optimization with Artificial Intelligence in Nutrition. Appl Sci (Switzerland). https://doi.org/10.3390/app13137835
Cohen Y, Valdés-Mas R, Elinav E (2023) The Role of Artificial Intelligence in Deciphering Diet–Disease Relationships: Case Studies. Annu Rev Nutr 43:225–250. https://doi.org/10.1146/annurev-nutr-061121-090535
Hart KH, Wilson-Barnes S, Stefanidis K et al (2022) The suitability of dietary recommendations suggested by artificial intelligence technology via a novel personalised nutrition mobile application. Proc Nutr Soc. https://doi.org/10.1017/s0029665122000374
Dhall Devanshi, Kaur R (2020) and JM Machine Learning: A Review of the Algorithms and Its Applications. In: Singh Pradeep Kumar and Kar AK and SY and KMH and TS (ed) Proceedings of ICRIC 2019. Springer International Publishing, Cham, pp 47–63
Nomura A, Noguchi M, Kometani M et al (2021) Artificial Intelligence in Current Diabetes Management and Prediction. Curr Diab Rep 21:61. https://doi.org/10.1007/s11892-021-01423-2
Article PubMed PubMed Central Google Scholar
Bini SA (2018) Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: what do these terms Mean and how will they Impact Health Care? J Arthroplasty. https://doi.org/10.1016/j.arth.2018.02.067
Naylor CD (2018) On the prospects for a (Deep) Learning Health Care System. JAMA 320:1099. https://doi.org/10.1001/jama.2018.11103
Ethem Alpaydın (2014) Introduction to machine learning, 3rd edn. MIT Press, Cambridge
Javaid M, Haleem A, Pratap Singh R et al (2022) Significance of machine learning in healthcare: features, pillars and applications. Int J Intell Networks. https://doi.org/10.1016/j.ijin.2022.05.002. 3:
Galal A, Talal M, Moustafa A (2022) Applications of machine learning in metabolomics: Disease modeling and classification. Front Genet. https://doi.org/10.3389/fgene.2022.1017340
Article PubMed PubMed Central Google Scholar
Biswas A, Saran I, Wilson FP (2021) Introduction to supervised machine learning. Kidney360 2:878–880. https://doi.org/10.34067/KID.0000182021
van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605
McInnes L, Healy J, Melville J (2018) UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Libbrecht MW, Noble WS (2015) Machine learning applications in genetics and genomics. Nat Rev Genet 16:321–332. https://doi.org/10.1038/nrg3920
Article CAS PubMed PubMed Central Google Scholar
Migdadi L, Lambert J, Telfah A et al (2021) Automated metabolic assignment: semi-supervised learning in metabolic analysis employing two dimensional nuclear magnetic resonance (NMR). Comput Struct Biotechnol J 19:5047–5058. https://doi.org/10.1016/j.csbj.2021.08.048
Article CAS PubMed PubMed Central Google Scholar
Abram KJ, McCloskey D (2022) A comprehensive evaluation of Metabolomics Data Preprocessing methods for Deep Learning. Metabolites 12:202. https://doi.org/10.3390/metabo12030202
Article CAS PubMed PubMed Central Google Scholar
Iqbal T, Elahi A, Wijns W, Shahzad A (2022) Exploring unsupervised machine learning classification methods for physiological stress detection. Front Med Technol. https://doi.org/10.3389/fmedt.2022.782756
Article PubMed PubMed Central Google Scholar
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539
Article CAS PubMed Google Scholar
Liu X, Faes L, Kale AU et al (2019) A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. https://doi.org/10.1016/S2589-7500(19)30123-2. 1:
Fanni SC, Febi M, Aghakhanyan G, Neri E (2023) Natural Language Processing. pp 87–99
Khurana D, Koli A, Khatter K, Singh S (2023) Natural language processing: state of the art, current trends and challenges. Multimed Tools Appl 82:3713–3744. https://doi.org/10.1007/s11042-022-13428-4
Iroju OG, Olaleke JO (2015) A systematic review of Natural Language Processing in Healthcare. Int J Inform Technol Comput Sci 7:44–50. https://doi.org/10.5815/ijitcs.2015.08.07
留言 (0)