{ "status": true, "message": "Task executed", "data": { "results": [ { "title": "A guide to machine learning for biologists.", "link": "https://pubmed.ncbi.nlm.nih.gov/34518686/", "authors": "Greener JG, Kandathil SM, Moffat L, Jones DT.", "source": "Nat Rev Mol Cell Biol. 2022 Jan;23(1):40-55. doi: 10.1038/s41580-021-00407-0. Epub 2021 Sep 13.", "date": "Nat Rev Mol Cell Biol. 2022 Jan;23(1):40-55. doi: 10.1038/s41580-021-00407-0. Epub 2021 Sep 13.", "snippet": "All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. ...We describe how different techniques may be suited to specific types of biological data, and also discuss some best practi …", "pmid": "34518686", "is_free": false }, { "title": "Introduction to Machine Learning, Neural Networks, and Deep Learning.", "link": "https://pubmed.ncbi.nlm.nih.gov/32704420/", "authors": "Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP.", "source": "Transl Vis Sci Technol. 2020 Feb 27;9(2):14. doi: 10.1167/tvst.9.2.14.", "date": "Transl Vis Sci Technol. 2020 Feb 27;9(2):14. doi: 10.1167/tvst.9.2.14.", "snippet": "PURPOSE: To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning. METHODS: A systematic literature search in PubMed was perfo …", "pmid": "32704420", "is_free": true }, { "title": "Machine Learning in Medicine.", "link": "https://pubmed.ncbi.nlm.nih.gov/26572668/", "authors": "Deo RC.", "source": "Circulation. 2015 Nov 17;132(20):1920-30. doi: 10.1161/CIRCULATIONAHA.115.001593.", "date": "Circulation. 2015 Nov 17;132(20):1920-30. doi: 10.1161/CIRCULATIONAHA.115.001593.", "snippet": "The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical da …", "pmid": "26572668", "is_free": true }, { "title": "eDoctor: machine learning and the future of medicine.", "link": "https://pubmed.ncbi.nlm.nih.gov/30102808/", "authors": "Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H.", "source": "J Intern Med. 2018 Dec;284(6):603-619. doi: 10.1111/joim.12822. Epub 2018 Sep 3.", "date": "J Intern Med. 2018 Dec;284(6):603-619. doi: 10.1111/joim.12822. Epub 2018 Sep 3.", "snippet": "Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. ...", "pmid": "30102808", "is_free": true }, { "title": "Supervised Machine Learning: A Brief Primer.", "link": "https://pubmed.ncbi.nlm.nih.gov/32800297/", "authors": "Jiang T, Gradus JL, Rosellini AJ.", "source": "Behav Ther. 2020 Sep;51(5):675-687. doi: 10.1016/j.beth.2020.05.002. Epub 2020 May 16.", "date": "Behav Ther. 2020 Sep;51(5):675-687. doi: 10.1016/j.beth.2020.05.002. Epub 2020 May 16.", "snippet": "Machine learning offers new tools to overcome challenges for which traditional statistical methods are not well-suited. This paper provides an overview of machine learning with a specific focus on supervised learning (i.e., methods that are desi …", "pmid": "32800297", "is_free": true }, { "title": "Machine learning for cardiology.", "link": "https://pubmed.ncbi.nlm.nih.gov/34338485/", "authors": "Arfat Y, Mittone G, Esposito R, Cantalupo B, DE Ferrari GM, Aldinucci M.", "source": "Minerva Cardiol Angiol. 2022 Feb;70(1):75-91. doi: 10.23736/S2724-5683.21.05709-4. Epub 2021 Aug 2.", "date": "Minerva Cardiol Angiol. 2022 Feb;70(1):75-91. doi: 10.23736/S2724-5683.21.05709-4. Epub 2021 Aug 2.", "snippet": "We specifically focus on the principal Machine learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in …", "pmid": "34338485", "is_free": true }, { "title": "Machine learning model for predicting malaria using clinical information.", "link": "https://pubmed.ncbi.nlm.nih.gov/33290932/", "authors": "Lee YW, Choi JW, Shin EH.", "source": "Comput Biol Med. 2021 Feb;129:104151. doi: 10.1016/j.compbiomed.2020.104151. Epub 2020 Nov 28.", "date": "Comput Biol Med. 2021 Feb;129:104151. doi: 10.1016/j.compbiomed.2020.104151. Epub 2020 Nov 28.", "snippet": "Various studies have aimed at developing machine learning models to diagnose malaria using blood smear images; however, this approach has many limitations. ...CONCLUSIONS: The results demonstrated that machine learning techniques can be successfully ap …", "pmid": "33290932", "is_free": false }, { "title": "A Review on Machine Learning for EEG Signal Processing in Bioengineering.", "link": "https://pubmed.ncbi.nlm.nih.gov/32011262/", "authors": "Hosseini MP, Hosseini A, Ahi K.", "source": "IEEE Rev Biomed Eng. 2021;14:204-218. doi: 10.1109/RBME.2020.2969915. Epub 2021 Jan 22.", "date": "IEEE Rev Biomed Eng. 2021;14:204-218. doi: 10.1109/RBME.2020.2969915. Epub 2021 Jan 22.", "snippet": "In this review, we will be examining specifically machine learning methods that have been developed for EEG analysis with bioengineering applications. ...From this information, we are able to determine the overall effectiveness of each machine learning …", "pmid": "32011262", "is_free": false }, { "title": "Review of machine learning and deep learning models for toxicity prediction.", "link": "https://pubmed.ncbi.nlm.nih.gov/38057999/", "authors": "Guo W, Liu J, Dong F, Song M, Li Z, Khan MKH, Patterson TA, Hong H.", "source": "Exp Biol Med (Maywood). 2023 Nov;248(21):1952-1973. doi: 10.1177/15353702231209421. Epub 2023 Dec 6.", "date": "Exp Biol Med (Maywood). 2023 Nov;248(21):1952-1973. doi: 10.1177/15353702231209421. Epub 2023 Dec 6.", "snippet": "These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using vario …", "pmid": "38057999", "is_free": true }, { "title": "Machine learning for clinical decision support in infectious diseases: a narrative review of current applications.", "link": "https://pubmed.ncbi.nlm.nih.gov/31539636/", "authors": "Peiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Georgiou P, Lescure FX, Birgand G, Holmes AH.", "source": "Clin Microbiol Infect. 2020 May;26(5):584-595. doi: 10.1016/j.cmi.2019.09.009. Epub 2019 Sep 17.", "date": "Clin Microbiol Infect. 2020 May;26(5):584-595. doi: 10.1016/j.cmi.2019.09.009. Epub 2019 Sep 17.", "snippet": "BACKGROUND: Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID). ...", "pmid": "31539636", "is_free": true } ], "related_searches": [ { "title": "machine learning prediction" }, { "title": "cancer machine learning" }, { "title": "prediction machine learning" }, { "title": "machine learning cancer" }, { "title": "disease machine learning" }, { "title": "machine learning drug" }, { "title": "artificial intelligence machine learning" }, { "title": "machine learning predict" }, { "title": "machine learning algorithms" }, { "title": "machine learning medicine" } ], "current_page": 1, "total_pages": "18,573" } }