Articles

Artificial Intelligence in Healthcare

AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone

Academic Edition, Chapter 16 - References/Further Reading

 

  1. CORD-19 [Internet]. Semanticscholar.org. [cited 2021 Jan 21]. Available from: https://www.semanticscholar.org/cord19
  2. Cord-19: covid-19 open research dataset — allen institute for ai [Internet]. [cited 2021 Jan 21]. Available from: https://allenai.org/data/[id]?id=cord-19
  3. Covid-19 open research dataset challenge(CORD-19) [Internet]. [cited 2021 Jan 21]. Available from: https://kaggle.com/allen-institute-for-ai/CORD-19-research-challenge
  4. Amazon introduces “distance assistant” [Internet]. About Amazon. 2020 [cited 2021 Jan 21]. Available from: https://www.aboutamazon.com/news/operations/amazon-introduces-distance-assistant
  5. IntelliSpace Portal 12 | AI based imaging analysis | Philips Healthcare [Internet]. Philips. [cited 2021 Jan 21]. Available from: https://www.usa.philips.com/healthcare/product/HC881101/intellispace-portal-12
  6. Mc Call CJ. Building a COVID-19 Vulnerability Index [Internet]. Arxiv.org. [cited 2021 Jan 21]. Available from: http://arxiv.org/abs/2003.07347v3
  7. Chinese hospitals deploy ai to help diagnose covid-19. Wired [Internet]. [cited 2021 Jan 21]; Available from: https://www.wired.com/story/chinese-hospitals-deploy-ai-help-diagnose-covid-19/
  8. Full page reload [Internet]. IEEE Spectrum: Technology, Engineering, and Science News. [cited 2021 Jan 21]. Available from: https://spectrum.ieee.org/biomedical/devices/contact-tracing-apps-struggle-to-be-both-effective-and-private
  9. Prototype ai tool predicts which coronavirus patients are most likely to develop severe disease [Internet]. GEN - Genetic Engineering and Biotechnology News. 2020 [cited 2021 Jan 21]. Available from: https://www.genengnews.com/news/prototype-ai-tool-predicts-which-coronavirus-patients-are-most-likely-to-develop-severe-disease/
  10. Rensselaer-developed algorithm accurately predicts covid-19 patient outcomes [Internet]. [cited 2021 Jan 21]. Available from: https://news.rpi.edu/content/2020/11/23/rensselaer-developed-algorithms-accurately-predict-covid-19-patient-outcomes
  11. Munsell BC, Wee C-Y, Keller SS, Weber B, Elger C, da Silva LAT, et al. Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data. NeuroImage. 2015 Sep 1;118:219–30.
  12. Trebeschi S, Drago SG, Birkbak NJ, Kurilova I, Cǎlin AM, Delli Pizzi A, et al. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann Oncol. 2019 Jun;30(6):998–
  13. Council J. Hospitals tap ai to help manage coronavirus outbreak. Wall Street Journal [Internet]. 2020 Mar 20 [cited 2021 Jan 21]; Available from: https://www.wsj.com/articles/hospitals-tap-ai-to-help-manage-coronavirus-outbreak-11584696601
  14. Silva JG da. Thailand Performance and Best Management Practices that saved lives against Covid-19: a comparison against ten critical countries. IJIER. 2020 Nov 1;8(11):119–54.
  15. IEEE Spectrum: Technology, Engineering, and Science News. [cited 2021 Jan 21]. Available from: https://spectrum.ieee.org/news-from-around-ieee/the-institute/ieee-member-news/this-temperaturescreening-system-for-covid19-can-check-up-to-9-people-at-once
  16. Baidu’s ai-related patented technologies: doing battle with covid-19 [Internet]. [cited 2021 Jan 21]. Available from: https://www.wipo.int/wipo_magazine/en/2020/02/article_0003.html
  17. Radin JM, Wineinger NE, Topol EJ, Steinhubl SR. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. The Lancet Digital Health. 2020 Feb 1;2(2):e85–93.
  18. Marino A. A closer look at the Apple Watch Series 6 and how to review it [Internet]. The Verge. 2020 [cited 2021 Jan 22]. Available from: https://www.theverge.com/2020/9/29/21493297/apple-watch-series-6-joanna-stern-interview-vergecast
  19. oura-admin. Ucsf tempredict study [Internet]. The Pulse Blog. 2020 [cited 2021 Jan 22]. Available from: https://blog.ouraring.com/ucsf-tempredict-study/
  20. Quer G, Radin JM, Gadaleta M, Baca-Motes K, Ariniello L, Ramos E, et al. Wearable sensor data and self-reported symptoms for COVID-19 detection. Nature Medicine. 2021 Jan;27(1):73–7.
  21. Hadas Bitran, Group Manager, Microsoft Healthcare Israel, and Jean Gabarra, General Manager, Health AI. Delivering information and eliminating bottlenecks with CDC’s COVID-19 assessment bot [Internet]. Microsoft.com. 2020 [cited 2021 Apr 13]. Available from: https://blogs.microsoft.com/blog/2020/03/20/delivering-information-and-eliminating-bottlenecks-with-cdcs-covid-19-assessment-bot/
  22. Meet “watson,” the ai chatbot answering coronavirus questions [Internet]. The Atlantic. [cited 2021 Jan 22]. Available from: https://www.theatlantic.com/sponsored/salesforce-2020/IBM/3391/
  23. Bespoke’s ai-based chatbot “bebot” implemented by niseko resort area [Internet]. [cited 2021 Jan 22]. Available from: https://www.prnewswire.com/news-releases/bespokes-ai-based-chatbot-bebot-implemented-by-niseko-resort-area-301182848.html
  24. 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.
  25. Belfiore MP, Urraro F, Grassi R, Giacobbe G, Patelli G, Cappabianca S, et al. Artificial intelligence to codify lung CT in Covid-19 patients. Radiol Med. 2020 May 4;1–5.
  26. Wang L, Lin ZQ, Wong A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Scientific Reports. 2020 Nov 11;10(1):19549.
  27. Farooq M, Hafeez A. Covid-resnet: a deep learning framework for screening of covid19 from radiographs. arXiv:200314395 [cs, eess] [Internet]. 2020 Mar 31 [cited 2021 Jan 22]; Available from: http://arxiv.org/abs/2003.14395
  28. Ekins S, Freundlich JS, Clark AM, Anantpadma M, Davey RA, Madrid P. Machine learning models identify molecules active against the Ebola virus in vitro. F1000Research. 2015;4.
  29. Zhang L, Ai H-X, Li S-M, Qi M-Y, Zhao J, Zhao Q, et al. Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function. Oncotarget. 2017 Sep 15;8(47):83142–54.
  30. Tracetogether [Internet]. [cited 2021 Jan 22]. Available from: https://www.tracetogether.gov.sg
  31. Biofourmis’ ai-powered remote monitoring platform deployed to monitor covid-19 patients in singapore [Internet]. [cited 2021 Jan 22]. Available from: https://www.prnewswire.com/news-releases/biofourmis-ai-powered-remote-monitoring-platform-deployed-to-monitor-covid-19-patients-in-singapore-301102529.html
  32. Predictive analytics takes center stage in the fight against COVID-19 and the Critically Ill [Internet]. Clew. 2020 [cited 2021 Jan 22]. Available from: https://clewmed.com/predictive-analytics-takes-center-stage-in-the-fight-against-covid-19-and-the-critically-ill/
  33. Richardson P, Griffin I, Tucker C, Smith D, Oechsle O, Phelan A, et al. Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. The Lancet. 2020 Feb 15;395(10223):e30–1.
  34. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and Structural Biotechnology Journal. 2020 Jan 1;18:784–90.
  35. Ge Y, Tian T, Huang S, Wan F, Li J, Li S, et al. A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19 [Internet]. bioRxiv. 2020 [cited 2021 Jan 22]. Available from: https://www.researchgate.net/publication/339910608_A_data-driven_drug_repositioning_framework_discovered_a_potential_therapeutic_agent_targeting_COVID-19
  36. Allam Z. The rise of machine intelligence in the covid-19 pandemic and its impact on health policy. Surveying the Covid-19 Pandemic and its Implications. 2020;89–96.
  37. Facebook announces to use Artificial Intelligence to predict the spike of COVID-19 fourteen days before [Internet]. [cited 2021 Jan 22]. Available from: https://www.digitalinformationworld.com/2020/10/facebook-announces-to-use-artificial.html
  38. Alphafold [Internet]. Deepmind. [cited 2021 Jan 22]. Available from: https://deepmind.com/research/case-studies/alphafold
  39. Srinivasa Rao ASR, Vazquez JA. Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine. Infect Control Hosp Epidemiol. :1–5.
  40. Zhang H, Zhang L, Li Z, Liu K, Liu B, Mathews DH, et al. LinearDesign: Efficient algorithms for optimized mRNA sequence design [Internet]. arXiv [q-bio.BM]. 2020 [cited 2021 Jan 22]. Available from: http://arxiv.org/abs/2004.10177
  41. Signorini A, Segre AM, Polgreen PM. The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS One. 2011;6(5):e19467.
  42. Babayan SA, Orton RJ, Streicker DG. Predicting reservoir hosts and arthropod vectors from evolutionary signatures in RNA virus genomes. Science. 2018 Nov 2;362(6414):577–80.
  43. Briand F-X, Schmitz A, Ogor K, Le Prioux A, Guillou-Cloarec C, Guillemoto C, et al. Emerging highly pathogenic H5 avian influenza viruses in France during winter 2015/16: phylogenetic analyses and markers for zoonotic potential. Eurosurveillance [Internet]. 2017 Mar 2 [cited 2021 Jan 22];22(9). Available from: https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2017.22.9.30473
  44. Eng CL, Tong JC, Tan TW. Predicting host tropism of influenza A virus proteins using random forest. BMC Med Genomics. 2014 Dec 8;7(3):S1.
  45. Predicting the effects of the covid pandemic on us health system capacity [Internet]. Qventus, Inc. 2020 [cited 2021 Jan 22]. Available from: https://qventus.com/blog/predicting-the-effects-of-the-covid-pandemic-on-us-health-system-capacity/
  46. Naudé W. Artificial intelligence vs COVID-19: limitations, constraints and pitfalls. AI Soc. 2020 Apr 28;1–5.
  47. Luong T, Pham H, Manning CD. Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics; 2015.