AI Weekly: Why a slow movement for machine learning could be a good thing K Wiggers, Venture Beat, February 21, 2020

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Last Posted: Feb 24, 2020

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• AI Weekly: Why a slow movement for machine learning could be a good thing
  K Wiggers, Venture Beat, February 21, 2020
• Powerful antibiotics discovered using AI- Machine learning spots molecules that work even against ‘untreatable’ strains of bacteria.
  J Marchant, Nature, February 20, 2020
• Hidden in Plain Sight: Machine Learning in Acute Kidney Injury
  EL Gill et al, Clin Chem, February 13, 2020
• Machine learning application for development of a data-driven predictive model able to investigate quality of life scores in a rare disease.
  Spiga Ottavia et al. Orphanet journal of rare diseases 2020 Feb 15(1) 46
• A fitting machine learning prediction model for short-term mortality following percutaneous catheterization intervention: a nationwide population-based study.
  Hsieh Meng-Hsuen et al. Annals of translational medicine 2019 Dec 7(23) 732
• Application of Machine Learning in Developing a Novelty Five-Pseudogene Signature to Predict Prognosis of Head and Neck Squamous Cell Carcinoma: A New Aspect of "Junk Genes" in Biomedical Practice.
  Xing Lu et al. DNA and cell biology 2020 Feb
• Associations between persistent organic pollutants and endometriosis: A multipollutant assessment using machine learning algorithms.
  Matta Komodo et al. Environmental pollution (Barking, Essex : 1987) 2020 Jan 260114066
• Evaluation of machine learning methods to stroke outcome prediction using a nationwide disease registry.
  Lin Ching-Heng et al. Computer methods and programs in biomedicine 2020 Feb 190105381
• Evaluation of Significant Coronary Artery Disease Based on CT Fractional Flow Reserve and Plaque Characteristics Using Random Forest Analysis in Machine Learning.
  Kawasaki Tomohiro et al. Academic radiology 2020 Feb
• Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial.
  Shepherd-Banigan Megan et al. Trials 2020 Feb 21(1) 189

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