Literature DB >> 18694055

Using machine learning to predict asthma exacerbations.

Joseph Finkelstein1, Aryya Gangopadhyay.   

Abstract

The objective of the current study was to explore the value of machine learning techniques for forecasting asthma exacerbations based on data obtained home asthma telemonitoring systems.

Mesh：

Year:  2007        PMID： 18694055

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN： 1559-4076


  4 in total

1.  Modeling asynchronous event sequences with RNNs.

Authors:  Stephen Wu; Sijia Liu; Sunghwan Sohn; Sungrim Moon; Chung-Il Wi; Young Juhn; Hongfang Liu
Journal:  J Biomed Inform       Date:  2018-06-05       Impact factor: 6.317

2.  Application of intelligent systems in asthma disease: designing a fuzzy rule-based system for evaluating level of asthma exacerbation.

Authors:  Maryam Zolnoori; Mohammad Hossein Fazel Zarandi; Mostafa Moin
Journal:  J Med Syst       Date:  2011-03-12       Impact factor: 4.460

Review 3.  Telehealthcare for asthma.

Authors:  Susannah McLean; David Chandler; Ulugbek Nurmatov; Joseph Liu; Claudia Pagliari; Josip Car; Aziz Sheikh
Journal:  Cochrane Database Syst Rev       Date:  2010-10-06

4.  XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury.

Authors:  Tomoo Inoue; Daisuke Ichikawa; Taro Ueno; Maxwell Cheong; Takashi Inoue; William D Whetstone; Toshiki Endo; Kuniyasu Nizuma; Teiji Tominaga
Journal:  Neurotrauma Rep       Date:  2020-07-23
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.