Research Output
Open-source Data Analysis and Machine Learning for Asthma Hospitalisation Rates
  Long-term conditions in Scotland account for 80% of all GP consultations; they also account for 60% of all deaths in Scotland. Asthma and Chronic Obstructive Pulmonary Disease (COPD) are common long-term respiratory diseases [1]. Asthma is a heterogeneous disease, usually characterized by chronic airway inflammation. It is defined by the history of respiratory symptoms such as wheeze, shortness of breath, chest tightness and cough that vary over time and in intensity, together with variable expiratory airflow limitation [2]. So far, we know that there are many different things – such as viruses, allergens, and pollution – that cause asthma or trigger attacks but not why or how they do it. This paper outlines how an open source dataset can be used to estimate asthma hospitalisation rates and uses machine learning to predict these rates, within ±7.5%, and for an 86.67% success rate.

  • Date:

    18 November 2018

  • Publication Status:


  • Library of Congress:

    QA76 Computer software

  • Dewey Decimal Classification:

    005.8 Data security

  • Funders:

    DHI Digital Health Institute


Rooney, L., Chute, C., Buchanan, W. J., Smales, A., & Hepburn, L. (2018). Open-source Data Analysis and Machine Learning for Asthma Hospitalisation Rates. In Proceedings of ThinkMind - GLOBAL HEALTH 2018, The Seventh International Conference on Global Health Challenges



asthma, copd, machine learning, open source

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