Heverin, Mark

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Heverin, Mark
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Heverin, Mark

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  • Publication
    Development of an Explainable Clinical Decision Support System for the Prediction of Patient Quality of Life in Amyotrophic Lateral Sclerosis
    (ACM, 2021-03-26)
    Antoniadi, Anna Markella 
    ;
    Galvin, Miriam 
    ;
    Heverin, Mark 
    ;
    Hardiman, Orla 
    ;
    Mooney, Catherine 
    Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative and currently incurable disease. It causes a rapid decline in motor functions and has a fatal trajectory. The aim of the treatment is mostly to alleviate symptoms and improve the patient’s quality of life (QoL). The goal of this study is to develop a Clinical Decision Support System (CDSS) in order to alert clinicians when a patient is at risk of experiencing a low QoL, so that they are better supported. The source of the data was the Irish ALS Registry and interviews with the 90 patients and their primary informal caregiver at three time-points. In this dataset, there were two different scores to measure a person’s overall QoL, based on the McGill QoL (MQoL) Questionnaire and we worked towards the prediction of both. The method we used for the development of the predictive models was Extreme Gradient Boosting (XGBoost), which was compared to a logistic regression baseline model. We used the SHAP (SHapley Additive exPlanations) values as a technique to provide local and global explanations to the outputs as well as to select the most important features. The total calculated MQoL score was predicted accurately by three features, with a F1-score on the test set equal to 0.81, a recall score of 0.78, and a precision score of 0.84, while, the addition of two features produced similar outcomes (0.79, 0.70 and 0.90 respectively). The three most important features were the age at disease onset, ALSFRS score for orthopnoea and the caregiver’s status pre-caregiving.
      56Scopus© Citations 5
  • Publication
    Using Patient Information for the Prediction of Caregiver Burden in Amyotrophic Lateral Sclerosis
    (ACM, 2020-09-21)
    Antoniadi, Anna Markella 
    ;
    Galvin, Miriam 
    ;
    Heverin, Mark 
    ;
    Hardiman, Orla 
    ;
    Mooney, Catherine 
    The aim of this study is to create a Clinical Decision Support System (CDSS) to assist in the early identification and support of caregivers at risk of experiencing burden while caring for a person with Amyotrophic Lateral Sclerosis. We work towards a system that uses a minimum amount of data that could be routinely collected. We investigated if the impairment of patients alone provides sufficient information for the prediction of caregiver burden. Results reveal a better performance of our system in identifying those at risk of high burden, but more information is needed for an accurate CDSS.
      285
  • Publication
    Prediction of quality of life in people with ALS: on the road towards explainable clinical decision support
    (ACM, 2021-06)
    Antoniadi, Anna Markella 
    ;
    Galvin, Miriam 
    ;
    Heverin, Mark 
    ;
    Hardiman, Orla 
    ;
    Mooney, Catherine 
    Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative disease that causes a rapid decline in motor functions and has a fatal trajectory. ALS is currently incurable, so the aim of the treatment is mostly to alleviate symptoms and improve quality of life (QoL) for the patients. The goal of this study is to develop a Clinical Decision Support System (CDSS) to alert clinicians when a patient is at risk of experiencing low QoL. The source of data was the Irish ALS Registry and interviews with the 90 patients and their primary informal caregiver at three time-points. In this dataset, there were two different scores to measure a person's overall QoL, based on the McGill QoL (MQoL) Questionnaire and we worked towards the prediction of both. We used Extreme Gradient Boosting (XGBoost) for the development of the predictive models, which was compared to a logistic regression baseline model. Additionally, we used Synthetic Minority Over-sampling Technique (SMOTE) to examine if that would increase model performance and SHAP (SHapley Additive explanations) as a technique to provide local and global explanations to the outputs as well as to select the most important features. The total calculated MQoL score was predicted accurately using three features - age at disease onset, ALSFRS-R score for orthopnoea and the caregiver's status pre-caregiving - with a F1-score on the test set equal to 0.81, recall of 0.78, and precision of 0.84. The addition of two extra features (caregiver's age and the ALSFRS-R score for speech) produced similar outcomes (F1-score 0.79, recall 0.70 and precision 0.90).
      91