Causal Dynamic Bayesian Networks for the Management of Glucose Control in Gestational Diabetes

EasyChair Preprint 5428

Abstract

Patients suffering from chronic conditions may need to make frequent decisions about the management of their condition in partnership with their health professionals. However, this may not be possible as appointments are not always scheduled according to necessity but instead at a fixed frequency. Remote monitoring technology has the potential to generate patient data but without intelligent systems capable of analysing the data and offering advice, more data just increase the person’s dependency on clinical staff for its interpretation. Decision-support systems that can give people more autonomy in the management of their condition can therefore benefit both the affected person and clinicians. We propose the use of Dynamic Bayesian Networks built from expert knowledge to interpret data and support decision-making, offering advice to patients suffering from a chronic condition. We argue that expert knowledge is needed as well as data to build such a decision-support system as the data that would be required to use machine learning will never be available in the current clinical system with all treatment decisions made at appointments scheduled at fixed intervals. We illustrate the methodology using a case study in Gestational Diabetes.

Keyphrases: Dynamic Bayesian Networks, Gestational Diabetes Mellitus, chronic diseases, patient monitoring

@booklet{EasyChair:5428,
  author    = {Mariana Neves and Bridget Daley and Graham Hitman and Mohammed Huda and Scott McLachlan and Sarah Finer and William Marsh},
  title     = {Causal Dynamic Bayesian Networks for the Management of Glucose Control in Gestational Diabetes},
  howpublished = {EasyChair Preprint 5428},
  year      = {EasyChair, 2021}}