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From personalised medicine to decision-making for emergency care: Role of smart devices and analytics

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If you have a question about this talk, please contact Shuya Zhong.

The aim of this talk would be to present the following two projects:

(i) Using smartphones for detecting and monitoring the symptoms of Parkinson’s disease (PD) – we demonstrate that using inbuilt smartphone sensors, we can assess key motor symptoms associated with PD such as: voice, balance, gait, finger tapping, reaction time, rest tremor, and postural tremor. Using a range of features extracted from these smartphone recordings, we were able to: (a) distinguish PD participants from controls with a high accuracy, (b) monitor symptom severity remotely, and© identify participants who are at risk of developing Parkinson’s. These findings are a step towards developing a remote diagnostic support tool for PD.

(ii) Predicting patient waiting times in the emergency department (ED) – Accurate predictions of individual patient waiting times in the ED can help improve overall patient satisfaction and assist healthcare organisations to streamline patient-flow based on informed staff and resource allocation. Waiting time is inherently uncertain, and so a point forecast is unhelpful and potentially misleading, and can thus result in greater dissatisfaction among patients. Using anonymized patient-level ED data, in this ongoing study, we adopt a machine learning approach to: (a) predict patient waiting times for both major and minor triage categories, and (b) identify the key variables that have the highest impact on modelling accuracy. These predictions can assist patients select a hospital in their vicinity that has the shortest waiting time.

This talk is part of the DIAL seminars series.

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