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Combining Patient Pathway Visualisation with Prediction Outcomes for Chemotherapy Treatments

Authors:
Agastya Silvina
Juliana Bowles
Peter Hall

Keywords: Health Data; Diagnosis; Treatment Time- line; Machine Learning; Oncology

Abstract:
The Edinburgh Cancer Centre (ECC) contains NHS Lothian cancer patient data from multiple resources. However, the lack of proxy between numerous scattered resources hinders the capability to use the information collected in a useful way. ECC data is very varied and includes patient characteristics (e.g., age, weight, height, gender), information on diagnosis (e.g., stage, site, comorbidities) and treatments (e.g., surgery, chemotherapy, radiotherapy). The visualisation of a fraction of ECC data in the form of a patient timeline can aid and enhance the process of observing and identifying the overall condition of the patient, as well as understand how it may compare with cohorts of patients with similar characteristics. We have previously developed machine learning models for predicting treatment outcomes for breast cancer patient data that have undergone chemotherapy. In this paper, we describe, examine, and propose a solution to connect all these aspects and provide a bridge for several resources. This will make it easier for clinicians and other healthcare professionals to support service planning, deliver better quality of care and consequently improve service outcome within NHS Lothian.

Pages: 94 to 99

Copyright: Copyright (c) IARIA, 2020

Publication date: March 22, 2020

Published in: conference

ISSN: 2308-4359

ISBN: 978-1-61208-763-4

Location: Valencia, Spain

Dates: from November 21, 2020 to November 25, 2020