Combining patient pathway visualisation with prediction outcomes for chemotherapy treatments
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.
Citation
Silvina , A , Kuster Filipe Bowles , J & Hall , P 2020 , Combining patient pathway visualisation with prediction outcomes for chemotherapy treatments . in S Sendra , Y Murata , J Civit-Masot & A Rajh (eds) , 12th International Conference on eHealth, Telemedicine, and Social Medicine (eTELEMED 2020), 21-25 November 2020, Valencia, Spain . eTELEMED the International Conference on eHealth, Telemedicine, and Social Medicine , International Academy, Research, and Industry Association , pp. 110-115 , Twelfth International Conference on eHealth, Telemedicine, and Social Medicine (eTELEMED 2020) , Valencia , Spain , 21/11/20 . < http://www.thinkmind.org/index.php?view=article&articleid=etelemed_2020_7_20_40057 > conference
Publication
12th International Conference on eHealth, Telemedicine, and Social Medicine (eTELEMED 2020), 21-25 November 2020, Valencia, Spain
ISSN
2308-4359Type
Conference item
Rights
Copyright © 2020 IARIA. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the final published version of the work, which was originally published at http://www.thinkmind.org/index.php?view=article&articleid=etelemed_2020_7_20_40057
Description
Funding: NHS Lothian, the DATALab, and the EU H2020 project Serums: Securing Medical Data in Smart Patient-Centric Healthcare Systems(grant code:826278).Collections
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