| 1. |
- Tzelves, Lazaros, et al.
(författare)
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Artificial intelligence supporting cancer patients across Europe - The ASCAPE project
- 2022
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Ingår i: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 17:4
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Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTION: Breast and prostate cancer survivors can experience impaired quality of life (QoL) in several QoL domains. The current strategy to support cancer survivors with impaired QoL is suboptimal, leading to unmet patient needs. ASCAPE aims to provide personalized- and artificial intelligence (AI)-based predictions for QoL issues in breast- and prostate cancer patients as well as to suggest potential interventions to their physicians to offer a more modern and holistic approach on cancer rehabilitation.METHODS AND ANALYSES: An AI-based platform aiming to predict QoL issues and suggest appropriate interventions to clinicians will be built based on patient data gathered through medical records, questionnaires, apps, and wearables. This platform will be prospectively evaluated through a longitudinal study where breast and prostate cancer survivors from four different study sites across the Europe will be enrolled. The evaluation of the AI-based follow-up strategy through the ASCAPE platform will be based on patients' experience, engagement, and potential improvement in QoL during the study as well as on clinicians' view on how ASCAPE platform impacts their clinical practice and doctor-patient relationship, and their experience in using the platform.ETHICS AND DISSEMINATION: ASCAPE is the first research project that will prospectively investigate an AI-based approach for an individualized follow-up strategy for patients with breast- or prostate cancer focusing on patients' QoL issues. ASCAPE represents a paradigm shift both in terms of a more individualized approach for follow-up based on QoL issues, which is an unmet need for cancer survivors, and in terms of how to use Big Data in cancer care through democratizing the knowledge and the access to AI and Big Data related innovations.TRIAL REGISTRATION: Trial Registration on clinicaltrials.gov: NCT04879563.
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| 2. |
- Lampropoulos, Konstantinos, et al.
(författare)
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ASCAPE : An open AI ecosystem to support the quality of life of cancer patients
- 2021
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Ingår i: 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI). - : IEEE Computer Society. - 9781665401326 - 9781665429801 ; , s. 301-310
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Konferensbidrag (refereegranskat)abstract
- The latest cancer statistics indicate a decrease in cancer-related mortality. However, due to the growing and ageing population, the absolute number of people living with cancer is set to keep increasing. This paper presents ASCAPE, an open AI infrastructure that takes advantage of the recent advances in Artificial Intelligence (AI) and Machine Learning (ML) to support cancer patients' quality of life (QoL). With ASCAPE health stakeholders (e.g. hospitals) can locally process their private medical data and then share the produced knowledge (ML models) through the open AI infrastructure.
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| 3. |
- Savic, Milos, et al.
(författare)
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The Application of Machine Learning Techniques in Prediction of Quality of Life Features for Cancer Patients
- 2023
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Ingår i: Computer Science and Information Systems. - : ComSIS Consortium. - 1820-0214. ; 20:1, s. 381-404
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Tidskriftsartikel (refereegranskat)abstract
- Quality of life (QoL) is one of the major issues for cancer patients. With the advent of medical databases containing large amounts of relevant QoL infor-mation it becomes possible to train predictive QoL models by machine learning (ML) techniques. However, the training of predictive QoL models poses several challenges mostly due to data privacy concerns and missing values in patient data. In this paper, we analyze several classification and regression ML models predicting QoL indicators for breast and prostate cancer patients. Three different approaches are employed for imputing missing values, and several settings for data privacy pre-serving are tested. The examined ML models are trained on datasets formed from two databases containing a large number of anonymized medical records of can-cer patients from Sweden. Two learning scenarios are considered: centralized and federated learning. In the centralized learning scenario all patient data coming from different data sources is collected at a central location prior to model training. On the other hand, federated learning enables collective training of machine learning models without data sharing. The results of our experimental evaluation show that the predictive power of federated models is comparable to that of centrally trained models for short-term QoL predictions, whereas for long-term periods centralized models provide more accurate QoL predictions. Furthermore, we provide insights into the quality of data preprocessing tasks (missing value imputation and differen-tial privacy).
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