The CLARIFY Decision Support Platform (DSP) is a responsive web application designed to support decision-making in cancer care through real-time data integration and predictive analytics. Built on Big Data Europe, the platform integrates a variety of data, including scientific publications, clinical anonymized data from EHR, genomic data, wearable monitoring data, and quality of life questionnaires. This data is presented in an intuitive and user-friendly dashboard. The goal of the CLARIFY project (Cancer Long Survivor Artificial Intelligence Follow-Up) is to use an Artificial Intelligence (AI)-driven decision support platform to stratify cancer patients by risk factors, predict survival rates and improve the personalization of post-treatment care. The target audience includes cancer patients, caregivers, healthcare professionals, and public health policymakers. The project addresses several areas of digital health, particularly precision medicine, data integration, and big data predictive analytics.
From Big Data to Big Decisions: How AI Stratifies Cancer Cases by Risk Factors From Big Data to Big Decisions: How AI Stratifies Cancer Cases by Risk Factors From Big Data to Big Decisions: How AI Stratifies Cancer Cases by Risk Factors From Big Data to Big Decisions: How AI Stratifies Cancer Cases by Risk Factors
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FROM BIG DATA TO BIG DECISIONS: HOW AI STRATIFIES CANCER CASES BY RISK FACTORS
Publication type: Article Summary
Original title: An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study
Article publication date: August 2022
Source: Cancers (Basel)
Authors: María Torrente, Pedro A. Sousa, Roberto Hernández, Mariola Blanco, Virginia Calvo, Ana Collazo, Gracinda R. Guerreiro, Beatriz Núñez, Joao Pimentao, Juan Cristóbal Sánchez, Manuel Campos, Luca Costabello, Vit Novacek, Ernestina Menasalvas, María Esther Vidal & Mariano Provencio
What is the goal, target audience, and areas of digital health it addresses?
The goal of the CLARIFY project (Cancer Long Survivor Artificial Intelligence Follow-Up) is to use an Artificial Intelligence (AI)-driven decision support platform to stratify cancer patients by risk factors, predict survival rates and improve the personalization of post-treatment care. The target audience includes cancer patients, caregivers, healthcare professionals, and public health policymakers. The project addresses several areas of digital health, particularly precision medicine, data integration, and big data predictive analytics.
What is the context?
The rising complexity of cancer care, along with the increasing volume of healthcare data from electronic health records (EHR), wearable devices, and genomic testing, creates both challenges and opportunities for effective patient management. As cancer remains a leading cause of death worldwide, the integration of AI in healthcare is essential for enhancing patient outcomes.
AI has significantly advanced the resolution of biomedical challenges by enabling the analysis of large-scale, multidimensional data. While AI advancements in early detection are notable, implementing personalized care remains challenging due to lack of standardization and insufficient clinical validation. Despite these barriers, the potential for AI to revolutionize oncology care by improving survival rates and the overall efficiency of clinical workflows is increasingly recognized.
What are the current approaches?
Current approaches in oncology typically involve standardized treatment protocols that adopt a reactive “one-size-fits-all” strategy, often neglecting individual patient characteristics and genetic profiles. These conventional methods rely primarily on the clinical experience of healthcare professionals, which can result in variability in healthcare for each patient.
What does CLARIFY platform consist of? How is the impact of CLARIFY project assessed?
The CLARIFY Decision Support Platform (DSP) is a responsive web application designed to support decision-making in cancer care through real-time data integration and predictive analytics. Built on Big Data Europe, the platform integrates a variety of data, including scientific publications, clinical anonymized data from EHR, genomic data, wearable monitoring data, and quality of life questionnaires. This data is presented in an intuitive and user-friendly dashboard.
The CLARIFY DSP uses statistical tools like Kaplan-Meier estimates to calculate the survival probabilities over time and Cox Regression models to analyze how factors such as age affect survival time, facilitating detailed individual and population-level analyses. Additionally, it employs machine learning algorithms, including neural networks, to predict patient outcomes as well as statistical relational learning to discover data patterns. Explainable AI is also integrated to enhance comprehension of AI decision-making, enabling clinicians to make evidence-based treatment decisions and personalized follow-up care while improving their understanding of disease progression and risk factors.
To test the CLARIFY project, a prognostic model was developed using data from 1,348 early-stage non-small cell lung cancer (NSCLC) patients. This model used AI for tumor recurrence prediction, relying solely on clinical data to analyze similar patient profiles and generate individual recurrence probabilities along with AI-generated explanations for each prediction. The model focused on identifying risk factors as gender, age, smoking status and treatment. The CLARIFY project’s impact was also evaluated in patients with breast cancer and non-Hodgkin’s lymphoma across multiple institutions in Spain, using the various data that the platform was designed to integrate.
What are the main results? What is the impact of these results? What is the future of this technology?
The prognostic model for patients with NSCLC identified high-risk characteristics: men over 70 years old, former smokers, who underwent surgery, and low-risk factors: women between 20 and 50 years old, non-smokers, who underwent surgery and adjuvant chemotherapy. Overall, the CLARIFY project demonstrated effectiveness in stratifying patients by risk and identifying significant prognostic factors. The predictive models demonstrated the platform’s ability to forecast outcomes across various cancers, enabling oncologists to compare results between different pathologies and patient groups from multiple hospitals.
The CLARIFY project’s findings have deepened the understanding of cancer treatment dynamics, which can have a positive impact on public health strategies to optimize care delivery. Future enhancements will incorporate sequencing data and rare mutations to enrich predictive capabilities and advance precision medicine. In the future, healthcare professionals will explain AI’s role in decision-making, empowering patients to take charge of their health through effective self-monitoring.
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Home / Publications / Publication
FROM BIG DATA TO BIG DECISIONS: HOW AI STRATIFIES CANCER CASES BY RISK FACTORS
Publication type: Article Summary
Original title: An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study
Article publication date: August 2022
Source: Cancers (Basel)
Authors: María Torrente, Pedro A. Sousa, Roberto Hernández, Mariola Blanco, Virginia Calvo, Ana Collazo, Gracinda R. Guerreiro, Beatriz Núñez, Joao Pimentao, Juan Cristóbal Sánchez, Manuel Campos, Luca Costabello, Vit Novacek, Ernestina Menasalvas, María Esther Vidal & Mariano Provencio
What is the goal, target audience, and areas of digital health it addresses?
The goal of the CLARIFY project (Cancer Long Survivor Artificial Intelligence Follow-Up) is to use an Artificial Intelligence (AI)-driven decision support platform to stratify cancer patients by risk factors, predict survival rates and improve the personalization of post-treatment care. The target audience includes cancer patients, caregivers, healthcare professionals, and public health policymakers. The project addresses several areas of digital health, particularly precision medicine, data integration, and big data predictive analytics.
What is the context?
The rising complexity of cancer care, along with the increasing volume of healthcare data from electronic health records (EHR), wearable devices, and genomic testing, creates both challenges and opportunities for effective patient management. As cancer remains a leading cause of death worldwide, the integration of AI in healthcare is essential for enhancing patient outcomes.
AI has significantly advanced the resolution of biomedical challenges by enabling the analysis of large-scale, multidimensional data. While AI advancements in early detection are notable, implementing personalized care remains challenging due to lack of standardization and insufficient clinical validation. Despite these barriers, the potential for AI to revolutionize oncology care by improving survival rates and the overall efficiency of clinical workflows is increasingly recognized.
What are the current approaches?
Current approaches in oncology typically involve standardized treatment protocols that adopt a reactive “one-size-fits-all” strategy, often neglecting individual patient characteristics and genetic profiles. These conventional methods rely primarily on the clinical experience of healthcare professionals, which can result in variability in healthcare for each patient.
What does CLARIFY platform consist of? How is the impact of CLARIFY project assessed?
The CLARIFY Decision Support Platform (DSP) is a responsive web application designed to support decision-making in cancer care through real-time data integration and predictive analytics. Built on Big Data Europe, the platform integrates a variety of data, including scientific publications, clinical anonymized data from EHR, genomic data, wearable monitoring data, and quality of life questionnaires. This data is presented in an intuitive and user-friendly dashboard.
The CLARIFY DSP uses statistical tools like Kaplan-Meier estimates to calculate the survival probabilities over time and Cox Regression models to analyze how factors such as age affect survival time, facilitating detailed individual and population-level analyses. Additionally, it employs machine learning algorithms, including neural networks, to predict patient outcomes as well as statistical relational learning to discover data patterns. Explainable AI is also integrated to enhance comprehension of AI decision-making, enabling clinicians to make evidence-based treatment decisions and personalized follow-up care while improving their understanding of disease progression and risk factors.
To test the CLARIFY project, a prognostic model was developed using data from 1,348 early-stage non-small cell lung cancer (NSCLC) patients. This model used AI for tumor recurrence prediction, relying solely on clinical data to analyze similar patient profiles and generate individual recurrence probabilities along with AI-generated explanations for each prediction. The model focused on identifying risk factors as gender, age, smoking status and treatment. The CLARIFY project’s impact was also evaluated in patients with breast cancer and non-Hodgkin’s lymphoma across multiple institutions in Spain, using the various data that the platform was designed to integrate.
What are the main results? What is the impact of these results? What is the future of this technology?
The prognostic model for patients with NSCLC identified high-risk characteristics: men over 70 years old, former smokers, who underwent surgery, and low-risk factors: women between 20 and 50 years old, non-smokers, who underwent surgery and adjuvant chemotherapy. Overall, the CLARIFY project demonstrated effectiveness in stratifying patients by risk and identifying significant prognostic factors. The predictive models demonstrated the platform’s ability to forecast outcomes across various cancers, enabling oncologists to compare results between different pathologies and patient groups from multiple hospitals.
The CLARIFY project’s findings have deepened the understanding of cancer treatment dynamics, which can have a positive impact on public health strategies to optimize care delivery. Future enhancements will incorporate sequencing data and rare mutations to enrich predictive capabilities and advance precision medicine. In the future, healthcare professionals will explain AI’s role in decision-making, empowering patients to take charge of their health through effective self-monitoring.
Would you like to know all the details?
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Do you have an innovative idea in healthcare field?
Share it with us and see it come to life.
We will help bring your projects to life!
Follow Us
Co-funded by