ArticleDigital health
Development of a digital treatment analyzer for the management of prostate cancer patients, with the help of real world data and use of predictive modelling.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence (AI)-based tools in the diagnosis and management of prostate cancer: a systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- Electrolyte trajectory-based subphenotypes and their association with hospital length of stay in prostate cancer patients.Translational andrology and urology · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer is the second most diagnosed cancer in the world. Treatment guidelines involve a multitude of therapies, however adherence to them is not fully established, while lack of personalized treatment strategies fails to put the patient as an individual clinical profile at the center of their treatment. We aim to present the concept of a digital treatment analyzer (TA) for the management of prostate cancer (PC) patients, leveraging real-world data (RWD) and predictive modeling to enhance personalized disease management strategies and adherence to PC guidelines, ultimately aiming to optimize therapeutic efficacy and improve outcomes. The TA comprises digital tools integrated into one user-intuitive interface, facilitating the development of patient-specific clinical profiles, classification of patients into matched historical RWD cohorts, presentation of relevant clinical guidelines, visual representation of treatment and outcomes, and mortality risk prediction based on a validated machine learning models. The Medical Information Mart for Intensive Care (MIMIC) IV dataset was utilized, including structured and unstructured data from the patient journey. The developed TA represents a promising approach to enhance personalized disease management strategies and adherence to PC guidelines. By integrating contemporary clinical guidelines, RWD and AI-driven insights, our digital TA aims to optimize therapeutic efficacy and improve patient outcomes. The presented concept demonstrates the potential for using a digital approach that integrates RWD into a treatment journey, to provide healthcare stakeholders with a holistic approach to PC management involving all available modern tools to achieve optimal outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.