Using Machine Learning to Forecast Patient Reactions in Customized Prostate Cancer Treatments: A Systematic Review

Machine learning takes a central spot in steering individualized treatments for prostate cancer with data-driven observations. This often connects to wider care aspects such as molecular profiling decisions. Barriers like outcome inconsistencies or information silos can hinder regimen choices. These issues produce varied treatment efficacies. Conventional oncology practices sometimes overlook ML forecasts and emphasize standard guidelines over tailored simulations. Recent explorations show that forecasting models improve response projections and results. This collection integrates components from ongoing research and clinical accounts. It examines modifications in analytical techniques, indicator fusions, and decision frameworks. These have increased control rates and refined protocols with decreased trial-based adjustments. Covering narratives from various oncology settings, the overview displays real advantages. Observations suggest these approaches promote enduring controls and unified care strategies.

کلیدواژه ها:

forecasting models ، outcome prediction ، cancer customization ، indicator evaluation ، treatment results ، insight-based choices