Application of machine learning to large clinical data sets to risk stratify MSK patients and predict outcomes of MSK intervention

这个项目的灵感来自于MSK临床团队希望通过更早地识别症状来改善患者护理，以便他们能够更早地被引导到适当的治疗路径，并避免排长队去看临床团队。它还旨在通过评估患者的健康并确定可以优化的风险因素来改善正在接受干预的患者的健康，以改善他们的健康和减少并发症。通过确定这如何改善患者的结果，数字健康团队可以建立智能技术，在患者考虑治疗方案的同时向他们显示风险，以便他们可以考虑他们面临的所有风险。

This project is inspired by the desire of MSK clinical teams to improve patient care by identifying there symptoms much earlier so they can be directed to appropriate treatment pathways much earlier and avoid long waiting lists to see clinical teams.It also aims to improve the health of any patient undergoing intervention by assessing their health and identifying risk factors that can be optimised to improve their health and reduce complications. By identifying how this improves patient outcomes the digital health team can build smart technology that shows patients there risk whilst they are considering treatment options so they can consider all the risks that they face.