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

该项目的灵感来自于MSK临床团队的愿望，即通过更早地识别症状来改善患者护理，从而可以更早地将患者引导到适当的治疗途径，避免长时间等待就诊临床团队。它还旨在通过评估任何接受干预的患者的健康状况和确定可以优化的风险因素来改善他们的健康和减少并发症，从而改善他们的健康。通过确定这如何改善患者的治疗效果，数字医疗团队可以构建智能技术，在患者考虑治疗方案时显示存在的风险，以便他们能够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.