Background: Hip replacement procedures improve patient lives by relieving pain and restoring mobility. Predicting hip replacement in advance could reduce pain by enabling timely interventions, prioritising individuals for surgery or rehabilitation, and utilising physiotherapy to potentially delay the need for joint replacement. This study predicts hip replacement a year in advance to enhance quality of life and health service efficiency. Methods: Adapting previous work using Temporal Graph Convolutional Neural Network (TG-CNN) models, we construct temporal graphs from primary care medical event codes, sourced from ResearchOne EHRs of 40-75-year-old patients, to predict hip replacement risk. We match hip replacement cases to controls by age, sex, and Index of Multiple Deprivation. The model, trained on 9,187 cases and 9,187 controls, predicts hip replacement one year in advance. We validate the model on two unseen datasets, recalibrating for class imbalance. Additionally, we conduct an ablation study and compare against four baseline models. Results: Our best model predicts hip replacement risk one year in advance with an AUROC of 0.724 (95% CI: 0.715-0.733) and an AUPRC of 0.185 (95% CI: 0.160-0.209), achieving a calibration slope of 1.107 (95% CI: 1.074-1.139) after recalibration. Conclusions: The TG-CNN model effectively predicts hip replacement risk by identifying patterns in patient trajectories, potentially improving understanding and management of hip-related conditions.

EHRs are a potential treasure trove of information beyond the needs and circumstances of specific patients, but who has time to pore through them to find it? It turns out, perhaps, another machine. That's the thinking behind a study published recently at npj Digital Medicine that specifically explored using deep machine learning methods to extract information regarding the post-surgical safety performance of implanted medical devices, in this case using hip replacements as a test case. To be sure, any medical device has to meet safety standards set by the FDA, but as Nigam Shah, PhD, associate professor of medicine and biomedical data science at Stanford and the study's lead author, pointed out, "The safety standards required by the FDA are for initial approval of the device's use. What we need is a scalable way -- beyond self-reporting -- to see how safe and effective these devices are in a population after years of use."

The Transhumanism Trap is out there. I am seeing it more and more often. If you do not know what transhumanism is let alone know that it has a logical trap into which the average person is likely to fall, don't worry. Transhumanism is a movement that wants to use technology to go beyond curing or preventing disease or disability. Transhumanists hunger for technology that will take an otherwise healthy individual and enhance him or her beyond normal human ability.