Transmitting Chinese Medicine: Changing Perceptions of Body, Pathology, and Treatment in Late Imperial China

Historians of Chinese medicine acknowledge the plurality of Chinese medicine along both synchronic and diachronic dimensions. Yet, there remains a tendency to think of tradition as being defined by some unchanging features. The Chinese medical body is a case in point. This is assumed to have been formalised by the late Han dynasty around a system of internal organs, conduits, collaterals, and associated body structures. Although criticism was voiced from time to time, this body and the micro/ macrocosmic cosmological resonances that underpin it are seen to persist until the present day. I challenge this view by attending to attempts by physicians in China and Japan in the period from the mid 16th to the late 18th century to reimagine this body. Working within the domain of cold damage therapeutics and combining philological scholarship, empirical observations, and new hermeneutic strategies these physicians worked their way towards a new territorial understanding of the body and of medicine as warfare that required an intimate familiarity with the body’s topography. In late imperial China this new view of the body and medicine was gradually re-absorbed into the mainstream. In Japan, however, it led to a break with this orthodoxy that in the Republican era became influential in China once more. I argue that attending further to the innovations of this period—commonly portrayed as one of decline—from a transnational perspective may help to go beyond the modern insistence to frame East Asian medicines as traditional.

Risk Modelling Framework for Emergency Hospital Readmission, Using Hospital Episode Statistics Inpatient Data

The objective of this study was to develop, test and benchmark a framework and a predictive risk model for hospital emergency readmission within 12 months. We performed the development using routinely collected Hospital Episode Statistics data covering inpatient hospital admissions in England. Three different timeframes were used for training, testing and benchmarking: 1999 to 2004, 2000 to 2005 and 2004 to 2009 financial years. Each timeframe includes 20% of all inpatients admitted within the trigger year. The comparisons were made using positive predictive value, sensitivity and specificity for different risk cut-offs, risk bands and top risk segments, together with the receiver operating characteristic curve. The constructed Bayes Point Machine using this feature selection framework produces a risk probability for each admitted patient, and it was validated for different timeframes, sub-populations and cut-off points. At risk cut-off of 50%, the positive predictive value was 69.3% to 73.7%, the specificity was 88.0% to 88.9% and sensitivity was 44.5% to 46.3% across different timeframes. Also, the area under the receiver operating characteristic curve was 73.0% to 74.3%. The developed framework and model performed considerably better than existing modelling approaches with high precision and moderate sensitivity.