Machine learning using longitudinal prescription and medical claims for the detection of non-alcoholic steatohepatitis (NASH).

Objectives: To develop and evaluate machine learning models to detect patients with suspected undiagnosed non-alcoholic steatohepatitis (NASH) for diagnostic screening and clinical management.

Methods: In this retrospective observational non-interventional study using administrative medical claims data from 1 463 089 patients, gradient-boosted decision trees were trained to detect patients with likely NASH from an at-risk patient population with a history of obesity, type 2 diabetes mellitus, metabolic disorder or non-alcoholic fatty liver (NAFL). Models were trained to detect likely NASH in all at-risk patients or in the subset without a prior NAFL diagnosis (at-risk non-NAFL patients).

An epidemiological study of major surgical procedures in an urban population of East delhi.

Surgery has become an integral part of global health care, with an estimated 234 million operations performed yearly. The World Bank in 2002 reported that an estimated 164 million disability-adjusted life years, representing 11% of the entire disease burden, were attributable to surgically treatable conditions. To study the prevalence of various surgical operations undergone by individuals in their life time, in an urbanized community of Delhi.