A social engineering model for poverty alleviation.

Poverty, the quintessential denominator of a developing nation, has been traditionally defined against an arbitrary poverty line; individuals (or countries) below this line are deemed poor and those above it, not so! This has two pitfalls. First, absolute reliance on a single poverty line, based on basic food consumption, and not on total consumption distribution, is only a partial poverty index at best. Second, a single expense descriptor is an exogenous quantity that does not evolve from income-expenditure statistics. Using extensive income-expenditure statistics from India, here we show how a self-consistent endogenous poverty line can be derived from an agent-based stochastic model of market exchange, combining all expenditure modes (basic food, other food and non-food), whose parameters are probabilistically estimated using advanced Machine Learning tools. Our mathematical study establishes a consumption based poverty measure that combines labor, commodity, and asset market outcomes, delivering an excellent tool for economic policy formulation.

Wireless monitoring and real-time adaptive predictive indicator of deterioration.

To assist in the early warning of deterioration in hospitalised children we studied the feasibility of collecting continuous wireless physiological data using Lifetouch (ECG-derived heart and respiratory rate) and WristOx2 (pulse-oximetry and derived pulse rate) sensors. We compared our bedside paediatric early warning (PEW) score and a machine learning automated approach: a Real-time Adaptive Predictive Indicator of Deterioration (RAPID) to identify children experiencing significant clinical deterioration. 982 patients contributed 7,073,486 min during 1,263 monitoring sessions. The proportion of intended monitoring time was 93% for Lifetouch and 55% for WristOx2. Valid clinical data was 63% of intended monitoring time for Lifetouch and 50% WristOx2. 29 patients experienced 36 clinically significant deteriorations. The RAPID Index detected significant deterioration more frequently (77% to 97%) and earlier than the PEW score ≥ 9/26. High sensitivity and negative predictive value for the RAPID Index was associated with low specificity and low positive predictive value. We conclude that it is feasible to collect clinically valid physiological data wirelessly for 50% of intended monitoring time. The RAPID Index identified more deterioration, before the PEW score, but has a low specificity. By using the RAPID Index with a PEW system some life-threatening events may be averted.