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Background Thirty-day hospital readmissions are a quality metric for health care systems. Predictive models aim to identify patients likely to readmit to more effectively target preventive strategies. Many risk of readmission models have been developed on retrospective data, but prospective validation of readmission models is rare. To the best of our knowledge, none of these developed models have been evaluated or prospectively validated in a military hospital. Objectives The objectives of this study are to demonstrate the development and prospective validation of machine learning (ML) risk of readmission models to be utilized by clinical staff at a military medical facility and demonstrate the collaboration between the U.S. Department of Defense's integrated health care system and a private company. Methods We evaluated multiple ML algorithms to develop a predictive model for 30-day readmissions using data from a retrospective cohort of all-cause inpatient readmissions at Madigan Army Medical Center (MAMC). This predictive model was then validated on prospective MAMC patient data. Precision, recall, accuracy, and the area under the receiver operating characteristic curve (AUC) were used to evaluate model performance. The model was revised, retrained, and rescored on additional retrospective MAMC data after the prospective model's initial performance was evaluated. Results Within the initial retrospective cohort, which included 32,659 patient encounters, the model achieved an AUC of 0.68. During prospective scoring, 1,574 patients were scored, of whom 152 were readmitted within 30 days of discharge, with an all-cause readmission rate of 9.7%. The AUC of the prospective predictive model was 0.64. The model achieved an AUC of 0.76 after revision and addition of further retrospective data. Conclusion This work reflects significant collaborative efforts required to operationalize ML models in a complex clinical environment such as that seen in an integrated health care system and the importance of prospective model validation.

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Salt stress causes nutritional imbalance and ion toxicity which affects wheat growth and production. A population of recombinant inbred lines (RILs) were developed by crossing Pasban90 (salt tolerant) and Frontana (salt suceptible) for identification of quantitative trait loci (QTLs) for physiological traits including relative water content, membrane stability index, water potential, osmotic potential, total chlorophyll content, chlorophyll a, chlorophyll b and biochemical traits including proline contents, superoxide dismutase, sodium content, potassium content, chloride content and sodium/potassium ratio by tagging 202 polymorphic simple sequence repeats (SSR) markers. Linkage map of RILs comprised of 21 linkage group covering A, B and D genome for tagging and maped a total of 60 QTLs with major and minor effect. B genome contributed to the highest number of QTLs under salt stress condition. Xgwm70 and Xbarc361 mapped on chromosome 6B was linked with Total chlorophyll, water potential and sodium content. The increasing allele for all these QTLs were advanced from parent Pasban90. Current study showed that Genome B and D had more potentially active genes conferring plant tolerance against salinity stress which may be exploited for marker assisted selection to breed salinity tolerant high yielding wheat varieties.