Genetic analyses of nutrient digestibility measured by fecal near-infrared spectroscopy in pigs.

Improved nutrient digestibility is an important trait in genetic improvement in pigs due to global resource scarcity, increased human population and greenhouse gas emissions from pork production. Further, poor nutrient digestibility represents a direct nutrient loss, which affects the profit of the farmer. The aim of this study was to estimate genetic parameters for apparent total tract digestibility of nitrogen (ATTDn), crude fat (ATTDCfat), dry matter (ATTDdm), and organic matter (ATTDom) and to investigate their genetic relationship to other relevant production traits in pigs. Near-infrared spectroscopy was used for prediction of total nitrogen content and crude fat content in feces. The predicted content was used to estimate apparent total tract digestibility of the different nutrients by using an indicator method, where acid insoluble ash was used as an indigestible marker. Average ATTDdm, ATTDom, ATTDn, and ATTDCfat ranged from 61% to 75.3%. Moderate heritabilities was found for all digestibility traits and ranged from 0.15 to 0.22. The genetic correlations among the digestibility traits were high (>0.8), except for ATTDCfat, which had no significant genetic correlation to the other digestibility traits. Significant genetic correlations were found between ATTDn and feed consumption between 40 and 120 kg live weight (F40120) (-0.54 ± 0.11) and ATTDdm and F40120 (-0.35 ± 0.12) and ATTDom and F40120 (-0.28 ± 0.13). No significant genetic correlations were found between digestibility traits and loin depth at 100 kg, nor backfat thickness at 100 kg (BF), except between BF and ATTDn (-0.31 ± 0.14). These results suggested that selection for improved feed efficiency through reduced feed intake within a weight interval, also has led to improved ATTDdm, ATTDom, and ATTDn. Further, the digestibility traits are heritable, but mainly related to feed intake and general function of the intestines, as opposed to allocation of feed resources to different tissues in the body.

Improved nutrient digestibility is an important trait in genetic improvement of pigs due to global resource scarcity, increased human population and greenhouse gas emissions from pork production. The main aim of this study was to investigate whether nutrient digestibility traits in pigs are heritable, and if they are genetically linked to other production traits. The results showed that digestibility of dry matter, organic matter, nitrogen, and crude fat are heritable, and can be selected for in a pig breeding program. The traits are genetically linked to other relevant production traits, such as feed intake, but not to carcass traits, such as loin depth. The results suggest that nutrient digestibility are traits that can be selected for, and that the traits are under indirect selection through other traits in the pig breeding program. The results also indicate that the nutrient digestibility traits express how well the animal utilizes consumed feed, rather than allocating feed to different tissue deposition.

Artificial Intelligence Algorithms and Natural Language Processing for the Recognition of Syncope Patients on Emergency Department Medical Records.

BACKGROUND: Enrollment of large cohorts of syncope patients from administrative data is crucial for proper risk stratification but is limited by the enormous amount of time required for manual revision of medical records. AIM: To develop a Natural Language Processing (NLP) algorithm to automatically identify syncope from Emergency Department (ED) electronic medical records (EMRs). METHODS: De-identified EMRs of all consecutive patients evaluated at Humanitas Research Hospital ED from 1 December 2013 to 31 March 2014 and from 1 December 2015 to 31 March 2016 were manually annotated to identify syncope. Records were combined in a single dataset and classified. The performance of combined multiple NLP feature selectors and classifiers was tested. Primary Outcomes: NLP algorithms' accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F3 score. RESULTS: 15,098 and 15,222 records from 2013 and 2015 datasets were analyzed. Syncope was present in 571 records. Normalized Gini Index feature selector combined with Support Vector Machines classifier obtained the best F3 value (84.0%), with 92.2% sensitivity and 47.4% positive predictive value. A 96% analysis time reduction was computed, compared with EMRs manual review. CONCLUSIONS: This artificial intelligence algorithm enabled the automatic identification of a large population of syncope patients using EMRs.

ICT and mobile health to improve clinical process delivery. a research project for therapy management process innovation.

The volume and the complexity of clinical and administrative information make Information and Communication Technologies (ICTs) essential for running and innovating healthcare. This paper tells about a project aimed to design, develop and implement a set of organizational models, acknowledged procedures and ICT tools (Mobile & Wireless solutions and Automatic Identification and Data Capture technologies) to improve actual support, safety, reliability and traceability of a specific therapy management (stem cells). The value of the project is to design a solution based on mobile and identification technology in tight collaboration with physicians and actors involved in the process to ensure usability and effectivenes in process management.

Evicase: an evidence-based case structuring approach for personalized healthcare.

The personalized medicine era stresses a growing need to combine evidence-based medicine with case based reasoning in order to improve the care process. To address this need we suggest a framework to generate multi-tiered statistical structures we call Evicases. Evicase integrates established medical evidence together with patient cases from the bedside. It then uses machine learning algorithms to produce statistical results and aggregators, weighted predictions, and appropriate recommendations. Designed as a stand-alone structure, Evicase can be used for a range of decision support applications including guideline adherence monitoring and personalized prognostic predictions.

The evolution of hospital information systems and the role of electronic patient records: from the Italian scenario to a real case.

Health care organizations can gain great value from Information and Communication Technologies (ICT), yet, although there is growing awareness of the potential benefits associated with their use, results often fall far short of expectations. Each year, the "ICT in Health Care" Observatory--part of the Politecnico di Milano School of Management--outlines a profile of the role of ICT in the Italian health care industry, investigating current projects in terms of their impact on processes and organizations, implementation state of the art, governance models, and prospective pathways. The 2009 collaborative research process outlines the need for a change in the way health care CIOs approach technological and organizational evolutions. ICT departments lack vision, governance mechanisms, skilled resources, and top management commitment. This has led to a series of distortions in the innovation of Hospital Information Systems (HISs) and ICT departments themselves. Currently they are too concerned with day-to-day operations and delay comprehensive initiatives capable of leading to effective ICT-driven innovations. The paper points out the problems that health care organizations are tackling and how they are trying to solve them. The case of the Italian National Cancer Institute in Milan provides a valuable example of how a health care organization is developing its HIS.