RESUMEN
The Himalayan mountains are early indicators of climate change, wherein slight changes in climate can lead to a drastic variation in faunal diversity, distribution, invasion of fauna into higher altitudes, rapid population growth, shortening of life cycle and increased number of overwintering species. The insects best represent the faunal diversity. In recent years, due to variation in pattern of rainfall and temperature regimes, several insect pests have moved northwards and are posing great threat to hill agriculture. Few among them are greenhouse whiteflies, thrips and mites in protected cultivation system; blister beetles on flowers of cereals, pulses and oilseeds; invasive insect pests like fall armyworm of maize and tomato pin worm and sporadic pests like grasshoppers that are reaching a status of major key pest in various crops. Keeping in mind the phenomenon of climate change and associated changes in pest population, the present article focuses on emerging insect pest problems in cereals, millets, pulses, oilseeds and vegetables of Indian Himalayas, along with their changing population density with respect to different climatic parameters, the per cent increase in the pest damage over the years and their potential of gaining the status of major pests in near future and causing huge economic losses to hill agriculture.
Asunto(s)
Productos Agrícolas , Insectos , Agricultura , Animales , Cambio Climático , TemperaturaRESUMEN
The paper applies an artificial intelligence centered method to classify 12 clinical safety incident (CSI) classes. The paper aims to establish a taxonomy that classifies the CSI reports into their correct classes automatically and with high accuracy. The study investigates feasibility of applying the C4.5 decision tree (DT) classifier and the random forest (RF) classifier for this purpose. The classifiers were trained using randomly selected 3600 CSIs from an Incident Information Management System (IIMS) used by seven hospitals. The taxonomies investigated were the Generic Reference Model (GRM) and the World Health Organization (WHO) patient safety classification. The classifiers trained 13 GRM CSI classes and 9 WHO CSI classes using a bag-of-words approach. The overall taxonomies performance on the RF classifier was better than on the DT classifier. The performance achieved by the classifier applying the WHO taxonomy was better than the GRM taxonomy. Four of the five poorly performing classes in the GRM taxonomy significantly improved their performance on changing the taxonomy. To improve the WHO taxonomy performance the improved WHO (WHO-I) taxonomy was built by adding a new class that did not exist in WHO but existed in GRM. The performance of the RF classifier applied to the WHO-I taxonomy further improved.
Asunto(s)
Inteligencia Artificial , Árboles de Decisión , Gestión de Riesgos , Humanos , Seguridad del PacienteRESUMEN
Cabbage is a cross-pollinated crop because of sporophytic self-incompatibility, and honey bees play an important role in its pollination. Though Asian honey bees, Apis cerana F., are used in pollination of cabbage, the rate of visitation, behavior, pollinator efficacy, and impact on seed-set are to be determined. Apis cerana occupy a share of 19.18% of all the flower visitors of cabbage in natural habitat of North Western Indian Himalayas. Pollination behavior in terms of peak activity, flowers processed per unit time, time spent per flower, and time spent in search of flowers are studied separately for both pollen and nectar foragers. Pollinator effectiveness as measured by seed set in flowers excluded from bee visitation, single bee visit, and unrestricted pollinator visits was 0.11. Studies on the impact of A. cerana bee pollination in cabbage seed production revealed an increase of 17.28% in siliqua per panicle, with 26.11% increase in seed yield. For assessing the requirement of A. cerana to pollinate one hectare of cabbage, flower availability and the speed with which the pollen and nectar foragers process the flowers are taken into consideration. A forager is estimated to pollinate 4,780 flowers a day, but cabbage flower requires 9.09 visits of A. cerana for optimum seed set. Thus, a maximum of 4,999 bee foragers or 8.33 colonies are needed to effectively pollinate 1 ha of cabbage. Though A. cerana is a good pollinator, our findings suggest that it is not an ideal pollinator of cabbage.
Asunto(s)
Abejas/fisiología , Polinización , Animales , Brassica/crecimiento & desarrollo , Brassica/fisiología , Conducta Alimentaria , IndiaRESUMEN
We consider the task of automatic classification of clinical incident reports using machine learning methods. Our data consists of 5448 clinical incident reports collected from the Incident Information Management System used by 7 hospitals in the state of New South Wales in Australia. We evaluate the performance of four classification algorithms: decision tree, naïve Bayes, multinomial naïve Bayes and support vector machine. We initially consider 13 classes (incident types) that were then reduced to 12, and show that it is possible to build accurate classifiers. The most accurate classifier was the multinomial naïve Bayes achieving accuracy of 80.44% and AUC of 0.91. We also investigate the effect of class labelling by an ordinary clinician and an expert, and show that when the data is labelled by an expert the classification performance of all classifiers improves. We found that again the best classifier was multinomial naïve Bayes achieving accuracy of 81.32% and AUC of 0.97. Our results show that some classes in the Incident Information Management System such as Primary Care are not distinct and their removal can improve performance; some other classes such as Aggression Victim are easier to classify than others such as Behavior and Human Performance. In summary, we show that the classification performance can be improved by expert class labelling of the training data, removing classes that are not well defined and selecting appropriate machine learning classifiers.
Asunto(s)
Sistemas de Información en Hospital/clasificación , Sistemas de Información en Hospital/estadística & datos numéricos , Aprendizaje Automático , Errores Médicos/clasificación , Gestión de Riesgos/clasificación , Gestión de Riesgos/estadística & datos numéricos , Teorema de Bayes , Errores Médicos/estadística & datos numéricos , Nueva Gales del Sur , Reconocimiento de Normas Patrones Automatizadas/métodos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
Patient safety is the buzz word in healthcare. Incident Information Management System (IIMS) is electronic software that stores clinical mishaps narratives in places where patients are treated. It is estimated that in one state alone over one million electronic text documents are available in IIMS. In this paper we investigate the data density available in the fields entered to notify an incident and the validity of the built in classification used by clinician to categories the incidents. Waikato Environment for Knowledge Analysis (WEKA) software was used to test the classes. Four statistical classifier based on J48, Naïve Bayes (NB), Naïve Bayes Multinominal (NBM) and Support Vector Machine using radial basis function (SVM_RBF) algorithms were used to validate the classes. The data pool was 10,000 clinical incidents drawn from 7 hospitals in one state in Australia. In first part of the study 1000 clinical incidents were selected to determine type and number of fields worth investigating and in the second part another 5448 clinical incidents were randomly selected to validate 13 clinical incident types. Result shows 74.6% of the cells were empty and only 23 fields had content over 70% of the time. The percentage correctly classified classes on four algorithms using categorical dataset ranged from 42 to 49%, using free-text datasets from 65% to 77% and using both datasets from 72% to 79%. Kappa statistic ranged from 0.36 to 0.4. for categorical data, from 0.61 to 0.74. for free-text and from 0.67 to 0.77 for both datasets. Similar increases in performance in the 3 experiments was noted on true positive rate, precision, F-measure and area under curve (AUC) of receiver operating characteristics (ROC) scores. The study demonstrates only 14 of 73 fields in IIMS have data that is usable for machine learning experiments. Irrespective of the type of algorithms used when all datasets are used performance was better. Classifier NBM showed best performance. We think the classifier can be improved further by reclassifying the most confused classes and there is scope to apply text mining tool on patient safety classifications.