In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

[EX Q]: Background: Adverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls. Methods/design: This validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart. Discussion: There is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time.
[EX A]: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

[EX Q]: The coronavirus (CoV) responsible for severe acute respiratory syndrome (SARS), SARS-CoV, encodes two large polyproteins (pp1a and pp1ab) that are processed by two viral proteases to yield mature non-structural proteins (nsps). Many of these nsps have essential roles in viral replication, but several have no assigned function and possess amino acid sequences that are unique to the CoV family. One such protein is SARS-CoV nsp1, which is processed from the N-terminus of both pp1a and pp1ab. The mature SARS-CoV protein is present in cells several hours post-infection and co-localizes to the viral replication complex, but its function in the viral life cycle remains unknown. Furthermore, nsp1 sequences are highly divergent across the CoV family, and it has been suggested that this is due to nsp1 possessing a function speciWc to viral interactions with its host cell or acting as a host speciWc virulence factor. In order to initiate structural and biophysical studies of SARS-CoV nsp1, a recombinant expression system and a puriWcation protocol have been developed, yielding milligram quantities of highly puriWed SARS-CoV nsp1. The puriWed protein was characterized using circular dichroism, size exclusion chromatography, and multi-angle light scattering.
[EX A]: Expression, puriWcation and characterization of recombinant severe acute respiratory syndrome coronavirus non-structural protein 1

[EX Q]: The International Health Regulations (2005) and the emergence and global spread of infectious diseases have triggered a re-assessment of how rich countries should support capacity development for communicable disease control in low and medium income countries (LMIC). In LMIC, three types of public health training have been tried: the university-based model; streamed training for specialised workers; and field-based programs. The first has low rates of production and teaching may not always be based on the needs and priorities of the host country. The second model is efficient, but does not accord the workers sufficient status to enable them to impact on policy. The third has the most potential as a capacity development measure for LMIC, but in practice faces challenges which may limit its ability to promote capacity development. We describe Australia's first Master of Applied Epidemiology (MAE) model (established in 1991), which uses field-based training to strengthen the control of communicable diseases. A central attribute of this model is the way it partners and complements health department initiatives to enhance workforce skills, health system performance and the evidencebase for policies, programs and practice. The MAE experience throws light on ways Australia could collaborate in regional capacity development initiatives. Key needs are a shared vision for a regional approach to integrate training with initiatives that strengthen service and research, and the pooling of human, financial and technical resources. We focus on communicable diseases, but our findings and recommendations are generalisable to other areas of public health.
[EX A]:
Strengthening field-based training in low and middle-income countries to build public health capacity: Lessons from Australia's Master of Applied Epidemiology program