Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Machine learning in healthcare : an investigation into model stability

Current machine learning algorithms, when directly applied to medical data, often fail to provide a good understanding of prognosis. This study provides three pathways to make predictive models stable and usable for healthcare. When tested on heart failure and diabetes patients from a local hospital, this study demonstrated 20% improvement over existing methods.<br /

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming

Transmission of Respiratory Syncytial Virus in Households: Who Acquires Infection From Whom?

Households represent a setting of frequent and intense contacts and hence are conducive to the spread of respiratory viruses, such as respiratory syncytial virus (RSV). Infants are most vulnerable to severe RSV disease but a vaccine is not yet available hence the need to explore alternate strategies of protecting them. Such strategies would require better understanding of who infects the infants. During the RSV season of 2009/2010, we undertook a prospective study in rural Kenya involving 493 members of 47 households each with a child bom after the preceding RSV epidemic and at least one elder sibling. Throughout the epidemic a nasopharyngeal swab (NPS) was collected every 3-4 days irrespective of symptoms, from all household members, and tested for a range of respiratory viruses including RSV using a molecular diagnostic assay. Partial sequencing of the attachment protein (G) gene from positive swabs was used to compare RSV strains within the household. In addition, once-a-week a specimen of oral fluid (OF) from around the gums was collected for RSV-specific antibodies screening and for assessment of sensitivity of the OF in detection of RSV using molecular diagnostics. This is the first prospective study to investigate introduction and transmission of RSV in families using molecular techniques over a complete RSV season. Analysis of RSV infection data is reported in this thesis with particular interest to identifying from where infants derive their infection, estimating the duration of RSV shedding and identity factors influencing the recovery rates, and estimating parameters of RSV susceptibility and transmission probability. In addition, data on diagnostic performance of OF in detection of RSV by molecular methods is presented. A total of 16,924 NPS were collected, representing 86% of planned. RSV was detected in 40 (85%) households and 179 (36%) of the participants. In 28 of the 44 households with complete data, there was transmission of RSV to the infants experiencing their first epidemic. The probable source of RSV infection of the naive infants was a household member in at least 54% of the cases. Co-primary infection between a household member and the RSV-naive infant was ascribed in 4 of the cases. Older children were assigned the primary case for 11 (39%) of the infant cases and 10 (91%) of these were attending school. The infants appeared to play a role transmitting the introduced infections to the other members of the household including to the mothers. These findings support vaccination strategies that target school age children and pregnant women. Both of these vaccination strategies can have profound benefits to RSV naive infants directly by augmenting neutralizing antibodies against RSV (immunization of the pregnant women) and indirectly by reducing transmission from siblings to RSV-naive infants. Results from this study provide increased confidence in the rationale for RSV vaccination of individuals who are not the key targets for protection

Developing a standardized tool for interpretation of radiology diagnostic accuracy trials

Summary Within the health sciences, action research is a methodology well suited to the goal of collaboratively improving practice. As the Royal College of Radiology recommends the use of published clinical trials as guides for achieving higher standards of accuracy, it is important for radiologists to reflect deeply on the results from diagnostic accuracy studies. When the results of the gold standard (or reference standard) are used to confirm a particular diagnosis or disease by comparing the diagnostic accuracy to a newer or index test, this is referred to as diagnostic accuracy research. In the reporting of all research, every effort must be made to reduce the incidence of bias. In 2003, the STARD (Standards for Reporting Diagnostic Accuracy) tool was developed for clinicians to enhance the quality of reporting diagnostic accuracy studies. Based on previous studies, experiential knowledge, and an extensive review of the literature, this research demonstrates that the STARD tool is not being fully optimized. The overall aim of this research was to conduct a work-based project within the department of radiology to develop a revised tool, based on the current STARD, which could then be used to more accurately report and interpret the results of radiology diagnostic accuracy trials. This study was conducted in accordance with participatory action research. Methods The development of this new reporting tool was conducted in collaboration with a group of physicians, and in two distinct phases. First, a needs assessment was sent to eight radiological experts who had agreed to participate in the study. Based on their responses, and feedback from my mentor and colleagues, the next phase of tool development was done using the Delphi technique, after two rounds of which consensus was met. Each phase and cycle iteration to complete the needs assessment and Delphi technique are synonymous with the cycles of action research. The new reporting tool was named the RadSTARD (Radiology Standards for the Reporting of Diagnostic Accuracy Studies), and an elaboration document was written to provide guidance to the end-user. Radiology residents and Fellows at The Ottawa Hospital were then asked to rate their level of confidence in interpreting a diagnostic accuracy article specific to radiology while referring to the RadSTARD. They were also provided a second diagnostic article, the STARD tool, and an elaboration document for comparison. Data was collected using questionnaires that allowed for additional comments. Findings The validation phase of the RadSTARD tool was completed via triangulation of data, as both a quantitative and qualitative analysis was completed. The results found no significant statistical difference between the two groups as per the Mann-Whitney and chi-square analysis. Likewise, both physician groups indicated that they found RadSTARD increased their level of confidence when interpreting the diagnostic accuracy article. Concomitantly, when combined, 96% of the two physician groups indicated they would use the tool again. Interpretation These results may be interpreted as generalizable, as there was no discrepancy or statistical difference found in the results between the radiology residents’ and Fellows’ scores, despite the differences in their level of training. Both groups found the RadSTARD tool and elaboration document to be beneficial to them when interpreting the literature. RadSTARD is thus a reliable tool that can be used to validate the results of diagnostic accuracy studies specific to radiology. It will aid radiologists in reporting and interpreting radiology diagnostic accuracy studies, impacting their practice for generations to come

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe