CLINICAL DIAGNOSTIC WHOLE EXOME SEQUENCING FOR INFANTS IN INTENSIVE CARE SETTINGS: OUTCOMES ANALYSIS AND ECONOMIC EVALUATION

Whole exome sequencing (ES) is an extensive form of genetic testing and increasingly used as a diagnostic tool. Clinical uptake of genome-scale sequencing occurred without clear guidelines for application or robust information regarding potential impact on patient health outcomes or cost of care. For infants in intensive care with suspected genetic conditions, ES can be especially powerful to identify a specific diagnosis and inform crucial decisions about medical care. However, little is known about the cost-effectiveness of ES compared to other diagnostic strategies. This project first assessed the literature on pediatric clinical ES. Then, using electronic medical record, diagnostic laboratory, and hospital cost data, we analyzed and compared outcomes and costs of care for patients with suspected genetic etiologies admitted to intensive care within the first year of life in two patient cohorts: those who had ES (ES, n=368) and did not have ES (No-ES, n=368) as part of a diagnostic workup at a large children’s hospital. Molecular diagnostic yield (25.8% No-ES, 27.7% ES; p=0.56) and 1-year survival (84.8% No-ES, 80.2% ES; p=0.10) were similar between cohorts, while ES patients had higher total cost, diagnostic investigation cost, and genetic test cost during the index admission and for the year after the date of first inpatient genetics consultation (all p\u3c0.01). ES demonstrated important diagnostic utility for patients with monogenic disease, yet other genetic tests, especially chromosomal microarray, remain important given the burden of chromosomal abnormalities in this population. As clinically applied over the first 5 years, ES does not appear to be a cost-effective diagnostic tool for the broad population of newborns and infants with suspected genetic disease compared to standard diagnostic tests such as chromosomal microarray analysis and panel/single gene testing. Further work is needed to develop outcome measures to capture utility of ES results – both diagnostic and non-diagnostic – for clinicians, patients, and patients’ families, and to specify clinical guidelines for appropriate ES application

Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges

Combinations of healthcare claims data with additional datasets provide large and rich sources of information. The dimensionality and complexity of these combined datasets can be challenging to handle with standard statistical analyses. However, recent developments in artificial intelligence (AI) have led to algorithms and systems that are able to learn and extract complex patterns from such data. AI has already been applied successfully to such combined datasets, with applications such as improving the insurance claim processing pipeline and reducing estimation biases in retrospective studies. Nevertheless, there is still the potential to do much more. The identification of complex patterns within high dimensional datasets may find new predictors for early onset of diseases or lead to a more proactive offering of personalized preventive services. While there are potential risks and challenges associated with the use of AI, these are not insurmountable. As with the introduction of any innovation, it will be necessary to be thoughtful and responsible as we increasingly apply AI methods in healthcare

Prospect patents, data markets, and the commons in data-driven medicine : openness and the political economy of intellectual property rights

Scholars who point to political influences and the regulatory function of patent courts in the USA have long questioned the courts’ subjective interpretation of what ‘things’ can be claimed as inventions. The present article sheds light on a different but related facet: the role of the courts in regulating knowledge production. I argue that the recent cases decided by the US Supreme Court and the Federal Circuit, which made diagnostics and software very difficult to patent and which attracted criticism for a wealth of different reasons, are fine case studies of the current debate over the proper role of the state in regulating the marketplace and knowledge production in the emerging information economy. The article explains that these patents are prospect patents that may be used by a monopolist to collect data that everybody else needs in order to compete effectively. As such, they raise familiar concerns about failure of coordination emerging as a result of a monopolist controlling a resource such as datasets that others need and cannot replicate. In effect, the courts regulated the market, primarily focusing on ensuring the free flow of data in the emerging marketplace very much in the spirit of the ‘free the data’ language in various policy initiatives, yet at the same time with an eye to boost downstream innovation. In doing so, these decisions essentially endorse practices of personal information processing which constitute a new type of public domain: a source of raw materials which are there for the taking and which have become most important inputs to commercial activity. From this vantage point of view, the legal interpretation of the private and the shared legitimizes a model of data extraction from individuals, the raw material of information capitalism, that will fuel the next generation of data-intensive therapeutics in the field of data-driven medicine

Pseudomonas populations causing pith necrosis of tomato and pepper in Argentina are highly diverse

Pseudomonas species causing pith necrosis symptoms on tomato and pepper collected in different areas of Argentina were identified as Pseudomonas corrugata, P. viridiflava and Pseudomonas spp. Their diversity was analysed and compared with reference strains on the basis of their phenotypic characteristics, copper and antibiotic sensitivity tests, serology, pathogenicity, DNA fingerprinting and restriction fragment length polymorphism (RFLP) analysis of a 16S rRNA gene fragment. All P. corrugata strains tested were copper-resistant while P. viridiflava strains were more variable. Numerical analysis of phenotypic data showed that all P. corrugata strains formed a single phenon that clustered at a level of about 93%, while all the P. viridiflava strains clustered in a separated phenon at a level of 94%. Genomic analysis by repetitive (rep)-PCR and 16S rRNA-RFLP fingerprinting and serological analysis showed that the two species contained considerable genetic diversity. Inoculations of tomato and pepper plants with strains from both hosts caused similar pith necrosis symptoms. Strains of both P. corrugata and P. viridiflava were grouped according to their geographical origin and not according to the original host. This is the first report of Pseudomonas viridiflava causing pith necrosis on pepper.Facultad de Ciencias Agrarias y ForestalesInstituto de Biotecnologia y Biologia Molecula

Pseudomonas populations causing pith necrosis of tomato and pepper in Argentina are highly diverse

Pseudomonas species causing pith necrosis symptoms on tomato and pepper collected in different areas of Argentina were identified as Pseudomonas corrugata, P. viridiflava and Pseudomonas spp. Their diversity was analysed and compared with reference strains on the basis of their phenotypic characteristics, copper and antibiotic sensitivity tests, serology, pathogenicity, DNA fingerprinting and restriction fragment length polymorphism (RFLP) analysis of a 16S rRNA gene fragment. All P. corrugata strains tested were copper-resistant while P. viridiflava strains were more variable. Numerical analysis of phenotypic data showed that all P. corrugata strains formed a single phenon that clustered at a level of about 93%, while all the P. viridiflava strains clustered in a separated phenon at a level of 94%. Genomic analysis by repetitive (rep)-PCR and 16S rRNA-RFLP fingerprinting and serological analysis showed that the two species contained considerable genetic diversity. Inoculations of tomato and pepper plants with strains from both hosts caused similar pith necrosis symptoms. Strains of both P. corrugata and P. viridiflava were grouped according to their geographical origin and not according to the original host. This is the first report of Pseudomonas viridiflava causing pith necrosis on pepper.Facultad de Ciencias Agrarias y ForestalesInstituto de Biotecnologia y Biologia Molecula

Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms

Customer churn is a serious problem, which is a critical issue encountered by large businesses and organizations. Due to the direct impact on the company's revenues, particularly in sectors such as the telecommunications as well as the banking, companies are working to promote ways to identify the churn of prospective consumers. Hence it is vital to investigate issues that influence customer churn to yield appropriate measures to diminish churn. The major objective of this work is to advance a model of churn prediction that helps telecom operatives to envisage clients that are most probable to be subjected to churn. The experimental approach for this study uses the machine learning procedures on the telecom churn dataset, using an improved Relief-F feature selection algorithm to pick related features from the huge dataset. To quantify the model's performance, the result of classification uses CART and ANN, the accuracy shows that ANN has a high predictive capacity of 93.88% compared to the 91.60% CART classifie

Presented Abstracts from the Thirty Third Annual Education Conference of the National Society of Genetic Counselors (New Orleans, LA, September 2014)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146864/1/jgc41067.pd

On the design of custom packs: grouping of medical disposable items for surgeries

A custom pack combines medical disposable items into a single sterile package that is used for surgical procedures. Although custom packs are gaining importance in hospitals due to their potential benefits in reducing surgery setup times, little is known on methodologies to configure them, especially if the number of medical items, procedure types and surgeons is large. In this paper, we propose a mathematical programming approach to guide hospitals in developing or reconfiguring their custom packs. In particular, we are interested in minimising points of touch, which we define as a measure for physical contact between staff and medical materials. Starting from an integer non-linear programming model, we develop both an exact linear programming (LP) solution approach and an LP-based heuristic. Next, we also describe a simulated annealing approach to benchmark the mathematical programming methods. A computational experiment, based on real data of a medium-sized Belgian hospital, compares the optimised results with the performance of the hospital’s current configuration settings and indicates how to improve future usage. Next to this base case, we introduce scenarios in which we examine to what extent the results are sensitive for waste, i.e. adding more items to the custom pack than is technically required for some of the custom pack’s procedures, since this can increase its applicability towards other procedures. We point at some interesting insights that can be taken up by the hospital management to guide the configuration and accompanying negotiation processes

Holistic, data-driven, service and supply chain optimisation: linked optimisation.

The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis