Curative Costs Research Articles

Developing high-dimensional machine learning models to improve generalization ability and overcome data insufficiency for mixed sugar fermentation simulation

Biorefinery can be promoted by building accurate machine learning models. This work proposed a strategy to enhance model’s generalization ability and overcome insufficient data conditions for mixed sugar fermentation simulation. Multiple inputs single output models, using initial glucose, initial xylose, and time together as inputs, have higher generalization ability than single input single output models with time as sole input in predicting glucose, xylose, ethanol, or biomass separately. Multiple inputs multiple outputs models, integrating outputs, enhanced model accuracy and resulted in an average R2 at 0.99. To overcome data insufficiency conditions, consensus yeast (CY) model, through consolidating data from 4 yeasts, obtained R2 at 0.90. By adjusting the pretrained CY model, the model can save more than 50% data and get R2 at 0.95 and 0.93 for yeast and bacterial fermentation simulation. The strategy can expand the application range and save costs of data curation for ANN models.

Autoencoder-based 3D representation learning for industrial seedling abnormality detection

Industrial seedling quality assessment, such as attempting to find abnormal seedlings, is a challenging task where assessment methods must contend with the natural variability of seedlings, as well as the subjective nature of expert judgements. Furthermore, obtaining expert judgements is expensive and time-consuming, so machine learning approaches which rely on fewer judgements would be useful in practice. We investigate autoencoders, operating on 3D point clouds obtained from 6732 seedlings to address this challenge, exploiting such systems’ ability to work with partially labelled data. Point clouds from tomato seedlings are recorded using a 3D data capture platform, MARVIN™, and the quality of each seedling is determined by expert judgement. An existing system is used to establish baseline performance scores using a rule-based expert system and machine learning with handcrafted features. Autoencoders are trained on the point clouds to learn representations for subsequent use in classification. We examine scenarios where large amounts of partially labelled data are available, and compare with the case where fully labelled data is available. To improve performance, we compare the architectural subcomponents based on PointNet and PointNet++, as well as the effect of different training strategies. We find, with 13.6% of training data labelled, our model has correct classification rates of 97.7% and 82.7% for normals and abnormals respectively. With further improvements and fully labelled data, we find that correct classification rates of 97.6% and 96.1% can be reached. The results demonstrate that semi-supervised learning supported by partially labelled data has the potential to greatly reduce the cost of data curation, with minimal impact on overall accuracy.

Autohighlight: Highlight detection in League of Legends esports broadcasts via crowd-sourced data

Every minute, 500 h of footage is uploaded to Youtube.com, and ∼1900 h of footage is livestreamed on Twitch.tv. It can therefore be challenging for viewers to find the content they are most likely to enjoy. Highlight videos can entertain users who did not watch a broadcast, e.g. due to a lack of awareness, availability, or willingness. Furthermore, livestream content creators can grow their audiences by using highlights as advertisement, while also engaging casual followers who do not watch full broadcasts. However, hand-generating these videos is laborious, thus automatic highlight detection is an active research challenge. We examine automatic highlight detection by focusing on esports broadcasts. Esports are an emerging genre of sport played using a video games. We focus on League of Legends, a popular title with multiple professional leagues. Esports broadcasts are high-quality and professionally produced, mirroring traditional sports. We tackle the problem in a weakly supervised manner, utilising two datasets, one of ‘crowd-sourced’ highlight videos and one of unedited broadcasts. These datasets allow us to leverage massive data while hugely reducing the human cost of data curation and annotation. We propose two novel extensions to state-of-the-art rank-based highlight detection architectures. Firstly, a multimodal hybrid–fusion architecture that enables audio-visual highlight detection, and secondly, a smoothing step to incorporate context into decision making. Both extensions show significant improvement over state-of-the-art ranking models, in places performing nearly twice as well as competing architectures. Additionally, we examine the effectiveness of each modality and compare ranking models with classification based systems.

Opportunities and Challenges in High-Quality Contemporary Data Collection in Traumatic Brain Injury: The CENTER-TBI Experience

Strong evidence in support of guidelines for traumatic brain injury (TBI) is lacking. Large-scale observational studies may offer a complementary source of evidence to clinical trials to improve the care and outcome for patients with TBI. They are, however, challenging to execute. In this review, we aim to characterize opportunities and challenges of large-scale collaborative research in neurotrauma. We use the setup and conduct of Collaborative European Neurotrauma Effectiveness Research in TBI (CENTER-TBI) as an illustrative example. We highlight the importance of building a team and of developing a network for younger researchers, thus investing toward the future. We involved investigators early in the design phase and recognized their efforts in a group contributor list on all publications. We found, however, that translation to academic credits often failed, and we suggest that the current system of academic credits be critically appraised. We found substantial variability in consent procedures for participant enrollment within and between countries. Overall, obtaining approvals typically required 4-6months, with outliers up to 18months. Research costs varied considerably across Europe and should be defined by center. We substantially underestimated costs of data curation, and we suggest that 15-20% of the budget be reserved for this purpose. Streamlining analyses and accommodating external research proposals demanded a structured approach. We implemented a systematic inventory of study plans and found this effective in maintaining oversight and in promoting collaboration between research groups. Ensuring good use of the data was a prominent feature in the review of external proposals. Multiple interactions occurred with industrial partners, mainly related to biomarkers and neuroimaging, and resulted in various formal collaborations, substantially extending the scope of CENTER-TBI. Overall, CENTER-TBI has been productive, with over 250 international peer-reviewed publications. We have ensured mechanisms to maintain the infrastructure and continued analyses. We see potential for individual patient data meta-analyses in connection to other large-scale projects. Our collaboration with Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) has taught us that although standardized data collection and coding according to common data elements can facilitate such meta-analyses, further data harmonization is required for meaningful results. Both CENTER-TBI and TRACK-TBI have demonstrated the complexity of the conduct of large-scale collaborative studies that produce high-quality science and new insights.

Economic evaluation of severe malaria in children under 14\xa0years in Zambia

IntroductionMalaria exerts a significant economic burden on health care providers and households and our study attempts to make claims on the cost effectiveness of artesunate against quinine in patients under 14 years of age in Zambia. Also, to find the average total costs involved in the treatment of severe malaria in children and their impact on household expenditure.MethodsCost-effectiveness analysis of severe malaria treatment was conducted from a healthcare provider perspective using a Markov model. Standard costing was performed for the identification, measurement and assessment phases with data from quantification reports for anti-malaria commodities as these documents provides drug procurement costs from suppliers and freight costs. Average and incremental cost-effectiveness ratio were estimated and uncertainties were assessed through probabilistic sensitivity analysis.ResultsIn Zambia severe malaria in children has been shown to account for over 45% of the total monthly curative healthcare costs incurred by households compared to the mean per capita monthly income. The cost of treating severe malaria depleted 7.67% of the monthly average household income. According, to the cost effectiveness analysis the of artesunate with quinine the ICER was $105 per death averted.ConclusionThe use of artesunate over quinine in the treatment of severe malaria in children under 14 years is a highly cost-effective strategy for the healthcare provider in Zambia.

Open Science and Data Science

Data Science (DS) as defined by Jim Gray is an emerging paradigm in all research areas to help finding non-obvious patterns of relevance in large distributed data collections. “Open Science by Design” (OSD), i.e., making artefacts such as data, metadata, models, and algorithms available and re-usable to peers and beyond as early as possible, is a pre-requisite for a flourishing DS landscape. However, a few major aspects can be identified hampering a fast transition: (1) The classical “Open Science by Publication” (OSP) is not sufficient any longer since it serves different functions, leads to non-acceptable delays and is associated with high curation costs. Changing data lab practices towards OSD requires more fundamental changes than OSP. 2) The classical publication-oriented models for metrics, mainly informed by citations, will not work anymore since the roles of contributors are more difficult to assess and will often change, i.e., other ways for assigning incentives and recognition need to be found. (3) The huge investments in developing DS skills and capacities by some global companies and strong countries is leading to imbalances and fears by different stakeholders hampering the acceptance of Open Science (OS). (4) Finally, OSD will depend on the availability of a global infrastructure fostering an integrated and interoperable data domain—“one data-domain” as George Strawn calls it—which is still not visible due to differences about the technological key pillars. OS therefore is a need for DS, but it will take much more time to implement it than we may have expected.

The Future of Platform Power: Making Middleware Work

Unlike many other proposals to curtail platform power, middleware does not violate the First Amendment of the U.S. Constitution. In the United States, that makes middleware a path forward in a neighborhood full of dead ends. Before we can execute on the middleware vision, however, at least four problems must be solved. First, how technologically feasible is it for competitors to remotely process massive quantities of platform data? Second, how is everyone going to get paid? Third, who will bear the enormous costs of content curation? And fourth, how does middleware address privacy issues regarding users' friends' data? We have to solve these problems to make middleware work—but this is also what makes the concept so promising.

Estimation of household health cost and climate adaptation cost with its health related determinants: empirical evidences from western Nepal

Limited evidence is available concerning the household-level costs of prevailing diseases and the potential cost of climate adaptation in Nepal. This study estimates these costs and assesses the relationships between prevalent diseases and climate adaptation at the household level using survey data from 420 households. An ingredients-based approach was used to estimate the cost of health and adaptation, and a Probit regression model was used to analyze the relationship between prevalent diseases and climate adaptation costs. Household direct curative costs are the highest among health cost components. Two-thirds of total health costs are direct costs for households. On average, 15.90% of household income is used for direct cost of health care. The climate hazard cost among afflicted households is estimated to be high. In addition, diseases like malaria, typhoid and jaundice, their costs, climate awareness program, droughts, family size and loss of per capita income are more likely to raise the cost of climate adaptation. The occurrence of gastritis, prevalence of diarrhea and cold waves are less likely to affect the cost. Policymakers should implement health financing schemes and adaptation strategies to prevent the loss of human health in western Nepal.

From Grandma’s Attic to Amnesty Programs: Adventures in Accessioning Archaeological Collections

Canyons of the Ancients Visitor Center and Museum (the Center) located in Dolores, Colorado is a federal Bureau of Land Management curation facility for archaeological collections generated from permitted projects on public lands in southwest Colorado. As with many museums, the Center receives artifacts without contextual information that were collected without permits and transferred through a variety of sources like law enforcement actions. Numerous donation scenarios are provided. The curation of artifacts without provenience or contextual information can be difficult to justify because of high curation costs and limited space. However, the Center has identified and clarified the value of unprovenienced collections and found paths to provide public benefit through the application of Department of Interior and Bureau of Land Management policy and guidance.

A checklist of attributes for effective monitoring of threatened species and threatened ecosystems

Monitoring of threatened species and threatened ecosystems is critical for determining population trends, identifying urgency of management responses, and assessing the efficacy of management interventions. Yet many threatened species and threatened ecosystems are not monitored and for those that are, the quality of the monitoring is often poor. Here we provide a checklist of factors that need to be considered for inclusion in robust monitoring programs for threatened species and threatened ecosystems. These factors can be grouped under four broad themes – the design of monitoring programs, the structure and governance of monitoring programs, data management and reporting, and appropriate funding and legislative support. We briefly discuss key attributes of our checklist under these themes. Key topics in our first theme of the design of monitoring programs include appropriate objective setting, identification of the most appropriate entities to be measured, consistency in methodology and protocols through time, ensuring monitoring is long-term, and embedding monitoring into management. Under our second theme which focuses on the structure and governance of monitoring programs for threatened species and ecosystems, we touch on the importance of adopting monitoring programs that: test the effectiveness of management interventions, produce results that are relevant to management, and engage with (and are accepted by) the community. Under Theme 3, we discuss why data management is critical and highlight that the costs of data curation, analysis and reporting need to be factored into budgets for monitoring programs. This requires that appropriate levels of funding are made available for monitoring programs, beyond just the cost of data collection – a key topic examined in Theme 4. We provide examples, often from Australia, to highlight the importance of each of the four themes. We recognize that these themes and topics in our checklist are often closely inter-related and therefore provide a conceptual model highlighting these linkages. We suggest that our checklist can help identify the parts of existing monitoring programs for threatened species and threatened ecosystems that are adequate for the purpose or may be deficient and need to be improved.

Associative attention networks for temporal relation extraction from electronic health records.

Temporal relations are crucial in constructing a timeline over the course of clinical care, which can help medical practitioners and researchers track the progression of diseases, treatments and adverse reactions over time. Due to the rapid adoption of Electronic Health Records (EHRs) and high cost of manual curation, using Natural Language Processing (NLP) to extract temporal relations automatically has become a promising approach. Typically temporal relation extraction is formulated as a classification problem for the instances of entity pairs, which relies on the information hidden in context. However, EHRs contain an overwhelming amount of entities and a large number of entity pairs gathering in the same context, making it difficult to distinguish instances and identify relevant contextual information for a specific entity pair. All these pose significant challenges towards temporal relation extraction while existing methods rarely pay attention to. In this work, we propose the associative attention networks to address these issues. Each instance is first carved into three segments according to the entity pair to obtain the differentiated representation initially. Then we devise the associative attention mechanism for a further distinction by emphasizing the relevant information, and meanwhile, for the reconstruction of association among segments as the final representation of the whole instance. In addition, position weights are utilized to enhance the performance. We validate the merit of our method on the widely used THYME corpus and achieve an average F1-score of 64.3% over three runs, which outperforms the state-of-the-art by 1.5%.

Do Neural Information Extraction Algorithms Generalize Across Institutions?

Natural language processing (NLP) techniques have been adopted to reduce the curation costs of electronic health records. However, studies have questioned whether such techniques can be applied to data from previously unseen institutions. We investigated the performance of a common neural NLP algorithm on data from both known and heldout (ie, institutions whose data were withheld from the training set and only used for testing) hospitals. We also explored how diversity in the training data affects the system's generalization ability. We collected 24,881 breast pathology reports from seven hospitals and manually annotated them with nine key attributes that describe types of atypia and cancer. We trained a convolutional neural network (CNN) on annotations from either only one (CNN1), only two (CNN2), or only four (CNN4) hospitals. The trained systems were tested on data from five organizations, including both known and heldout ones. For every setting, we provide the accuracy scores as well as the learning curves that show how much data are necessary to achieve good performance and generalizability. The system achieved a cross-institutional accuracy of 93.87% when trained on reports from only one hospital (CNN1). Performance improved to 95.7% and 96%, respectively, when the system was trained on reports from two (CNN2) and four (CNN4) hospitals. The introduction of diversity during training did not lead to improvements on the known institutions, but it boosted performance on the heldout institutions. When tested on reports from heldout hospitals, CNN4 outperformed CNN1 and CNN2 by 2.13% and 0.3%, respectively. Real-world scenarios require that neural NLP approaches scale to data from previously unseen institutions. We show that a common neural NLP algorithm for information extraction can achieve this goal, especially when diverse data are used during training.

Automatic extraction of gene-disease associations from literature using joint ensemble learning

A wealth of knowledge concerning relations between genes and its associated diseases is present in biomedical literature. Mining these biological associations from literature can provide immense support to research ranging from drug-targetable pathways to biomarker discovery. However, time and cost of manual curation heavily slows it down. In this current scenario one of the crucial technologies is biomedical text mining, and relation extraction shows the promising result to explore the research of genes associated with diseases. By developing automatic extraction of gene-disease associations from the literature using joint ensemble learning we addressed this problem from a text mining perspective. In the proposed work, we employ a supervised machine learning approach in which a rich feature set covering conceptual, syntax and semantic properties jointly learned with word embedding are trained using ensemble support vector machine for extracting gene-disease relations from four gold standard corpora. Upon evaluating the machine learning approach shows promised results of 85.34%, 83.93%,87.39% and 85.57% of F-measure on EUADR, GAD, CoMAGC and PolySearch corpora respectively. We strongly believe that the presented novel approach combining rich syntax and semantic feature set with domain-specific word embedding through ensemble support vector machines evaluated on four gold standard corpora can act as a new baseline for future works in gene-disease relation extraction from literature.

LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC

The identification and interpretation of genomic variants play a key role in the diagnosis of genetic diseases and related research. These tasks increasingly rely on accessing relevant manually curated information from domain databases (e.g. SwissProt or ClinVar). However, due to the sheer volume of medical literature and high cost of expert curation, curated variant information in existing databases are often incomplete and out-of-date. In addition, the same genetic variant can be mentioned in publications with various names (e.g. ‘A146T’ versus ‘c.436G>A’ versus ‘rs121913527’). A search in PubMed using only one name usually cannot retrieve all relevant articles for the variant of interest. Hence, to help scientists, healthcare professionals, and database curators find the most up-to-date published variant research, we have developed LitVar for the search and retrieval of standardized variant information. In addition, LitVar uses advanced text mining techniques to compute and extract relationships between variants and other associated entities such as diseases and chemicals/drugs. LitVar is publicly available at https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/LitVar.

Micropublication: incentivizing community curation and placing unpublished data into the public domain.

Large volumes of data generated by research laboratories coupled with the required effort and cost of curation present a significant barrier to inclusion of these data in authoritative community databases. Further, many publicly funded experimental observations remain invisible to curation simply because they are never published: results often do not fit within the scope of a standard publication; trainee-generated data are forgotten when the experimenter (e.g. student, post-doc) leaves the lab; results are omitted from science narratives due to publication bias where certain results are considered irrelevant for the publication. While authors are in the best position to curate their own data, they face a steep learning curve to ensure that appropriate referential tags, metadata, and ontologies are applied correctly to their observations, a task sometimes considered beyond the scope of their research and other numerous responsibilities. Getting researchers to adopt a new system of data reporting and curation requires a fundamental change in behavior among all members of the research community. To solve these challenges, we have created a novel scholarly communication platform that captures data from researchers and directly delivers them to information resources via Micropublication. This platform incentivizes authors to publish their unpublished observations along with associated metadata by providing a deliberately fast and lightweight but still peer-reviewed process that results in a citable publication. Our long-term goal is to develop a data ecosystem that improves reproducibility and accountability of publicly funded research and in turn accelerates both basic and translational discovery. Database URL: www.micropublication.org

How much does curation cost?

NIH administrators have recently expressed concerns about the cost of curation for biological databases. However, they did not articulate the exact costs of curation. Here we calculate the cost of biocuration of articles for the EcoCyc database as $219 per article over a 5-year period. That cost is 6–15% of the cost of open-access publication fees for publishing biomedical articles, and we estimate that cost is 0.088% of the cost of the overall research project that generated the experimental results. Thus, curation costs are small in an absolute sense, and represent a miniscule fraction of the cost of the research.

Crowd-sourcing and author submission as alternatives to professional curation.

Can we decrease the costs of database curation by crowd-sourcing curation work or by offloading curation to publication authors? This perspective considers the significant experience accumulated by the bioinformatics community with these two alternatives to professional curation in the last 20 years; that experience should be carefully considered when formulating new strategies for biological databases. The vast weight of empirical evidence to date suggests that crowd-sourced curation is not a successful model for biological databases. Multiple approaches to crowd-sourced curation have been attempted by multiple groups, and extremely low participation rates by ‘the crowd’ are the overwhelming outcome. The author-curation model shows more promise for boosting curator efficiency. However, its limitations include that the quality of author-submitted annotations is uncertain, the response rate is low (but significant), and to date author curation has involved relatively simple forms of annotation involving one or a few types of data. Furthermore, shifting curation to authors may simply redistribute costs rather than decreasing costs; author curation may in fact increase costs because of the overhead involved in having every curating author learn what professional curators know: curation conventions, curation software and curation procedures.

Comparison of curative effect and cost between total hip arthroplasty and internal fixation for displaced femoral neck fractures in geratic patients

Objective To compare the curative effects and costs between total hip arthroplasty and internal fixation for displaced femoral neck fractures in geratic patients. Methods Ninety-eight geratic patients with displaced femoral neck fracture were treated between 2000 January and 2004 January. They were 44 males and 54 females, with an average age of 65.5±3.1 years (range, from 60 to 69 years). There were 42 cases of Garden's type Ⅲ and 56 cases of Garden's type Ⅳ. Forty-nine patients received total hip arthroplasty (group A) and the other 49 internal fixation (group B). Postoperative complications, reoperation rate, Harris hip score at the last follow-up and the total cost of medical treatment of were compared between the 2 groups. Results The patients included in this study were followed up for an average of 11.0 years (range, from 10 to 13 years). The postoperative complications [8.2% (4/49) ] and reoperation rate [8.2% (4/49) ] in group A were lower than those in group B [28.6% (14/49) and 28.6% (14/49), respectively]. The Harris hip score in group A (94.9±3.1) was higher than that in group B (88.0±3.2). Group A incurred much lower costs for reoperation (2, 367±891 Yuan) and for medical care and medication (382±98 Yuan) than did group B (16,089±2,567 Yuan and 6,603±1, 025 Yuan, respectively), but higher cost for primary operation (35, 124±5, 789 Yuan) and total cost (38, 785±3, 981 Yuan) than group B (16, 546±3, 892 Yuan and 36, 654±3, 768 Yuan, respectively). All the above differences were statistically significant (P<0.05). Conclusions In the treatment of displaced femoral neck fractures in geratic patients, compared with internal fixation, total hip arthroplasty may be a better choice because it leads to a better curative effect but a slightly higher total cost Key words: Femoral neck fractures; Arthroplasty, replacement, hip; Fracture fixation, internal; Aged

How to measure effects of integrated primary care?

Project description: The project “Added value of Integrated Primary Health Care” aims to study the association between integrated primary health care and quality and costs of health care. It is assumed that integrated primary care is related to more cost-effective care. Empirical evidence for such relationships is scarce up till now because instruments to measure these components are missing. The main aim of this project is to develop an instrument to measure “integration of care” and to relate this to quality of the provided care and the health care costs generated in these practices by patients. In the first stage of the project, instruments to measure the level of integration, the quality, and the costs of health care were developed. In a pilot study, it was tested whether different types of data could be linked on an individual level. In this contribution we present the early progress of the project. Methods: Questionnaires to measure integrated primary care were developed based on literature research and a Delphi method with expert interviews. Indicators for quality of care can be measured by using data from GP’s Electronic Patient Records and with questionnaires among the three most prevalent providers in primary health care (GPs, physical therapists, pharmacists). At the information center Vektis, we have access to health claims data on patient level from all Dutch health care insurance companies. These data include curative and care costs for the whole GP 14th International Conference on Integrated Care, Brussels, April 2-4 2014 International Journal of Integrated Care – Volume 14, 01 October – URN:NBN:NL:UI:10-1-116149– http://www.ijic.org/ population and information about which hospital the patients went to. In a pilot study, curative and care costs of the patient population of 5 GP practices were linked to survey data of over 500 patients by using a Trusted-Third-Party as linking partner. Innovation: The project “Added value of Integrated Primary Health Care” includes a number of innovative elements. First, the development of an integrated care instrument is a novelty. We hope to inspire other researchers to collaborate with us on further improvements of this instrument. We developed measures of quality of care from mono-centered to integrated care. The use of health claims data is still new in health services research and very promising. Next, it was shown that linking data on patient level is possible which is very important for answering the final research question whether integrated primary care produces cost-effective care.

Overview of the gene ontology task at BioCreative IV.

Gene Ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation.Database URL: http://www.biocreative.org/tasks/biocreative-iv/track-4-GO/.