Multi-EPL: Accurate multi-source domain adaptation.

Given multiple source datasets with labels, how can we train a target model with no labeled data? Multi-source domain adaptation (MSDA) aims to train a model using multiple source datasets different from a target dataset in the absence of target data labels. MSDA is a crucial problem applicable to many practical cases where labels for the target data are unavailable due to privacy issues. Existing MSDA frameworks are limited since they align data without considering labels of the features of each domain. They also do not fully utilize the target data without labels and rely on limited feature extraction with a single extractor. In this paper, we propose Multi-EPL, a novel method for MSDA. Multi-EPL exploits label-wise moment matching to align the conditional distributions of the features for the labels, uses pseudolabels for the unavailable target labels, and introduces an ensemble of multiple feature extractors for accurate domain adaptation. Extensive experiments show that Multi-EPL provides the state-of-the-art performance for MSDA tasks in both image domains and text domains, improving the accuracy by up to 13.20%.

An adaptive spark-based framework for querying large-scale NoSQL and relational databases.

The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others' efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios' benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.

Development and validation of a simple algorithm to estimate common gestational age categories using standard administrative birth record data in Ontario, Canada.

Gestational age is often incompletely recorded in administrative records, despite being critical to paediatric and maternal health research. Several algorithms exist to estimate gestational age using administrative databases; however, many have not been validated or use complicated methods that are not readily adaptable. We developed a simple algorithm to estimate common gestational age categories from routine administrative data. We leveraged a population-based registry of all hospital births occurring in Ontario, Canada over 2002-2016 including 1.8 million birth records. In this sample, this simple algorithm had excellent performance compared to a verified measure of gestational age; 87.61% agreement (95% CI: 87.49, 87.74). The accuracy of the algorithm exceeded 98% for all of the gestational age categories. Agreement notably increased over time and was greatest among singleton births and infants born at 2500-2999 g. This study provides a straight-forward algorithm for accurately estimating common gestational age categories that is easily adaptable for use in other countries.Impact StatementWhat is already known on this subject? Gestational age is often incompletely or inaccurately recorded in administrative health databases, despite being critical to the study of many paediatric and maternal health outcomes. Consequently, researchers must rely on various methods to estimate gestational age, many of these methods are either overly simple (i.e. assuming a uniform duration) or analytically complicated and difficult to adapt for new populations (e.g. regression-based approaches).What the results of this study add? This study, based on a population-based registry of all 1.8 million births occurring in Ontario, Canada 2003-2016, found that a simple, sex-specific algorithm using three commonly recorded birth record characteristics performs almost perfectly compared to a clinical estimate recorded near birth.What the implications are of these findings for clinical practice and/or further research? This study suggests that a straight-forward, sex-specific algorithm based on routinely collected birth record data is able to accurately estimate common gestational age categories (i.e. extreme preterm, <28 weeks; very preterm, 28-32 weeks; moderate-to-late preterm, 33-26 weeks; and term, 37 weeks of completed gestational age). This work will be of greatest interest to perinatal researchers using routinely collected health administrative data.

Plain Language Summaries of Clinical Trial Results: A Preliminary Study to Assess Availability of Easy-to-Understand Summaries and Approaches to Improving Public Engagement.

BACKGROUND: Easy-to-understand, stand-alone factual summaries of clinical trial results have the potential to improve public understanding of and engagement with pharmaceutical research. The European Clinical Trial Regulation (EU) No. 536/2014 is a major regulatory initiative that will result in a large number of such plain language summaries (PLSs) posted in the public domain. Today, however, little is known about the extent to which PLSs are written and are available to the general public. OBJECTIVES: This preliminary study assessed (i) 20 top pharmaceutical companies' positions on improving transparency and commitment to disclosing trial result summaries in an easy-to-understand format and (ii) the availability of such summaries in the public domain and the ease of locating them via general web searches. METHODS: The availability of PLSs in the public domain was estimated based on the number of EudraCT technical result summaries in four disease areas: chronic obstructive pulmonary disease, asthma, meningitis, and influenza. The likelihood of PLSs being easy to find through internet search engine queries by members of the public was assessed using Google. RESULTS: All 20 sponsors had committed to improve clinical trial transparency, 17 committed to sharing PLSs with trial participants, and 14 had at least one PLS available in the public domain. A total of 99 clinical studies in these four disease areas had technical summaries posted on EudraCT between 1 January 2017 and 30 June 2020. Of these 99, 14 studies had PLSs in the public domain. A total of 12 of 14 PLSs were directly captured by search engine. However, the sponsor trial identifier or EudraCT number had to be included in the search term to locate them. Generic search terms resulted in large volumes of non-relevant results. CONCLUSION: Despite the progressive movement towards clinical trial transparency, easily accessible PLSs on clinical trials are currently scarce. The provision of a European mandate and framework for non-technical result summaries by Regulation (EU) 536/2014 will be a major step to bring about positive change.

More patient and public involvement in healthcare research will help to speed the process of making new medicines. This is known by both the regulators and the healthcare industry. The healthcare industry wants to make more information on human research studies available to patients and the public. One way to help achieve this is to write simple summaries of clinical study results. Here, we use the term plain language summary (PLS) to describe them. The PLS allows people to understand human research studies more clearly. A new law will soon make it necessary to write a PLS for every clinical study done in Europe. But, today, is the PLS being used to inform the public about clinical research studies? And what is its potential? We found only  a few researchers have already begun to write PLSs. PLSs on most studies are not available to the public. Even those PLSs on public websites are very hard to find through a Google search. To better understand the potential of PLSs we are doing more research. This research will look at what the public wants to know about these studies and how they will retrieve this information.

Ontological approach to the knowledge systematization of a toxic process and toxic course representation framework for early drug risk management.

Various types of drug toxicity can halt the development of a drug. Because drugs are xenobiotics, they inherently have the potential to cause injury. Clarifying the mechanisms of toxicity to evaluate and manage drug safety during drug development is extremely important. However, toxicity mechanisms, especially hepatotoxic mechanisms, are very complex. The significant exposure of liver cells to drugs can cause dysfunction, cell injury, and organ failure in the liver. To clarify potential risks in drug safety management, it is necessary to systematize knowledge from a consistent viewpoint. In this study, we adopt an ontological approach. Ontology provides a controlled vocabulary for sharing and reusing of various data with a computer-friendly manner. We focus on toxic processes, especially hepatotoxic processes, and construct the toxic process ontology (TXPO). The TXPO systematizes knowledge concerning hepatotoxic courses with consistency and no ambiguity. In our application study, we developed a toxic process interpretable knowledge system (TOXPILOT) to bridge the gaps between basic science and medicine for drug safety management. Using semantic web technology, TOXPILOT supports the interpretation of toxicity mechanisms and provides visualizations of toxic courses with useful information based on ontology. Our system will contribute to various applications for drug safety evaluation and management.

Openness and trust in data-intensive science: the case of biocuration.

Data-intensive science comes with increased risks concerning quality and reliability of data, and while trust in science has traditionally been framed as a matter of scientists being expected to adhere to certain technical and moral norms for behaviour, emerging discourses of open science present openness and transparency as substitutes for established trust mechanisms. By ensuring access to all available information, quality becomes a matter of informed judgement by the users, and trust no longer seems necessary. This strategy does not, however, take into consideration the networks of professionals already enabling data-intensive science by providing high-quality data. In the life sciences, biological data- and knowledge bases managed by expert biocurators have become crucial for data-intensive research. In this paper, I will use the case of biocurators to argue that openness and transparency will not diminish the need for trust in data-intensive science. On the contrary, data-intensive science requires a reconfiguration of existing trust mechanisms in order to include those who take care of and manage scientific data after its production.

Application of Hereditary Renal Cell Carcinoma Risk Criteria to a Large Prospective Database.

AIMS: To evaluate the clinical impact of the Canadian criteria for identifying patients and families at risk for hereditary renal cell carcinoma (RCC). MATERIALS AND METHODS: The Canadian hereditary RCC risk criteria were applied to patients from 16 centres in the Canadian Kidney Cancer information system (CKCis) prospective database. The primary end point was the proportion of patients who met at least one criterion. RESULTS: Between January 2011 and May 2017, 8388 patients were entered in the database; 291 had inadequate risk data; 2827 (35%) met at least one criterion for genetic testing (at-risk population). Most (83%) met just one criterion. The criterion of non-clear cell histology contributed the largest proportion of at-risk patients (59%), followed by age ≤ 45 years (28%). Sixty-one patients had documentation of genetic testing, with 56 being classified at-risk (2% of at-risk). Twenty patients (35%) of the patients at risk and tested for hereditary RCC were found to harbour a germline mutation. CONCLUSIONS: Application of the Canadian hereditary RCC risk criteria to a large prospective database resulted in 35% of patients being identified at risk for hereditary RCC who could qualify for genetic testing. However, the true incidence of hereditary RCC in this population is unknown as most patients did not have documented genetic testing carried out and, thus, the sensitivity and specificity of the criteria cannot be determined. The low proportion of at-risk patients who underwent genetic testing is disappointing and highlights that there may be gaps in reporting, knowledge and/or barriers in access to genetic testing.

Clinical Outcomes and Healthcare Resource Utilization for Gastrointestinal Acute Graft-versus-Host Disease after Allogeneic Transplantation for Hematologic Malignancy: A Retrospective US Administrative Claims Database Analysis.

Graft-versus-host disease (GVHD) is the leading cause of nonrelapse mortality among patients who receive allogeneic hematopoietic cell transplantation (allo-HCT). In its acute form (aGVHD), GVHD involves the skin, liver, and gastrointestinal (GI) tract, with GI involvement most strongly associated with poor prognosis. This retrospective cohort study used US healthcare claims data for 2008 to 2015 to identify patients who developed GI aGVHD after allo-HCT performed as curative treatment for hematologic malignancy and compared them with patients who did not develop aGVHD in terms of outcomes related to survival, infections, healthcare resource utilization (HRU), and costs. Whereas the patients without aGVHD saw a 66% improvement in 1-year survival between 2009 and 2015, this effect was not observed in patients with GI aGVHD. Compared with patients without evidence of aGVHD, patients with GI aGVHD were 3.9-fold more likely to develop an infection in the year after allo-HCT. Similarly, patients who developed GI aGVHD were 4.3-fold more likely to have an inpatient admission after allo-HCT discharge, and such an admission cost on average 47% more than an admission for patients without aGVHD. Our findings confirm that GI involvement in aGVHD is associated with higher mortality, risk of infection, HRU, and cost compared with absence of aGVHD.

[Opportunities and challenges of blockchain technology in the healthcare industry]. / Opportunités et enjeux de la technologie blockchain dans le secteur de la santé.

Often described as a tool to build trust among stakeholders with divergent interests, blockchain technology has been of interest to many sectors since it was first used in 2008. Initially designed to record financial transactions between individuals, its applications have largely evolved with technological advances and the growing interest of international companies. In the healthcare sector, blockchain is interesting for many of its features: its immutability which makes it an excellent support for authenticating sensitive data such as clinical trials consents, the possibility of publishing smart contracts that automate and facilitate many processes or the constitution of a network that agrees on the state of the information. Much acclaimed, blockchain technology is still to be tested in real-life conditions and adapted to a particularly complex regulatory and economic context in the healthcare sector.

Hospital enterprise Architecture Framework (Study of Iranian University Hospital Organization).

BACKGROUNDS: Nowadays developing smart and fast services for patients and transforming hospitals to modern hospitals is considered a necessity. Living in the world inundated with information systems, designing services based on information technology entails a suitable architecture framework. OBJECTIVES: This paper aims to present a localized enterprise architecture framework for the Iranian university hospital. METHODS AND RESULTS: Using two dimensions of implementation and having appropriate characteristics, the best 17 enterprises frameworks were chosen. As part of this effort, five criteria were selected according to experts' inputs. According to these criteria, five frameworks which had the highest rank were chosen. Then 44 general characteristics were extracted from the existing 17 frameworks after careful studying. Then a questionnaire was written accordingly to distinguish the necessity of those characteristics using expert's opinions and Delphi method. The result showed eight important criteria. In the next step, using AHP method, TOGAF was chosen regarding having appropriate characteristics and the ability to be implemented among reference formats. In the next step, enterprise architecture framework was designed by TOGAF in a conceptual model and its layers. For determining architecture framework parts, a questionnaire with 145 questions was written based on literature review and expert's opinions. The results showed during localization of TOGAF for Iran, 111 of 145 parts were chosen and certified to be used in the hospital. CONCLUSION: The results showed that TOGAF could be suitable for use in the hospital. So, a localized Hospital Enterprise Architecture Modelling is developed by customizing TOGAF for an Iranian hospital at eight levels and 11 parts. This new model could be used to be performed in other Iranian hospitals.

A Fast Healthcare Interoperability Resources (FHIR) layer implemented over i2b2.

BACKGROUND: Standards and technical specifications have been developed to define how the information contained in Electronic Health Records (EHRs) should be structured, semantically described, and communicated. Current trends rely on differentiating the representation of data instances from the definition of clinical information models. The dual model approach, which combines a reference model (RM) and a clinical information model (CIM), sets in practice this software design pattern. The most recent initiative, proposed by HL7, is called Fast Health Interoperability Resources (FHIR). The aim of our study was to investigate the feasibility of applying the FHIR standard to modeling and exposing EHR data of the Georges Pompidou European Hospital (HEGP) integrating biology and the bedside (i2b2) clinical data warehouse (CDW). RESULTS: We implemented a FHIR server over i2b2 to expose EHR data in relation with five FHIR resources: DiagnosisReport, MedicationOrder, Patient, Encounter, and Medication. The architecture of the server combines a Data Access Object design pattern and FHIR resource providers, implemented using the Java HAPI FHIR API. Two types of queries were tested: query type #1 requests the server to display DiagnosticReport resources, for which the diagnosis code is equal to a given ICD-10 code. A total of 80 DiagnosticReport resources, corresponding to 36 patients, were displayed. Query type #2, requests the server to display MedicationOrder, for which the FHIR Medication identification code is equal to a given code expressed in a French coding system. A total of 503 MedicationOrder resources, corresponding to 290 patients, were displayed. Results were validated by manually comparing the results of each request to the results displayed by an ad-hoc SQL query. CONCLUSION: We showed the feasibility of implementing a Java layer over the i2b2 database model to expose data of the CDW as a set of FHIR resources. An important part of this work was the structural and semantic mapping between the i2b2 model and the FHIR RM. To accomplish this, developers must manually browse the specifications of the FHIR standard. Our source code is freely available and can be adapted for use in other i2b2 sites.

Examining database persistence of ISO/EN 13606 standardized electronic health record extracts: relational vs. NoSQL approaches.

BACKGROUND: The objective of this research is to compare the relational and non-relational (NoSQL) database systems approaches in order to store, recover, query and persist standardized medical information in the form of ISO/EN 13606 normalized Electronic Health Record XML extracts, both in isolation and concurrently. NoSQL database systems have recently attracted much attention, but few studies in the literature address their direct comparison with relational databases when applied to build the persistence layer of a standardized medical information system. METHODS: One relational and two NoSQL databases (one document-based and one native XML database) of three different sizes have been created in order to evaluate and compare the response times (algorithmic complexity) of six different complexity growing queries, which have been performed on them. Similar appropriate results available in the literature have also been considered. RESULTS: Relational and non-relational NoSQL database systems show almost linear algorithmic complexity query execution. However, they show very different linear slopes, the former being much steeper than the two latter. Document-based NoSQL databases perform better in concurrency than in isolation, and also better than relational databases in concurrency. CONCLUSION: Non-relational NoSQL databases seem to be more appropriate than standard relational SQL databases when database size is extremely high (secondary use, research applications). Document-based NoSQL databases perform in general better than native XML NoSQL databases. EHR extracts visualization and edition are also document-based tasks more appropriate to NoSQL database systems. However, the appropriate database solution much depends on each particular situation and specific problem.

Should biomedical research be like Airbnb?

The thesis presented here is that biomedical research is based on the trusted exchange of services. That exchange would be conducted more efficiently if the trusted software platforms to exchange those services, if they exist, were more integrated. While simpler and narrower in scope than the services governing biomedical research, comparison to existing internet-based platforms, like Airbnb, can be informative. We illustrate how the analogy to internet-based platforms works and does not work and introduce The Commons, under active development at the National Institutes of Health (NIH) and elsewhere, as an example of the move towards platforms for research.

Data science, learning, and applications to biomedical and health sciences.

The last decade has seen an unprecedented increase in the volume and variety of electronic data related to research and development, health records, and patient self-tracking, collectively referred to as Big Data. Properly harnessed, Big Data can provide insights and drive discovery that will accelerate biomedical advances, improve patient outcomes, and reduce costs. However, the considerable potential of Big Data remains unrealized owing to obstacles including a limited ability to standardize and consolidate data and challenges in sharing data, among a variety of sources, providers, and facilities. Here, we discuss some of these challenges and potential solutions, as well as initiatives that are already underway to take advantage of Big Data.

Development and evaluation of a record linkage protocol for Utah's Controlled Substance Database.

Utah's Controlled Substance Database prescription registry does not include master identifiers to link records for individual patients. We describe and evaluate a linkage protocol for Utah's Controlled Substance Database. Prescriptions (N = 22,401,506) dated 2005-2009 were linked using The Link King software and patient identifiers (e.g. names, dates of birth) for 2,232,725 patients. Review of 998 randomly selected record pairs classified 46 percent as definitely correct links and 54 percent as probably correct links. A correct link could not be confirmed for <1 percent. None were classified as probably incorrect links or definitely incorrect links. Record set reviews (N = 100 patients/set for 10 set sizes, randomly selected) classified 27-49 percent as definitely correct links and 39-63 percent as probably correct links. Fewer had too little information to confirm a link (5%-22%) or were probably incorrect (0%-6%). None were definitely incorrect. Overall, results suggest that Utah's Controlled Substance Database records were correctly linked. These data may be useful for cross-sectional and longitudinal studies of patient-controlled substance prescription histories.

Improving Quality and Safety Through Use of Secondary Data: Methods Case Study.

Researchers need to evaluate the strengths and weaknesses of data sets to choose a secondary data set to use for a health care study. This research method review informs the reader of the major issues necessary for investigators to consider while incorporating secondary data into their repertoire of potential research designs and shows the range of approaches the investigators may take to answer nursing research questions in a variety of context areas. The researcher requires expertise in locating and judging data sets and in the development of complex data management skills for managing large numbers of records. There are important considerations such as firm knowledge of the research question supported by the conceptual framework and the selection of appropriate databases, which guide the researcher in delineating the unit of analysis. Other more complex issues for researchers to consider when conducting secondary data research methods include data access, management and security, and complex variable construction.

Improving treatment plan evaluation with automation.

The goal of this work is to evaluate the effectiveness of Plan-Checker Tool (PCT) which was created to improve first-time plan quality, reduce patient delays, increase the efficiency of our electronic workflow, and standardize and automate the phys-ics plan review in the treatment planning system (TPS). PCT uses an application programming interface to check and compare data from the TPS and treatment management system (TMS). PCT includes a comprehensive checklist of automated and manual checks that are documented when performed by the user as part of a plan readiness check for treatment. Prior to and during PCT development, errors identified during the physics review and causes of patient treatment start delays were tracked to prioritize which checks should be automated. Nineteen of 33checklist items were automated, with data extracted with PCT. There was a 60% reduction in the number of patient delays in the six months after PCT release. PCT was suc-cessfully implemented for use on all external beam treatment plans in our clinic. While the number of errors found during the physics check did not decrease, automation of checks increased visibility of errors during the physics check, which led to decreased patient delays. The methods used here can be applied to any TMS and TPS that allows queries of the database.