dataesr/scientific-paragraphs-categorization huggingface dataset:
To summarize recent research and select the best papers published in 2017 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook. Methods: A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation. Results: Among the 1,194 retrieved papers, the entire review process resulted in the selection of four best papers. The first paper studies the impact of recency and of longitudinal extent of electronic health record (EHR) datasets used to train a data-driven predictive model of inpatient admission orders.
Paper Selection Method A comprehensive literature search on topics related to CDSSs and CPOE systems was performed to identify candidate best papers following the established protocol applied in the past years  [2]. We used two bibliographic databases, primarily the PubMed/MEDLINE database (from NCBI, National Center for Biotechnology Information) as it is dedicated to biomedical literature and, secondarily, Web of Science® (WoS, from Thomson Reuters) to retrieve publications which are not referenced in PubMed, since WoS has a broader scope. Both databases were searched with similar queries targeting journal papers published in 2017, written in English, and on the aforementioned topics. The adopted strategy, which was first implemented last year  [3]  and replicated this year, was based on four exclusive queries yielding four disjoint citation subsets: Q Pub_plain , based on a plain-text search in PubMed titles and abstracts using keywords; Q Pub_indexed , based on the PubMed indexing scheme using MeSH terms and exclusive of the previous set; Q WoS_restricted , based on a WoS search on non PubMed-indexed papers restricted to the two subject areas "Medical Informatics" and "Health Care Sciences & Services" and, finally, Q WoS_filtered , based on other non-PubMed-indexed papers filtered by non-relevant subject areas.
It should be noted that, due to the delay in the PubMed population process, some papers published during  2016  were not yet retrievable at the date we queried the bibliographic database last year; thus, PubMed queries were modified accordingly so that papers missed last year could be considered for the 2017 selection. A first review of the four subsets of retrieved citations was performed by the two section editors to select 15 candidate best papers. These candidate best papers were then individually reviewed and rated by external reviewers from the international Medical Informatics community. Based on reviewers' ratings and comments, the Yearbook editorial committee had to select three to six best papers of the year in the decision support domain.
In their paper, the authors studied how varying longitudinal historical training data can impact the prediction of future clinical decisions. A clinical order recommender system, analogous to Netflix or Amazon's product recommenders based on customers' prior purchase, was used to predict admission orders in a tertiary academic hospital based on patients' diagnoses and recorded orders at admission. The objective of the study was to assess the impact on the accuracy of decision prediction of varying historical datasets used to train the clinical order recommender system and to estimate the decay rate of the relevance of prior data. Nine training sets were built on available EHR data from 2009 to 2012 considering different periods varying in duration, from one month to the whole period (4 years), and in starting year.
In parallel, "classical" order sets, expert-based and human-authored, attached to admission diagnoses were elaborated. Predicted orders as well as human-authored order sets were compared to actual 2013 data. Results showed that the accuracy of predicted decisions for the reference period (2013) was significantly better when the system was trained on just one month on recent data (2012) than with one year of old data  (2009). Using more data from a longer period, four years in the past, was not better than using the most recent data (2012) except when applying a decaying weighting scheme. In this context, testing several values, an efficient half-life of data relevance was estimated at four months. The authors concluded that data-driven models predict decisions better when trained on small recent datasets than on larger sets augmented with older data.
Likewise, given that such data-driven approaches are conservative, the question about the consideration of the perpetual evolution of medicine needs to be thought, especially if the training decision sets could themselves be biased by data-driven decision support systems. The second paper, authored by Ebadi et al. [5] , introduced DisTeam, a decision support tool aiming to address optimal surgical team selection having as its cornerstone a genetic algorithm. To this end, DisTeam entails a "training" mode and an "operational" mode. The "training" mode consists of the "patient clustering" module (allowing the tailoring of the surgical team selection procedure to patient characteristics) and the "extracting existing teams" module (fetching distinct surgical service providers that are being used to formulate intermediate solutions), while the optimization procedure is performed in the "operational" mode through the genetic algorithm.
More specifically, for the "patient clustering" a retrospective dataset of patients forming groups of similar patients was employed (based on features including age, ethnicity, race, Charlson comorbidity index, and body mass index). This allowed the identification of the most representative cluster for a given patient using K-Prototypes, a variation of the well-known K-means clustering method. The "extracting existing teams" module extracts the surgical team (consisting of all the healthcare professionals who provided care to the patient, e. g. a surgeon, an anesthesiologist, a nurse, etc. ) associated with each patient. Whenever the information of a new patient is entered into DisTeam, the best possible team is suggested through the "operational" mode by finding the most similar patient cluster, extracting candidate teams and, ultimately, selecting the best candidate.
Through the "patient clustering" module, DisTeam determines which cluster best represents the new patient and then selects the target cluster, which is being used by the "optimization" module to select the best surgical team. The respective fitness function relies on the number of complications that have occurred during the surgery, which are in turn associated with providers at three different levels, i. e. collective level (any past surgical case involving "all" current team members), pairwise level (any past surgical case involving any "two" current team members), and individual level (any past surgical case involving any current team member). First, past collective surgical cases of the given candidate team are considered. If there are no collective surgical cases, then pairwise surgical cases of any members of the candidate team are checked and, if no pairwise cooperation is found, individual provider's performance is considered.
Finally, all KBs as well as the ONC list were applied to an actual dataset of 14 million prescriptions to trigger DDI alerts and simulate their effect for clinical decision support. Five drug KB vendors were contacted and three accepted to participate to the study. It must be noted that vendor representatives are co-authors of the published paper. Results showed that the number of drug-drug pairs listed in each KB varied from a factor of three. A total of 8. 6 million unique drug pairs were identified in the three KBs, among which 79% were present in only one KB and 5% in all three KBs. This low number supports the finding that DDI resources are highly variable. Further content analysis showed however that within the subset of common pairs, there was more agreement than disagreement in the severity ranking of the DDIs, especially for contraindications, which represent the most important category of DDIs.
When considering the high priority DDIs of the ONC list, each KB covered them at least in 99%. This result showed that DDIs identified as among the most important were correctly handled despite quantitative variations in size and contents. Applied to the prescription dataset, the total number of alerts varied according to the KB. However, the ONC list alerts were again all covered by each KB, though differences in the severity ranking were observed. Notably, two statins and QT-prolonging agents were responsible for more than 97% of all ONC alerts. While KBs significantly cover the reference ONC DDIs, the authors suggested that other contraindicated DDIs shared by all KBs might complement the current ONC list. They concluded that observed variations in size and contents call for better standardization of drug KBs supported by better evidence, preferably obtained from EHR-derived patient outcomes rather than from expert panel consensus.
The paper provides a comprehensive description of all the steps entailed in the application of the proposed framework, as well as the limitations of the study. The proposed framework was applied in a quite large number of patient EHRs, which were extracted from the Department of Gastrointestinal Surgery (DGS) at the University Hospital of North Norway from 2004 to 2012. The dataset included both structured information, such as ICD-10 diagnosis codes, age, sex, length of surgery, Contributions from the 2017 Literature on Clinical Decision Support blood test results, etc. , as well as free-text from documents such as doctor notes, radiology reports, and semi-structured nurse notes. For testing, a clinician created a list of major abdominal surgeries requiring general anesthesia, defining a cohort of patients who could potentially suffer from postoperative delirium.
Oliveira et al. [12]  presented a temporally-oriented healthcare assistant, the underlying model of which provides a comprehensive representation of temporal constraints in Clinical Practice Guidelines (CPGs). The expressiveness of the model is illustrated via a case study featuring CPGs for the diagnosis and management of colon cancer. Mohammadhassanzadeh et al. [13]  elaborated on semantics-based plausible reasoning, aiming to extend the coverage of medical KBs for improved clinical decision support. The work relied on Semantic Web technology to solve complex clinical decision support queries and it was evaluated using a real-world medical dataset of patients with hepatitis, from which different percentages of data were randomly removed to reflect scenarios with increasing amounts of incomplete medical knowledge.
This trend is to some extent expected, given the extraordinary attention that "Big Data" and data-driven artificial intelligence are currently experiencing in the health domain overall and in decision support systems in particular. Contributions from the 2017 Literature on Clinical Decision Support sets. The authors conclude that data-driven models predict decisions better when trained on small recent datasets than on larger sets augmented with older data. Adding older training data may lead to less efficient predictions, unless a decaying weighting function is used. Ebadi A, Tighe PJ, Zhang L, Rashidi P DisTeam: A decision support tool for surgical team selection Artif Intell Med 2017 Feb;76:  [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26]  DisTeam is a novel decision support tool for surgical team selection aspiring to contribute in reduced conflicts, better coordination, and better patient outcomes.
Specifically, DisTeam converges quickly to the optimal solution, providing the best surgical team as well as additional teams that could be employed alternatively. DisTeam was evaluated using intra-operative data from 6,065 unique orthopedic surgery cases, involving 60 distinct surgeons, 157 anesthesiologists, and 223 circulators. Compared to the current state-of-the-art, DisTeam extends existing scheduling software by considering team structure and history as well as patient's specific characteristics, beyond personnel preferences (about days, shifts, units, etc. ) as well as regulatory and union requirements. Fung KW, Kapusnik-Uner J, Cunningham J, Higby-Baker S, Bodenreider O Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support J Am Med Inform Assoc 2017 Jul 1;24(4):806-12 Medication safety is of primary importance to healthcare delivery.
Known DDIs may be among the most preventable adverse events. The authors compared three commercial drug KBs largely used in the US for automated decision support in CPOE systems generating DDI alerts. In a normalization phase, all drug resources were mapped to RxNorm to allow for comparisons. The contents of KBs were statically compared to assess how they overlapped. It was also checked whether the KBs covered a reference list of highly significant DDIs from the Office of the National Coordinator for Health Information Technology (ONC). Finally, each KB and the ONC list were applied to an actual dataset of 14 million prescriptions to simulate their effect as clinical decision support. Results showed that the number of drug-drug pairs listed in each KB varied from a factor of three. Among the 8.
6 million unique pairs, 79% were present in only one KB and 5% in all three KBs. Content analysis showed that there was more agreement than disagreement in the severity ranking of the DDIs in common pairs, especially for contraindications. The selected DDIs of the ONC list were covered at 99% or more in the three KBs. Applied to the prescription dataset, the number of alerts varied according to the KB. However, ONC alerts were all covered by the KBs, though differences in the severity ranking were observed. Notably, two statins and QT-prolonging agents were responsible of more than 97% of all ONC alerts. Observed variations in size and contents call for better standardization of drug KBs. Despite this, KBs significantly cover the reference ONC DDIs. The authors suggest that other contraindicated DDIs shared by all KBs might complement the current ONC list.
Taking note that the current knowledge-based decision support approaches to promote best practice are limited by classical trial-based clinical research and human authoring, Chen et al. advocate for data-driven approaches that would take advantage of data accumulated in EHRs to predict clinical practice patterns and then offer automated decision support. A clinical order recommender system, analogous to Netflix or Amazon's product recommenders, was used to predict admission orders in a tertiary academic hospital based on admission diagnoses. The objective of the study was to assess the impact of this approach on the accuracy of decision prediction for varying historical datasets used to train the clinical order recommender system and to estimate the decay rate of the relevance of prior data.
The training sets were built on available data from 2009 to 2012 with different periods varying in duration, from one to 48 months, and varying in their starting year. Predicted orders and human-authored order sets were compared to actual 2013 data. Results showed that the accuracy of predicted decisions for the reference period (2013) was significantly better when the system was trained on just one month and on recent data (2012) than when it was trained on one year and on older data (2009). Interestingly, using more data from a longer period (2009-2012) was not better than using the most recent data (2012), except when applying a decaying weighting scheme. In this context, the half-life of data relevance was estimated at four months. Whatever the training set, the predicted decisions using data mining were more accurate than the predefined, human-authored, order Although being a common complication after major surgery with serious consequences, especially in the elderly population, postoperative delirium remains often undetected.
Despite the availability of whole genome sequences of apple and peach, there has been a considerable gap between genomics and breeding. To bridge the gap, the European Union funded the FruitBreedomics project (March 2011 to August 2015) involving 28 research institutes and private companies. Three complementary approaches were pursued: (i) tool and software development, (ii) deciphering genetic control of main horticultural traits taking into account allelic diversity and (iii) developing plant materials, tools and methodologies for breeders. Decisive breakthroughs were made including the making available of ready-to-go DNA diagnostic tests for Marker Assisted Breeding, development of new, dense SNP arrays in apple and peach, new phenotypic methods for some complex traits, software for gene/ QTL discovery on breeding germplasm via Pedigree Based Analysis (PBA).
This resulted in the discovery of highly predictive molecular markers for traits of horticultural interest via PBA and via Genome Wide Association Studies (GWAS) on several European genebank collections. FruitBreedomics also developed pre-breeding plant materials in which multiple sources of resistance were pyramided and software that can support breeders in their selection activities. Through FruitBreedomics, significant progresses were made in the field of apple and peach breeding, genetics, genomics and bioinformatics of which advantage will be made by breeders, germplasm curators and scientists. A major part of the data collected during the project has been stored in the FruitBreedomics database and has been made available to the public. This review covers the scientific discoveries made in this major endeavour, and perspective in the apple and peach breeding and genomics in Europe and beyond.
Implementation of bioinformatics tools to store and access to the data of the project Bioinformatics infrastructures were designed and deployed to provide efficient storage and access of phenotypic data and genome sequence variation information. We developed and managed web-based relational data banks specifically created for apple and peach data, and provided specific bioinformatics tools for data integration and visualization  15. All data are accessible through a stable and well-maintained interface, available at the address http://bioinformatics. tecnoparco. org/ fruitbreedomics. The database contains a shared nomenclature for phenotypes attributes and descriptors, and a list of consensus cultivar names from several countries. It also stores genotypes and phenotypes collected during several years of observations on collections of plants, characterised by cultivars and geographical locations.
Two separate database systems have been developed, along with their two interfaces. The first one stores phenotype data relying on an experiment-based model. This database reflects, in its structure, the natural data shape of the phenotyping information: trees, experimental orchards, phenotypes and genotypes are represented as separate and interconnected tables, in a classic relational data model; this facilitates standardization and importing/integration procedures, as well as update of this particular type of data. While an automatic procedure allows to upload large amounts of data, performing data validation in the meantime, a second interface, constituted by web pages containing modules and forms, allows researchers to manually input information (ensuring integrity and correctness of data) and to surf and update database content.
All the information included in this part of the system is periodically copied, together with other types of data (e. g. , genotypes) to the final database, into a Chado-like structure (http://gmod. org/wiki/Chado), the same underlying many installations including The Genome Database of Rosaceae (GDR-http://rosaceae. org/). With this infrastructure, Fruibreedomics data landscape were aligned to GDR, easing data exchange between the two projects. On top of this Chado database, a Drupal/Tripal interface has been developed, integrating several tools. Two are the objectives of this web application: to leverage the interconnections between several separated datasets collected in the FruitBreedomics database, and to provide the functionalities to easily perform analyses. A particular effort was devoted to Linkage Disequilibrium Analysis (LD), and Marker Based Analysis (MBA), with the development of two custom online tools, namely Ldexplorer and The Breeder's Interface.
This evaluation revealed that >75% of the SNP assays were polymorphic and segregated in a Mendelian fashion. The genetic linkage maps constructed on individual families had an average of 6. 8K SNPs uniformly distributed along the genome  19  , which provided ~7000 highquality markers suitable for pedigree-based QTL mapping on multi-parent populations (see below). The genetic position of ca. 16K SNPs was used for the re-ordering of contigs from the first apple genome sequence into an improved version of the apple genome (v3. 0) now available in a recent de novo assembly of a 'Golden Delicious' doubled-haploid whole genome sequence  21  both of which are available through the GDR databasewww. rosaceae. org. Using the information obtained from the 20K array on wide germplasm it was also possible to study the decay of linkage disequilibrium (LD) for each apple chromosome.
New software Software packages were developed to provide an efficient workflow for genetic studies using SNP markers from the filtering and calling of markers through the phasing of SNP marker alleles, the building of haploblocks and haplotypes to PBA-based QTL discovery software. Firstly, the ASSIsT software allows a smooth, fast and reliable filtering and calling of Infinium derived SNP markers. The format of its input files are compatible with output files from GenomeStudio (Illumina Inc. , San Diego, CA, USA; http://www. illumina. com)  29. FruitBreedomics successfully extended the software FlexQTL for use on SNP markers. FlexQTL was developed in the previous EU-HiDRAS project  30  to enable multi-family analyses and was ready for the use of SSR markers  27. As thousands SNP markers can now be tested for less than the price of a few SSR marker, sizes of datasets increased dramatically.
On the other hand, single SNP markers are more difficult to use on complex germplasm because single markers are less informative. Thanks to collaborative efforts between FruitBreedomics and RosBREED 31 , FlexQTL was successfully adapted for application to high density SNP data sets, which included increased phasing performance for SNP data. Also its user friendliness has been considerably improved through the development of the Graphical User Interface Visual FlexQTL which provides support in the creation of input files, the running of the FlexQTL software itself, and for a structured examination of the FlexQTL output. Both softwares are publicly available at www. flexqtl. nl. To further enhance the applicability of SNP data in genetic analyses, and dramatically reduce computation time of QTL discovery studies, the PediHaplotyper software was developed  32.