State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China.
Peking-Tsinghua Center for Life Sciences, Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang, Shandong 261325, China.
Laboratoire Reproduction et Developpement des Plantes, Univ Lyon, ENS de Lyon, UCB Lyon1, CNRS, INRA, Lyon, France. Electronic address: teva.vernoux@ens-lyon.fr.
Laboratoire de Reproduction et Développement des Plantes, Université de Lyon, UCB Lyon 1, ENS de Lyon, INRA, CNRS, 46 Allée d'Italie, 69364 Lyon Cedex 07, France christophe.trehin@ens-lyon.fr olivier.hamant@ens-lyon.fr.
Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907, USA.
Purdue Center for Plant Biology, Purdue University, West Lafayette, IN 47907, USA.
Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China.
Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China.
Division of Biological Sciences, Interdisciplinary Plant Group, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. Electronic address: mcsteenp@missouri.edu.
New York University, The Department of Biology, The Center for Genomics and Systems Biology, New York, NY, United States. Electronic address: kdb5@nyu.edu.
State Key Laboratory of Plant Environmental Resilience, College of Biological Sciences, China Agricultural University, Beijing 100193, China. Electronic address: guoyan@cau.edu.cn.
Laboratoire de Reproduction et Développement des Plantes, Université de Lyon, ENS de Lyon, UCBL, INRAE, CNRS, INRIA 46 Allée d'Italie, 69364, Lyon, France.
The Key Laboratory of Plant Development and Environmental Adaptation Biology, Ministry of Education, College of Life Sciences, Shandong University, Qingdao, China.
In the medical field, we face many challenges, including the high cost of data collection and processing, difficult standards issues, and complex preprocessing techniques. It is necessary to establish...
We defined the data quality management system from three aspects (Construction - Operation - Utilization) based on the life cycle of medical data. Based on this, we proposed the "SMART DATA" concept a...
We define "SMART DATA" as systematized, high-quality data collected based on the life cycle of data construction, operation, and utilization through quality control activities for medical data. In thi...
In this study, we conducted primary research to develop a medical data quality management system. This will standardize medical data extraction and quality control methods and increase the utilization...
To assess the prevalence of overly positive interpretation, also called 'spin',-of results in diagnostic accuracy studies of infectious diseases and to identify suggestions for improvement....
A PubMed search was performed to identify diagnostic accuracy studies of infectious diseases published between January and March 2019. Each article was assessed by two authors independently to identif...
The final analysis included 120 studies. Favourable or promising recommendations were made in the main text of 101 (84%) of the included studies. Evidence of actual over-interpretation (spin) was foun...
Evidence of over-interpretation of results was found in one-third of the included studies. We have proposed possible interventions to prevent overly positive interpretations of results in diagnostic a...
We describe the design, implementation, and impact of a data harmonization, data quality checking, and dynamic report generation application in an international observational HIV research network. The...
For a long time, the reliability of medical-scientific research was, without further verification, based on real data. It is becoming increasingly clear that this assumption is unjustified and that pr...
Although ChatGPT was not developed for medical use, there is growing interest in its use in medical fields. Understanding its capabilities and precautions for its use in the medical field is an urgent...
A non-clinical experimental study....
We administered the Japanese National Medical Examination to GPT-3.5 and GPT-4 to examine the rates of accuracy and consistency in their responses. We counted the total number of documents in the Web ...
For GPT-4, we confirmed an accuracy rate of 81.0 % and a consistency rate of 88.8 % on the exam; both showed improvement compared to those for GPT-3.5. A positive correlation was observed between the ...
Checking consistency may help identify incorrect answers when using ChatGPT. Users should be aware that the accuracy of the answers by ChatGPT may decrease when it is asked about topics with limited p...
Pairing of the T cell receptor (TCR) with its cognate peptide-MHC (pMHC) is a cornerstone in T cell-mediated immunity. Recently, single-cell sequencing coupled with DNA-barcoded MHC multimer staining ...
Digital transformation in healthcare and the growth of health data generation and collection are important challenges for the secondary use of healthcare records in the health research field. Likewise...
To capture the different data governance behind health data hubs across Europe, a survey focused on analysing the feasibility of linking individual-level data between data collections and the generati...
In total, 41 survey responses received until June 2022 were analysed. Stratification methods were performed to cover the different levels of granularity identified in some data hubs' characteristics. ...
The analysis of the responses from health data hub respondents across Europe provided a list of the most frequent aspects, which concluded with a set of specific best practices on data management and ...
Unstructured text data (UTD) are increasingly found in many databases that were never intended to be used for research, including electronic medical record (EMR) databases. Data quality can impact the...
Our objective was to systematically document current research and practices about NLP preprocessing methods to describe or improve the quality of UTD, including UTD found in EMR databases....
A scoping review was undertaken of peer-reviewed studies published between December 2002 and January 2021. Scopus, Web of Science, ProQuest, and EBSCOhost were searched for literature relevant to the ...
A total of 41 articles were included in the scoping review; over 50% were published between 2016 and 2021. Almost 20% of the articles were published in health science journals. Common preprocessing me...
Multiple NLP techniques have been proposed to preprocess UTD, with some differences in techniques applied to EMR data. There are similarities in the data quality dimensions used to characterize struct...
In this evaluation, we aim to strengthen Routine Health Information Systems (RHIS) through the digitization of data quality assessment (DQA) processes. We leverage electronic data from the Kenya Healt...
We evaluated 187 HIV care facilities with electronic medical records across Kenya. Using quarterly, longitudinal KHIS data from January 2011 to June 2018 (total N = 30 quarters), we extracted indicato...
A total of 5,610 unique facility-quarters were extracted from KHIS. The mean completeness score was 61.1% [standard deviation (SD) = 27%]. The mean consistency score was 80% (SD = 16.4%). The mean dis...
We observed a lack of correlation between the completeness score and the other two scores. As such, for a holistic DQA, completeness assessment should be paired with the measurement of either consiste...
Groundwater overuse in different domains will eventually lead to global freshwater scarcity. To meet the anticipated demands, many governments worldwide are employing innovative and traditional techni...
