Advancements in regenerative medicine have highlighted the need for increased standardization and sharing of stem cell products to help drive these innovative interventions toward public availability and to increase collaboration in the scientific community. Although numerous attempts and numerous databases have been made to store this data, there is still a lack of a platform that incorporates heterogeneous stem cell information into a harmonized project-based framework. The aim of the platform described in this study, ReMeDy, is to provide an intelligent informatics solution which integrates diverse stem cell product characteristics with study subject and omics information. In the resulting platform, heterogeneous data is validated using predefined ontologies and stored in a relational database. In this initial feasibility study, testing of the ReMeDy functionality was performed using published, publically-available induced pluripotent stem cell projects conducted in in vitro, preclinical and intervention evaluations. It demonstrated the robustness of ReMeDy for storing diverse iPSC data, by seamlessly harmonizing diverse common data elements, and the potential utility of this platform for driving knowledge generation from the aggregation of this shared data. Next steps include increasing the number of curated projects by developing a crowdsourcing framework for data upload and an automated pipeline for metadata abstraction. The database is publically accessible at https//remedy.mssm.edu/.In recent years, microbiota has become an increasingly relevant factor for the understanding and potential treatment of diseases. In this work, based on the data reported by the largest study of microbioma in the world, a classification model has been developed based on Machine Learning (ML) capable of predicting the country of origin (United Kingdom vs United States) according to metagenomic data. The data were used for the training of a glmnet algorithm and a Random Forest algorithm. Both algorithms obtained similar results (0.698 and 0.672 in AUC, respectively). Furthermore, thanks to the application of a multivariate feature selection algorithm, eleven metagenomic genres highly correlated with the country of origin were obtained. An in-depth study of the variables used in each model is shown in the present work.Transfer learning has demonstrated its potential in natural language processing tasks, where models have been pre-trained on large corpora and then tuned to specific tasks. We applied pre-trained transfer models to a Spanish biomedical document classification task. The main goal is to analyze the performance of text classification by clinical specialties using state-of-the-art language models for Spanish, and compared them with the results using corresponding models in English and with the most important pre-trained model for the biomedical domain. The outcomes present interesting perspectives on the performance of language models that are pre-trained for a particular domain. In particular, we found that BioBERT achieved better results on Spanish texts translated into English than the general domain model in Spanish and the state-of-the-art multilingual model.Registries of clinical studies such as ClinicalTrials.gov are an important source of information. However, the process of manually entering metadata is prone to errors which impedes their use and thereby the overall usefulness of the registry. In this work, we propose a generic approach towards detection of errors in the metadata by using the Shapes Constraint Language for defining rule templates covering constraints regarding value type and cardinality. click here We developed a Python 3 algorithm for the automatic validation of 15 rule instances applied to the whole ClinicalTrials.gov database (355,862 studies; 27th October 2020) resulting in more than 5 million metadata verifications. Our results show a large number of errors in different metadata fields, such as i) missing values, ii) values not coming from a predefined set or iii) wrong cardinalities, can be detected using this approach. Since 2015 approximately 5% of all studies contain one or more errors. In the future, we will apply this technique to other registries and develop more complex rules by focusing on the semantics of the metadata. This could render the possibility of automatically correcting entries, increasing the value of registries of clinical studies.This paper describes the development and evaluation of a Canadian drug ontology (OCRx), built to provide a normalized and standardized description of drugs that are authorized to be marketed in Canada. OCRx aims to improve the usability and interoperability of drugs terminologies for a non-ambiguous access to drugs information that is available in electronic health record systems. We present the first release of OCRx that is described in Web Ontology Language and aligned to the Identification of Medicinal Product (IDMP) standards. For comparison purposes, OCRx is mapped to RxNorm, its US variant.eMass project aims to digitalize the medical examination procedure of recruitment phase of conscripts in the Hellenic Navy. eMass integrates recruits’ Electronic Health Record (EHR), while allows a pre-screening test, through portable telemedicine equipment. The data will be exploited to assess the individual’s cardiovascular risk through appropriate digital tools and algorithms. The eMass digital platform, will be accessible to health experts involved in the recruitment procedure for further assessment and processing. Recruits’ personal data is stored in the database encrypted using Advanced Encryption Standard (AES). eMass solution contributes to beneficial management and medical data analysis, preventing inessential physical or medical examinations minimizing danger of possible errors and reducing time-consuming processes. Moreover, eMass exploits Electronic Health Record data through a machine-learning based cardiovascular risk assessment tool.

To evaluate the accuracy of the French health administrative database to describe patients’ medication and primary care visits, in the context of a transitional care intervention including an in-hospital medication reconciliation followed by a structured community follow-up by the patient’s general practitioner and pharmacist.

A retrospective cohort study of older persons enrolled in the transitional care intervention between January 1st, 2015 and December 31st, 2018.

Only 46.1% of the community follow-up were timely billed, in the 3 months after the patient discharge. The sensitivity of the health administrative database to identify medications was 90.0%. Its positive predictive value was 50.1%.

This study reveals that the French health administrative database was poorly reliable to identify both community follow-up and chronic medications.

This study reveals that the French health administrative database was poorly reliable to identify both community follow-up and chronic medications.