The text data in this dataset has been collected from the publicly available MIMIC-III (Medical Information Mart for Intensive Care) database. MIMIC-III is an extensive, single-center database containing information about patients admitted to critical care units at a large tertiary care hospital. We extracted patient reports from this database by considering the most crucial attributes that determine drug reactions and side effects. The labeled data has been manually annotated by healthcare professionals to classify each entry as either an ADR (Adverse Drug Reaction) or a non-ADR. Additional Another file contains text data that has not been labeled. It can be used for unsupervised learning, clustering, or as a dataset for future labeling efforts.

ADR provides an authoritative data store for shared administrative, demographic, enrollment, and eligibility information which is managed as a corporate asset. This administrative database system offers mission-critical database support for all VA Medical 21st Century Core applications such as Enrollment Systems, Identity Management System, Community Care Program, Veterans's Choice program, President's Affordable Care Act project, Patient Advocacy Tracking System, Veterans 360, and others.

ADR

## Overview

ADR is a dataset for object detection tasks - it contains ADR Signs annotations for 1,303 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

ACOT-ADR is a comprehensive evaluation dataset designed to assess Adaptive Chain-of-Thought reasoning approaches in Chinese adverse drug reaction (ADR) extraction and standardization.

This repository contains text data and code related to the identification and clustering of Adverse Drug Reactions (ADR) using Sentence-BERT (S-BERT) embeddings and the SS-DBSCAN clustering algorithm. The dataset includes both labeled and unlabeled patient reports extracted from the publicly available MIMIC-III database.

The labeled data has been manually annotated to distinguish between ADR and non-ADR cases. The unlabeled dataset is used for unsupervised clustering experiments, particularly to assess high-dimensional data clustering performance.

New in This Version:- Added Jupyter Notebook: mimic-5k_PCA_tSNE_clustering.ipynb- Included detailed README_ADR_Clustering_Task.txt with step-by-step instructions to reproduce clustering results- Explained how to scale experiments from 1,000 to full dataset size

The Administrative Data Repository (ADR) was established to provide support for the administrative data elements relative to multiple categories of a person entity such as demographic and eligibility information. Although initially focused on the computing needs of the Veterans Health Administration, the ADR is positioned to provide identity management and demographics support for all IT systems within the Department of Veterans Affairs.

*NA = not applicable.

We investigated factors affecting the timing of signal detection by comparing variations in reporting time of known and unknown ADRs after initial drug release in the USA. Data on adverse event reactions (AERs) submitted to U.S. FDA was used. Six ADRs associated with 6 drugs (rosuvastatin, aripiprazole, teriparatide, telithromycin, exenatide, varenicline) were investigated: Changes in the proportional reporting ratio, reporting odds ratio, and information component as indexes of signal detection were followed every 3 months after each drugs release, and the time for detection of signals was investigated. The time for the detection of signal to be detected after drug release in the USA was 2–10 months for known ADRs and 19–44 months for unknown ones. The median lag time for known and unknown ADRs was 99.0–122.5 days and 185.5–306.0 days, respectively. When the FDA released advisory information on rare but potentially serious health risks of an unknown ADR, the time lag to report from the onset of ADRs to the FDA was shorter. This study suggested that one factor affecting signal detection time is whether an ADR was known or unknown at release.

Objective: To analyze the situation and clinical characteristics of adverse drug reactions (ADRs) induced by camrelizumab, and provide references for clinical medication safety. Methods: Adverse drug reaction (ADR) of camrelizumab reported by the hospital and in the medical records from Jan. 2020 to Sep. 2022 were collected. Meanwhile, PubMed, Web of Science, CNKI, Vip and Wanfang databases were searched for ADRs induced by camrelizumab, and collected literatures were further analyzed statistically. Results: A total of 10 cases of ADR were reported in our hospital, including 9 male and 1 female. The patients’ ages ranged from 48 to 87 years, with an average age of (67.7±13.2) years. ADRs of camrelizumab frequently occurred within 120 days after medication. ADRs involved skin and adnexal, digestive system, cardiovascular system and endocrine system, etc. 8 patients got improved or recovered after discontinuation of medication and/or symptomatic support. Totally 65 papers of ADRs induced by camrelizumab were included, involving 72 patients(49 males and 23 females). The patients’ ages ranged from 32 to 87 years. Patients of 61 to 70 years showed the highest incidence (n=24, 33.3%). ADRs of camrelizumab frequently occurred within 90 days after medication. The main ADRs induced by camrelizumab were skin and adnexal lesions (n=27, 37.5%). 59 patients (81.9%) got improved or recovered after discontinuation of medication and/or symptomatic support. After the improvement of ADRs, 2 patients (2.8%) were retreated with camrelizumab and showed the same ADRs as before. There were also 5 patients (6.9%) who did not have ADR after ADRs had improved or recovered. Conclusion: The risk factors associated with ADR should be carefully evaluated in the clinical use of camrelizumab to improve patient medication safety.

The Neonatal Drug ADR Association Dataset, curated from the FDA Adverse Event Reporting System (FAERS), focuses on over 140 neonatal health conditions. It comprises approximately 145130 reported cases, from which 32441 unique instances were extracted after deduplication (Version 1). Each instance documents a suspect drug, its associated adverse drug reaction (ADR), and related metadata including drug outcome, patient gender, age, and weight. While demographic details are included, some instances have missing values due to unreported information by patients or healthcare professionals. This comprehensive and refined dataset offers valuable insights for neonatal pharmacovigilance, supporting research in drug safety, ADR pattern analysis, and treatment outcomes in this vulnerable population.

This dataset presents a comprehensive analysis of adverse drug reactions (ADRs) associated with neonatal drug use, curated from the FDA Adverse Event Reporting System (FAERS). It is based on over 140 clinically relevant neonatal reaction terms, and includes (i)Total Cases, (ii) # of Serious Cases (including deaths), and (iii) # of Death Cases, for each of the 141 neonatal conditions. The dataset is designed to support research in neonatal pharmacovigilance, drug safety assessment, and ADR risk modeling.

Metadata Included in the Dataset:

(i) Neonatal Reaction Summary (Neonatal Reaction Terms_Summary.xlsx) that contains over 140 neonatal reaction terms with corresponding Total Cases, Serious Cases (including deaths), and Death Cases. (ii) Year-wise Reported Cases for Neonatal Conditions (xxx. Neonatal Reaction Term.xlsx) provides yearly distribution of ADR reports for each neonatal reaction term to understand longitudinal trends in ADR frequency. (iii) Total Case Statistics (xxx. Neonatal Reaction Term_Total cases.xlsx) that tabulates Total, Serious, and Death cases to support risk quantification across neonatal conditions. (iv) Detailed Case Listings (xxx. Neonatal Reaction Term_List of cases.xlsx) that includes individual reported case details with 24 attributes per record, notably Suspect Drug(s), Reported Reaction(s), Severity Classification, Patient Demographics (Patient Age, Sex, and Weight), and Outcome Information, and Reason for Use

Unwanted side effects of drugs are a burden on patients and a severe impediment in the development of new drugs. At the same time, adverse drug reactions (ADRs) recorded during clinical trials are an important source of human phenotypic data. It is therefore essential to combine data on drugs, targets and side effects into a more complete picture of the therapeutic mechanism of actions of drugs and the ways in which they cause adverse reactions. To this end, we have created the SIDER (‘Side Effect Resource’, http://sideeffects.embl.de) database of drugs and ADRs. The current release, SIDER 4, contains data on 1430 drugs, 5880 ADRs and 140 064 drug–ADR pairs, which is an increase of 40% compared to the previous version. For more fine-grained analyses, we extracted the frequency with which side effects occur from the package inserts. This information is available for 39% of drug–ADR pairs, 19% of which can be compared to the frequency under placebo treatment. SIDER furthermore contains a data set of drug indications, extracted from the package inserts using Natural Language Processing. These drug indications are used to reduce the rate of false positives by identifying medical terms that do not correspond to ADRs.

Example of a dataset for analyzing the ADR (adr) for the concomitant use of two drugs (d1 and d2) for the listds data.

Includes records such as:rn- agreements to use ADR rn- records of intake and processrn- records of settlement or discontinuance of case rn- parties’ written evaluations of the processrnrnInformal process - Records not associated with another employee dispute, complaint or grievance process.rnrnFormal process - Records generated in response to a referral from another dispute, grievance or complaint process, such as EEO complaints or grievances.

Reported characteristics of the dataset.

AY027/ADR dataset hosted on Hugging Face and contributed by the HF Datasets community

ADR 2 CLONE

## Overview

ADR 2 CLONE is a dataset for object detection tasks - it contains ADR Signs Qpuo annotations for 1,638 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

ADR is a strategy to streamline court processes and encourage alternatives to court. It focuses on a more strengths-based, inclusive and collaborative approach to resolving child protection disputes, and encourages the involvement and support of the family, extended family, and the community, in planning and decision-making for children. The dataset describes the number of referrals in child protection cases that are made to ADR service providers funded by the Ministry of Children and Youth Services, including referrals tracked by Children's Aid Societies. An ADR service provider is a third-party transfer payment agency that is funded by the Ministry of Children and Youth Services to deliver and/or coordinate the delivery of ADR services. The data set is broken down by: * fiscal year * ministry region * children's aid society * total number of new referrals * number of referrals that are carried over from the previous fiscal year * referrals made prior to court involvement * referrals made post court involvement * referrals declined *[ADR]: alternative dispute resolution

The docked protein responsible for the association with the ADR is identified in the first, second, and third columns, using the UniProt name and ID and the corresponding PDB ID, respectively. Columns 4,5, and 6 give data on the statistical significance of the association with the p-value of the association, the associated false discovery rate (q-value), and the corresponding beta coefficient in the median AUC logistic regression model. Column 7 is the PubMed results that confirm the drug-protein or drug-side effect. The number of hits is shown in parentheses. Bold UniProt IDs are off-target proteins (i.e. not intended targets of the 732 drugs we consider).Top-ranked ADR-protein associations derived from models built using the 560×409 docking score matrix.

By 2028, the average daily rate (ADR) was forecasted to reach above *** U.S. dollars in the Gulf Cooperation Council (GCC). The average daily rate in the GCC region was showing signs of recovery post the COVID-19 outbreak.

ADR System Food Sorter Market Outlook

The global ADR System Food Sorter market size was valued at approximately USD 2.5 billion in 2023 and is expected to reach USD 4.8 billion by 2032, growing at a CAGR of 7.1% from 2024 to 2032. The growth of this market is primarily driven by increasing consumer awareness regarding food safety and quality, as well as the rising adoption of automation in the food processing industry.

One of the significant growth factors in the ADR System Food Sorter market is the increasing focus on food safety and quality. Governments and regulatory bodies across the globe are implementing stringent food safety standards and regulations to ensure the quality and safety of food products. This has led food processing companies to adopt advanced sorting technologies, including ADR systems, to comply with these regulations and maintain product quality. Additionally, the growing awareness among consumers regarding the importance of food safety has further fueled the demand for ADR system food sorters.

Another key driver of market growth is the rising adoption of automation in the food processing industry. Food processing companies are increasingly integrating automated systems to enhance operational efficiency, reduce labor costs, and minimize human error. ADR system food sorters offer precise sorting capabilities, high efficiency, and the ability to handle large volumes of food products. These advantages have led to their widespread adoption in the food processing industry, thereby driving market growth.

Technological advancements in sorting technologies have also played a crucial role in the growth of the ADR System Food Sorter market. The development of advanced sorting systems, such as optical sorters, X-ray sorters, and laser sorters, has significantly improved the accuracy and efficiency of sorting processes. These technologies enable the detection and removal of foreign materials, defects, and contaminants from food products, ensuring high-quality output. The continuous innovation in sorting technologies is expected to further boost the market growth in the coming years.

Food Sorting Machines have become an integral part of the food processing industry, revolutionizing the way food products are sorted and ensuring high standards of quality and safety. These machines utilize advanced technologies such as optical sensors, X-rays, and lasers to detect and remove foreign materials, defects, and contaminants from food products. The implementation of Food Sorting Machines not only enhances the efficiency and accuracy of sorting processes but also helps in maintaining compliance with stringent food safety regulations. As consumer demand for safe and high-quality food products continues to rise, the adoption of Food Sorting Machines is expected to grow, driving further advancements in sorting technologies.

From a regional perspective, North America holds a significant share in the ADR System Food Sorter market, driven by the robust food processing industry and stringent food safety regulations in the region. Europe also presents substantial growth opportunities, supported by the increasing demand for high-quality food products and the presence of leading food processing companies. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the expanding food processing industry, rising population, and growing awareness about food safety and quality. Additionally, the increasing investments in infrastructure development and technological advancements in emerging economies like China and India are expected to further propel the market growth in this region.

Product Type Analysis

The ADR System Food Sorter market is segmented into various product types, including Optical Sorters, X-ray Sorters, Laser Sorters, and Others. Each product type offers distinct advantages and is suitable for different applications in the food processing industry. Optical sorters hold a significant share in the market due to their high efficiency and accuracy in detecting and removing defective food products. These sorters utilize advanced imaging technology to identify defects, foreign materials, and contaminants, ensuring high-quality output. The increasing adoption of optical sorters in the food processing industry is driven by their ability to handle large volumes of food products and improve operational efficiency.

X-ray sorters are another key segment in the