Detection Of Adverse Drug Reactions Research Articles

Development and Validation of 'Prediction of Adverse Drug Reactions in Older Inpatients (PADROI)' Risk Assessment Tool.

BackgroundAdverse drug reactions (ADR) detection and prediction methods in hospitalized older adults remain imprecise. The identification of the risk factors for ADRs in this group of patients is crucial to develop plausible prediction models.ObjectiveThis study aimed at developing and validating a “Prediction of ADR in Older Inpatients (PADROI)” risk assessment tool in hospitalized older adults.Methods and MaterialsWe had previously conducted a derivational study that aimed to determine the risk factors of ADRs in hospitalized older adults. We developed the PADROI model as a potential ADR risk assessment tool incorporating 8 predictors each given a score by rounding off the respective adjusted odds ratios (AORs) to the nearest whole number. Subsequently, we conducted another prospective cohort among adults aged 60 years and older admitted to Gynecology and Obstetrics, Medical, Oncology, Surgery, and Psychiatry wards at Mbarara Regional Referral Hospital (MRRH) from July 5 to September 17, 2021.ResultsA total of 124 participants, 70 females and 54 males aged 60–95 years, were included in this validation cohort; 62 of them experienced 90 ADRs. When applied to the derivational cohort, the area under receiver operating characteristic curve (AUROC) for the PADROI model was shown to be 0.896 (0.869–0.923; at 95% CI). In the validation study, AUROC of PADROI was 0.917 (0.864–0.971 at 95% CI; p < 0.001). Overall, PADROI correctly predicted 91.7% of those who experienced an ADR.ConclusionUsing the adjusted odds ratios from our derivational cohort, we developed an ADR prediction tool (PADROI) that achieved an excellent AUROC (0.917), high sensitivity (87.1%) and specificity (90.3%). The current model demonstrated a high potential for clinical applicability which can be strengthened if similar results are reproduced in larger and multi-centered studies.

High Burden of Adverse Drug Reactions to Isoniazid Preventive Therapy in People Living With HIV at 3 Tertiary Hospitals in Uganda: Associated Factors.

HIV is one of the most important risk factors of tuberculosis (TB)-related morbidity and mortality. Isoniazid preventive therapy (IPT) is recommended to prevent latent TB reactivation in patients with HIV. However, due to multiple therapies and comorbidities, these patients are predisposed to adverse drug reactions (ADRs) that lead to increased morbidity and mortality. The aim of this study was to determine the prevalence and associated factors of suspected IPT-linked ADRs in HIV-positive patients using IPT. A cross-sectional study was conducted between February and March 2020 at 3 regional referral hospitals (RRHs) in central Uganda. We sampled 660 HIV-positive patients aged 10 years or older who received IPT between July and December 2019 inclusive. Patients were interviewed using a pretested structured questionnaire, and their treatment records were reviewed. A modified Poisson regression model with clustered robust standard errors was used to identify factors associated with suspected IPT-linked ADRs. The prevalence of the suspected ADRs was 51% (334 of the 660; 95% confidence interval [CI]: 18% to 83%). Patients self-reported 7-fold the number of suspected ADRs documented in the clinical files by the health care workers. Musculoskeletal symptoms were the most frequently experienced reaction (14%), followed by dizziness (13%) and peripheral neuropathy (11%). Serious suspected ADRs were experienced by 12% of the study participants; the most common were hepatotoxicity (26%), dizziness (23%), and neuropathy (17%). Female sex (aPR [adjusted prevalence ratio]: 0.92, 95% CI: = 0.88 to 0.95), study site (aPR: 1.09, 95% CI: = 1.09 to 1.18), level of education (aPR: 0.94, 95% CI: = 0.94 to 0.99), history of TB (aPR: 0.93, 95% CI: = 0.87 to 0.99), good IPT adherence (aPR: 1.16, 95% CI: = 1.05 to 1.29), and use of protease inhibitor (PI)-based antiretroviral therapy (aPR: 1.01, 95% CI: = 1.00 to 1.02) were significantly associated with suspected IPT-linked ADRs. The prevalence of suspected IPT-linked ADRs is high, and hepatotoxicity is the most commonly reported serious suspected ADR. Patients self-reported more suspected ADRs than those documented in clinical files by health care workers. Patient engagement could improve ADR detection and potentially strengthen the pharmacovigilance system. Patients with a high risk of ADR ought to be monitored regularly to enable early detection and management.

Review on tuberculosis

Tuberculosis has been one of the contagious diseases in human communities during the past 50 years. World Health Organization (WHO) has been reported that one third of the world’s population is infected with Mycobacterium tuberculosis this high occurrence of infection has caused a large number of morbidity and mortality. Which is partly due to serious adverse reactions induced by Anti-TB drugs. Mycobacterium tuberculosis normally affects the lungs along with other parts of the body examples: lymph nodes, spin, cervix and kidney etc. India is the highest TB burden country accounting more than one third of the global occurrences. There are many adverse drug reactions (ADR's) are developed during the treatment of Tuberculosis with anti-tubercular drugs. The detection of adverse drug reactions (ADR's) has become increasingly significant because of introduction of a large number potent toxic chemicals. Pharmacovigilance is an arm of patient care and surveillance ADR'S effect both the children and adults with carrying the magnitudes causing morbidity and mortality. A good pharmacovigilance study will identify the risks within the shortest possible time after the medicine has been marketed and will help to establish or identify the risk factors. The current prospective observational study of six months duration was designed to assess the rate of adverse drug reactions (ADRs) occurrence by anti-tubercular drugs in patients admitted in the department of Chest &amp; Tuberculosis from whom a proper consent obtained. The results of the study showed that the incidence of all major adverse effects was 1.08 per 50 person’s month of exposure. The occurrence of any major side effect in the study was associated with female sex and increase in age. In our study the major cause of admission was adverse drug reactions 47(47%).In our study Hepatitis was observed in 11 (11.56%) patients, leading to the death of 3 patients. In conclusion, Anti-TB drugs that used in tubercular treatment were result in significant adverse effects both in quantity and severity. These reactions may lead to hospitalization, and even death. To confirm this hypothesis many more studies with large population is needed.

SCAN: A shared causal attention network for adverse drug reactions detection in tweets

Twitter is a popular social media site on which people post millions of Tweets every day. As patients often share their experiences with drugs on Twitter, Tweets can also be considered as a rich alternative source of adverse drug reaction (ADR)-related information. This information can be useful for health authorities and drug manufacturing companies to monitor the post-marketing effectiveness of drugs. However, the automatic detection of ADRs in Tweets is challenging, as Tweets are informal and prone to grammatical errors. The existing approaches to automatically detecting ADRs do not consider the cause-effect relationships between a drug and an ADR. In this paper, we propose a novel shared causal attention network that exploits such cause-effect relationships to detect ADRs in Tweets. In our approach, we split a Tweet into the prefix, midfix, and postfix segments based on the position of the drug name in the Tweet and separately extract causal features from the segments. We then share these separate causal features with both word and parts-of-speech features, and apply the multi-head self-attention mechanism. We run extensive experiments on three publicly available benchmark datasets to illustrate the effectiveness of the proposed approach.

Design and Implementation of a Computerized Management System for Adverse Drug Reactions at the Sourô SANOU University Hospital (CHUSS) in Bobo-Dioulasso, Burkina Faso.

Pharmacovigilance is the science and activities related to the detection, evaluation, understanding and prevention of adverse drug reactions or any other possible drug-related problems. In our tropics, this discipline is in an embryonic state. The availability of a management system capable of responding to pharmacovigilance activities is the main objective of our study. The coding was done on the DJANGO Framework. Signal detection was done using the ROR method. We designed three modules which are the notification module, the analysis module and the statistics module. This study has allowed us to launch the basis for a computerization of the pharmacovigilance information system and partly meets our objective. However, it could lead to the integration of the dictionary of adverse effects such as MedDRA as well as the International Classification of Medicines (ATC, EphMRA).

Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs.

Epilepsy is a common neurological disorder worldwide and antiepileptic drug (AED) therapy is the cornerstone of its treatment. It has a laudable aim of achieving seizure freedom with minimal, if any, adverse drug reactions (ADRs). Too often, AED treatment is a long-lasting journey, in which ADRs have a crucial role in its administration. Therefore, from a pharmacovigilance perspective, detecting the ADRs of AEDs is a task of utmost importance. Typically, this task is accomplished by analyzing relevant data from spontaneous reporting systems. Despite their wide adoption for pharmacovigilance activities, the passiveness and high underreporting ratio associated with spontaneous reporting systems have encouraged the consideration of other data sources such as electronic health databases and pharmaceutical databases. Social media is the most recent alternative data source with many promising potentials to overcome the shortcomings of traditional data sources. Although in the literature some attempts have investigated the validity and utility of social media for ADR detection of different groups of drugs, none of them was dedicated to the ADRs of AEDs. Hence, this paper presents a novel investigation of the validity and utility of social media as an alternative data source for the detection of AED ADRs. To this end, a dataset of consumer reviews from two online health communities has been collected. The dataset is preprocessed; the unigram, bigram, and trigram are generated; and the ADRs of each AED are extracted with the aid of consumer health vocabulary and ADR lexicon. Three widely used measures, namely, proportional reporting ratio, reporting odds ratio, and information component, are used to measure the association between each ADR and AED. The resulting list of signaled ADRs for each AED is validated against a widely used ADR database, called Side Effect Resource, in terms of the precision of ADR detection. The validation results indicate the validity of online health community data for the detection of AED ADRs. Furthermore, the lists of signaled AED ADRs are analyzed to answer questions related to the common ADRs of AEDs and the similarities between AEDs in terms of their signaled ADRs. The consistency of the drawn answers with the existing pharmaceutical knowledge suggests the utility of the data from online health communities for AED-related knowledge discovery tasks.

Suspected Adverse Drug Reaction Detection using Association Rules Mining and Fuzzy sets

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

A Comprehensive Review and Application of Interpretable Deep Learning Model for ADR Prediction

Drug safety is a pressing need in today's healthcare. Minimizing drug toxicity and improving the individual’s health and society is the key objective of the healthcare domain. Drugs are clinically tested in laboratories before marketing as medicines. However, the unintended and harmful effects of drugs are called Adverse Drug Reactions (ADRs). The impact of ADRs can range from mild discomfort to more severe health hazards leading to hospitalization and in some cases death. Therefore, the objective of this research paper is to design a framework based on which research papers are collected from both ADR detection and prediction domain. Around 172 research articles are collected from the sites like ResearchGate, PubMed, etc. After applying the elimination criteria the author categorized them into ADR detection and prediction themes. Further, common data sources and algorithms as well as the evaluation metrics were analyzed and their contribution to their respective domains is stated in terms of percentages. A deep learning framework is also designed and implemented based on the research gaps identified in the existing ADR detection and prediction models. The performance of the deep learning model with two hidden layers was found to be optimum for ADR prediction and further, the non-interpretability part of the model is addressed using a global surrogate model. The proposed architecture has successfully addressed multiple limitations of existing models and also highlights the importance of early detection & prediction of adverse drug reactions in the healthcare industry.

An evaluation of trigger tool method for adverse drug reaction monitoring at a tertiary care teaching hospital.

OBJECTIVE:Trigger tool method (TTM) is an active surveillance method for adverse drug reaction (ADR) monitoring. The study aimed to evaluate TTM for ADR monitoring in indoor patients of the surgery department.MATERIALS AND METHODS:This prospective, observational study was conducted at the Department of Surgery of a Tertiary Care Teaching Hospital in Gujarat. Patients of either gender and more than 18 years of age admitted to two selected surgery units were enrolled with prior informed consent. Preliminary trigger tool list (PTTL) comprising 13 drug triggers (DTs), 13 patient triggers (PTs), 9 laboratory triggers (LTs), and 12 surgical module triggers (STs) were used. Patients were followed up till discharge to monitor the occurrence of triggers and adverse events.RESULTS:A total of 400 patients were included (male: female ratio of 2.3:1; mean age: 43.07 ± 16.4 years; and mean length of hospital stay: 5.75 ± 3.12 days). Of 400 patients, triggers were present in 359 patients (89.75%) and no trigger was observed in 41 patients (10.25%). Of the 47 triggers in PTTL, 24 triggers were observed 1155 times, of these 14 triggers lead to the detection of 49 ADRs in 43 patients. The rate of adverse drug events was 12.25/100 patients. DT was the most common trigger identified (81.64%). Positive predictive values (PPV) for PTs, STs, DTs, LTs were 26.88%, 23.07%, 10.3%, and 5.55%, respectively. The comprehensive PPV of PTTL was 11.97%. Modified trigger tool list consists of 14 triggers.CONCLUSION:TTM is an effective method of ADR monitoring in the surgery department. An awareness of TT helps better detection of ADRs.

Detection of adverse drug reactions from online health communities’ data: a case study of anti-epileptic drugs

Detection of adverse drug reactions from online health communities’ data: a case study of anti-epileptic drugs

Signal Detection of Unknown Adverse Drug Reactions of Biologic DMARDs Using Real-World Data from the Korean College of Rheumatology BIOlogics &amp;amp; Targeted Therapy Registry (KOBIO)

Signal Detection of Unknown Adverse Drug Reactions of Biologic DMARDs Using Real-World Data from the Korean College of Rheumatology BIOlogics &amp;amp; Targeted Therapy Registry (KOBIO)

The Importance of Direct Patient Reporting of Adverse Drug Reactions in the Safety Monitoring Process.

All medicinal products authorized in the European Union are subjects of constant drug-safety monitoring processes. It is organized in a pharmacovigilance system that is designed to protect human health and life by the detection, analysis and prevention of adverse drug reactions (ADRs) and other drug-related problems. The main role of the aforementioned system is to collect and analyze adverse drug reaction reports. Legislation introduced several years ago allowed patients, their legal representatives and caregivers to report adverse drug reactions, which caused them to be an additional source of safety data. This paper presents the analysis of EudraVigilance data related to adverse drug reactions provided by patients, their representatives, as well as those obtained from healthcare professionals related to medicines which belong to M01A anti-inflammatory and antirheumatic products, a non-steroid group. The objective of the study was to identify the changes in the number and structure of adverse reaction reporting after the introduction of pharmacovigilance (PV) obligations in EU. A review of scientific literature was also conducted to assess the differences in adverse reactions reported by patients or their representatives and by healthcare professionals. We also identified other factors which, according to literature review, influenced the number of adverse reaction reports provided by patients. Analysis of data collected from the EudraVigilance showed that from 2011 to 2013 the number of reports made by patients and their caregivers increased by approx. 24 percentage points, and then, from 2014, it constituted around 30% of the total of reported reactions every year, so patient reporting is an important part of pharmacovigilance system and a source of drugs’ safety information throughout their use in healthcare practice. Additionally, there was no interrelationship between the seriousness of reported adverse reactions and the overall number of patient reports when compared to reports form healthcare professionals.

Adverse Drug Reaction Monitoring of Antidepressant Drugs in a Mental Health Institute in Odisha

Introduction: Antidepressants are used primarily in the management of depressive and anxiety disorders. The occurrence of adverse drug reactions (ADRs) to antidepressants is a major challenge as it influences patient compliance. Aim: The aim of this study was to find out the ADR profile of antidepressant drugs in a mental health institute in Odisha. Materials and Methods: This is a cross sectional observational study conducted in Department of Pharmacology in collaboration with Mental Health Institute (Centre of Excellence) S.C.B Medical College and Hospital, Cuttack from September 2017 to September 2019. Patients who received at least one antidepressant drug were included in the study irrespective of age and sex. Data were collected by interviewing the patients or attendants and on detection of ADR, it was recorded on suspected ADR reporting form designed by PvPI. Causality, severity and preventability of ADRs were assessed by, WHO-UMC causality assessment, modified Hartwig-Siegel Scale and modified Schumock-Thornton criteria respectively. Results: Out of 180 patients taking antidepressants, ADRs were reported in 24% of patients, with either possible or probable causality. None were labelled as certain. ADRs were observed in 50% of patients who received TCAs and among 34.5% who received polytherapy. Insomnia (27%), fatigue (17%) and agitation (13%) were most common ADRs. Most of the ADRs were of mild severity (91%) and not preventable (84%). Conclusion: Insomnia, fatigue and agitation were among most common ADRs. There was increased chance of ADRs with polytherapy and use of TCAs. Most ADRs were mild and not preventable.

The Impact of Mandatory Reporting of Non-Serious Safety Reports to EudraVigilance on the Detection of Adverse Reactions

Introduction and ObjectiveEuropean Union legislation has mandated the submission of European Economic Area non-serious reports to the EudraVigilance database since November 2017. As spontaneous reports of suspected adverse reactions to medicines represent a key source of safety signals, the European Medicines Agency has undertaken this work to assess the effects of this requirement on the characteristics of the reports submitted to EudraVigilance and on the detection of adverse drug reactions through routine analyses of the database.MethodsChanges in the numbers of serious and non-serious reports transmitted to EudraVigilance were examined over the period during which the legislation was implemented. The numbers and nature of potential safety signals emerging from established statistical algorithms used at the European Medicines Agency applied either to only the serious reports or to all reports in EudraVigilance were compared.ResultsUp to November 2017, less than 25% of European Economic Area reports in EudraVigilance were classified as non-serious, since than this figure was slightly above 60%. This change accompanied an increase in the total number of reports received. Addition of non-serious reports to the signal detection process resulted in a small overall increase in signals of disproportionate reporting with some new signals of disproportionate reporting appearing and some existing signals of disproportionate reporting disappearing; the sensitivity of the signal detection system was slightly increased and the proportion of signals of disproportionate reporting that corresponded to known adverse drug reactions (a measure of efficiency) was unchanged.ConclusionsThe change in legislation has led to a small increase in sensitivity, without affecting the efficiency of the routine statistical measures used. The number of non-serious reports as a proportion of reports in EudraVigilance is likely to increase over time and further monitoring of the impact on signal detection is required. Further work is also required on the qualitative impact of non-serious reports on the nature of signals detected and on their evaluation.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40264-021-01137-0.

Decision Tree-Based Data Stratification Method for the Minimization of the Masking Effect in Adverse Drug Reaction Signal Detection

Data masking is an inborn defect of measures of disproportionality in adverse drug reactions signal detection. Some improved methods which used gender and age for data stratification only considered the patient-related confounding factors, ignoring the drug-related influencing factors. Due to a large number of reports and the high proportion of antibiotics in the Chinese spontaneous reporting database, this paper proposes a decision tree-stratification method for the minimization of the masking effect by integrating the relevant factors of patients and drugs. The adverse drug reaction monitoring reports of Jiangsu Province in China from 2011 to 2018 were selected for this study. First, the age division interval was determined based on the statistical analysis of antibiotic-related data. Secondly, correlation analysis was conducted based on the patient’s gender and age respectively with the drug category attributes. Thirdly, the decision tree based on age and gender was constructed by the J48 algorithm, which was used to determine if drugs belonged to antibiotics as a classification label. Fourthly, some performance evaluation indicators were constructed based on the data of drug package inserts as a standard signal library: recall, precision, and F (the arithmetic harmonic mean of recall and precision). Finally, four experiments were carried out by means of the proportional reporting ratio method: non-stratification (total data), gender-stratification, age-stratification and decision tree-stratification, and the performance of the signal detection results was compared. The experimental results showed that the decision tree-stratification was superior to the other three methods. Therefore, the data-masking effect can be further minimized by comprehensively considering the patient and drug-related confounding factors.

Adverse effects and duration of treatment of TB in Canterbury, New Zealand.

BACKGROUND Treatment of TB is often extended beyond the recommended duration. The aim of this study was to assess prevalence of extended treatment and to identify associated risk factors. We also aimed to determine the frequency and type of adverse drug reactions (ADR) experienced by this study population.METHODS We performed a retrospective cohort study of all patients treated for active TB at Christchurch Hospital, Christchurch, New Zealand, between 1 March 2012 and 31 December 2018. Data for 192 patients were collected on patient demographics, disease characteristics and treatment characteristics, including planned and actual duration of treatment and ADRs.RESULTS Of 192 patients, 35 (18.2%) had treatment extended, and 85 (46.5%) of 183 with fully drug-susceptible TB received ≥9 months treatment. The most common reasons for extension were persistent or extensive disease and ADR. Extended treatment duration was not associated with any patient or disease characteristics. We found 35 (18.2%) patients experienced at least one ADR. The most common ADRs were hepatitis, rash and peripheral neuropathy.CONCLUSION TB treatment extension beyond WHO guidelines is common. Further research is needed to guide management of those with slow response to treatment. Methods for early detection of ADR, systems to improve adherence and therapeutic drug monitoring are potentially useful strategies.

Pharmacovigilance in Pakistan: A Peek into the Future Prospects

Pharmacovigilance in Pakistan: A Peek into the Future Prospects

Privacy-Preserving Anonymity for Periodical Releases of Spontaneous Adverse Drug Event Reporting Data: Algorithm Development and Validation.

BackgroundSpontaneous reporting systems (SRSs) have been increasingly established to collect adverse drug events for fostering adverse drug reaction (ADR) detection and analysis research. SRS data contain personal information, and so their publication requires data anonymization to prevent the disclosure of individuals’ privacy. We have previously proposed a privacy model called MS(k, θ*)-bounding and the associated MS-Anonymization algorithm to fulfill the anonymization of SRS data. In the real world, the SRS data usually are released periodically (eg, FDA Adverse Event Reporting System [FAERS]) to accommodate newly collected adverse drug events. Different anonymized releases of SRS data available to the attacker may thwart our single-release-focus method, that is, MS(k, θ*)-bounding.ObjectiveWe investigate the privacy threat caused by periodical releases of SRS data and propose anonymization methods to prevent the disclosure of personal privacy information while maintaining the utility of published data.MethodsWe identify potential attacks on periodical releases of SRS data, namely, BFL-attacks, mainly caused by follow-up cases. We present a new privacy model called PPMS(k, θ*)-bounding, and propose the associated PPMS-Anonymization algorithm and 2 improvements: PPMS+-Anonymization and PPMS++-Anonymization. Empirical evaluations were performed using 32 selected FAERS quarter data sets from 2004Q1 to 2011Q4. The performance of the proposed versions of PPMS-Anonymization was inspected against MS-Anonymization from some aspects, including data distortion, measured by normalized information loss; privacy risk of anonymized data, measured by dangerous identity ratio and dangerous sensitivity ratio; and data utility, measured by the bias of signal counting and strength (proportional reporting ratio).ResultsThe best version of PPMS-Anonymization, PPMS++-Anonymization, achieves nearly the same quality as MS-Anonymization in both privacy protection and data utility. Overall, PPMS++-Anonymization ensures zero privacy risk on record and attribute linkage, and exhibits 51%-78% and 59%-82% improvements on information loss over PPMS+-Anonymization and PPMS-Anonymization, respectively, and significantly reduces the bias of ADR signal.ConclusionsThe proposed PPMS(k, θ*)-bounding model and PPMS-Anonymization algorithm are effective in anonymizing SRS data sets in the periodical data publishing scenario, preventing the series of releases from disclosing personal sensitive information caused by BFL-attacks while maintaining the data utility for ADR signal detection.

Supporting Personalized Health Care With Social Media Analytics: An Application to Hypothyroidism

Social media analytics can considerably contribute to understanding health conditions beyond clinical practice, by capturing patients’ discussions and feelings about their quality of life in relation to disease treatments. In this article, we propose a methodology to support a detailed analysis of the therapeutic experience in patients affected by a specific disease, as it emerges from health forums. As a use case to test the proposed methodology, we analyze the experience of patients affected by hypothyroidism and their reactions to standard therapies. Our approach is based on a data extraction and filtering pipeline, a novel topic detection model named Generative Text Compression with Agglomerative Clustering Summarization ( GTCACS ), and an in-depth data analytic process. We advance the state of the art on automated detection of adverse drug reactions ( ADRs ) since, rather than simply detecting and classifying positive or negative reactions to a therapy, we are capable of providing a fine characterization of patients along different dimensions, such as co-morbidities, symptoms, and emotional states.

Adversarial neural network with sentiment-aware attention for detecting adverse drug reactions

Adverse drug reaction (ADR) detection is an important issue in drug safety. ADRs are health threats caused by medication. Identifying ADRs in a timely manner can reduce harm to patients and can also assist doctors in the rational use of drugs. Many studies have investigated potential ADRs based on social media due to the openness and timeliness of this resource; however, they have ignored the fine-grained emotional expression in social media text. In addition, the benchmark datasets from social media are usually small, which can result in the problem of over-fitting. In this paper, we propose the Adversarial Neural Network with Sentiment-aware Attention (ANNSA) model, which enhances the sentimental element in social media and improves the performance of neural networks via data augmentation. Specifically, a sentiment-aware attention mechanism is proposed to extract the word-level sentiment features associated with sentiment words and learn task-related information by optimizing a task-specific loss. For low-resource datasets, we use an adversarial training approach to generate perturbations of the word embeddings via an implicit regularization technique. ANNSA was tested on three social media ADR detection datasets, namely, Twitter, TwiMed (Twitter) and CADEC. The experimental results indicated the ability to achieve F1 values of 48.84%, 64.18% and 83.06%, respectively, comparable to the best results reported for state-of-the-art methods. Our study demonstrates that sentiment words are highly correlated with ADRs and that word-level sentiment features can assist in detecting ADRs from social media datasets.