Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure

Automated data analysis of unstructured grey literature in health research: A mapping review

The amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead-times of producing high-quality peer-reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or tools for health-related grey literature and soft data, with a focus on (semi)automating horizon scans, health technology assessments (HTA), evidence maps, or other literature reviews. We searched six databases to cover both health- and computer-science literature. After deduplication, 10% of the search results were screened by two reviewers, the remainder was single-screened up to an estimated 95% sensitivity; screening was stopped early after screening an additional 1000 results with no new includes. All full texts were retrieved, screened, and extracted by a single reviewer and 10% were checked in duplicate. We included 84 papers covering automation for health-related social media, internet fora, news, patents, government agencies and charities, or trial registers. From each paper, we extracted data about important functionalities for users of the tool or method; information about the level of support and reliability; and about practical challenges and research gaps. Poor availability of code, data, and usable tools leads to low transparency regarding performance and duplication of work. Financial implications, scalability, integration into downstream workflows, and meaningful evaluations should be carefully planned before starting to develop a tool, given the vast amounts of data and opportunities those tools offer to expedite research.

Extracting adverse drug events from clinical Notes: A systematic review of approaches used

BACKGROUND

An adverse drug event (ADE) is any unfavorable effect that occurs due to the use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text extraction research because it helps with pharmacovigilance and patient medication studies.

OBJECTIVE

From the considerable amount of clinical narrative text, natural language processing (NLP) researchers have developed methods for extracting ADEs and their related attributes. This work presents a systematic review of current methods.

METHODOLOGY

Two biomedical databases have been searched from June 2022 until December 2023 for relevant publications regarding this review, namely the databases PubMed and Medline. Similarly, we searched the multi-disciplinary databases IEEE Xplore, Scopus, ScienceDirect, and the ACL Anthology. We adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement guidelines and recommendations for reporting systematic reviews in conducting this review. Initially, we obtained 5,537 articles from the search results from the various databases between 2015 and 2023. Based on predefined inclusion and exclusion criteria for article selection, 100 publications have undergone full-text review, of which we consider 82 for our analysis.

RESULTS

We determined the general pattern for extracting ADEs from clinical notes, with named entity recognition (NER) and relation extraction (RE) being the dual tasks considered. Researchers that tackled both NER and RE simultaneously have approached ADE extraction as a "pipeline extraction" problem (n = 22), as a "joint task extraction" problem (n = 7), and as a "multi-task learning" problem (n = 6), while others have tackled only NER (n = 27) or RE (n = 20). We further grouped the reviews based on the approaches for data extraction, namely rule-based (n = 8), machine learning (n = 11), deep learning (n = 32), comparison of two or more approaches (n = 11), hybrid (n = 12) and large language models (n = 8). The most used datasets are MADE 1.0, TAC 2017 and n2c2 2018.

CONCLUSION

Extracting ADEs is crucial, especially for pharmacovigilance studies and patient medications. This survey showcases advances in ADE extraction research, approaches, datasets, and state-of-the-art performance in them. Challenges and future research directions are highlighted. We hope this review will guide researchers in gaining background knowledge and developing more innovative ways to address the challenges.

The use of artificial intelligence for automating or semi-automating biomedical literature analyses: a scoping review

OBJECTIVE

Evidence-based medicine (EBM) is a decision-making process based on the conscious and judicious use of the best available scientific evidence. However, the exponential increase in the amount of information currently available likely exceeds the capacity of human-only analysis. In this context, artificial intelligence (AI) and its branches such as machine learning (ML) can be used to facilitate human efforts in analyzing the literature to foster EBM. The present scoping review aimed to examine the use of AI in the automation of biomedical literature survey and analysis with a view to establishing the state-of-the-art and identifying knowledge gaps.

MATERIALS AND METHODS

Comprehensive searches of the main databases were performed for articles published up to June 2022 and studies were selected according to inclusion and exclusion criteria. Data were extracted from the included articles and the findings categorized.

RESULTS

The total number of records retrieved from the databases was 12,145, of which 273 were included in the review. Classification of the studies according to the use of AI in evaluating the biomedical literature revealed three main application groups, namely assembly of scientific evidence (n=127; 47%), mining the biomedical literature (n=112; 41%) and quality analysis (n=34; 12%). Most studies addressed the preparation of systematic reviews, while articles focusing on the development of guidelines and evidence synthesis were the least frequent. The biggest knowledge gap was identified within the quality analysis group, particularly regarding methods and tools that assess the strength of recommendation and consistency of evidence.

CONCLUSION

Our review shows that, despite significant progress in the automation of biomedical literature surveys and analyses in recent years, intense research is needed to fill knowledge gaps on more difficult aspects of ML, deep learning and natural language processing, and to consolidate the use of automation by end-users (biomedical researchers and healthcare professionals).

Impact of digital transformation on the future of medical education and practice

In this article, the author provides synopses of the factors that have finally propelled health-care education and practice to join, at times reluctantly, the overarching digital transformative process that has been swept other industries over the last few decades. The key contributors and driving forces that have energized the entry of health-care education and practices are mentioned. The roles of major universities, large technology companies, and the expanding roles of Artificial Intelligence and Machine Learning are described. The projected future developments are predicted to continue to be substantial, sweeping, and forcing changes that are unprecedented. Thus, academicians and practitioners should be alerted to what the rapidly changing landscape is likely to become and accordingly take steps to manage and preserve their roles or risk be left behind or worse be forced out.

Optimization of Data Mining and Analysis System for Chinese Language Teaching Based on Convolutional Neural Network

Chinese language is also an important way to understand Chinese culture and an important carrier to inherit and carry forward Chinese traditional culture. Chinese language teaching is an important way to inherit and develop Chinese language. Therefore, in the era of big data, data mining and analysis of Chinese language teaching can effectively sum up experience and draw lessons, so as to improve the quality of Chinese language teaching and promote Chinese language culture. Text clustering technology can analyze and process the text information data and divide the text information data with the same characteristics into the same category. Based on big data, combined with convolutional neural network and K-means algorithm, this paper proposes a text clustering method based on convolutional neural network (CNN), constructs a Chinese language teaching data mining analysis system, and optimizes it so that the system can better mine Chinese character data in Chinese language teaching data in depth and comprehensively. The results show that the optimized k-means algorithm needs 683 iterations to achieve the target accuracy. The average K-measure value of the optimized system is 0.770, which is higher than that of the original system. The results also show that K-means algorithm can significantly improve the clustering effect, optimize the data mining analysis system of Chinese language teaching, and deeply mine the Chinese data in Chinese language teaching, so as to improve the quality of Chinese language teaching.

Application of Improved LSTM Algorithm in Macroeconomic Forecasting

From a macro perspective, futures index of agricultural products can reflect the trend of macroeconomy and can also have an early warning effect on the possible crisis and provide a reference for the government's economic forecast and macro control. Therefore, it is necessary to strengthen the research on early warning and prediction of agricultural futures price. For the prediction of futures price, there are two kinds of common models: one is the traditional classic time series model, and the other is the neural network model under the wave of artificial intelligence. This paper selects the 1976 closing data of agricultural futures index from January 10, 2012, to February 27, 2020, and uses the time series differential autoregressive integrated moving average model (ARIMA model) and long short-term memory model (LSTM model) to study this work, respectively, and compares the predicted effects of the two models in some metrics. Based on the predicted results of the two models, a simple trading strategy is established, and the trading effects of the two models are compared. The results show that the LSTM model has obvious advantage over ARIMA time series model in the price index prediction of agricultural futures market.

Compensated Fuzzy Neural Network-Based Music Teaching Ability Assessment Model

College is the main place to carry out music teaching, and it is important to assess the music teaching ability in college effectively. Based on this, this paper firstly analyzes the necessity of music teaching ability assessment and briefly summarizes the application of neural network and deep learning technology in music teaching ability assessment and secondly designs an assessment model based on compensated fuzzy neural network algorithm and analyzes the accuracy of the model, finds out the causes of forming abnormal output by analysing the general dimensional conditions of the algorithm of the model, and proposes corresponding correction. Finally, the reliability and feasibility of the music teaching ability assessment model were experimentally verified by combining with teaching practice. The research results confirm the feasibility of the compensated fuzzy neural network algorithm in music teaching ability assessment, which has important reference significance for improving the quality of music teaching in colleges and universities.

Safety Risk Assessment of Tourism Management System Based on PSO-BP Neural Network

With the development of science and technology, system management is gradually applied to tourism management. How to correctly assess the security risks of the tourism management system has become an important means to maintain passenger information. The security risk index of the travel management system is input into the PSO-BP network as a sample, and the corresponding risk value of the index is used as the network output. The results show that the error results, accuracy (96.53%), training time (216 s), number of iterations (275 times), and convergence speed are all better than traditional BP network. The relative error of PSO-BP network (0.32%) is better than that of BP network, with 300 iterations, and the error is close to 10–5. The average evaluation accuracy of S based on PSO-BP network is 99.72%, and the average time consumed is 2.512 s. It is superior to the evaluation model based on fuzzy set and entropy weight theory and the evaluation model based on gray correlation analysis and radial basis function neural network. In conclusion, the security risk assessment of the tourism management system based on PSO-BP network can effectively assess the security risk of the tourism management system.

Sports Training System Based on Convolutional Neural Networks and Data Mining

In recent years, China's sports industry has achieved good development, but the efficiency of athletes in the training process is difficult to have scientific guarantee. How to use scientific algorithm and data mining technology to accurately guide the sports training process has become a hot spot. Based on this, this paper studies the gait recognition model of sports training based on convolutional neural network algorithm. First, this paper analyzes the research status of gait recognition in the process of training and optimizes and improves the deficiencies in sports training. Then, the convolutional neural network algorithm and data mining technology are optimized and analyzed in the gait recognition model. Finally, the experimental results show that the convolutional neural network algorithm can realize the recognition and model reconstruction of athletes' gait in the training process and can make the optimal strategy according to the gait differences of different athletes in the training process, and the recognition accuracy of athletes' gait can reach more than 97%.

Construction of the Reverse Resource Recovery System of e-Waste Based on DLRNN

The research on the reverse resource network of e-waste at home and abroad is still in its infancy, and most of it is only based on traditional forward logistics. Reverse resources are the process of moving goods from their typical final destination for recycling value or proper disposal. With the intensification of market competition and the strengthening of environmental protection legislation by the government, reverse resources are no longer a neglected corner in the supply chain. The DLRNN model of the e-waste reverse resource recovery system constructed in this paper can provide an important theoretical and empirical basis for the rational utilization of waste electronic products and fully tap the potential value of waste electronic products, which is of great significance to the recycling of natural resources. In this paper, a hybrid network framework DLRNN based on deep learning (DL) and cyclic neural network (RNN) is designed for problem classification. Experimental results show that the classification accuracy of this framework is improved by 2.4% on TREC and 2.5% on MSQC without additional word vector conversion tools.

Exchange Rate Forecasting Based on Deep Learning and NSGA-II Models

Today, the global exchange market has been the world's largest trading market, whose volume could reach nearly 5.345 trillion US dollars, attracting a large number of investors. Based on the perspective of investors and investment institutions, this paper combines theory with practice and creatively puts forward an innovative model of double objective optimization measurement of exchange forecast analysis portfolio. To be more specific, this paper proposes two algorithms to predict the volatility of exchange, which are deep learning and NSGA-II-based dual-objective measurement optimization algorithms for the exchange investment portfolio. Compared with typical traditional exchange rate prediction algorithms, the deep learning model has more accurate results and the NSGA-II-based model further optimizes the selection of investment portfolios and finally gives investors a more reasonable investment portfolio plan. In summary, the proposal of this article can effectively help investors make better investments and decision-making in the exchange market.

3D Virtual Reality Implementation of Tourist Attractions Based on the Deep Belief Neural Network

In today's society, information technology is widely used, and virtual reality technology, as one of the emerging frontier technologies, has entered a stage of rapid development. Virtual reality is the use of computer technology to simulate the real-life environment into a virtual simulation environment, with the help of special equipment to realize the natural interaction between users and technical environment, in which the tourism industry is the most widely used. In order to realize 3D virtual reality of tourist attractions and improve users' immersive experience in the process of interaction, the deep belief neural network is introduced to realize the target recognition and reconstruction in virtual reality. The results show that the algorithm has excellent performance in target recognition and target reconstruction, and deep belief networks improve the accuracy by 0.57% and 0.81% and the accuracy by 0.21% and 2.06%, respectively, compared with the current optimal algorithm in target recognition of 12 and 20 view regular projection images. Compared with the current optimal algorithm, deep belief networks are reduced by 0.2%, 3.7%, and 0.6%, respectively. The accuracy index was increased by 2%, 0.1%, and 0.1%, respectively. The above results show that the proposed algorithm based on the deep belief neural network can realize 3D virtual reality of complex scenes such as tourist attractions according to its excellent performance.

Performance Evaluation of Enterprise Supply Chain Management Based on the Discrete Hopfield Neural Network

In order to make up for the shortcomings of current performance evaluation methods, this paper proposes a new method of enterprise performance evaluation, discusses the construction principle of the evaluation index, and proposes a method of enterprise supply chain overall performance evaluation based on the discrete Hopfield neural network (DHNN) algorithm. Enterprise supply chain (SC) is an important way for enterprises to conduct business with other strategic partners in the market, and the improvement of SC performance is an important way to improve the core competitiveness of enterprises, so it is of great value to study the performance evaluation and index design of the enterprise SC. This method calculates the level value of the overall performance of the SC. This level value is a value between 0 and 1. The higher the value, the higher the overall performance level of the SC. Therefore, when evaluating the overall performance of the SC, appropriate index weights must be selected according to the characteristics of the industry, which helps to objectively evaluate the overall performance of the SC.

Analysis of Sports Performance Prediction Model Based on GA-BP Neural Network Algorithm

There are many factors that affect athletes' sports performance in sports competitions. The traditional sports performance prediction method is difficult to obtain more accurate sports performance prediction results and corresponding data analysis in a short time, which is not conducive for coaches to formulate targeted and scientific training sprint plans for athletes' problems. Therefore, based on GA-BP neural network algorithm, this paper constructs a sports performance prediction model and carries out experiments and analysis. The experimental results show that GA-BP neural network algorithm has a faster convergence speed than BP neural network and can achieve the expected error accuracy in a shorter time, which overcomes the problems of the BP neural network. At the same time, different from the previous models, GA-BP neural network algorithm can get the athlete training model according to the relationship between quality training indicators and special sports training results, which can more intuitively show the advantages and disadvantages of athletes. In the final sports performance prediction results, GA-BP neural network prediction results have higher accuracy, better stability, better prediction effect, and higher application value than BP neural network.

Advertising Click-Through Rate Prediction Based on CNN-LSTM Neural Network

In the era of big data information, how to effectively predict and analyze the click-through rate of information advertising is the key for enterprises in various fields to seek returns. The point rate prediction of advertising is one of the core contents of advertising calculation. The traditional shallow prediction model cannot meet the nonlinear relationship of data processing, and the manual processing of data information extraction method is very resource consuming. To solve the above problems, this paper proposes a CNN-LSTM (convolutional neural network-long short-term memory) convolution hybrid neural network algorithm to predict the click-through rate of advertisements. According to the neural network algorithm, the prediction model is constructed, and the effective features are extracted in the process of model establishment, and the prediction analysis is carried out according to the simplified LSTM neural network time serialization features. CNN convolution neural network is used to train the prediction model. This paper analyzes the characteristics of traditional prediction methods and the corresponding solutions and carries out feature learning and prediction model construction for advertising click-through rate prediction. Then, the unknown behavior of advertising users is judged and predicted. The results show that, compared with the single structure network of traditional prediction model, the prediction effect based on CNN-LSTM neural network algorithm has higher accuracy.

Optimization of Online Education and Teaching Evaluation System Based on GA-BP Neural Network

With the popularization and application of online education in the world, how to evaluate and analyze the classroom teaching effect through scientific methods has become one of the important teaching tasks in colleges. Based on this, this paper studies the application of the GA-BP neural network algorithm. Firstly, it gives a brief overview of the current situation of online education and GA-BP neural network algorithm. Secondly, through the investigation of the online education system in many aspects, it evaluates students' online education classroom teaching quality from five aspects, and this paper proposes a more scientific online education classroom teaching quality evaluation optimization model and finally verifies the reliability of the online education teaching evaluation model through the practice in a university. The results show that the GA-BP neural network-based evaluation optimization model can effectively evaluate the online education in the process of analyzing the quality of online education classroom teaching of most professional students.

Identification of Adverse Drug Event-Related Japanese Articles: Natural Language Processing Analysis

Background

Medical articles covering adverse drug events (ADEs) are systematically reported by pharmaceutical companies for drug safety information purposes. Although policies governing reporting to regulatory bodies vary among countries and regions, all medical article reporting may be categorized as precision or recall based. Recall-based reporting, which is implemented in Japan, requires the reporting of any possible ADE. Therefore, recall-based reporting can introduce numerous false negatives or substantial amounts of noise, a problem that is difficult to address using limited manual labor.

Objective

Our aim was to develop an automated system that could identify ADE-related medical articles, support recall-based reporting, and alleviate manual labor in Japanese pharmaceutical companies.

Methods

Using medical articles as input, our system based on natural language processing applies document-level classification to extract articles containing ADEs (replacing manual labor in the first screening) and sentence-level classification to extract sentences within those articles that imply ADEs (thus supporting experts in the second screening). We used 509 Japanese medical articles annotated by a medical engineer to evaluate the performance of the proposed system.

Results

Document-level classification yielded an F1 of 0.903. Sentence-level classification yielded an F1 of 0.413. These were averages of fivefold cross-validations.

Conclusions

A simple automated system may alleviate the manual labor involved in screening drug safety–related medical articles in pharmaceutical companies. After improving the accuracy of the sentence-level classification by considering a wider context, we intend to apply this system toward real-world postmarketing surveillance.

Discovering the Context of People With Disabilities: Semantic Categorization Test and Environmental Factors Mapping of Word Embeddings from Reddit

BACKGROUND

The World Health Organization's International Classification of Functioning Disability and Health (ICF) conceptualizes disability not solely as a problem that resides in the individual, but as a health experience that occurs in a context. Word embeddings build on the idea that words that occur in similar contexts tend to have similar meanings. In spite of both sharing "context" as a key component, word embeddings have been scarcely applied in disability. In this work, we propose social media (particularly, Reddit) to link them.

OBJECTIVE

The objective of our study is to train a model for generating word associations using a small dataset (a subreddit on disability) able to retrieve meaningful content. This content will be formally validated and applied to the discovery of related terms in the corpus of the disability subreddit that represent the physical, social, and attitudinal environment (as defined by a formal framework like the ICF) of people with disabilities.

METHODS

Reddit data were collected from pushshift.io with the pushshiftr R package as a wrapper. A word2vec model was trained with the wordVectors R package using the disability subreddit comments, and a preliminary validation was performed using a subset of Mikolov analogies. We used Van Overschelde's updated and expanded version of the Battig and Montague norms to perform a semantic categories test. Silhouette coefficients were calculated using cosine distance from the wordVectors R package. For each of the 5 ICF environmental factors (EF), we selected representative subcategories addressing different aspects of daily living (ADLs); then, for each subcategory, we identified specific terms extracted from their formal ICF definition and ran the word2vec model to generate their nearest semantic terms, validating the obtained nearest semantic terms using public evidence. Finally, we applied the model to a specific subcategory of an EF involved in a relevant use case in the field of rehabilitation.

RESULTS

We analyzed 96,314 comments posted between February 2009 and December 2019, by 10,411 Redditors. We trained word2vec and identified more than 30 analogies (eg, breakfast - 8 am + 8 pm = dinner). The semantic categorization test showed promising results over 60 categories; for example, s(A relative)=0.562, s(A sport)=0.475 provided remarkable explanations for low s values. We mapped the representative subcategories of all EF chapters and obtained the closest terms for each, which we confirmed with publications. This allowed immediate access (≤ 2 seconds) to the terms related to ADLs, ranging from apps "to know accessibility before you go" to adapted sports (boccia). For example, for the support and relationships EF subcategory, the closest term discovered by our model was "resilience," recently regarded as a key feature of rehabilitation, not yet having one unified definition. Our model discovered 10 closest terms, which we validated with publications, contributing to the "resilience" definition.

CONCLUSIONS

This study opens up interesting opportunities for the exploration and discovery of the use of a word2vec model that has been trained with a small disability dataset, leading to immediate, accurate, and often unknown (for authors, in many cases) terms related to ADLs within the ICF framework.

Application of Augmented Intelligence for Pharmacovigilance Case Seriousness Determination

INTRODUCTION

Identification of adverse events and determination of their seriousness ensures timely detection of potential patient safety concerns. Adverse event seriousness is a key factor in defining reporting timelines and is often performed manually by pharmacovigilance experts. The dramatic increase in the volume of safety reports necessitates exploration of scalable solutions that also meet reporting timeline requirements.

OBJECTIVE

The aim of this study was to develop an augmented intelligence methodology for automatically identifying adverse event seriousness in spontaneous, solicited, and medical literature safety reports. Deep learning models were evaluated for accuracy and/or the F1 score against a ground truth labeled by pharmacovigilance experts.

METHODS

Using a stratified random sample of safety reports received by Celgene, we developed three neural networks for addressing identification of adverse event seriousness: (1) a binary adverse-event level seriousness classifier; (2) a classifier for determining seriousness categorization at the adverse-event level; and (3) an annotator for identifying seriousness criteria terms to provide supporting evidence at the document level.

RESULTS

The seriousness classifier achieved an accuracy of 83.0% in post-marketing reports, 92.9% in solicited reports, and 86.3% in medical literature reports. F1 scores for seriousness categorization were 77.7 for death, 78.9 for hospitalization, and 75.5 for important medical events. The seriousness annotator achieved an F1 score of 89.9 in solicited reports, and 75.2 in medical literature reports.

CONCLUSIONS

The results of this study indicate that a neural network approach can provide an accurate and scalable solution for potentially augmenting pharmacovigilance practitioner determination of adverse event seriousness in spontaneous, solicited, and medical literature reports.

Adverse Drug Events and Contributing Factors Among Hospitalized Adult Patients at Jimma Medical Center, Southwest Ethiopia: A Prospective Observational Study

Background

Adverse drug events (ADEs) are common complications of clinical care resulting in significant morbidity, mortality, and high clinical expenditure. Population-level estimates of inpatient ADEs are limited in Ethiopia.

Objective

This study aimed to assess the incidence, contributing factors, severity, and preventability of ADEs among hospitalized adult patients at Jimma Medical Center, Ethiopia.

Methods

A prospective observational study design was conducted among hospitalized adult patients at tertiary hospital in Ethiopia. A structured data collection tool was prepared from relevant literatures for data collection. Data were analyzed using statistical software. Logistic regression was performed to identify factors contributing to ADE occurrence. P values < 0.05 were considered statistically significant.

Results

A total of 319 patients were included with follow-up period of 5667 person-days. About 50.5% were women. The mean (SD) age of patients was 43 (17.6) years. One hundred sixteen ADEs were identified with the incidence of 36.4 (95% CI, 30.1-43.6) per 100 admissions and 20.5 (95% CI, 16.9-24.6) per 1000 person-days. Antituberculosis agents (adjusted odds ratio [aOR] = 2.52; 95% CI, 1.06-5.98; P = 0.036), disease of the circulatory system (aOR = 2.67; 95% CI, 1.46-4.89; P = 0.001), disease of the digestive system (aOR = 2.84; 95% CI, 1.45-5.57; P = 0.002), being on medication during admission (aOR = 3.09; 95% CI, 1.77-5.41; P < 0.001), and hospital stay more than 2 weeks (aOR = 3.93; 95% CI, 1.39-11.12; P = 0.010) were independent predictors of ADE occurrence.

Conclusions

One in every 4 patients admitted to the hospital experienced ADEs during their hospital stay. Most ADEs were moderate in severity. About two-thirds of the ADEs identified were deemed probably or definitely preventable. Therefore, it is high time to reinforce large-scale efforts to redesign safer, higher quality health care systems to adequately tackle the problem.

A review of big data and medical research

Universally, the volume of data has increased, with the collection rate doubling every 40 months, since the 1980s. "Big data" is a term that was introduced in the 1990s to include data sets too large to be used with common software. Medicine is a major field predicted to increase the use of big data in 2025. Big data in medicine may be used by commercial, academic, government, and public sectors. It includes biologic, biometric, and electronic health data. Examples of biologic data include biobanks; biometric data may have individual wellness data from devices; electronic health data include the medical record; and other data demographics and images. Big data has also contributed to the changes in the research methodology. Changes in the clinical research paradigm has been fueled by large-scale biological data harvesting (biobanks), which is developed, analyzed, and managed by cheaper computing technology (big data), supported by greater flexibility in study design (real-world data) and the relationships between industry, government regulators, and academics. Cultural changes along with easy access to information via the Internet facilitate ease of participation by more people. Current needs demand quick answers which may be supplied by big data, biobanks, and changes in flexibility in study design. Big data can reveal health patterns, and promises to provide solutions that have previously been out of society's grasp; however, the murkiness of international laws, questions of data ownership, public ignorance, and privacy and security concerns are slowing down the progress that could otherwise be achieved by the use of big data. The goal of this descriptive review is to create awareness of the ramifications for big data and to encourage readers that this trend is positive and will likely lead to better clinical solutions, but, caution must be exercised to reduce harm.

Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med Project

The large-scale use of social media by the population has gained the attention of stakeholders and researchers in various fields. In the domain of pharmacovigilance, this new resource was initially considered as an opportunity to overcome underreporting and monitor the safety of drugs in real time in close connection with patients. Research is still required to overcome technical challenges related to data extraction, annotation, and filtering, and there is not yet a clear consensus concerning the systematic exploration and use of social media in pharmacovigilance. Although the literature has mainly considered signal detection, the potential value of social media to support other pharmacovigilance activities should also be explored. The objective of this paper is to present the main findings and subsequent recommendations from the French research project Vigi4Med, which evaluated the use of social media, mainly web forums, for pharmacovigilance activities. This project included an analysis of the existing literature, which contributed to the recommendations presented herein. The recommendations are categorized into three categories: ethical (related to privacy, confidentiality, and follow-up), qualitative (related to the quality of the information), and quantitative (related to statistical analysis). We argue that the progress in information technology and the societal need to consider patients' experiences should motivate future research on social media surveillance for the reinforcement of classical pharmacovigilance.

deepBioWSD: effective deep neural word sense disambiguation of biomedical text data

OBJECTIVE

In biomedicine, there is a wealth of information hidden in unstructured narratives such as research articles and clinical reports. To exploit these data properly, a word sense disambiguation (WSD) algorithm prevents downstream difficulties in the natural language processing applications pipeline. Supervised WSD algorithms largely outperform un- or semisupervised and knowledge-based methods; however, they train 1 separate classifier for each ambiguous term, necessitating a large number of expert-labeled training data, an unattainable goal in medical informatics. To alleviate this need, a single model that shares statistical strength across all instances and scales well with the vocabulary size is desirable.

MATERIALS AND METHODS

Built on recent advances in deep learning, our deepBioWSD model leverages 1 single bidirectional long short-term memory network that makes sense prediction for any ambiguous term. In the model, first, the Unified Medical Language System sense embeddings will be computed using their text definitions; and then, after initializing the network with these embeddings, it will be trained on all (available) training data collectively. This method also considers a novel technique for automatic collection of training data from PubMed to (pre)train the network in an unsupervised manner.

RESULTS

We use the MSH WSD dataset to compare WSD algorithms, with macro and micro accuracies employed as evaluation metrics. deepBioWSD outperforms existing models in biomedical text WSD by achieving the state-of-the-art performance of 96.82% for macro accuracy.

CONCLUSIONS

Apart from the disambiguation improvement and unsupervised training, deepBioWSD depends on considerably less number of expert-labeled data as it learns the target and the context terms jointly. These merit deepBioWSD to be conveniently deployable in real-time biomedical applications.

Inferring new relations between medical entities using literature curated term co-occurrences

Objectives

Identifying new relations between medical entities, such as drugs, diseases, and side effects, is typically a resource-intensive task, involving experimentation and clinical trials. The increased availability of related data and curated knowledge enables a computational approach to this task, notably by training models to predict likely relations. Such models rely on meaningful representations of the medical entities being studied. We propose a generic features vector representation that leverages co-occurrences of medical terms, linked with PubMed citations.

Materials and Methods

We demonstrate the usefulness of the proposed representation by inferring two types of relations: a drug causes a side effect and a drug treats an indication. To predict these relations and assess their effectiveness, we applied 2 modeling approaches: multi-task modeling using neural networks and single-task modeling based on gradient boosting machines and logistic regression.

Results

These trained models, which predict either side effects or indications, obtained significantly better results than baseline models that use a single direct co-occurrence feature. The results demonstrate the advantage of a comprehensive representation.

Discussion

Selecting the appropriate representation has an immense impact on the predictive performance of machine learning models. Our proposed representation is powerful, as it spans multiple medical domains and can be used to predict a wide range of relation types.

Conclusion

The discovery of new relations between various medical entities can be translated into meaningful insights, for example, related to drug development or disease understanding. Our representation of medical entities can be used to train models that predict such relations, thus accelerating healthcare-related discoveries.

Artificial intelligence in medical education

Artificial intelligence (AI) is a growing phenomenon, and will soon facilitate wide-scale changes in many professions, including medical education. In order for medical educators to be properly prepared for AI, they will need to have at least a fundamental knowledge of AI in relation to learning and teaching, and the extent to which it will impact on medical education. This Guide begins by introducing the broad concepts of AI by using fairly well-known examples to illustrate AI's implications within the context of education. It then considers the impact of AI on medicine and the implications of this impact for educators trying to educate future doctors. Drawing on these strands, it then identifies AI's direct impact on the methodology and content of medical education, in an attempt to prepare medical educators for the changing demands and opportunities that are about to face them because of AI.

An automated pipeline for analyzing medication event reports in clinical settings

BACKGROUND

Medication events in clinical settings are significant threats to patient safety. Analyzing and learning from the medication event reports is an important way to prevent the recurrence of these events. Currently, the analysis of medication event reports is ineffective and requires heavy workloads for clinicians. An automated pipeline is proposed to help clinicians deal with the accumulated reports, extract valuable information and generate feedback from the reports. Thus, the strategy of medication event prevention can be further developed based on the lessons learned.

METHODS

In order to build the automated pipeline, four classic machine learning classifiers (i.e., support vector machine, Naïve Bayes, random forest, and multi-layer perceptron) were compared to identify the event originating stages, event types, and event causes from the medication event reports. The precision, recall and F-1 measure were calculated to assess the performance of the classifiers. Further, a strategy to measure the similarity of medication event reports in our pipeline was established and evaluated by human subjects through a questionnaire.

RESULTS

We developed three classifiers to identify the medication event originating stages, event types and causes, respectively. For the event originating stages, a support vector machine classifier obtains the best performance with an F-1 measure of 0.792. For the event types, a support vector machine classifier exhibits the best performance with an F-1 measure of 0.758. And for the event causes, a random forest classifier reaches an F-1 measure of 0.925. The questionnaire results show that the similarity measurement is consistent with the domain experts in the task of identifying similar reports.

CONCLUSION

We developed and evaluated an automated pipeline that could identify three attributes from the medication event reports and calculate the similarity scores between the reports based on the attributes. The pipeline is expected to improve the efficiency of analyzing the medication event reports and to learn from the reports in a timely manner.

Analyzing Medication Error Reports in Clinical Settings: An Automated Pipeline Approach

Medication error is a severe patient safety event in the United States. Medication error reports collected by Patient Safety Organizations provide an opportunity to analyze and learn from previous errors. However, the current workflow of analyzing the error reports is labor-intensive and time-consuming. To reduce the workloads for clinicians and save time, we developed a pipeline for medication error report pre-analysis by applying automated text classification techniques. The pipeline was proven functional in two tasks, i.e., identifying the error originated stages, error types and error causes from the medication error reports, and calculating the similarity scores between the error reports for re-organization. The proposed pipeline holds promise in helping clinicians understand the nature of medication error in an error report, and better manage the error reports, which could further facilitate the prevention of medication errors in healthcare settings.

MicroShare: Privacy-Preserved Medical Resource Sharing through MicroService Architecture

This paper takes up the problem of medical resource sharing through MicroService architecture without compromising patient privacy. To achieve this goal, we suggest refactoring the legacy EHR systems into autonomous MicroServices communicating by the unified techniques such as RESTFul web service. This lets us handle clinical data queries directly and far more efficiently for both internal and external queries. The novelty of the proposed approach lies in avoiding the data de-identification process often used as a means of preserving patient privacy. The implemented toolkit combines software engineering technologies such as Java EE, RESTful web services, JSON Web Tokens to allow exchanging medical data in an unidentifiable XML and JSON format as well as restricting users to the need-to-know principle. Our technique also inhibits retrospective processing of data such as attacks by an adversary on a medical dataset using advanced computational methods to reveal Protected Health Information (PHI). The approach is validated on an endoscopic reporting application based on openEHR and MST standards. From the usability perspective, the approach can be used to query datasets by clinical researchers, governmental or non-governmental organizations in monitoring health care and medical record services to improve quality of care and treatment.