dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction In recent years, there has been increasing interest from regulatory agencies and scientific organisations into the recording, coding and reporting of medication errors. Accuracy and consistency in the handling of medication error reports ensure the safety and effectiveness of medicines and provide reliable information to both healthcare professionals and patients. Objective The authors have examined a sample of Medical Dictionary for Regulatory Activities (MedDRA ® ) coded reports that describe medication errors to assess the accuracy and consistency of MedDRA ® coding, and to identify the main types of coding errors for the newly introduced COVID-19 vaccines. Methods The sample of coded terms was assessed by two MedDRA ® experts applying the Four Eyes Principle. It included 1500 reported terms drawn from the Uppsala Monitoring Centre database reported up to 25 August, 2021, describing medication errors for COVID-19 vaccines with their assigned MedDRA ® terms.
Results One third of the records could not be assessed because of incomplete or unclear verbatims. In one third, MedDRA ® term assignments were correct, but another third of the sample was not adequately coded. The most frequent coding errors corresponded to vague MedDRA ® Preferred Term assignments despite more detailed information being available in the verbatim for a more precise coding. This observation is similar to findings in the EudraVigilance database, where some of the most frequently assigned MedDRA ® terms for medication errors also represent vague concepts.
Key Points Precise, accurate and consistent MedDRA ® coding in pharmacovigilance databases is a crucial prerequisite for a reliable and reproducible safety data analysis to identify medication errors as one of the main causes of preventable harm to patients' health, with the aim of preventing such incidents. A quality review of coding within cases submitted to the Uppsala Monitoring Centre database for reporting medication errors with COVID-19 vaccines revealed substantial quality deficiencies in the extraction of medication error data from the Individual Case Safety Reports and their code assignments for a large proportion of the reported incidents. Raising awareness of the deficiencies in documentation and coding of medication errors will help to establish a reliable basis for the assessment of such errors, to ensure adequate drug and vaccine use in line with valid labelling requirements.
Methods The chosen sample for this investigation corresponded to a data extract of coded data for adverse events representing medication errors reported for COVID-19 vaccines drawn from the Uppsala Monitoring Centre (UMC) VigiBase ®. VigiBase ® is the WHO's global database of Individual Case Safety Reports (ICSRs) from national pharmacovigilance centres in the countries participating in the WHO Program for International Drug Monitoring. Thereby, it provides a comprehensive overview of global reporting and coding practices. According to the WHO and UMC policy and guidelines, reports sent from the Program for International Drug Monitoring member countries to VigiBase ® are anonymised and do not allow patient identification. To further ensure anonymisation of the data, the dataset did not contain information on the countries of origin of the reported incidents nor case narratives.
EudraVigilance is a centralised European database for reporting and evaluating suspected adverse reactions of medicines, maintained by the European Medicines Agency on behalf of the regulatory network in the European Union. The increased awareness of the topic of medication errors since 2005 is reflected by an increase of case reports of medication errors in EudraVigilance  [6]. Medication error safety concerns were listed in the risk management plan for almost a quarter of centrally authorised products approved in the European Union in the years 2010-17  [7]. In addition, the owner of the Medical Dictionary for Regulatory Activities (MedDRA ® ), i. e. the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use, provides detailed guidance for MedDRA ® coding purposes, including medication errors and other product use errors and issues  [8, 9].
Results The results of the review of the 1500 data sets for medication errors with COVID-19 vaccines extracted from UMC database are summarised below. A total of 518 verbatims (34. 5%) could not be further evaluated because of category 1 errors described in the table above. Clarifications of these errors were not possible in the absence of access to the ICSRs and narratives. Among the category 1 errors, in 219 cases, the free text zone describing the medication error was not available. When available, the text was mostly limited to a brief description that in the remaining 299 cases would have required further clarification from the reporter. The remaining 982 verbatims (65%) were evaluated for coding accuracy. Of these 982 verbatims, 499 (50. 8%) have not been adequately coded: • In 270 cases (27.
Up to this version, many additions have already been made to the terminology to reflect clinical practice. MedDRA ® users need to be familiar with the granularity of MedDRA ® terms that can be used for the coding of medication errors and issues to avoid resorting to simpler solutions such as assigning non-specific terms. The remaining third of records were coded correctly. The selection of vague terminology terms can result in a misleading data output that compromises signal detection, understanding of trends and data analysis  [8, 18]. Such errors may ultimately prevent the frequency of different types of medication errors from being understood as a basis for remedial action. A similar analysis on COVID-19 vaccine medication error data has not been retrieved from bibliographic searches. However, as the UMC database draws data from multiple sources, the high percentage of inadequately coded terms can be considered a reflection of overall coding practices.
This assertion is supported by the fact that the authors' observations are comparable to findings in the EudraVigilance database. Looking at the ten most frequently assigned PTs from HLGT Medication errors and other product use errors and issues for reported medication errors and issues (the same HLGT on which the study presented here is based), these incidents are frequently coded to very general and vague MedDRA ® PTs such as: PT Medication error, PT Product use issue, PT Product administration error. The PT Product use in unapproved indication and PT Product dose omission issue are also vague and do not allow differentiation between medication error/issue, medicinal product misuse and off-label use (Table  2 ). The consistent findings in the two datasets indicate that there is a need to improve the understanding of the documentation and assessment of medication errors as well as of MedDRA ® content and related coding guidelines.
Background: Clinical pharmacists have a pivotal role in the management of the patient's medication. However, it is necessary to know how pharmacistmediated deprescribing could be implemented in a hospital setting according to hospital physicians. Objective: To explore physicians' views on the involvement of hospital pharmacists in the deprescribing process using the example of PPIs. Methods: A qualitative study using two focus groups with hospital physicians was conducted to determine their attitudes regarding deprescribing initiated by the hospital pharmacist. The interviews were recorded and transcribed using the NVivo analysis software. A thematic analysis led to a categorization of all the verbatims. Results: Hospital doctors are reluctant to deprescribe drugs initiated by a colleague and feel that it is the responsibility of the general practitioner (GP), who fails to do so due to lack of time.
| Data analyses The interviews were transcribed using the qualitative analysis software NVivo ® (V. 12. 7. 0) immediately after each session and were then completely anonymized. A phenomenological design was used to explore the perceptions. The qualitative analysis was carried out by the first, third and last authors using the six-step thematic analysis by Braun and Clarke. 27 Briefly, it consisted of categorization and thematization of all the verbatims from each group through systematic inductive coding. The results of the two groups were analysed independently. The verbatims were categorized into sub-themes and then into themes. The themes from each of the two groups were then pooled and rearranged to ensure coherence, and quotes were extracted to better illustrate the themes. 28.
However, obtaining the labels of failures is not so straightforward and, in certain cases, can introduce significant noise. In fact, failures are not directly stored as a statistic, but instead, one can record parts that have been replaced at the garage and try to predict parts which will need to be replaced. When a vehicle is handled in a garage because of a failure, replaced parts are not always linked to the real failure due to expertise error and habits. Therefore, only a subset of possible failures can be covered by this kind of approach. In practice, datasets of parts' replacements tended to be heavily unbalanced. On the other hand, the production of failures labels by experts would be very expensive considering the complexity of parts and car models' and scenarios' variations. Unsupervised anomaly detection could therefore be considered as a solution to this problem.
The Data and the Car A car is a complex assembly of mechanical and electrical parts. The control and sensing of all of those parts are processed by electric computing units (ECU). ECUs are microcontroller-based units that can receive, process, and send information from and to other ECUs. Specifically, information transits through a controller area network (CAN) bus. For example, one ECU might control the oil temperature while receiving and processing information about the motor rotational speed that has been sent from the ECU managing the motor area. In a modern car, tens of ECUs can process information simultaneously while the car is running. Information transiting through the CAN bus is thus a very good reflection of the car's global state. In order to extract this information, a spy system can listen to the information transiting on the CAN bus and save it in a structured manner, associating a sample to its variable and timestamp.
Then, the resulting data can be sent to a cloud database and into an anomaly detector, as shown in Figure  1. Flow chart presenting the process of data generation in a modern car leading to anomaly detection. Within the car, ECUs handle sensors and communicate some of their values through the CAN bus. That CAN bus can be read in order to extract those sensors' data and can then be sent to the cloud or be exploited internally by another ECU. The resulting time series are then resampled, a prediction is emitted after each sample, and an anomaly is either detected or not given the prediction error.
Our Dataset The dataset used in this paper consists of the recordings of 486 variables gathered from an Alpine Renault car during driving tests conducted on circuits. Those tests were conducted over the course of 4 months, resulting in 17 GB of data. The sampling rate of the recordings was 10 Hz. In order to simplify the training and evaluation of the model, we filtered variables with respect to their complexity and retained only the richest ones. As many variables correspond to commands with only one to three discrete values and are rarely triggered, most of the signals show very few variations through time and are therefore poorly informative. Those are the variables which we filtered out. This process resulted in an 85-variable system. As recordings do not have the same temporal length, this dataset can be seen as a set of multivariate time series with different lengths.
Finally, we resampled the data to 1 Hz using the average in order to save time on training and testing. This dataset is not public but is necessary in order to apply state-of-the-art anomaly detectors to real case studies. Nevertheless, we have included a comparison of different state-of-the-art models as well as the ones we have chosen in Table  1. These were applied to an open-source benchmark in order to ensure that our method and implementation were valid. Flow chart presenting the process of data generation in a modern car leading to anomaly detection. Within the car, ECUs handle sensors and communicate some of their values through the CAN bus. That CAN bus can be read in order to extract those sensors' data and can then be sent to the cloud or be exploited internally by another ECU. The resulting time series are then resampled, a prediction is emitted after each sample, and an anomaly is either detected or not given the prediction error.
Finally, we resampled the data to 1 Hz using the average in order to save time on training and testing. This dataset is not public but is necessary in order to apply state-of-the-art anomaly detectors to real case studies. Nevertheless, we have included a comparison of different state-of-the-art models as well as the ones we have chosen in Table  1. These were applied to an open-source benchmark in order to ensure that our method and implementation were valid. Table  1. Comparison of state-of-the-art anomaly detectors on the public benchmark Yahoo  [32]. The LSTM (ours) corresponds to the LSTM with 50 cells and two layers, as presented in Section 4, using the semi-supervised threshold. A1 to A4 correspond to different groups of time-series that can be found within the Yahoo benchmark.
Anomaly Detection During training, for each sample x t , the predictor output a prediction xt+1. The mean square error (MSE) over all dimensions and time was then computed between the predicted and true samples of one time series. MSE = 1 nm n-1 ∑ t=0 ||(x t -x t ) 2 || 1 ( 3 ) where ||. || 1 stands for the norm 1 of a vector. This error was used as a loss function in order to optimize the predictor using a gradient descent optimization. Using this method and using an LSTM or a GRU, we were able to take full advantage of having time series of different lengths in the dataset. No information was lost due to padding or cutting the time series. The predictor was able to efficiently adapt to the variation in time series length. In a vehicle, most of variables that we exploited here are correlated; for example, a failure in the oil pressure impacts the engine temperature.
Results After learning the predictor parameters, we ran our unsupervised anomaly detector through all the time series of our dataset and then exploited the results through different evaluations. For all models, during the learning phase we used 300 epochs, a learning rate of 10 -4 with the Adam optimization, and 2000 subsequences of 100 points each; additionally, we used 20% of the data for testing. The Adam optimization is one of the best and most-utilized gradient-descent-based optimization algorithms for neural networks such as CNN and RNN that exists in the field of machine learning  [35]. Below, we will first evaluate how well our algorithm catches the given anomalies using our specific labels, and then we will propose measuring the correlation between labels and variables using the multidimensional nature of our algorithm.
Evaluation with Labels (Oil Pressure Failure) As explained in Section 2, labels that have been provided to our team express a specific abnormal behavior due to failures in oil pressure and do not cover all possible abnormal behaviors. These labels point to specific timestamps in the dataset where this oil pressure failure has been detected. This abnormal behavior is not instantaneous and appears for several seconds. Thus, we have to consider a time window w a around those timestamps' labels to assess whether an abnormal sample found by our algorithm matches one label. We define the set of true positives TP as: TP = {t ∈ A T |∃t l ∈ L, t l -w a ≤ t ≤ t l + w a } (12) With L and A T being, respectively, the set of labels and found anomalies timestamps. We only consider one true positive sample with respect to one label even if there are several true positives in that label window, hence the usage of the set formalism.
LSTM Computation Costs Comparison Here we evaluate LSTM models with respect to different amounts of cells and layers. We start with our baseline LSTM (50-50) and run smaller architectures. We also compute the number of parameters and multiply-accumulate operations (MACs) for each model in order to evaluate their computational costs using the pytorch-OpCounter (access date: 12 January 2022) library https://github. com/Lyken17/pytorch-OpCounter. In Table  4 , the LSTM  (10)  shows the best TPR, but, for a better evaluation, the FPR has to be considered, as discussed below. When evaluating the anomaly detection results, the two main factors to be considered are the TPR and the FPR. We want the highest TPR while keeping the FPR as low as possible. Depending on the threshold used during anomaly detection, TPR and FPR vary.
Discussion and Future Work In this paper, we have proposed unsupervised anomaly detection models using different state-of-the-art algorithms such as a LSTM, GRU, or CNN and anomaly likelihood or a semi-supervised anomaly threshold optimization. We applied these models in order to effectively point out abnormal temporal behaviors over a real, complex, multivariate sensor time series that was extracted from a vehicle CAN bus during circuit tests. The evaluation of these models with respect to some limited labels shows an acceptable rate of detection, although there is still room for improvement. First, as our labels only express one specific abnormal behavior, it is difficult to fully evaluate our model. Second, further advanced data prepossessing and feature engineering conducted with experts would certainly help.
Estimation in the NCC design is based on a stratified version of the partial likelihood, where for each stratum the risk set, created at the time of case's event, is limited to a case and his/her matched controls  (Prentice & Breslow, 1978). The partial likelihood used to estimate the Cox proportional hazards model depends on the ordering of events in time. Yet, due to imprecise measurement of time (e. g. , in days, weeks, or months), datasets may contain several tied event times, that is, events that occur at the same time, which poses special problems for partial likelihood estimation  (Hertz-Picciotto & Rockhill, 1997). In the presence of ties, exact expression of the partial likelihood considers all possible permutations at each time when more than one event occurs  (Peto, 1972). However, this is time-consuming and there are situations in which this approach may become computationally impossible due to the large number of ties in the dataset.
2. 1. 2 Analyses of simulated data Simulated data were analyzed using two alternative approaches: one corresponding to a full cohort design and another assuming an NCC study was embedded in the same cohort. In the absence of covariates, data from the entire cohort were analyzed using a conventional univariate unadjusted Cox proportional hazards model with either time-invariant A(i) or time-varying A(i,t) exposure, depending on the structure of the simulated data. Log-HR estimates for exposure, together with a 95% confidence interval (CI), were obtained using the coxph function from the survival package in R, with Efron's approximation for handling ties  (Efron, 1977). This method is recommended because it is faster than the exact method  (Borucka, 2014)  and more accurate than Breslow's approximation  (Hertz-Picciotto & Rockhill, 1997).
This function groups all cases who experienced the event of interest at the same time, and their respective controls in the same stratum, therefore generating ties in those strata where two or more cases had the same event time. To handle ties within the strata, in three alternative analyses of the same dataset, we used the exact method  (Peto, 1972 ) as well as the  Breslow (1974)  and the  Efron (1977)  approximations. As the fourth analytical method, we modified the ccwc function so that cases who experienced the event at the same time were allocated to separate strata, ensuring that each stratum included only a single case and, thus, avoiding ties. This approach, referred to as the "modified ccwc approach" in the remainder of the paper, is described in Supporting Information Appendix 1.
The NCC samples with either one or five controls per case were analyzed using conditional logistic regression to estimate the association between exposure and the occurrence of the event of interest, by conditioning on the matched sets. Log-odds ratio estimates for conditional logistic regression, equivalent to log-HR  (Prentice & Breslow, 1978) , were obtained using the clogit function from the survival package in R. For the second set of simulations considering confounders, data from the entire cohort were analyzed using the Cox model adjusted for sex and age (grouped into five classes of 5 years each) with Efron's approximation. We considered NCC analyses with Efron's approximation either adjusted for or matched on sex and age (categorized into five classes of 5 years each). In all scenarios, we compared the performance of each analytical approach with respect to estimated log-HR for exposure, across the 1000 samples simulated for a given scenario, using: (i) relative and absolute bias, (ii) average standard error estimator (SEE), that is, the observed standard deviation of the 1000 estimates, (iii) root mean square error (RMSE), and (iv) coverage probability of the 95% CI.
Population and methods To compare the estimates obtained from the two study designs using real-world data, we examined the risk of breast cancer associated with ever use of MHT in the French E3N women cohort (Étude Épidémiologique auprès de Femmes de la Mutuelle Générale de l'Éducation Nationale)  (Fournier et al. , 2014). The E3N cohort received ethical approval from the French National Commission for Computed Data and Individual Freedom (Commission Nationale de l'Informatique et des Libertés, CNIL) and all participants in the study provided informed consent. Our study population was composed of 38,091 postmenopausal women who were free of any cancer when they completed a detailed questionnaire on their past use of any MHT in 1992. The participants were actively followed-up until 2008 through self-administered questionnaires every 2-3 years, which included updated information on recent MHT use.
RW of 95%CI Full proportion of women were already exposed at baseline in the E3N data whereas all subjects were considered unexposed at the beginning of their follow-up in the simulations. The NCC analyses with the conditional logistic regression model were repeated 100 times for each of one and five controls per case, using four different approaches to handle ties: the exact method, Breslow's and Efron's approximations, as well as the modified ccwc approach. In secondary analyses, to increase the number of ties, the time unit of analysis was changed from day to month. We compared the estimates obtained from each approach using unadjusted HR with empirical 95% CI, relative difference of log-HR (difference between mean log-HR of NCC and log-HR of cohort design over the latter), and relative width of 95% CI (ratio of the width of the 95% CI from each of NCC analyses over the width of 95% CI from full cohort design).