Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Adverse drug reactions (ADRs) are common and they are the underlying cause of over a million serious injuries and deaths each year. The most familiar method to detect ADRs is relying on spontaneous reports. Unfortunately, the low reporting rate of spontaneous reports is a serious limitation of pharmacovigilance. Objective: Our objective was to identify a method to detect potential ADRs of drugs automatically using a deep neural network (DNN). Methods: We designed a DNN model that utilizes the chemical, biological, and biomedical information of drugs to detect ADRs. This model aimed to fulfil two main purposes, identifying the potential ADRs of drugs and predicting the possible ADRs of a new drug. For improving the detection performance, we distributed representations of the target drugs in a vector space to capture the drug relationships by using the word embedding approach to process amounts of biomedical literature. Moreover, we built a mapping function to address new drugs that do not appear in the dataset. Results: We predicted the ADRs of drugs recorded up to 2012, by using the drug information and the ADRs reported up to 2009. There were contained 746 drugs and 232 new drugs which only recorded in 2012 with 1,325 ADRs. The experimental results showed that the overall performance of our model with mean average precision (MAP) at top-10 is achieved 0.523 for ADR prediction on the dataset. Conclusions: Our model was effective in identifying the potential ADRs of a drug and the possible ADRs of a new drug. Most importantly, it can detect potential ADRs irrespective of whether they have been reported in the past.

Background: Automated interpretation of echocardiography by deep neural networks could support clinical reporting and improve efficiency. Whilst prior studies have evaluated spatial relationships using still frame images, our aim was to train and test a deep neural network for video analysis by combining spatial and temporal information, to automate the recognition of left ventricular regional wall motion abnormalities. Methods: We collected a series of transthoracic echocardiography examinations performed between July 2017 and April 2018 in two tertiary care hospitals. Regional wall abnormalities were defined by experienced physiologists and confirmed by trained cardiologists. First, we developed a 3-dimensional (3D) convolutional neural network (CNN) model for view selection ensuring stringent image quality control. Second, a U-net model segmented images to annotate the location of each left ventricular wall. Third, a final 3D CNN model evaluated echocardiographic videos from four standard views, before and after segmentation, and calculated a wall motion abnormality confidence level (0-1) for each segment. To evaluate model stability, we performed 5-fold cross-validation and external validation. Results: In a series of 10,638 echocardiograms, our view selection model identified 6,454 (61%) examinations with sufficient image quality in all standard views. In this training set, 2,740 frames were annotated to develop the segmentation model, which achieved a Dice similarity coefficient of 0.756. External validation was performed in 1,756 examinations from an independent hospital. A regional wall motion abnormality was observed in 8.9% and 4.9% in the training and external validation datasets, respectively. The final model recognized regional wall motion abnormalities in the cross-validation and external validation datasets with an area under the receiver operating characteristic curve of 0.912 (95% confidence interval [CI] 0.896 to 0.928) and 0.891 (95% CI 0.834 to 0.948), respectively. In the external validation dataset, the sensitivity was 81.8% (95% CI 73.8 to 88.2%) and specificity was 81.6% (95% CI 80.4 to 82.8%). Conclusions: In echocardiographic examinations of sufficient image quality, it is feasible for deep neural networks to automate the recognition of regional wall motion abnormalities using temporal and spatial information from moving images. Further investigation is required to optimise model performance and evaluate clinical applications.

Recommendation systems have become more important since the widespread use of the Internet. The large amount of information means that it is difficult for users to discover what they really need. However, users’ preferences can change over time because of the age and the impact of social networks. Recommendation systems must capture users’ preferences and recommend suitable items, which is a difficult challenge. This study proposes a novel recommendation system that adapts to the changing preferences of users. By learning and adapting to users’ changing preferences better recommendations are provided. This study uses context factors as additional information to model users’ preferences more accurately. For the proposed recommendation system, the context is based on users’ most recent interaction items. This study proposes two novel attention mechanisms for the recommendation system. The contextual item attention module captures contextual information, changing pattern in users’ preference and the importance of items. The multi-head attention module extends the diversity of users’ preferences and adapts to changing preferences. The recommendation performance is improved using additional item's temporal information to model the contextual item's representation. Experiments compare the proposed algorithm with several state-of-the-art recommendation methods using three real-world datasets. The experimental results demonstrate that the proposed context-aware recommendation model outperforms traditional methods and demonstrate the effectiveness with which contextual information is captured by the attention mechanism.

Background: General severity of illness scores are not well calibrated to predict mortality among patients receiving renal replacement therapy (RRT) for acute kidney injury (AKI). We developed machine learning models to make mortality prediction and compared their performance to that of the Sequential Organ Failure Assessment (SOFA) and HEpatic failure, LactatE, NorepInephrine, medical Condition, and Creatinine (HELENICC) scores. Methods: We extracted routinely collected clinical data for AKI patients requiring RRT in the MIMIC and eICU databases. The development models were trained in 80% of the pooled dataset and tested in the rest of the pooled dataset. We compared the area under the receiver operating characteristic curves (AUCs) of four machine learning models (multilayer perceptron [MLP], logistic regression, XGBoost, and random forest [RF]) to that of the SOFA, nonrenal SOFA, and HELENICC scores and assessed calibration, sensitivity, specificity, positive (PPV) and negative (NPV) predicted values, and accuracy. Results: The mortality AUC of machine learning models was highest for XGBoost (0.823; 95% confidence interval [CI], 0.791–0.854) in the testing dataset, and it had the highest accuracy (0.758). The XGBoost model showed no evidence of lack of fit with the Hosmer–Lemeshow test (p > 0.05). Conclusion: XGBoost provided the highest performance of mortality prediction for patients with AKI requiring RRT compared with previous scoring systems.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

BACKGROUND Adverse drug reactions (ADRs) are common and are the underlying cause of over a million serious injuries and deaths each year. The most familiar method to detect ADRs is relying on spontaneous reports. Unfortunately, the low reporting rate of spontaneous reports is a serious limitation of pharmacovigilance. OBJECTIVE The objective of this study was to identify a method to detect potential ADRs of drugs automatically using a deep neural network (DNN). METHODS We designed a DNN model that utilizes the chemical, biological, and biomedical information of drugs to detect ADRs. This model aimed to fulfill two main purposes: identifying the potential ADRs of drugs and predicting the possible ADRs of a new drug. For improving the detection performance, we distributed representations of the target drugs in a vector space to capture the drug relationships using the word-embedding approach to process substantial biomedical literature. Moreover, we built a mapping function to address new drugs that do not appear in the dataset. RESULTS Using the drug information and the ADRs reported up to 2009, we predicted the ADRs of drugs recorded up to 2012. There were 746 drugs and 232 new drugs, which were only recorded in 2012 with 1325 ADRs. The experimental results showed that the overall performance of our model with mean average precision at top-10 achieved is 0.523 and the rea under the receiver operating characteristic curve (AUC) score achieved is 0.844 for ADR prediction on the dataset. CONCLUSIONS Our model is effective in identifying the potential ADRs of a drug and the possible ADRs of a new drug. Most importantly, it can detect potential ADRs irrespective of whether they have been reported in the past.