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This newly inaugurated research database for 12-lead electrocardiogram signals was created under the auspices of Chapman University and Shaoxing People’s Hospital (Shaoxing Hospital Zhejiang University School of Medicine) and aims to enable the scientific community in conducting new studies on arrhythmia and other cardiovascular conditions. Certain types of arrhythmias, such as atrial fibrillation, have a pronounced negative impact on public health, quality of life, and medical expenditures. As a non-invasive test, long term ECG monitoring is a major and vital diagnostic tool for detecting these conditions. This practice, however, generates large amounts of data, the analysis of which requires considerable time and effort by human experts. Advancement of modern machine learning and statistical tools can be trained on high quality, large data to achieve exceptional levels of automated diagnostic accuracy. Thus, we collected and disseminated this novel database that contains 12-lead ECGs of 10,646 patients with a 500 Hz sampling rate that features 11 common rhythms and 67 additional cardiovascular conditions, all labeled by professional experts. The dataset consists of 10-second, 12-dimension ECGs and labels for rhythms and other conditions for each subject. The dataset can be used to design, compare, and fine-tune new and classical statistical and machine learning techniques in studies focused on arrhythmia and other cardiovascular conditions.Measurement(s)cardiac arrhythmiaTechnology Type(s)12 lead electrocardiography • digital curationFactor Type(s)sex • experimental condition • age groupSample Characteristic - OrganismHomo sapiensMachine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11698521

Background Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes with respect to different disease class imbalance scenarios. Methods In this study, we employed seven artificial intelligence models, a CNN (Convolutional Neural Network), a Transformer encoder, a pretrained BERT (Bidirectional Encoder Representations from Transformers), and four typical sequence neural networks models, namely, RNN (Recurrent Neural Network), GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), and Bi-LSTM (Bi-directional Long Short-Term Memory) to classify the presence or absence of 16 disease conditions from patients’ discharge summary notes. We analyzed this question as a composition of 16 binary separate classification problems. The model performance of the seven models on each of the 16 datasets with various levels of imbalance between classes were compared in terms of AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic), AUC-PR (Area Under the Curve of Precision and Recall), F1 Score, and Balanced Accuracy as well as the training time. The model performances were also compared in combination with different word embedding approaches (GloVe, BioWordVec, and no pre-trained word embeddings). Results The analyses of these 16 binary classification problems showed that the Transformer encoder model performs the best in nearly all scenarios. In addition, when the disease prevalence is close to or greater than 50%, the Convolutional Neural Network model achieved a comparable performance to the Transformer encoder, and its training time was 17.6% shorter than the second fastest model, 91.3% shorter than the Transformer encoder, and 94.7% shorter than the pre-trained BERT-Base model. The BioWordVec embeddings slightly improved the performance of the Bi-LSTM model in most disease prevalence scenarios, while the CNN model performed better without pre-trained word embeddings. In addition, the training time was significantly reduced with the GloVe embeddings for all models. Conclusions For classification tasks on medical notes, Transformer encoders are the best choice if the computation resource is not an issue. Otherwise, when the classes are relatively balanced, CNNs are a leading candidate because of their competitive performance and computational efficiency.

Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead ECGs from 40,258 patients with four arrhythmia classes: atrial fibrillation, general supraventricular tachycardia, sinus bradycardia and sinus rhythm including sinus irregularity rhythm. Our results show that the optimal approach consisted of Low Band Pass filter, Robust LOESS, Non Local Means smoothing, a proprietary feature extraction method based on percentiles of the empirical distribution of ratios of interval lengths and magnitudes of peaks and valleys, and Extreme Gradient Boosting Tree classifier, achieved an F 1 -Score of 0.988 on patients without additional cardiac conditions. The same noise reduction and feature extraction methods combined with Gradient Boosting Tree classifier achieved an F 1 -Score of 0.97 on patients with additional cardiac conditions. Our method achieved the highest classification accuracy (average 10-fold cross-validation F 1 -Score of 0.992) using an external validation data, MIT-BIH arrhythmia database. The proposed optimal multi-stage arrhythmia classification approach can dramatically benefit automatic ECG data analysis by providing cardiologist level accuracy and robust compatibility with various ECG data sources.

The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder–Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards their targets. We successfully integrated the Encoder–Decoder Transformer architecture, which generates molecular structures (drugs) from scratch with the RL-MCTS, serving as a reinforcement learning framework. The RL-MCTS combines the exploitation and exploration capabilities of a Monte Carlo Tree Search with the machine translation of a transformer-based Encoder–Decoder model. This dynamic approach allows the model to iteratively refine its drug candidate generation process, ensuring that the generated molecules adhere to essential physicochemical and biological constraints and effectively bind to their targets. The results from drugAI showcase the effectiveness of the proposed approach across various benchmark datasets, demonstrating a significant improvement in both the validity and drug-likeness of the generated compounds, compared to two existing benchmark methods. Moreover, drugAI ensures that the generated molecules exhibit strong binding affinities to their respective targets. In summary, this research highlights the real-world applications of drugAI in drug discovery pipelines, potentially accelerating the identification of promising drug candidates for a wide range of diseases.

Alzheimer’s disease is the most common type of dementia that currently affects over 6.5 million people in the U.S. Currently there is no cure and the existing drug therapies attempt to delay the mental decline and improve cognitive abilities. Two of the most commonly prescribed such drugs are Donepezil and Memantine. We formally tested and confirmed the presence of a beneficial drug-drug interaction of Donepezil and Memantine using a causal inference analysis. We applied doubly robust estimators to one of the largest and high-quality medical databases to estimate the effect of two commonly prescribed Alzheimer’s disease (AD) medications, Donepezil and Memantine, on the average number of hospital or emergency department visits per year among patients diagnosed with AD. Our results show that, compared to the absence of medication scenario, the Memantine monotherapy, and the Donepezil monotherapy, the combined use of Donepezil and Memantine treatment significantly reduces the average number of hospital or emergency department visits per year by 0.078 (13.8%), 0.144 (25.5%), and 0.132 days (23.4%), respectively. The assessed decline in the average number of hospital or emergency department visits per year is consequently associated with a substantial reduction in medical costs. As of 2022, according to the Alzheimer’s Disease Association, there were over 6.5 million individuals aged 65 and older living with AD in the US alone. If patients who are currently on no drug treatment or using either Donepezil or Memantine alone were switched to the combined used of Donepezil and Memantine therapy, the average number of hospital or emergency department visits could decrease by over 613 thousand visits per year. This, in turn, would lead to a remarkable reduction in medical expenses associated with hospitalization of AD patients in the US, totaling over 940 million dollars per year.

Cardiac catheter ablation has shown the effectiveness of treating the idiopathic premature ventricular complex and ventricular tachycardia. As the most important prerequisite for successful therapy, criteria based on analysis of 12-lead ECGs are employed to reliably speculate the locations of idiopathic ventricular arrhythmia before a subsequent catheter ablation procedure. Among these possible locations, right ventricular outflow tract and left outflow tract are the major ones. We created a new 12-lead ECG database under the auspices of Chapman University and Ningbo First Hospital of Zhejiang University that aims to provide high quality data enabling detection of the distinctions between idiopathic ventricular arrhythmia from right ventricular outflow tract to left ventricular outflow tract. The dataset contains 334 subjects who successfully underwent a catheter ablation procedure that validated the accurate origins of idiopathic ventricular arrhythmia.Measurement(s)Ventricular arrhythmiaTechnology Type(s)electrocardiographyFactor Type(s)sexSample Characteristic - OrganismHomo sapiensMachine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11926506

OBJECTIVESThe aim of this study was to assess the degree of compassion satisfaction and compassion fatigue (CF) among critical care, oncology and charge nurses. BACKGROUNDCumulative grief resulting from caring for critically/terminally ill patients may result in CF, leading to lower quality care and higher nurse attrition. METHODData were collected from 38 direct care nurses and 10 charge nurses, using the Professional Quality of Life. RESULTSCharge nurses had higher secondary traumatic stress (STS) than direct care nurses. Nurses with less than 10 years of experience had lower CS than experienced nurses. Higher levels of burnout (BO) and STS were reported among charge nurses, whereas less direct care nurses had average to high BO and STS ratings. CONCLUSIONSPrevious studies focused on direct care nurses; our findings suggest that CF is prevalent among charge nurses as well. Interventions should be considered for clinical providers and charge nurses including debriefing, stress reduction, peer support, and team building.

Background Although prior research has estimated the overarching cost burden of heart failure (HF), a thorough analysis examining medical expense differences and trends, specifically among commercially insured patients with heart failure, is still lacking. Thus, the study aims to examine historical trends and differences in medical costs for commercially insured heart failure patients in the United States from 2006 to 2021. Methods A population-based, cross-sectional analysis of medical and pharmacy claims data (IQVIA PharMetrics ® Plus for Academic) from 2006 to 2021 was conducted. The cohort included adult patients (age > = 18) who were enrolled in commercial insurance plans and had healthcare encounters with a primary diagnosis of HF. The primary outcome measures were the average total annual payment per patient and per cost categories encompassing hospitalization, surgery, emergency department (ED) visits, outpatient care, post-discharge care, and medications. The sub-group measures included systolic, diastolic, and systolic combined with diastolic, age, gender, comorbidity, regions, states, insurance payment, and self-payment. Results The study included 422,289 commercially insured heart failure (HF) patients in the U.S. evaluated from 2006 to 2021. The average total annual cost per patient decreased overall from $9,636.99 to $8,201.89, with an average annual percentage change (AAPC) of -1.11% (95% CI: -2% to -0.26%). Hospitalization and medication costs decreased with an AAPC of -1.99% (95% CI: -3.25% to -0.8%) and − 3.1% (95% CI: -6.86–0.69%). On the other hand, post-discharge, outpatient, ED visit, and surgery costs increased by an AAPC of 0.84% (95% CI: 0.12–1.49%), 4.31% (95% CI: 1.03–7.63%), 7.21% (95% CI: 6.44–8.12%), and 9.36% (95% CI: 8.61–10.19%). Conclusions The study’s findings reveal a rising trend in average total annual payments per patient from 2006 to 2015, followed by a subsequent decrease from 2016 to 2021. This decrease was attributed to the decline in average patient costs within the Medicare Cost insurance category after 2016, coinciding with the implementation of the Medicare Access and CHIP Reauthorization Act (MACRA) of 2015. Additionally, expenses related to surgical procedures, emergency department (ED) visits, and outpatient care have shown substantial growth over time. Moreover, significant differences across various variables have been identified.

Background Alzheimer’s disease (AD) is the most common neurodegenerative disease. Studying the effects of drug treatments on multiple health outcomes related to AD could be beneficial in demonstrating which drugs reduce the disease burden and increase survival. Methods We conducted a comprehensive causal inference study implementing doubly robust estimators and using one of the largest high-quality medical databases, the Oracle Electronic Health Records (EHR) Real-World Data. Our work was focused on the estimation of the effects of the two common Alzheimer’s disease drugs, Donepezil and Memantine, and their combined use on the five-year survival since initial diagnosis of AD patients. Also, we formally tested for the presence of interaction between these drugs. Results Here, we show that the combined use of Donepezil and Memantine significantly elevates the probability of five-year survival. In particular, their combined use increases the probability of five-year survival by 0.050 (0.021, 0.078) (6.4%), 0.049 (0.012, 0.085), (6.3%), 0.065 (0.035, 0.095) (8.3%) compared to no drug treatment, the Memantine monotherapy, and the Donepezil monotherapy respectively. We also identify a significant beneficial additive drug-drug interaction effect between Donepezil and Memantine of 0.064 (0.030, 0.098). Conclusions Based on our findings, adopting combined treatment of Memantine and Donepezil could extend the lives of approximately 303,000 people with AD living in the USA to be beyond five-years from diagnosis. If these patients instead have no drug treatment, Memantine monotherapy or Donepezil monotherapy they would be expected to die within five years. Plain language summary Alzheimer’s disease is the most common type of dementia, affecting millions of people worldwide. In this study, we investigated the effects of two drugs commonly prescribed to people with Alzheimer’s disease called Donepezil and Memantine to see whether they had an impact on when people died. We found that the combined use of Donepezil and Memantine significantly increased the probability of a person surviving five years compared to no drug treatment or treatment with Donepezil or Memantine alone. Our results suggest that the lives of many Alzheimer’s patients in the USA who are currently on no drug treatment or just Donepezil or Memantine could be extended if they were treated with both drugs simultaneously. Yaghmaei et al. utilize doubly robust causal inference estimators and a large medical data set to evaluate the impact of combining Donepezil and Memantine on five-year survival since initial diagnosis of Alzheimer’s disease patients. A significant benefit to survival is seen when the drugs are combined.

Objective Emergency department (ED) return visits within 72 h may be a sign of poor quality of care and entail unnecessary use of healthcare resources. In this study, we compare the performance of two leading statistical and machine learning classification algorithms, and we use the best performing approach to identify novel risk factors of ED return visits. Methods We analyzed 3.2 million ED encounters with at least one diagnosis under “injury, poisoning and certain other consequences of external causes” and “external causes of morbidity.” These encounters included patients 18 years or older from across 128 emergency room facilities in the USA. For each encounter, we calculated the 72-h ED return status and retrieved 57 features from demographics, diagnoses, procedures, and medications administered during the process of administration of medical care. We implemented a mixed-effects model to assess the effects of the covariates while accounting for the hierarchical structure of the data. Additionally, we investigated the predictive accuracy of the extreme gradient boosting tree ensemble approach and compared the performance of the two methods. Results The mixed-effects model indicates that certain blunt force and non-blunt trauma inflates the risk of a return visit. Notably, patients with trauma to the head and patients with burns and corrosions have elevated risks. This is in addition to 11 other classes of both blunt force and non-blunt force traumas. In addition, prior healthcare resource utilization, patients who have had one or more prior return visits within the last 6 months, prior ED visits, and the number of hospitalizations within the 6 months are associated with increased risk of returning to the ED after discharge. On the one hand, the area under the receiver characteristic curve (AUROC) of the mixed-effects model was 0.710 (0.707, 0.712). On the other hand, the gradient boosting tree ensemble had a lower AUROC of 0.698 CI (0.696, 0.700) on the independent test model. Conclusions The proposed mixed-effects model achieved the highest known AUC and resulted in the identification of novel risk factors. The model outperformed one of the leading machine learning ensemble classifiers, the extreme gradient boosting tree in terms of model performance. The risk factors we identified can assist emergency departments to decrease the number of unplanned return visits within 72 h.