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We propose ENCASE to combine expert features and DNNs (Deep Neural Networks) together for ECG classification. We first explore and implement expert features from statistical area, signal processing area and medical area. Then, we build DNNs to automatically extract deep features. Besides, we propose a new algorithm to find the most representative wave (called centerwave) among long ECG record, and extract features from centerwave. Finally, we combine these features together and put them into ensemble classifiers. Experiment on 4-class ECG data classification reports 0.84 F 1 score, which is much better than any of the single model.

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Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities. Recently, deep learning models showed initial success on modeling ECG data, however they are mostly black-box, thus lack interpretability needed for clinical usage. In this work, we propose MultIlevel kNowledge-guided Attention networks (MINA) that predict heart diseases from ECG signals with intuitive explanation aligned with medical knowledge. By extracting multilevel (beat-, rhythm-and frequency-level) domain knowledge features separately, MINA combines the medical knowledge and ECG data via a multilevel attention model, making the learned models highly interpretable. Our experiments showed MINA achieved PR-AUC 0.9436 (outperforming the best baseline by 5.51%) in real world ECG dataset. Finally, MINA also demonstrated robust performance and strong interpretability against signal distortion and noise contamination.

Objective: We aim to combine deep neural networks and engineered features (hand-crafted features based on medical domain knowledge) for cardiac arrhythmia detection from short single-lead ECG recordings. Approach: We propose a two-stage method named for cardiac arrhythmia detection. The first stage is feature extraction and the second stage is classifier building. In the feature extraction stage, we extract both deep features and engineered features. Deep features are obtained by modifying deep neural networks into a deep feature extractor. Engineered features are extracted by summarizing existing approaches into four feature groups. Then, we propose a feature aggregation approach to combine these features. In the classifier building stage, we build multiple gradient boosting decision trees and combine them to get the final detector. Main results: Experiments are performed on the PhysioNet/Computing in Cardiology Challenge 2017 dataset (Clifford et al 2017 Computing in Cardiology vol 44). Using F1 scores reported on the hidden test set as measurements, got 0.9117 on Normal (F1N), 0.8128 on Atrial Fibrillation (AF) (F1A), 0.7505 on Others (F1O), and 0.5671 on Noise (F1P). It placed 5th in the Challenge and 8th in the follow-up challenge (ranked by considering the average of Normal, AF, and Others (F1NAO  =  0.825)). When rounding to two decimal places, we were part a three-way tie for 1st place and were part a seven-way tie for 2nd place in the follow-up challenge. Further experiments show that combined features perform better than individual features, and deep features show more importance scores than other features. Significance: can benefit from both feature engineering-based methods and recent deep neural networks. It is flexible and can easily assimilate the ability of new cardiac arrhythmia detection methods.

Background The coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide since December 2019. From the beginning of infection, patients will progress through different symptom stages, such as fever, dyspnea or even death. Identifying disease progression and predicting patient outcome at an early stage helps target treatment and resource allocation. However, there is no clear COVID-19 stage definition, and few studies have addressed characterizing COVID-19 progression, making the need for this study evident. Methods We proposed a temporal deep learning method, based on a time-aware long short-term memory (T-LSTM) neural network and used an online open dataset, including blood samples of 485 patients from Wuhan, China, to train the model. Our method can grasp the dynamic relations in irregularly sampled time series, which is ignored by existing works. Specifically, our method predicted the outcome of COVID-19 patients by considering both the biomarkers and the irregular time intervals. Then, we used the patient representations, extracted from T-LSTM units, to subtype the patient stages and describe the disease progression of COVID-19. Results Using our method, the accuracy of the outcome of prediction results was more than 90% at 12 days and 98, 95 and 93% at 3, 6, and 9 days, respectively. Most importantly, we found 4 stages of COVID-19 progression with different patient statuses and mortality risks. We ranked 40 biomarkers related to disease and gave the reference values of them for each stage. Top 5 is Lymph, LDH, hs-CRP, Indirect Bilirubin, Creatinine. Besides, we have found 3 complications - myocardial injury, liver function injury and renal function injury. Predicting which of the 4 stages the patient is currently in can help doctors better assess and cure the patient. Conclusions To combat the COVID-19 epidemic, this paper aims to help clinicians better assess and treat infected patients, provide relevant researchers with potential disease progression patterns, and enable more effective use of medical resources. Our method predicted patient outcomes with high accuracy and identified a four-stage disease progression. We hope that the obtained results and patterns will aid in fighting the disease.

Background Acute graft-versus-host disease (aGVHD) remains the major cause of early mortality after haploidentical related donor (HID) hematopoietic stem cell transplantation (HSCT). We aimed to establish a comprehensive model which could predict severe aGVHD after HID HSCT. Methods Consecutive 470 acute leukemia patients receiving HID HSCT according to the protocol registered at https://clinicaltrials.gov (NCT03756675) were enrolled, 70% of them (n = 335) were randomly selected as training cohort and the remains 30% (n = 135) were used as validation cohort. Results The equation was as follows: Probability (grade III–IV aGVHD) = $$\frac{1}{{1 + \exp \left( { - \,{\text{Y}}} \right)}}$$ 1 1 + exp - Y , where Y = –0.0288 × (age) + 0.7965 × (gender) + 0.8371 × (CD3 + /CD14 + cells ratio in graft) + 0.5829 × (donor/recipient relation) − 0.0089 × (CD8 + cell counts in graft) − 2.9046. The threshold of probability was 0.057392 which helped separate patients into high- and low-risk groups. The 100-day cumulative incidence of grade III–IV aGVHD in the low- and high-risk groups was 4.1% (95% CI 1.9–6.3%) versus 12.8% (95% CI 7.4–18.2%) (P = 0.001), 3.2% (95% CI 1.2–5.1%) versus 10.6% (95% CI 4.7–16.5%) (P = 0.006), and 6.1% (95% CI 1.3–10.9%) versus 19.4% (95% CI 6.3–32.5%) (P = 0.017), respectively, in total, training, and validation cohort. The rates of grade III–IV skin and gut aGVHD in high-risk group were both significantly higher than those of low-risk group. This model could also predict grade II–IV and grade I–IV aGVHD. Conclusions We established a model which could predict the development of severe aGVHD in HID HSCT recipients.

Steroid‐refractory (SR) acute graft‐versus‐host disease (aGVHD) is one of the leading causes of early mortality after allogeneic hematopoietic stem cell transplantation (allo‐HSCT). We investigated the efficacy, safety, prognostic factors, and optimal therapeutic protocol for SR‐aGVHD patients treated with basiliximab in a real‐world setting. Nine hundred and forty SR‐aGVHD patients were recruited from 36 hospitals in China, and 3683 doses of basiliximab were administered. Basiliximab was used as monotherapy (n = 642) or in combination with other second‐line treatments (n = 298). The cumulative incidence of overall response rate (ORR) at day 28 after basiliximab treatment was 79.4% (95% confidence interval [CI] 76.5%–82.3%). The probabilities of nonrelapse mortality and overall survival at 3 years after basiliximab treatment were 26.8% (95% CI 24.0%–29.6%) and 64.3% (95% CI 61.2%–67.4%), respectively. A 1:1 propensity score matching was performed to compare the efficacy and safety between the monotherapy and combined therapy groups. Combined therapy did not increase the ORR; conversely, it increased the infection rates compared with monotherapy. The multivariate analysis showed that combined therapy, grade III–IV aGVHD, and high‐risk refined Minnesota aGVHD risk score before basiliximab treatment were independently associated with the therapeutic response. Hence, we created a prognostic scoring system that could predict the risk of having a decreased likelihood of response after basiliximab treatment. Machine learning was used to develop a protocol that maximized the efficacy of basiliximab while maintaining acceptable levels of infection risk. Thus, real‐world data suggest that basiliximab is safe and effective for treating SR‐aGVHD.