DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom Representations

Diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient. A few studies use reinforcement learning (RL) to learn the optimal policy from the joint action space of symptoms and diseases. However, existing RL (or Non-RL) methods cannot achieve sufficiently good prediction accuracy, still far from its upper limit. To address the problem, we propose a decoupled automatic diagnostic framework DxFormer, which divides the diagnosis process into two steps: symptom inquiry and disease diagnosis, where the transition from symptom inquiry to disease diagnosis is explicitly determined by the stopping criteria. In DxFormer, we treat each symptom as a token, and formalize the symptom inquiry and disease diagnosis to a language generation model and a sequence classification model respectively. We use the inverted version of Transformer, i.e., the decoder-encoder structure, to learn the representation of symptoms by jointly optimizing the reinforce reward and cross entropy loss. Extensive experiments on three public real-world datasets prove that our proposed model can effectively learn doctors' clinical experience and achieve the state-of-the-art results in terms of symptom recall and diagnostic accuracy.Comment: 7 pages, 4 figures, 3 table

Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations for the target task. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarity is currently missing. In this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL by taking advantage of this idea. Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it by modeling multi-policy transfer as the option learning problem. PTF can be easily combined with existing deep RL approaches. Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202

Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder

Identifying genes associated with Parkinson's disease plays an extremely important role in the diagnosis and treatment of Parkinson's disease. In recent years, based on the guilt-by-association hypothesis, many methods have been proposed to predict disease-related genes, but few of these methods are designed or used for Parkinson's disease gene prediction. In this paper, we propose a novel prediction method for Parkinson's disease gene prediction, named N2A-SVM. N2A-SVM includes three parts: extracting features of genes based on network, reducing the dimension using deep neural network, and predicting Parkinson's disease genes using a machine learning method. The evaluation test shows that N2A-SVM performs better than existing methods. Furthermore, we evaluate the significance of each step in the N2A-SVM algorithm and the influence of the hyper-parameters on the result. In addition, we train N2A-SVM on the recent dataset and used it to predict Parkinson's disease genes. The predicted top-rank genes can be verified based on literature study

Predicting Disease-Related Genes Using Integrated Biomedical Networks

Background: Identifying the genes associated to human diseases is crucial for disease diagnosis and drug design. Computational approaches, esp. the network-based approaches, have been recently developed to identify disease-related genes effectively from the existing biomedical networks. Meanwhile, the advance in biotechnology enables researchers to produce multi-omics data, enriching our understanding on human diseases, and revealing the complex relationships between genes and diseases. However, none of the existing computational approaches is able to integrate the huge amount of omics data into a weighted integrated network and utilize it to enhance disease related gene discovery. Results: We propose a new network-based disease gene prediction method called SLN-SRW (Simplified Laplacian Normalization-Supervised Random Walk) to generate and model the edge weights of a new biomedical network that integrates biomedical data from heterogeneous sources, thus far enhancing the disease related gene discovery. Conclusions: The experiment results show that SLN-SRW significantly improves the performance of disease gene prediction on both the real and the synthetic data sets

Circulating tumor DNA determining hyperprogressive disease after CAR-T therapy alarms in DLBCL: a case report and literature review

Chimeric antigen receptor T-cell therapy (CAR-T) has been widely applied in the clinical practice of relapse/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) due to its promising effects. Hyperprogressive disease (HPD) has gained attention for rapid tumor progression and has become a therapeutic and prognostic challenge. Here, we present a patient who had suffered from several recurrences previously and controlled well with a very small tumor lesion left was infused with CD19/CD22 bispecific CAR-T, with no immune effector cell-associated neurotoxicity syndrome, or cytokine release syndrome observed. However, rapid deterioration, subsequent imaging examination, circulating tumor DNA, and serum biomarkers detection identified HPD. The patient did not respond to salvage treatment and died 40 days after infusion. To our knowledge, only one case of HPD in DLBCL after CAR-T therapy has been reported. This fatal case alarmed the risk of HPD and the ctDNA profile monitoring we used was performed as a non-invasive method to diagnose HPD, providing far-reaching practical instruction for CAR-T therapy

The effect of helminth infection on the microbial composition and structure of the caprine abomasal microbiome

Haemonchus contortus is arguably the most injurious helminth parasite for small ruminants. We characterized the impact of H. contortus infection on the caprine abomasal microbiome. Fourteen parasite naive goats were inoculated with 5,000 H. contortus infective larvae and followed for 50 days. Six age-matched naïve goats served as uninfected controls. Reduced bodyweight gain and a significant increase in the abosamal pH was observed in infected goats compared to uninfected controls. Infection also increased the bacterial load while reducing the abundance of the Archaea in the abomasum but did not appear to affect microbial diversity. Nevertheless, the infection altered the abundance of approximately 19% of the 432 species-level operational taxonomic units (OTU) detected per sample. A total of 30 taxa displayed a significantly different abundance between control and infected goats. Furthermore, the infection resulted in a distinct difference in the microbiome structure. As many as 8 KEGG pathways were predicted to be significantly affected by infection. In addition, H. contortus-induced changes in butyrate producing bacteria could regulate mucosal inflammation and tissue repair. Our results provided insight into physiological consequences of helminth infection in small ruminants and could facilitate the development of novel control strategies to improve animal and human health