Research on Recognition of Medical Image Detection Based on Neural Network

et al. 2020

Complexity

Lung nodules are an early symptom of lung cancer. The earlier they are found, the more beneficial it is for treatment. However, in practice, Chinese doctors are likely to cause misdiagnosis. Therefore, deep learning is introduced, an improved target detection network is used, and public datasets are used to diagnose and identify lung nodules. This paper selects the Mask-RCNN network and uses the dense block structure of Densenet and the channel shuffle convolution method to improve the Mask-RCNN network. The experimental results prove that proposed algorithm is extremely effective.

“…It is very harmful to the human body. It is an early manifestation of lung cancer [3]. It usually appears as a circle in medical imaging.…”

Section: Introductionmentioning

confidence: 99%

Research on Lung Nodule Detection Based on Improved Target Detection Network

et al. 2020

Complexity

“…In recent years, deep learning (DL) technology has been widely used because of its superior performance in various medical applications [28,29], such as medical image recognition [39] and medication recommendations [40]. In the past year, the spread of COVID-19 has had a peripheral effect on the global economy and health.…”

Section: Discussionmentioning

confidence: 99%

Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning

BMC Med Inform Decis Mak

Hong

Song

et al. 2021

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.

“…In recent years, deep learning (DL) technology has shown its superior performance in medical applications [ 41 – 44 ], such as medical image recognition [ 45 ] and medication recommendations [ 46 ]. And many methods have achieved good performance for specific disease prediction, such as Alzheimer's disease [ 47 ], sepsis [ 48 ], and heart disease [ 49 , 50 ].…”

Section: Discussionmentioning

confidence: 99%

Interpretable time-aware and co-occurrence-aware network for medical prediction

BMC Med Inform Decis Mak

Dui

2021

Background Disease prediction based on electronic health records (EHRs) is essential for personalized healthcare. But it’s hard due to the special data structure and the interpretability requirement of methods. The structure of EHR is hierarchical: each patient has a sequence of admissions, and each admission has some co-occurrence diagnoses. However, the existing methods only partially model these characteristics and lack the interpretation for non-specialists. Methods This work proposes a time-aware and co-occurrence-aware deep learning network (TCoN), which is not only suitable for EHR data structure but also interpretable: the co-occurrence-aware self-attention (CS-attention) mechanism and time-aware gated recurrent unit (T-GRU) can model multilevel relations; the interpretation path and the diagnosis graph can make the result interpretable. Results The method is tested on a real-world dataset for mortality prediction, readmission prediction, disease prediction, and next diagnoses prediction. Experimental results show that TCoN is better than baselines with 2.01% higher accuracy. Meanwhile, the method can give the interpretation of causal relationships and the diagnosis graph of each patient. Conclusions This work proposes a novel model—TCoN. It is an interpretable and effective deep learning method, that can model the hierarchical medical structure and predict medical events. The experiments show that it outperforms all state-of-the-art methods. Future work can apply the graph embedding technology based on more knowledge data such as doctor notes.