scholarly journals KGPA: Construction of Knowledge Graph for Pituitary Adenoma (Preprint)

2021 ◽  
Author(s):  
An Fang ◽  
Pei Lou ◽  
Jiahui Hu ◽  
Wanqing Zhao ◽  
Ming Feng ◽  
...  
Keyword(s):  
Pituitary Adenoma ◽  
Data Fusion ◽  
Medical Knowledge ◽  
Medical Data ◽  
Knowledge Graph ◽  
Fusion Method ◽  
Fusion Model ◽  
Construction Of Knowledge ◽  
Icd 10 ◽  
Disease Entities

BACKGROUND Pituitary adenoma is one of the most common central nervous system tumors. The diagnosis and treatment of pituitary adenoma are still very difficult. Misdiagnosis and recurrence occur from time to time, and experienced neurosurgeons are in serious shortage. Knowledge graphs can help interns quickly understand the medical knowledge related to pituitary tumor. OBJECTIVE The aim of this paper is to integrate the data of pituitary adenomas from reliable sources and construct a knowledge graph, and use the knowledge graph for knowledge discovery. METHODS A method of constructing a knowledge graph of diseases was introduced and used to build a knowledge graph for pituitary adenoma (KGPA). The schema of the KGPA was manually constructed. Information of pituitary adenoma were automatically extracted from EMR and the medical websites through the CRF model and web wrappers we designed. An entity fusion method was proposed, based on the head and tail entity fusion models, to fuse the data from heterogeneous sources. The disease entities were standardized to ICD-10. RESULTS Data was extracted from 300 EMRs of pituitary adenoma and 4 medical portals. Entity fusion was carried out by using the data fusion model we proposed. The accuracy of the head and tail entity fusion were more than 97%. Part of the triples were selected for evaluation, and the accuracy was 95.4%. CONCLUSIONS This paper introduced an approach to construct KGPA and proposed a data fusion method suitable for medical data. The evaluation results show that the data in KGPA is of high quality. The constructed KGPA can help physicians in their clinical practice.

scholarly journals A Comprehensive and Automated Fusion Method: The Enhanced Flexible Spatiotemporal DAta Fusion Model for Monitoring Dynamic Changes of Land Surface

2019 ◽  
Vol 9 (18) ◽  
pp. 3693 ◽  
Author(s):  
Shi ◽  
Wang ◽  
Zhang ◽  
Liang ◽  
Niu ◽  
...  
Keyword(s):  
Data Fusion ◽  
Land Surface ◽  
Abrupt Change ◽  
Spatiotemporal Data ◽  
Gradual Change ◽  
Fusion Method ◽  
Fusion Model ◽  
Dynamic Changes ◽  
Change Events ◽  
Fusion Methods

Spatiotemporal fusion methods provide an effective way to generate both high temporal and high spatial resolution data for monitoring dynamic changes of land surface. But existing fusion methods face two main challenges of monitoring the abrupt change events and accurately preserving the spatial details of objects. The Flexible Spatiotemporal DAta Fusion method (FSDAF) was proposed, which can monitor the abrupt change events, but its predicted images lacked intra-class variability and spatial details. To overcome the above limitations, this study proposed a comprehensive and automated fusion method, the Enhanced FSDAF (EFSDAF) method and tested it for Landsat–MODIS image fusion. Compared with FSDAF, the EFSDAF has the following strengths: (1) it considers the mixed pixels phenomenon of a Landsat image, and the predicted images by EFSDAF have more intra-class variability and spatial details; (2) it adjusts the differences between Landsat images and MODIS images; and (3) it improves the fusion accuracy in the abrupt change area by introducing a new residual index (RI). Vegetation phenology and flood events were selected to evaluate the performance of EFSDAF. Its performance was compared with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Spatial and Temporal Reflectance Unmixing Model (STRUM), and FSDAF. Results show that EFSDAF can monitor the changes of vegetation (gradual change) and flood (abrupt change), and the fusion images by EFSDAF are the best from both visual and quantitative evaluations. More importantly, EFSDAF can accurately generate the spatial details of the object and has strong robustness. Due to the above advantages of EFSDAF, it has great potential to monitor long-term dynamic changes of land surface.

Reinforcement learning based data fusion method for multi-sensors

2020 ◽  
Vol 7 (6) ◽  
pp. 1489-1497
Author(s):  
Tongle Zhou ◽  
Mou Chen ◽  
Jie Zou
Keyword(s):  
Reinforcement Learning ◽  
Data Fusion ◽  
Fusion Method

scholarly journals Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Steven A. Hicks ◽  
Jonas L. Isaksen ◽  
Vajira Thambawita ◽  
Jonas Ghouse ◽  
Gustav Ahlberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Medical Data ◽  
Medical Doctors ◽  
Medical Tests ◽  
Deep Learning Model

AbstractDeep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.

scholarly journals Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 46
Author(s):  
Gangqiang Zhang ◽  
Wei Zheng ◽  
Wenjie Yin ◽  
Weiwei Lei
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Groundwater Level ◽  
North China Plain ◽  
North China ◽  
Gradient Boosting ◽  
Fusion Model ◽  
The North ◽  
The North China Plain ◽  
Prediction Module

The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local scales. To overcome this limitation, this study develops a machine learning-based fusion model to obtain high-resolution (0.25°) groundwater level anomalies (GWLA) by integrating GRACE observations in the North China Plain. Specifically, the fusion model consists of three modules, namely the downscaling module, the data fusion module, and the prediction module, respectively. In terms of the downscaling module, the GRACE-Noah model outperforms traditional data-driven models (multiple linear regression and gradient boosting decision tree (GBDT)) with the correlation coefficient (CC) values from 0.24 to 0.78. With respect to the data fusion module, the groundwater level from 12 monitoring wells is incorporated with climate variables (precipitation, runoff, and evapotranspiration) using the GBDT algorithm, achieving satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m, and MAE: 0.87 m). By merging the downscaled TWSA and fused groundwater level based on the GBDT algorithm, the prediction module can predict the water level in specified pixels. The predicted groundwater level is validated against 6 in-situ groundwater level data sets in the study area. Compare to the downscaling module, there is a significant improvement in terms of CC metrics, on average, from 0.43 to 0.71. This study provides a feasible and accurate fusion model for downscaling GRACE observations and predicting groundwater level with improved accuracy.

A Data Fusion Method For The Delayering Of X-Ray Fluorescence Images Of Painted Works Of Art

2021 ◽  
Author(s):  
L. D. Fiske ◽  
A. K. Katsaggelos ◽  
M. C. G. Aalders ◽  
M. Alfeld ◽  
M. Walton ◽  
...  
Keyword(s):  
Data Fusion ◽  
Fusion Method ◽  
X Ray ◽  
Works Of Art ◽  
Fluorescence Images
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