scholarly journals Personalized Urination Activity Management Based on an Intelligent System Using a Wearable Device

2021 ◽  
Vol 25 (3) ◽  
pp. 229-235
Author(s):  
Sung-Jong Eun ◽  
Jun Young Lee ◽  
Han Jung ◽  
Khae-Hawn Kim
Keyword(s):  
Management System ◽  
Short Term Memory ◽  
Intelligent System ◽  
Interval Data ◽  
Memory Systems ◽  
Patient Specific ◽  
Sequential Data ◽  
Clinical Environment ◽  
Motor Patterns ◽  
Activity Management

Purpose: In this study, a urinary management system was established to collect and analyze urinary time and interval data detected through patient-worn smart bands, and the results of the analysis were shown through a web-based visualization to enable monitoring and appropriate feedback for urological patients.Methods: We designed a device that can recognize urination time and spacing based on patient-specific posture and consistent posture changes, and we built a urination patient management system based on this device. The order of body movements during urination was consistent in terms of time characteristics; therefore, sequential data were analyzed and urinary activity was recognized using repeated neural networks and long-term short-term memory systems. The results were implemented as a web (HTML5) service program, enabling visual support for clinical diagnostic assistance.Results: Experiments were conducted to evaluate the performance of the proposed recognition techniques. The effectiveness of smart band monitoring urination was evaluated in 30 men (average age, 28.73 years; range, 26–34 years) without urination problems. The entire experiment lasted a total of 3 days. The final accuracy of the algorithm was calculated based on urological clinical guidelines. This experiment showed a high average accuracy of 95.8%, demonstrating the soundness of the proposed algorithm.Conclusions: This urinary activity management system showed high accuracy and was applied in a clinical environment to characterize patients’ urinary patterns. As wearable devices are developed and generalized, algorithms capable of detecting certain sequential body motor patterns that reflect certain physiological behaviors can be a new methodology for studying human physiological behaviors. It is also thought that these systems will have a significant impact on diagnostic assistance for clinicians.

Working, Declarative, and Procedural Memory in Children With Developmental Language Disorder

2020 ◽  
Vol 63 (12) ◽  
pp. 4162-4178
Author(s):  
Emily Jackson ◽  
Suze Leitão ◽  
Mary Claessen ◽  
Mark Boyes
Keyword(s):  
Working Memory ◽  
Short Term Memory ◽  
Language Disorder ◽  
Declarative Memory ◽  
Memory Systems ◽  
Procedural Memory ◽  
Typically Developing ◽  
Short Term ◽  
Term Memory ◽  
Visual Spatial

Purpose Previous research into the working, declarative, and procedural memory systems in children with developmental language disorder (DLD) has yielded inconsistent results. The purpose of this research was to profile these memory systems in children with DLD and their typically developing peers. Method One hundred four 5- to 8-year-old children participated in the study. Fifty had DLD, and 54 were typically developing. Aspects of the working memory system (verbal short-term memory, verbal working memory, and visual–spatial short-term memory) were assessed using a nonword repetition test and subtests from the Working Memory Test Battery for Children. Verbal and visual–spatial declarative memory were measured using the Children's Memory Scale, and an audiovisual serial reaction time task was used to evaluate procedural memory. Results The children with DLD demonstrated significant impairments in verbal short-term and working memory, visual–spatial short-term memory, verbal declarative memory, and procedural memory. However, verbal declarative memory and procedural memory were no longer impaired after controlling for working memory and nonverbal IQ. Declarative memory for visual–spatial information was unimpaired. Conclusions These findings indicate that children with DLD have deficits in the working memory system. While verbal declarative memory and procedural memory also appear to be impaired, these deficits could largely be accounted for by working memory skills. The results have implications for our understanding of the cognitive processes underlying language impairment in the DLD population; however, further investigation of the relationships between the memory systems is required using tasks that measure learning over long-term intervals. Supplemental Material https://doi.org/10.23641/asha.13250180

Investigating Recurrent Neural Networks for OCT A-scan Based Tissue Analysis

2014 ◽  
Vol 53 (04) ◽  
pp. 245-249 ◽  
Author(s):  
S. Otte ◽  
L. Wittig ◽  
G. Hüttmann ◽  
C. Kugler ◽  
D. Drömann ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Pulmonary Nodules ◽  
Patient Specific ◽  
Specific Training ◽  
Reference Distribution ◽  
Human Lung Tissue ◽  
Additional Information ◽  
Cutoff Frequencies ◽  
Low Pass ◽  
Lower Cutoff

Summary Objectives: Optical Coherence Tomography (OCT) has been proposed as a high resolution image modality to guide transbronchial biopsies. In this study we address the question, whether individual A-scans obtained in needle direction can contribute to the identification of pulmonary nodules. Methods: OCT A-scans from freshly resected human lung tissue specimen were recorded through a customized needle with an embedded optical fiber. Bidirectional Long Short Term Memory networks (BLSTMs) were trained on randomly distributed training and test sets of the acquired A-scans. Patient specific training and different pre-processing steps were evaluated. Results: Classification rates from 67.5% up to 76% were archived for different training scenarios. Sensitivity and specificity were highest for a patient specific training with 0.87 and 0.85. Low pass filtering decreased the accuracy from 73.2% on a reference distribution to 62.2% for higher cutoff frequencies and to 56% for lower cutoff frequencies. Conclusion: The results indicate that a grey value based classification is feasible and may provide additional information for diagnosis and navigation. Furthermore, the experiments show patient specific signal properties and indicate that the lower and upper parts of the frequency spectrum contribute to the classification.

scholarly journals Three-dimensional technologies used for patient specific applications in orthopedics

2021 ◽  
Vol 67 (2) ◽  
pp. 77-85
Author(s):  
Flaviu Moldovan ◽  
Tiberiu Bataga
Keyword(s):  
Orthopedic Surgery ◽  
Three Dimensional ◽  
Medical Application ◽  
Surgical Instruments ◽  
Patient Specific ◽  
Innovative Technology ◽  
Clinical Environment ◽  
Clinical Practices ◽  
Content Description ◽  
Almost All

Abstract Background: Three-dimensional (3D) technologies have numerous medical applications and have gained a lot of interest in medical world. After the advent of three-dimensional printing technology, and especially in last decade, orthopedic surgeons began to apply this innovative technology in almost all areas of orthopedic traumatic surgery. Objective: The aim of this paper is to give an overview of 3D technologies current usage in orthopedic surgery for patient specific applications. Methods: Two major databases PubMed and Web of Science were explored for content description and applications of 3D technologies in orthopedic surgery. It was considered papers presenting controlled studies and series of cases that include descriptions of 3D technologies compatible with applications to human medical purposes. Results: First it is presented the available three-dimensional technologies that can be used in orthopedic surgery as well as methods of integration in order to achieve the desired medical application for patient specific orthopedics. Technology starts with medical images acquisition, followed by design, numerical simulation, and printing. Then it is described the state of the art clinical applications of 3D technologies in orthopedics, by selecting the latest reported articles in medical literature. It is focused on preoperative visualization and planning, trauma, injuries, elective orthopedic surgery, guides and customized surgical instrumentation, implants, orthopedic fixators, orthoses and prostheses. Conclusion: The new 3D digital technologies are revolutionizing orthopedic clinical practices. The vast potential of 3D technologies is increasingly used in clinical practice. These technologies provide useful tools for clinical environment: accurate preoperative planning for cases of complex trauma and elective cases, personalized surgical instruments and personalized implants. There is a need to further explore the vast potential of 3D technologies in many other areas of orthopedics and to accommodate healthcare professionals with these technologies, as well as to study their effectiveness compared to conventional methods.

Predicting Outcomes of Business Process Executions Based on LSTM Neural Networks and Attention Mechanism

2021 ◽  
Author(s):  
Jiaojiao Wang ◽  
Dongjin Yu ◽  
Chengfei Liu ◽  
Xiaoxiao Sun
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Sequential Data ◽  
Time Prediction ◽  
Prediction Time ◽  
Highly Sensitive ◽  
Long Short Term Memory ◽  
Early Decision ◽  
Lstm Network ◽  
Hidden Layer

Abstract To effectively predict the outcome of an on-going process instance helps make an early decision, which plays an important role in so-called predictive process monitoring. Existing methods in this field are tailor-made for some empirical operations such as the prefix extraction, clustering, and encoding, leading that their relative accuracy is highly sensitive to the dataset. Moreover, they have limitations in real-time prediction applications due to the lengthy prediction time. Since Long Short-term Memory (LSTM) neural network provides a high precision in the prediction of sequential data in several areas, this paper investigates LSTM and its enhancements and proposes three different approaches to build more effective and efficient models for outcome prediction. The first move on enhancement is that we combine the original LSTM network from two directions, forward and backward, to capture more features from the completed cases. The second move on enhancement is that we add attention mechanism after extracting features in the hidden layer of LSTM network to distinct them from their attention weight. A series of extensive experiments are evaluated on twelve real datasets when comparing with other approaches. The results show that our approaches outperform the state-of-the-art ones in terms of prediction effectiveness and time performance.

PREDICTION AND ANALYSIS OF GEOMECHANICAL PROPERTIES OF JIMUSAER SHALE USING A MACHINE LEARNING APPROACH

2021 ◽  
Author(s):  
Lianteng Song ◽  
◽  
Zhonghua Liu ◽  
Chaoliu Li ◽  
Congqian Ning ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Gamma Ray ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Sequential Data ◽  
Log Data ◽  
Geomechanical Properties ◽  
Single Well

Geomechanical properties are essential for safe drilling, successful completion, and exploration of both conven-tional and unconventional reservoirs, e.g. deep shale gas and shale oil. Typically, these properties could be calcu-lated from sonic logs. However, in shale reservoirs, it is time-consuming and challenging to obtain reliable log-ging data due to borehole complexity and lacking of in-formation, which often results in log deficiency and high recovery cost of incomplete datasets. In this work, we propose the bidirectional long short-term memory (BiL-STM) which is a supervised neural network algorithm that has been widely used in sequential data-based pre-diction to estimate geomechanical parameters. The pre-diction from log data can be conducted from two differ-ent aspects. 1) Single-Well prediction, the log data from a single well is divided into training data and testing data for cross validation; 2) Cross-Well prediction, a group of wells from the same geographical region are divided into training set and testing set for cross validation, as well. The logs used in this work were collected from 11 wells from Jimusaer Shale, which includes gamma ray, bulk density, resistivity, and etc. We employed 5 vari-ous machine learning algorithms for comparison, among which BiLSTM showed the best performance with an R-squared of more than 90% and an RMSE of less than 10. The predicted results can be directly used to calcu-late geomechanical properties, of which accuracy is also improved in contrast to conventional methods.

A Feature-Based Morphing Methodology for Biological Structures Applied to the Spatial Organization of Cardiomyocytes in the Left Atrium

2011 ◽  
Author(s):  
Alessandro Satriano ◽  
Edward J. Vigmond ◽  
Elena S. Di Martino
Keyword(s):  
Spatial Organization ◽  
Experimental Tests ◽  
Smooth Transition ◽  
Patient Specific ◽  
Clinical Environment ◽  
Individual Values ◽  
Tissue Properties ◽  
Biological Structures ◽  
Critical Issues ◽  
Parameter Values

When complex biological structures are modeled, one of the most critical issues is the assignment of geometrical, mechanical and electrical properties to the meshed surfaces. Properties of interest are commonly obtained from diagnostic imaging, experimental tests or anatomical observation. These parameters are usually lumped into individual values assigned to a specific region after subdividing the structure in sub-regions. This practice simplifies the problem avoiding the cumbersome assignment of parameter values to each element. However, sub-regions may not adequately represent the smooth transition between regions thus resulting in artificial discontinuities. In addition, some parameters, such as for example the organization of cardiomyocytes, which is the objective of our research, may be obtained through destructive tests or through sophisticated methods that can only be performed on a limited number of samples. Or else, data structure obtained for one animal species could be applied on a different species. Furthermore, in a clinical environment the need for fast turnout of patient-specific models would benefit from the assignment of tissue properties in a semi-automatic manner.

scholarly journals Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2102 ◽  
Author(s):  
Vo-Nguyen Tuyet-Doan ◽  
Tien-Tung Nguyen ◽  
Minh-Tuan Nguyen ◽  
Jong-Ho Lee ◽  
Yong-Hwa Kim
Keyword(s):  
Short Term Memory ◽  
Model Performance ◽  
Electrical Equipment ◽  
Fault Classification ◽  
Model Complexity ◽  
Sequential Data ◽  
Gas Insulated Switchgear ◽  
Proposed Model ◽  
Insulation Systems ◽  
Deep Learning Model

Detecting, measuring, and classifying partial discharges (PDs) are important tasks for assessing the condition of insulation systems used in different electrical equipment. Owing to the implementation of the phase-resolved PD (PRPD) as a sequence input, an existing method that processes sequential data, e.g., the recurrent neural network, using a long short-term memory (LSTM) has been applied for fault classification. However, the model performance is not further improved because of the lack of supporting parallel computation and the inability to recognize the relevance of all inputs. To overcome these two drawbacks, we propose a novel deep-learning model in this study based on a self-attention mechanism to classify the PD patterns in a gas-insulated switchgear (GIS). The proposed model uses a self-attention block that offers the advantages of simultaneous computation and selective focusing on parts of the PRPD signals and a classification block to finally classify faults in the GIS. Moreover, the combination of LSTM and self-attention is considered for comparison purposes. The experimental results show that the proposed method achieves performance superiority compared with the previous neural networks, whereas the model complexity is significantly reduced.

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