scholarly journals A fast, resource efficient and reliable rule-based system for COVID-19 symptom identification

JAMIA Open ◽  
2021 ◽  
Author(s):  
Himanshu S Sahoo ◽  
Greg M Silverman ◽  
Nicholas E Ingraham ◽  
Monica I Lupei ◽  
Michael A Puskarich ◽  
...  
Keyword(s):  
Real Time ◽  
Resource Utilization ◽  
Fine Tuning ◽  
Rule Based ◽  
Low Resource ◽  
Annotation System ◽  
High Resource ◽  
Industry Standard ◽  
Depth Analysis ◽  
Current Annotation

Abstract Objective With COVID-19 there was a need for rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from high resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution. Materials and Methods Performance, resource utilization and runtime of the rule-based gazetteer was compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP and MedTagger. Results This rule-based gazetteer was fastest, had low resource footprint and similar performance for weighted micro-average and macro-average measures of precision, recall and f1-score compared to other annotation systems. Discussion Opportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute nodes for faster runtime. Conclusion This rule-based gazetteer overcame key technical limitations facilitating real-time symptomatology identification for COVID-19 and integration of unstructured data elements into our CDS. It is ideal for large-scale deployment across a wide variety of health care settings for surveillance of acute COVID-19 symptoms for integration into prognostic modeling. Such a system is currently being leveraged for monitoring of post-acute sequelae of COVID-19 (PASC) progression in COVID-19 survivors. This study conducted the first in-depth analysis and developed a rule-based gazetteer for COVID-19 symptom extraction with the following key features: low processor and memory utilization, faster runtime and similar weighted micro-average and macro-average measures for precision, recall and f1-score compared to industry standard annotation systems. Lay Summary With COVID-19 came an unprecedented need to identify symptoms of COVID-19 patients under investigation (PUIs) in a time sensitive, resource-efficient and accurate manner. While available annotation systems perform well for smaller healthcare settings, they fail to scale in larger healthcare systems where 10,000+ clinical notes are generated a day. This study covers 3 improvements addressing key limitations of current annotation systems. (1) High resource utilization and poor scalability of existing annotation systems. The presented rule-based gazetteer is a high-throughput annotation system for processing high volume of notes, thus, providing opportunity for clinicians to make more informed time-sensitive decisions around patient care. (2) Equally important is our developed rule-based gazetteer performs similar or better than current annotation systems for symptom identification. (3) Due to minimal resource needs of the rule-based gazetteer, it could be deployed at healthcare sites lacking a robust infrastructure where industry standard annotation systems cannot be deployed because of low resource availability.

scholarly journals Low-Resource Named Entity Recognition via the Pre-Training Model

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 786
Author(s):  
Siqi Chen ◽  
Yijie Pei ◽  
Zunwang Ke ◽  
Wushour Silamu
Keyword(s):  
Data Augmentation ◽  
Language Model ◽  
Named Entity Recognition ◽  
Name Entity Recognition ◽  
Fine Tuning ◽  
Entity Recognition ◽  
Language Models ◽  
Low Resource ◽  
Named Entity ◽  
High Resource

Named entity recognition (NER) is an important task in the processing of natural language, which needs to determine entity boundaries and classify them into pre-defined categories. For low-resource languages, most state-of-the-art systems require tens of thousands of annotated sentences to obtain high performance. However, there is minimal annotated data available about Uyghur and Hungarian (UH languages) NER tasks. There are also specificities in each task—differences in words and word order across languages make it a challenging problem. In this paper, we present an effective solution to providing a meaningful and easy-to-use feature extractor for named entity recognition tasks: fine-tuning the pre-trained language model. Therefore, we propose a fine-tuning method for a low-resource language model, which constructs a fine-tuning dataset through data augmentation; then the dataset of a high-resource language is added; and finally the cross-language pre-trained model is fine-tuned on this dataset. In addition, we propose an attention-based fine-tuning strategy that uses symmetry to better select relevant semantic and syntactic information from pre-trained language models and apply these symmetry features to name entity recognition tasks. We evaluated our approach on Uyghur and Hungarian datasets, which showed wonderful performance compared to some strong baselines. We close with an overview of the available resources for named entity recognition and some of the open research questions.

scholarly journals Voice Activation for Low-Resource Languages

2021 ◽  
Vol 11 (14) ◽  
pp. 6298
Author(s):  
Aliaksei Kolesau ◽  
Dmitrij Šešok
Keyword(s):  
Fine Tuning ◽  
Test Accuracy ◽  
Data Set ◽  
Low Resource ◽  
Relative Improvement ◽  
High Resource ◽  
Joint Training ◽  
Activation System ◽  
Resource Data

Voice activation systems are used to find a pre-defined word or phrase in the audio stream. Industry solutions, such as “OK, Google” for Android devices, are trained with millions of samples. In this work, we propose and investigate several ways to train a voice activation system when the in-domain data set is small. We compare self-training exemplar pre-training, fine-tuning a model pre-trained on another domain, joint training on both an out-of-domain high-resource and a target low-resource data set, and unsupervised pre-training. In our experiments, the unsupervised pre-training and the joint-training with a high-resource data set from another domain significantly outperform a strong baseline of fine-tuning a model trained on another data set. We obtain 7–25% relative improvement depending on the model architecture. Additionally, we improve the best test accuracy on the Lithuanian data set from 90.77% to 93.85%.

P053 VARIATIONS IN HEALTHCARE UTILIZATION PATTERNS AMONG HIGH RISK INFLAMMATORY BOWEL DISEASE PATIENTS ENROLLED IN HIGH DEDUCTIBLE HEALTH PLANS

2020 ◽  
Vol 26 (Supplement_1) ◽  
pp. S67-S68
Author(s):  
Jeffrey Berinstein ◽  
Shirley Cohen-Mekelburg ◽  
Calen Steiner ◽  
Megan Mcleod ◽  
Mohamed Noureldin ◽  
...  
Keyword(s):  
At Risk ◽  
Resource Utilization ◽  
Time Trends ◽  
Healthcare Costs ◽  
Insurance Plan ◽  
High Resource ◽  
Patients At Risk ◽  
Inflammatory Bowel ◽  
Ed Visits ◽  
Out Of Pocket Costs

Abstract Background High-deductible health plan (HDHP) enrollment has increased rapidly over the last decade. Patients with HDHPs are incentivized to delay or avoid necessary medical care. We aimed to quantify the out-of-pocket costs of Inflammatory Bowel Disease (IBD) patients at risk for high healthcare resource utilization and to evaluate for differences in medical service utilization according to time in insurance period between HDHP and traditional health plan (THP) enrollees. Variations in healthcare utilization according to time may suggest that these patients are delaying or foregoing necessary medical care due to healthcare costs. Methods IBD patients at risk for high resource utilization (defined as recent corticosteroid and narcotic use) continuously enrolled in an HDHP or THP from 2009–2016 were identified using the Truven Health MarketScan database. Median annual financial information was calculated. Time trends in office visits, colonoscopies, emergency department (ED) visits, and hospitalizations were evaluated using additive decomposition time series analysis. Financial information and time trends were compared between the two insurance plan groups. Results Of 605,862 with a diagnosis of IBD, we identified 13,052 patients at risk for high resource utilization with continuous insurance plan enrollment. The median annual out-of-pocket costs were higher in the HDHP group (n=524) than in the THP group (n=12,458) ($1,920 vs. $1,205, p<0.001), as was the median deductible amount ($1,015 vs $289, p<0.001), without any difference in the median annual total healthcare expenses (Figure 1). Time in insurance period had a greater influence on utilization of colonoscopies, ED visits, and hospitalization in IBD patients enrolled in HDHPs compared to THPs (Figure 2). Colonoscopies peaked in the 4th quarter, ED visits peaked in the 1st quarter, and hospitalizations peaked in the 3rd and 4th quarter. Conclusion Among IBD patients at high risk for IBD-related utilization, HDHP enrollment does not change the cost of care, but shifts healthcare costs onto patients. This may be a result of HDHPs incentivizing delays with a potential for both worse disease outcomes and financial toxicity and needs to be further examined using prospective studies.

scholarly journals Evaluation of Optimal Vibrotactile Feedback for Force-Controlled Upper Limb Myoelectric Prostheses

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5209 ◽  
Author(s):  
Andrea Gonzalez-Rodriguez ◽  
Jose L. Ramon ◽  
Vicente Morell ◽  
Gabriel J. Garcia ◽  
Jorge Pomares ◽  
...  
Keyword(s):  
Real Time ◽  
Upper Limb ◽  
Closed Loop ◽  
Fine Tuning ◽  
Vibrotactile Feedback ◽  
Loop Control ◽  
Myoelectric Prostheses ◽  
Amplitude Level ◽  
Real Time Application ◽  
Selection Of

The main goal of this study is to evaluate how to optimally select the best vibrotactile pattern to be used in a closed loop control of upper limb myoelectric prostheses as a feedback of the exerted force. To that end, we assessed both the selection of actuation patterns and the effects of the selection of frequency and amplitude parameters to discriminate between different feedback levels. A single vibrotactile actuator has been used to deliver the vibrations to subjects participating in the experiments. The results show no difference between pattern shapes in terms of feedback perception. Similarly, changes in amplitude level do not reflect significant improvement compared to changes in frequency. However, decreasing the number of feedback levels increases the accuracy of feedback perception and subject-specific variations are high for particular participants, showing that a fine-tuning of the parameters is necessary in a real-time application to upper limb prosthetics. In future works, the effects of training, location, and number of actuators will be assessed. This optimized selection will be tested in a real-time proportional myocontrol of a prosthetic hand.

scholarly journals Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2727
Author(s):  
Hari Prasanth ◽  
Miroslav Caban ◽  
Urs Keller ◽  
Grégoire Courtine ◽  
Auke Ijspeert ◽  
...  
Keyword(s):  
Systematic Review ◽  
Gait Analysis ◽  
Real Time ◽  
Wearable Sensors ◽  
Pressure Sensors ◽  
Clinical Applications ◽  
Performance Comparison ◽  
Heel Strike ◽  
Rule Based ◽  
Pathological Gait

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

scholarly journals A framework for designing medical devices resilient to low-resource settings

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Davide Piaggio ◽  
Rossana Castaldo ◽  
Marco Cinelli ◽  
Sara Cinelli ◽  
Alessia Maccaro ◽  
...  
Keyword(s):  
Delphi Study ◽  
Middle Income ◽  
Low Resource Settings ◽  
Health Technologies ◽  
Low Resource ◽  
Middle Income Countries ◽  
High Resource ◽  
International Experts ◽  
Global Population ◽  
Different Parts

Abstract Background To date (April 2021), medical device (MD) design approaches have failed to consider the contexts where MDs can be operationalised. Although most of the global population lives and is treated in Low- and Middle-Income Countries (LMCIs), over 80% of the MD market share is in high-resource settings, which set de facto standards that cannot be taken for granted in lower resource settings. Using a MD designed for high-resource settings in LMICs may hinder its safe and efficient operationalisation. In the literature, many criteria for frameworks to support resilient MD design were presented. However, since the available criteria (as of 2021) are far from being consensual and comprehensive, the aim of this study is to raise awareness about such challenges and to scope experts’ consensus regarding the essentiality of MD design criteria. Results This paper presents a novel application of Delphi study and Multiple Criteria Decision Analysis (MCDA) to develop a framework comprising 26 essential criteria, which were evaluated and chosen by international experts coming from different parts of the world. This framework was validated by analysing some MDs presented in the WHO Compendium of innovative health technologies for low-resource settings. Conclusions This novel holistic framework takes into account some domains that are usually underestimated by MDs designers. For this reason, it can be used by experts designing MDs resilient to low-resource settings and it can also assist policymakers and non-governmental organisations in shaping the future of global healthcare.

scholarly journals Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon Tam ◽  
Mounir Boukadoum ◽  
Alexandre Campeau-Lecours ◽  
Benoit Gosselin
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Control Strategy ◽  
High Density ◽  
Fine Tuning ◽  
Mode Switching ◽  
Correct Prediction ◽  
Learning Approach ◽  
Predictive Values ◽  
Hand Prosthesis

AbstractMyoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

On the Performance Comparisons of Native and Clientless Real-Time Screen-Sharing Technologies

2021 ◽  
Vol 17 (2) ◽  
pp. 1-26
Author(s):  
Chun-ying Huang ◽  
Yun-chen Cheng ◽  
Guan-zhang Huang ◽  
Ching-ling Fan ◽  
Cheng-hsin Hsu
Keyword(s):  
Real Time ◽  
Computer Games ◽  
Performance Metrics ◽  
Video Quality ◽  
Dynamic Network ◽  
Web Browsers ◽  
Depth Analysis ◽  
Measurement Methodology ◽  
Overall Performance ◽  
Screen Sharing

Real-time screen-sharing provides users with ubiquitous access to remote applications, such as computer games, movie players, and desktop applications (apps), anywhere and anytime. In this article, we study the performance of different screen-sharing technologies, which can be classified into native and clientless ones. The native ones dictate that users install special-purpose software, while the clientless ones directly run in web browsers. In particular, we conduct extensive experiments in three steps. First, we identify a suite of the most representative native and clientless screen-sharing technologies. Second, we propose a systematic measurement methodology for comparing screen-sharing technologies under diverse and dynamic network conditions using different performance metrics. Last, we conduct extensive experiments and perform in-depth analysis to quantify the performance gap between clientless and native screen-sharing technologies. We found that our WebRTC-based implementation achieves the best overall performance. More precisely, it consumes a maximum of 3 Mbps bandwidth while reaching a high decoding ratio and delivering good video quality. Moreover, it leads to a steadily high decoding ratio and video quality under dynamic network conditions. By presenting the very first rigorous comparisons of the native and clientless screen-sharing technologies, this article will stimulate more exciting studies on the emerging clientless screen-sharing technologies.

Cybersecurity Standards in the Context of Operating System

2021 ◽  
Vol 54 (3) ◽  
pp. 1-36
Author(s):  
Syed Wasif Abbas Hamdani ◽  
Haider Abbas ◽  
Abdul Rehman Janjua ◽  
Waleed Bin Shahid ◽  
Muhammad Faisal Amjad ◽  
...  
Keyword(s):  
Organizational Context ◽  
Cyber Attacks ◽  
Fine Tuning ◽  
Security Vulnerabilities ◽  
Compliance Testing ◽  
Depth Analysis ◽  
Minimum Requirements ◽  
Security Standards ◽  
High Degree ◽  
Selection Of

Cyber threats have been growing tremendously in recent years. There are significant advancements in the threat space that have led towards an essential need for the strengthening of digital infrastructure security. Better security can be achieved by fine-tuning system parameters to the best and optimized security levels. For the protection of infrastructure and information systems, several guidelines have been provided by well-known organizations in the form of cybersecurity standards. Since security vulnerabilities incur a very high degree of financial, reputational, informational, and organizational security compromise, it is imperative that a baseline for standard compliance be established. The selection of security standards and extracting requirements from those standards in an organizational context is a tedious task. This article presents a detailed literature review, a comprehensive analysis of various cybersecurity standards, and statistics of cyber-attacks related to operating systems (OS). In addition to that, an explicit comparison between the frameworks, tools, and software available for OS compliance testing is provided. An in-depth analysis of the most common software solutions ensuring compliance with certain cybersecurity standards is also presented. Finally, based on the cybersecurity standards under consideration, a comprehensive set of minimum requirements is proposed for OS hardening and a few open research challenges are discussed.

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