scholarly journals Human-efficient labeling of a solar flux emergence video dataset by a deep learning model

2021 ◽  
Author(s):  
Subhamoy Chatterjee ◽  
Andres Munoz-Jaramillo ◽  
Derek Lamb
Keyword(s):  
Complex Data ◽  
Flux Emergence ◽  
Emergence Time ◽  
Quality Of Data ◽  
Complex Dynamic ◽  
Dynamic Events ◽  
Maximum Change ◽  
Deep Learning Model ◽  
Manual Verification

Abstract Machine learning is becoming a critical tool for interrogation of large complex data. However, labeling large datasets is time-consuming. Here we show that convolutional neural networks (CNNs), trained on crudely labeled astronomical videos, can be leveraged to improve the quality of data labeling and reduce the need for human intervention. We use videos of the solar photospheric magnetic field, crudely labeled into two classes: emergence or non-emergence of large bipolar magnetic regions (BMRs). We train the CNN using crude labeling, manually verify, correct labeling vs. CNN disagreements, and repeat this process until convergence. This results in a high-quality labeled dataset requiring the manual verification of only ~50% of all videos. Furthermore, by gradually masking the videos and looking for maximum change in CNN inference, we locate BMR emergence time without retraining the CNN. This demonstrates the versatility of CNNs for simplifying the challenging task of labeling complex dynamic events.

Methods to Monitor and Improve the Performance of Specimen Holders for Transmission Electron Cryomicroscopy

1996 ◽  
Vol 54 ◽  
pp. 454-455
Author(s):  
B. L. Armbruster ◽  
B. Kraus ◽  
M. Pan
Keyword(s):  
Electron Microscopy ◽  
Electron Beam ◽  
Electron Beam Irradiation ◽  
Beam Irradiation ◽  
Quality Of Data ◽  
Electron Cryomicroscopy ◽  
Thermal Equilibration ◽  
Transmission Electron ◽  
Electron Microscopes

One goal in electron microscopy of biological specimens is to improve the quality of data to equal the resolution capabilities of modem transmission electron microscopes. Radiation damage and beam- induced movement caused by charging of the sample, low image contrast at high resolution, and sensitivity to external vibration and drift in side entry specimen holders limit the effective resolution one can achieve. Several methods have been developed to address these limitations: cryomethods are widely employed to preserve and stabilize specimens against some of the adverse effects of the vacuum and electron beam irradiation, spot-scan imaging reduces charging and associated beam-induced movement, and energy-filtered imaging removes the “fog” caused by inelastic scattering of electrons which is particularly pronounced in thick specimens.Although most cryoholders can easily achieve a 3.4Å resolution specification, information perpendicular to the goniometer axis may be degraded due to vibration. Absolute drift after mechanical and thermal equilibration as well as drift after movement of a holder may cause loss of resolution in any direction.

Pengaruh Kompetensi Coder terhadap Keakuratan dan Ketepatan Pengkodean Menggunakan ICD 10 di Rumah Sakit X Pekanbaru Tahun 2016

2018 ◽  
Vol 1 (1) ◽  
pp. 31-43
Author(s):  
Nur Maimun ◽  
Jihan Natassa ◽  
Wen Via Trisna ◽  
Yeye Supriatin
Keyword(s):  
Medical Record ◽  
Human Error ◽  
Medical Personnel ◽  
Quality Of Data ◽  
Accuracy And Precision ◽  
Result Show ◽  
Study Implementation ◽  
Icd 10 ◽  
The Cost

The accuracy in administering the diagnosis code was the important matter for medical recorder, quality of data was the most important thing for health information management of medical recorder. This study aims to know the coder competency for accuracy and precision of using ICD 10 at X Hospital in Pekanbaru. This study was a qualitative method with case study implementation from five informan. The result show that medical personnel (doctor) have never received a training about coding, doctors writing that hard and difficult to read, failure for making diagnoses code or procedures, doctor used an usual abbreviations that are not standard, theres still an officer who are not understand about the nomenclature and mastering anatomy phatology, facilities and infrastructure were supported for accuracy and precision of the existing code. The errors of coding always happen because there is a human error. The accuracy and precision in coding very influence against the cost of INA CBGs, medical and the committee did most of the work in the case of severity level III, while medical record had a role in monitoring or evaluation of coding implementation. If there are resumes that is not clearly case mix team check file needed medical record the result the diagnoses or coding for conformity. Keywords: coder competency, accuracy and precision of coding, ICD 10

Designing Information Product (IP) Maps On the Process of Data Processing and Academic Information

2017 ◽  
Vol 4 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Diana Effendi
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Management ◽  
Information Quality ◽  
Quality Data ◽  
Management Approach ◽  
Quality Of Data ◽  
Information Product ◽  
Academic Activities

Information Product Approach (IP Approach) is an information management approach. It can be used to manage product information and data quality analysis. IP-Map can be used by organizations to facilitate the management of knowledge in collecting, storing, maintaining, and using the data in an organized. The  process of data management of academic activities in X University has not yet used the IP approach. X University has not given attention to the management of information quality of its. During this time X University just concern to system applications used to support the automation of data management in the process of academic activities. IP-Map that made in this paper can be used as a basis for analyzing the quality of data and information. By the IP-MAP, X University is expected to know which parts of the process that need improvement in the quality of data and information management.   Index term: IP Approach, IP-Map, information quality, data quality. REFERENCES[1] H. Zhu, S. Madnick, Y. Lee, and R. Wang, “Data and Information Quality Research: Its Evolution and Future,” Working Paper, MIT, USA, 2012.[2] Lee, Yang W; at al, Journey To Data Quality, MIT Press: Cambridge, 2006.[3] L. Al-Hakim, Information Quality Management: Theory and Applications. Idea Group Inc (IGI), 2007.[4] “Access : A semiotic information quality framework: development and comparative analysis : Journal ofInformation Technology.” [Online]. Available: http://www.palgravejournals.com/jit/journal/v20/n2/full/2000038a.html. [Accessed: 18-Sep-2015].[5] Effendi, Diana, Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi MenggunakanCALDEA Dan EVAMECAL (Studi Kasus X University), Proceeding Seminar Nasional RESASTEK, 2012, pp.TIG.1-TI-G.6.

Pilot, open non-controlled trial to assess the feasibility of implementing objective parameters as primary endpoints in a clinical trial with patients affected by knee osteoarthritis. (Preprint)

2019 ◽  
Author(s):  
Bogdan Corneliu Andor ◽  
Dionisio Franco Barattini ◽  
Dumitru Emanuel Dogaru ◽  
Simone Guadagna ◽  
Serban Rosu
Keyword(s):  
Chondroitin Sulphate ◽  
Controlled Trial ◽  
Subjective Evaluation ◽  
Pilot Trial ◽  
Final Visit ◽  
Life Questionnaire ◽  
Quality Of Data ◽  
Open Label ◽  
Objective Measurements

BACKGROUND Osteoarthritis (OA) is one of the top five most disabling conditions and it affects more than one third of persons over 65 years of age. Currently 80% of persons affected by OA already report having some movement limitation, 20% of people are not be able to perform major activities of daily living, and about 11% of the total affected population need of personal care. On 2014 the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) suggested as first step of pharmacological treatment for knee OA a background therapy with chronic symptomatic slow-acting drugs for osteoarthritis (SYSADOAs), such as glucosamine sulphate, chondroitin sulphate and hyaluronic acid (HA). In studies with oral HA, symptoms of OA are often measured using subjective parameters such as the visual analog scale (VAS) or the quality of life questionnaire (QoL) and objective measurements as ultrasonography (US) or range of motion (ROM) are employed in very few trials. This affects the quality of data in the literature. OBJECTIVE The primary objective of this work is to assess the feasibility of implementing US and ROM as objective measurements to correlate the improvement of knee mobility with pain reduction, evaluated using a subjective scale (VAS) in patients assuming a nutraceutical containing HA. The secondary objective is to evaluate the enrollment rate in one month to verify the feasibility for time and budget of the planned future main study. The explorative objective of the trial is to obtain preliminary data on efficacy of the tested product. METHODS This open-label pilot trial is performed in an orthopedic clinic (Timisoara, Romania). Male and female subjects (from 50 to 70 years) diagnosed with symptomatic OA of the knee with mild joint discomfort for at least 6 months are included. Following protocol, 8 patients are administered for 8 weeks Syalox® 300 Plus (River Pharma, Italy), a product based on HA of high molecular weight. Baseline and final visit assessments include orthopedic assessment, US, Knee injury and Osteoarthritis Outcome Score (KOOS) questionnaire, VAS and ROM of knee. RESULTS Data collection occurred between February 2018 and June 2018. All results are expected to be available by the end of 2018. CONCLUSIONS This pilot trial will be the first study to analyze the potential correlation between subjective evaluation (VAS, KOOS questionnaire) and objective measurements (US, ROM and actigraphy). The data from this study will assess the feasibility of the planned monthly recruitment rate and the necessary time and budget, and should provide preliminary information on efficacy of the tested product. CLINICALTRIAL ClinicalTrials.gov (NCT number: NCT03421054).

Measuring patient engagement among cancer patients in eHealth and mHealth interventions on symptom management: an integrated review (Preprint)

2020 ◽  
Author(s):  
Juqing Zhao ◽  
Pei Chen ◽  
Guangming Wan
Keyword(s):  
Cancer Patients ◽  
Patient Engagement ◽  
Symptom Management ◽  
Subjective Experience ◽  
Participation Rate ◽  
Sensor Data ◽  
Quality Of Data ◽  
Mhealth Intervention

BACKGROUND There has been an increase number of eHealth and mHealth interventions aimed to support symptoms among cancer survivors. However, patient engagement has not been guaranteed and standardized in these interventions. OBJECTIVE The objective of this review was to address how patient engagement has been defined and measured in eHealth and mHealth interventions designed to improve symptoms and quality of life for cancer patients. METHODS Searches were performed in MEDLINE, PsychINFO, Web of Science, and Google Scholar to identify eHealth and mHealth interventions designed specifically to improve symptom management for cancer patients. Definition and measurement of engagement and engagement related outcomes of each intervention were synthesized. This integrated review was conducted using Critical Interpretive Synthesis to ensure the quality of data synthesis. RESULTS A total of 792 intervention studies were identified through the searches; 10 research papers met the inclusion criteria. Most of them (6/10) were randomized trial, 2 were one group trail, 1 was qualitative design, and 1 paper used mixed method. Majority of identified papers defined patient engagement as the usage of an eHealth and mHealth intervention by using different variables (e.g., usage time, log in times, participation rate). Engagement has also been described as subjective experience about the interaction with the intervention. The measurement of engagement is in accordance with the definition of engagement and can be categorized as objective and subjective measures. Among identified papers, 5 used system usage data, 2 used self-reported questionnaire, 1 used sensor data and 3 used qualitative method. Almost all studies reported engagement at a moment to moment level, but there is a lack of measurement of engagement for the long term. CONCLUSIONS There have been calls to develop standard definition and measurement of patient engagement in eHealth and mHealth interventions. Besides, it is important to provide cancer patients with more tailored and engaging eHealth and mHealth interventions for long term engagement.

scholarly journals Response Behavior and Quality of Survey Data: Comparing Elderly Respondents in Institutions and Private Households

2021 ◽  
pp. 004912412199553
Author(s):  
Jan-Lucas Schanze
Keyword(s):  
Data Quality ◽  
The Elderly ◽  
Response Behavior ◽  
Quality Of Data ◽  
Social Surveys ◽  
Private Households ◽  
Confounding Variables ◽  
Health Related ◽  
Survey Interviews

An increasing age of respondents and cognitive impairment are usual suspects for increasing difficulties in survey interviews and a decreasing data quality. This is why survey researchers tend to label residents in retirement and nursing homes as hard-to-interview and exclude them from most social surveys. In this article, I examine to what extent this label is justified and whether quality of data collected among residents in institutions for the elderly really differs from data collected within private households. For this purpose, I analyze the response behavior and quality indicators in three waves of Survey of Health, Ageing and Retirement in Europe. To control for confounding variables, I use propensity score matching to identify respondents in private households who share similar characteristics with institutionalized residents. My results confirm that most indicators of response behavior and data quality are worse in institutions compared to private households. However, when controlling for sociodemographic and health-related variables, differences get very small. These results suggest the importance of health for the data quality irrespective of the housing situation.

scholarly journals Ensemble-Based Out-of-Distribution Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 567
Author(s):  
Donghun Yang ◽  
Kien Mai Mai Ngoc ◽  
Iksoo Shin ◽  
Kyong-Ha Lee ◽  
Myunggwon Hwang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Metric Learning ◽  
Feature Space ◽  
Confidence Score ◽  
Distance Metric Learning ◽  
Current State ◽  
Overall Performance ◽  
Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

scholarly journals Bacterial superinfections among persons with coronavirus disease 2019:A comprehensive review of data from postmortem studies

2021 ◽  
Author(s):  
Cornelius J Clancy ◽  
Ilan S Schwartz ◽  
Brittany Kula ◽  
M Hong Nguyen
Keyword(s):  
Rank Order ◽  
Diffuse Alveolar Damage ◽  
Antimicrobial Treatment ◽  
Beta Lactam ◽  
Quality Of Data ◽  
Major Objective ◽  
Postmortem Studies ◽  
Lung Infections ◽  
Histopathologic Findings

Abstract Background Limited clinical data suggest ~16% prevalence of bacterial superinfections among critically ill patients with coronavirus disease 2019 (COVID-19). Methods We reviewed postmortem studies of patients with COVID-19 published in English through 26 September 2020 for histopathologic findings consistent with bacterial lung infections. Results Worldwide, 621 patients from 75 studies were included. The quality of data was uneven, likely because identifying superinfections was not a major objective in 96% (72/75) of studies. Histopathology consistent with potential lung superinfection was reported in 32% (200/621) of patients (22-96 years old; 66% men). Types of infections were pneumonia (95%), abscesses or empyema (3.5%), and septic emboli (1.5%). Seventy-three percent of pneumonias were focal rather than diffuse. Predominant histopathologic findings were intra-alveolar neutrophilic infiltrations that were distinct from those typical of COVID-19-associated diffuse alveolar damage. In studies with available data, 79% of patients received antimicrobial treatment; most common agents were beta-lactam/beta-lactamase inhibitors (48%), macrolides (16%), cephalosoprins (12%), and carbapenems (6%). Superinfections were proven by direct visualization or recovery of bacteria in 25.5% (51/200) of potential cases, and 8% of all patients in postmortem studies. In rank order, pathogens included Acinetobacter baumannii, Staphylococcus aureus, Pseudomonas aeruginosa and Klebsiella pneumoniae. Lung superinfections were causes of death in 16% of potential cases, and 3% of all patients with COVID-19. Conclusions Potential bacterial lung superinfections were evident at postmortem examination in 32% of persons who died with COVID-19 (proven, 8%; possible, 24%), but they were uncommonly the cause of death.

scholarly journals Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data

2021 ◽  
Vol 15 (4) ◽  
pp. 1-20
Author(s):  
Georg Steinbuss ◽  
Klemens Böhm
Keyword(s):  
Outlier Detection ◽  
Synthetic Data ◽  
Real Data ◽  
Detection Methods ◽  
Quality Of Data ◽  
Benchmark Data ◽  
Core Idea ◽  
Generic Process ◽  
Unsupervised Outlier Detection

Benchmarking unsupervised outlier detection is difficult. Outliers are rare, and existing benchmark data contains outliers with various and unknown characteristics. Fully synthetic data usually consists of outliers and regular instances with clear characteristics and thus allows for a more meaningful evaluation of detection methods in principle. Nonetheless, there have only been few attempts to include synthetic data in benchmarks for outlier detection. This might be due to the imprecise notion of outliers or to the difficulty to arrive at a good coverage of different domains with synthetic data. In this work, we propose a generic process for the generation of datasets for such benchmarking. The core idea is to reconstruct regular instances from existing real-world benchmark data while generating outliers so that they exhibit insightful characteristics. We propose and describe a generic process for the benchmarking of unsupervised outlier detection, as sketched so far. We then describe three instantiations of this generic process that generate outliers with specific characteristics, like local outliers. To validate our process, we perform a benchmark with state-of-the-art detection methods and carry out experiments to study the quality of data reconstructed in this way. Next to showcasing the workflow, this confirms the usefulness of our proposed process. In particular, our process yields regular instances close to the ones from real data. Summing up, we propose and validate a new and practical process for the benchmarking of unsupervised outlier detection.

scholarly journals Improving Specimen Labelling and Data Collection in Bio-science Research using Mobile and Web Applications

2020 ◽  
Vol 10 (1) ◽  
pp. 1-16
Author(s):  
Isaac Nyabisa Oteyo ◽  
Mary Esther Muyoka Toili
Keyword(s):  
Data Collection ◽  
Web Applications ◽  
Data Entry ◽  
Science Research ◽  
Quality Data ◽  
Quality Of Data ◽  
Data Entry Form ◽  
Research Activities ◽  
Daunting Task

AbstractResearchers in bio-sciences are increasingly harnessing technology to improve processes that were traditionally pegged on pen-and-paper and highly manual. The pen-and-paper approach is used mainly to record and capture data from experiment sites. This method is typically slow and prone to errors. Also, bio-science research activities are often undertaken in remote and distributed locations. Timeliness and quality of data collected are essential. The manual method is slow to collect quality data and relay it in a timely manner. Capturing data manually and relaying it in real time is a daunting task. The data collected has to be associated to respective specimens (objects or plants). In this paper, we seek to improve specimen labelling and data collection guided by the following questions; (1) How can data collection in bio-science research be improved? (2) How can specimen labelling be improved in bio-science research activities? We present WebLog, an application that we prototyped to aid researchers generate specimen labels and collect data from experiment sites. We use the application to convert the object (specimen) identifiers into quick response (QR) codes and use them to label the specimens. Once a specimen label is successfully scanned, the application automatically invokes the data entry form. The collected data is immediately sent to the server in electronic form for analysis.

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