scholarly journals Anomaly Detection Algorithm for Real-World Data and Evidence in Clinical Research (Preprint)

2021 ◽  
Author(s):  
Vendula Churová ◽  
Roman Vyškovský ◽  
Kateřina Maršálová ◽  
David Kudláček ◽  
Daniel Schwarz
Keyword(s):  
Anomaly Detection ◽  
Clinical Research ◽  
Data Quality ◽  
Real World ◽  
Detection Algorithm ◽  
Data Detection ◽  
Real World Data ◽  
World Data ◽  
Clinical Registries ◽  
Anomalous Data

BACKGROUND Statistical analysis, which has become an integral part of evidence-based medicine, relies heavily on data quality that is of critical importance in modern clinical research. Input data are not only at risk of being falsified or fabricated, but also of being mishandled by investigators. OBJECTIVE The urgent need to assure the highest data quality possible has led to implementation of various auditing strategies designed to monitor clinical trials and detect errors of different origin that frequently occur in the field. METHODS An automatic anomaly detection algorithm based on machine learning that combines clustering with a series of distance metrics is presented. RESULTS The algorithm is built in a particular electronic data capture (EDC) system that stores real-world data in clinical registries. These data, together with newly generated, simulated anomalous data were utilized to evaluate the detection performance of this algorithm. CONCLUSIONS The experimental results demonstrate that the algorithm, which is universal, and as such may be implemented in other EDC systems, is capable of anomalous data detection with sensitivity exceeding 85%.

scholarly journals Anomaly Detection Algorithm for Real-World Data and Evidence in Clinical Research (Preprint)

10.2196/27172 ◽  
2021 ◽  
Author(s):  
Vendula Churová ◽  
Roman Vyškovský ◽  
Kateřina Maršálová ◽  
David Kudláček ◽  
Daniel Schwarz
Keyword(s):  
Anomaly Detection ◽  
Clinical Research ◽  
Real World ◽  
Detection Algorithm ◽  
Real World Data ◽  
World Data

scholarly journals Gap between real-world data and clinical research within hospitals in China: a qualitative study

BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e038375
Author(s):  
Feifei Jin ◽  
Chen Yao ◽  
Xiaoyan Yan ◽  
Chongya Dong ◽  
Junkai Lai ◽  
...  
Keyword(s):  
Qualitative Study ◽  
Clinical Research ◽  
Real World ◽  
Data Standards ◽  
Medical Record Data ◽  
Real World Data ◽  
World Data ◽  
Electronic Medical Record Data ◽  
Research Platform ◽  
Record Data

ObjectiveTo investigate the gap between real-world data and clinical research initiated by doctors in China, explore the potential reasons for this gap and collect different stakeholders’ suggestions.DesignThis qualitative study involved three types of hospital personnel based on three interview outlines. The data analysis was performed using the constructivist grounded theory analysis process.SettingSix tertiary hospitals (three general hospitals and three specialised hospitals) in Beijing, China, were included.ParticipantsIn total, 42 doctors from 12 departments, 5 information technology managers and 4 clinical managers were interviewed through stratified purposive sampling.ResultsElectronic medical record data cannot be directly downloaded into clinical research files, which is a major problem in China. The lack of data interoperability, unstructured electronic medical record data and concerns regarding data security create a gap between real-world data and research data. Updating hospital information systems, promoting data standards and establishing an independent clinical research platform may be feasible suggestions for solving the current problems.ConclusionsDetermining the causes of gaps and targeted solutions could contribute to the development of clinical research in China. This research suggests that updating the hospital information system, promoting data standards and establishing a clinical research platform could promote the use of real-world data in the future.

Abstract P505: Real-world Data Validation Of A Novel P-wave Based Automatic Atrial Fibrillation Detection Algorithm

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Åke Olsson ◽  
Magnus Samulesson
Keyword(s):  
Real World ◽  
False Negative ◽  
Detection Algorithm ◽  
P Wave ◽  
Detection Accuracy ◽  
The Novel ◽  
Real World Data ◽  
P Waves ◽  
World Data ◽  
Rr Variability

Background: Automatic ECG algorithms using only RR-variability in ECG to detect AF have shown high false positive rates. By including P-wave presence in the algorithm, research has shown that it can increase detection accuracy for AF. Methods: A novel RR- and P-wave based automatic detection algorithm implemented in the Coala Heart Monitor ("Coala", Coala Life AB, Sweden) was evaluated for detection accuracy by the comparison to blinded manual ECG interpretation based on real-world data. Evaluation was conducted on 100 consecutive anonymous printouts of chest- and thumb-ECG waveforms, where the algorithm had detected both irregular RR-rhythms and strong P-waves in either chest or thumb recording (non-AF episodes classified by algorithm as Category 12).The recordings, without exclusions, were generated from 5,512 real-world data recordings from actual Coala users in Sweden (both OTC and Rx users) during the period of March 5 to March 22, 2019, with no control or influence by the researchers or any other organization or individual. The prevalence of cardiac conditions in the user population was unknown.The blinded recordings were each manually interpreted by a trained cardiologist. The manual interpretation was compared with the automatic analysis performed by the detection algorithm to determine the number of additional false negative indications for AF as presented to the user. Results: The trained cardiologist manually interpreted 0 of the 100 recordings as AF. Manual interpretation showed that the novel automatic AF algorithm yielded 0 % False Negative error and 100 % Negative Predictive Value (NPV) for detection of AF. Irregular RR-rhythms were detected in 569 recordings (10 % of a total of 5,512 recordings). The 100 non-AF recordings containing both irregular RR-rhythms and strong P-waves constituted 18% of all recordings with irregular RR-rhythms. Respiratory sinus arrhythmia was the single most prevalent condition and was found in 47% of irregular RR-rhythms with strong P-waves. Conclusion: The novel, P-wave based automatic ECG algorithm used in the Coala, showed a zero percent False Negative error rate for AF detection in ECG recordings with RR-variability but presence of P-waves, as compared to manual interpretation by a cardiologist.

scholarly journals PNS1 Real-World DATA Collection in China: Challenges and Recommendations to Ensure DATA Quality

2020 ◽  
Vol 22 ◽  
pp. S80
Author(s):  
M. Soni ◽  
L. Marshall ◽  
R. Zaha ◽  
J. Lee ◽  
Y. Huang
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Real World ◽  
Real World Data ◽  
World Data

scholarly journals Validate Real-World Data-based Endpoint Measures of Cancer Treatment Outcomes

2021 ◽  
Author(s):  
Qian Li ◽  
Hansi Zhang ◽  
Zhaoyi Chen ◽  
Yi Guo ◽  
Thomas George ◽  
...  
Keyword(s):  
Overall Survival ◽  
Clinical Trials ◽  
Data Quality ◽  
Treatment Outcomes ◽  
Real World ◽  
Gold Standard ◽  
Surrogate Marker ◽  
Real World Data ◽  
World Data ◽  
Real World Evidence

Recently, there is a growing interest in using real-world data (RWD) to generate real-world evidence (RWE) that complements clinical trials. Nevertheless, to quantify the treatment effects, it is important to develop meaningful RWD-based endpoints. In cancer trials, two real-world endpoints are particularly of interest: real-world overall survival (rwOS) and real-world time to next treatment (rwTTNT). In this work, we identified ways to calculate these real-world endpoints with structured EHR data, and validated these endpoints against the gold-standard measurements of these endpoints derived from linked EHR and TR data. In addition, we also examined and reported the data quality issues especially the inconsistency between the EHR and TR data. Using survival model, our result showed that patients (1) without subsequent chemotherapy or (2) with subsequent chemotherapy and longer rwTTNT, would have longer rwOS, showing the validity of using rwTTNT as a real-world surrogate marker for measuring cancer endpoints.

scholarly journals Mining moving object data

2012 ◽  
Vol 2 (3) ◽  
Author(s):  
Jaroslav Zendulka ◽  
Martin Pešek
Keyword(s):  
Outlier Detection ◽  
Real World ◽  
Moving Objects ◽  
Detection Algorithm ◽  
Moving Object ◽  
Location Based Services ◽  
Data Sets ◽  
Real World Data ◽  
World Data ◽  
Trajectory Outlier Detection

AbstractCurrently many devices provide information about moving objects and location-based services that accumulate a huge volume of moving object data, including trajectories. This paper deals with two useful analysis tasks — mining moving object patterns and trajectory outlier detection. We also present our experience with the TOP-EYE trajectory outlier detection algorithm, which we applied to two real-world data sets.

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