Dataset Information

CoDR: Correlation-based Data Reduction Scheme for Efficient Gathering of Heterogeneous Driving Data.

ABSTRACT: A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driving data, such as traffic conditions, driver behavior, vehicle status and location information. However, these different types of driving data easily become more than tens of GB per day, forming a significant hurdle due to the storage and network cost. To address this problem, this paper proposes a novel scheme, called CoDR, which can reduce data volume by considering the correlations among heterogeneous driving data. Among heterogeneous datasets, CoDR first chooses one set as a pivot data. Then, according to the objective of data collection, it identifies data ranges relevant to the objective from the pivot dataset. Finally, it investigates correlations among sets, and reduces data volume by eliminating irrelevant data from not only the pivot set but also other remaining datasets. CoDR gathers four heterogeneous driving datasets: two videos for front view and driver behavior, OBD-II and GPS data. We show that CoDR decreases data volume by up to 91%. We also present diverse analytical results that reveal the correlations among the four datasets, which can be exploited usefully for edge computing to reduce data volume on the spot.

SUBMITTER: Park J

PROVIDER: S-EPMC7146115 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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CoDR: Correlation-based Data Reduction Scheme for Efficient Gathering of Heterogeneous Driving Data.

Park Junho J Chung Yoojin Y Choi Jongmoo J

Sensors (Basel, Switzerland) 20200317 6

A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driving data, such as traffic conditions, driver behavior, vehicle status and location information. However, these different types of driving data easily become more than tens of GB per day, forming a significant hurdle due to the storage and network cost. To address this problem, this paper proposes a novel scheme, called CoDR, which can reduc ...[more]

PMID: 32192221

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Project description:BackgroundThe burden on healthcare systems is mounting continuously owing to population growth and aging, overuse of medical services, and the recent COVID-19 pandemic. This overload is also causing reduced healthcare quality and outcomes. One solution gaining momentum is the integration of intelligent self-assessment tools, known as symptom-checkers, into healthcare-providers' systems. To the best of our knowledge, no study so far has investigated the data-gathering capabilities of these tools, which represent a crucial resource for simulating doctors' skills in medical-interviews.ObjectivesThe goal of this study was to evaluate the data-gathering function of currently available chatbot symptom-checkers.MethodsWe evaluated 8 symptom-checkers using 28 clinical vignettes from the repository of MSD-Manual case studies. The mean number of predefined pertinent findings for each case was 31.8 ± 6.8. The vignettes were entered into the platforms by 3 medical students who simulated the role of the patient. For each conversation, we obtained the number of pertinent findings retrieved and the number of questions asked. We then calculated the recall-rates (pertinent-findings retrieved out of all predefined pertinent-findings), and efficiency-rates (pertinent-findings retrieved out of the number of questions asked) of data-gathering, and compared them between the platforms.ResultsThe overall recall rate for all symptom-checkers was 0.32(2,280/7,112;95 %CI 0.31-0.33) for all pertinent findings, 0.37(1,110/2,992;95 %CI 0.35-0.39) for present findings, and 0.28(1140/4120;95 %CI 0.26-0.29) for absent findings. Among the symptom-checkers, Kahun platform had the highest recall rate with 0.51(450/889;95 %CI 0.47-0.54). Out of 4,877 questions asked overall, 2,280 findings were gathered, yielding an efficiency rate of 0.46(95 %CI 0.45-0.48) across all platforms. Kahun was the most efficient tool 0.74 (95 %CI 0.70-0.77) without a statistically significant difference from Your.MD 0.69(95 %CI 0.65-0.73).ConclusionThe data-gathering performance of currently available symptom checkers is questionable. From among the tools available, Kahun demonstrated the best overall performance.

Project description:BACKGROUND:China still suffers heavily from rabies, although reported human cases continue to decrease year over year. There are far fewer laboratory-confirmed human cases than clinically diagnosed cases, which is a big problem that needs to be addressed. In this report, we summarize analyses of all specimens from human cases tested in our laboratory over the past 15?years, in order to promote laboratory diagnosis of rabies. METHODS:From 2005 to 2019, a total of 271 samples from 164 suspected rabies cases were collected from local hospitals by the local Centers for Disease Control and Prevention (CDCs) in China. Saliva, cerebrospinal fluid (CSF), serum (blood) and urine were collected for ante-mortem diagnosis, and brain tissue, neck skin tissue and cornea were collected for post-mortem diagnosis. All of the specimens were tested by reverse transcription-polymerase chain reaction (RT-PCR), and brain tissues were also tested using fluorescent antibody test (FAT). The number of positive test results obtained using different fluids or tissues, and at different stages of the disease, were compared using a chi-square test and a more effective sampling program is recommended. RESULTS:As the national reference laboratory for rabies surveillance in China, our laboratory has tested 271 samples from 164 suspected rabies cases collected by local CDCs since 2005. We found that saliva gave the highest number of positive test results (32%), compared with CSF and other fluids. We also found that serum or blood specimens collected in the last 3 days of life can test positive by RT-PCR. CONCLUSIONS:Serum or blood samples collected in the last 3 days of a patient's life can be used to measure viral RNA, which means that serum samples, as well as saliva and CSF, can be used to detect viral RNA for anti-mortem diagnosis of rabies. Because of our findings, we have modified our "National Surveillance Project for Human Rabies", by adding the collection and testing of serum samples from the end of the survival period. This will improve our national surveillance and laboratory diagnosis of human rabies.

Dataset Information

CoDR: Correlation-based Data Reduction Scheme for Efficient Gathering of Heterogeneous Driving Data.

Publications

CoDR: Correlation-based Data Reduction Scheme for Efficient Gathering of Heterogeneous Driving Data.

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