scholarly journals Half-century archives of occupational medical data on French nuclear workers: a dusty warehouse or gold mine for epidemiological research?

2014 ◽  
Vol 65 (4) ◽  
pp. 407-416 ◽  
Author(s):  
Jerome-Philippe Garsi ◽  
Eric Samson ◽  
Laetitia Chablais ◽  
Sergey Zhivin ◽  
Christine Niogret ◽  
...  
Keyword(s):  
Blood Test ◽  
Metabolic Diseases ◽  
Medical Data ◽  
Nuclear Industry ◽  
Internal Exposure ◽  
Test Results ◽  
Job Descriptions ◽  
Epidemiological Research ◽  
Nuclear Workers ◽  
Medical Visits

Abstract This article discusses the availability and completeness of medical data on workers from the AREVA NC Pierrelatte nuclear plant and their possible use in epidemiological research on cardiovascular and metabolic disorders related to internal exposure to uranium. We created a computer database from files on 394 eligible workers included in an ongoing nested case-control study from a larger cohort of 2897 French nuclear workers. For each worker, we collected records of previous employment, job positions, job descriptions, medical visits, and blood test results from medical history. The dataset counts 9,471 medical examinations and 12,735 blood test results. For almost all of the parameters relevant for research on cardiovascular risk, data completeness and availability is over 90 %, but it varies with time and improves in the latest time period. In the absence of biobanks, collecting and computerising available good-quality occupational medicine archive data constitutes a valuable alternative for epidemiological and aetiological research in occupational health. Biobanks rarely contain biological samples over an entire worker’s carrier and medical data from nuclear industry archives might make up for unavailable biomarkers that could provide information on cardiovascular and metabolic diseases

New Method to Assist Discrimination of Liver Diseases by Spherical SOM with Mahalanobis Distance

2012 ◽  
Vol 16 (1) ◽  
pp. 55-61 ◽  
Author(s):  
Norie Kanzaki ◽  
◽  
Akihiro Kanagawa ◽  
Keyword(s):  
Acute Hepatitis ◽  
Mahalanobis Distance ◽  
Blood Test ◽  
Input Data ◽  
Distance Measure ◽  
Medical Data ◽  
Data Conversion ◽  
Test Results ◽  
Automated Diagnosis ◽  
Test Items

Spherical SOM, an improved version of a kind of neutral network SOM, has successfully been applied to data analysis in a variety of fields achieving effective results. However, distance measure of commercial spherical SOM is limited to the Euclidean distance and it is not suitable enough to the analysis of biased data such as blood test results. The Mahalanobis distance is said to be effective for the analysis of such medical data. Therefore it is expected that better results should be obtained if spherical SOM with Mahalanobis distance is applied to the analysis of medical data. In this paper, we take blood test items as multi-dimensional vectors and convert the input data into Mahalanobis distance with the aim of developing an automated diagnosis system by spherical SOM with Mahalanobis distance as pseudo input data. Conversion of the input data into Mahalanobis distance ensures correct evaluations of the biased data of blood test results at the same time allowing automated diagnosis based on doctors’ intuitions and experiences. We have grouped subjects of diagnosis whose features were not well discriminated by conventional Mahalanobis distance and have administered detailed discrimination by the group and obtained better discrimination rates. While in the conventional studies TP rates for the following five categories, no liverrelated problem, hepatoma (liver cancer), acute hepatitis, chronic hepatitis and liver cirrhosis, were 100%, 70%, 100%, 80% and 60% respectively, they were 96%, 80%, 71%, 86% and 91% respectively with the proposed method. Significant results were obtained overall except for acute hepatitis.

scholarly journals The use of routine blood tests to assist the diagnosis of COVID-19 in symptomatic hospitalized patients

2021 ◽  
pp. 000456322199907
Author(s):  
IT Parsons ◽  
AT Parsons ◽  
E Balme ◽  
G Hazell ◽  
R Gifford ◽  
...  
Keyword(s):  
Cell Count ◽  
Retrospective Review ◽  
Blood Test ◽  
Patient Flow ◽  
White Cell Count ◽  
Area Under The Curve ◽  
White Cell ◽  
Test Results ◽  
Rt Pcr ◽  
Blood Tests

Introduction Specific patterns of blood test results are associated with COVID-19 infection. The aim of this study was to identify which blood tests could be used to assist in diagnosing COVID-19. Method A retrospective review was performed on consecutive patients referred to hospital with a clinical suspicion of COVID-19 over a period of four weeks. The patient’s clinical presentation and severe acute respiratory syndrome coronavirus 2 reverse-transcription polymerase chain reaction (SARS-CoV-2 RT-PCR) were recorded. The patients were divided by diagnosis into COVID (COVID-19 infection) or CONTROL (an alternate diagnosis). A retrospective review of consecutive patients over a further two-week period was used for the purposes of validation. Results Overall, 399 patients (53% COVID, 47% CONTROL) were analysed. White cell count, neutrophils and lymphocytes were significantly lower, while lactate dehydrogenase and ferritin were significantly higher, in the COVID group in comparison to CONTROL. Combining the white cell count, lymphocytes and ferritin results into a COVID Combined Blood Test (CCBT) had an area under the curve of 0.79. Using a threshold CCBT of –0.8 resulted in a sensitivity of 0.85 and a specificity of 0.63. Analysing this against a further retrospective review of 181 suspected COVID-19 patients, using the same CCBT threshold, resulted in a sensitivity of 0.73 and a specificity of 0.75. The sensitivity was comparable to the SARS-CoV-2 RT PCR. Discussion Mathematically combining the blood tests has the potential to assist clinical acumen allowing for rapid streaming and more accurate patient flow pending definitive diagnosis. This may be of particular use in low-resource settings.

Predicting abnormal laboratory blood test results in the intensive care unit using novel features based on information theory and historical conditional probability (Preprint)

2021 ◽  
Author(s):  
Camilo E. Valderrama ◽  
Daniel J. Niven ◽  
Henry T. Stelfox ◽  
Joon Lee
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Conditional Probability ◽  
Blood Test ◽  
Laboratory Tests ◽  
Conditional Entropy ◽  
Admission Diagnosis ◽  
Test Results ◽  
Abnormal Laboratory ◽  
Blood Tests

BACKGROUND Redundancy in laboratory blood tests is common in intensive care units (ICU), affecting patients' health and increasing healthcare expenses. Medical communities have made recommendations to order laboratory tests more judiciously. Wise selection can rely on modern data-driven approaches that have been shown to help identify redundant laboratory blood tests in ICUs. However, most of these works have been developed for highly selected clinical conditions such as gastrointestinal bleeding. Moreover, features based on conditional entropy and conditional probability distribution have not been used to inform the need for performing a new test. OBJECTIVE We aimed to address the limitations of previous works by adapting conditional entropy and conditional probability to extract features to predict abnormal laboratory blood test results. METHODS We used an ICU dataset collected across Alberta, Canada which included 55,689 ICU admissions from 48,672 patients with different diagnoses. We investigated conditional entropy and conditional probability-based features by comparing the performances of two machine learning approaches to predict normal and abnormal results for 18 blood laboratory tests. Approach 1 used patients' vitals, age, sex, admission diagnosis, and other laboratory blood test results as features. Approach 2 used the same features plus the new conditional entropy and conditional probability-based features. RESULTS Across the 18 blood laboratory tests, both Approach 1 and Approach 2 achieved a median F1-score, AUC, precision-recall AUC, and Gmean above 80%. We found that the inclusion of the new features statistically significantly improved the capacity to predict abnormal laboratory blood test results in between ten and fifteen laboratory blood tests depending on the machine learning model. CONCLUSIONS Our novel approach with promising prediction results can help reduce over-testing in ICUs, as well as risks for patients and healthcare systems. CLINICALTRIAL N/A

The moral construction of worry about radiation exposure: Emotion, knowledge, and tests

2017 ◽  
Vol 28 (6) ◽  
pp. 635-656 ◽  
Author(s):  
Aug Nishizaka
Keyword(s):  
Radiation Exposure ◽  
Conversation Analysis ◽  
Internal Exposure ◽  
Test Results ◽  
Communication Tool ◽  
Work Related ◽  
Video Recordings ◽  
Moral Construction ◽  
Fukushima Disaster ◽  
Do So

In the analysis of video recordings of the interactions between a doctor and the examinees following internal radiation exposure tests at a hospital in Fukushima Prefecture, I explore how the participants address one of the most serious consequences of the Fukushima disaster, that is, their concerns about radioactive materials. To do so, this study employs conversation analysis. The doctor’s presentation of the test results provides the examinees with a place to express relief and also makes relevant the justification work related to the expression of relief. In conclusion, I consider how the internal exposure tests also function as a communication tool in the context in which residents from affected areas face potential difficulties in expressing their worry about radiation.

Diseases

1932 ◽  
Vol 5 (3-4) ◽  
pp. 73-130
Keyword(s):  
No Value ◽  
Blood Test ◽  
Agglutination Test ◽  
Test Results ◽  
Agglutination Titer ◽  
Poultry Farmers

Value of Agglutination-Test. - Der Wert der Agglutinations Methode bei der weissen Ruhr. Dr. Sachweh, München. Berliner Tierärztliche Wochenschrift, 1931, p. 845.A number of chickens were subjected, every three months, to the agglutination test. During this test it appeared that the agglutination titer varied considerably. All animals, of which a great number reacted positively at the beginning of the test, reacted negatively at the end thereof. This made it possible to comprehend the strongly varying results of the different tests. A group of 400 positive reacting hens gave healthy chicks. The wide advertising of the rapid blood test results in the poultry farmers making their own researches, and owing to the varying results obtained, it forces them to attach no value thereto.

scholarly journals Early Cancer Detection from Multianalyte Blood Test Results

iScience ◽  
2019 ◽  
Vol 15 ◽  
pp. 332-341 ◽  
Author(s):  
Ka-Chun Wong ◽  
Junyi Chen ◽  
Jiao Zhang ◽  
Jiecong Lin ◽  
Shankai Yan ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Blood Test ◽  
Early Cancer ◽  
Early Cancer Detection ◽  
Test Results
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