Data Interpretation

2020 ◽  
pp. 193-206
Author(s):  
David M. Corey ◽  
Mark Zelig
Keyword(s):  
Clinical Decision Making ◽  
Clinical Decision ◽  
Data Interpretation ◽  
Integration Model ◽  
Fitness For Duty ◽  
Forensic Practice ◽  
Data Source ◽  
Single Data ◽  
And Behavior

This chapter begins with a discussion of the benefits of using a data integration model to guide clinical decision-making in suitability and fitness evaluations. The authors note that avoiding reliance on a single data source is a well-established principle in forensic practice. In their experience, in some cases the data fit together neatly like pieces of a jigsaw puzzle, and the resulting suitability and fitness determinations are “no-brainers.” But more often, there is some information that points to problems that are contradicted (or not reported) by other sources. These more typical cases reflect the reality that people can be complicated, and behavior is contextual. The authors then present several examples of integrative models, including one for guiding the integration and interpretation of data in a preemployment evaluation to reach a determination of suitability, and another to facilitate the determination of fitness for duty.

Do Adjunctive Flap-Monitoring Technologies Impact Clinical Decision Making? An Analysis of Microsurgeon Preferences and Behavior by Body Region

2015 ◽  
Vol 135 (3) ◽  
pp. 883-892 ◽  
Author(s):  
Justin L. Bellamy ◽  
Gerhard S. Mundinger ◽  
José M. Flores ◽  
Eric G. Wimmers ◽  
Georgia C. Yalanis ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Body Region ◽  
Flap Monitoring ◽  
Monitoring Technologies ◽  
And Behavior

scholarly journals Deep Learning of Markov Model Based Machines for Determination of Better Treatment Option Decisions for Infertile Women

2019 ◽  
Author(s):  
Arni S.R. Srinivasa Rao ◽  
Michael P. Diamond
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
Treatment Options ◽  
Clinical Decision ◽  
Infertile Women ◽  
Learning Techniques ◽  
Technical Article

AbstractIn this technical article, we are proposing ideas those we have been developing of how machine learning and deep learning techniques can potentially assist obstetricians / gynecologists in better clinical decision making using infertile women in their treatment options in combination with mathematical modeling in pregnant women as examples.

Diagnostic performance of the noninvasive prenatal FetoGnost RhD assay for the prediction of the fetal RhD blood group status

2021 ◽  
Author(s):  
Tobias J. Legler ◽  
Sandra Lührig ◽  
Irina Korschineck ◽  
Dieter Schwartz
Keyword(s):  
Diagnostic Accuracy ◽  
Diagnostic Performance ◽  
Clinical Decision Making ◽  
False Negative ◽  
Clinical Decision ◽  
Multiple Pregnancies ◽  
Group Status ◽  
Test Kit ◽  
Existing Data

Abstract Purpose: To evaluate the diagnostic accuracy of a commercially available test kit for noninvasive prenatal determination of the fetal RhD status (NIPT-RhD) with a focus on early gestation and multiple pregnancies. Methods: The FetoGnost RhD assay (Ingenetix, Vienna, Austria) is routinely applied for clinical decision making either in woman with anti-D alloimmunization or in order to target the application of routine antenatal anti-D prophylaxis (RAADP) to women with a RhD positive fetus. Based on existing data in the laboratory information system the newborn’s serological RhD status was compared with NIPT RhD results. Results: Since 2009 NIPT RhD was performed in 2,968 pregnant women between week 5+6 and 40+0 of gestation (median 12+6) and conclusive results were obtained in 2,888 (97.30%) cases. Diagnostic accuracy was calculated from those 2244 (77.70%) cases with the newborn’s serological RhD status reported. The sensitivity of the FetoGnost RhD assay was 99.93% (95% CI 99.61% - 99.99%) and the specificity was 99.61% (95% CI 98.86% - 99.87%). No false positive or false negative NIPT RhD result was observed in 203 multiple pregnancies. Conclusion: NIPT RhD results are reliable when obtained with FetoGnost RhD assay. Targeted routine anti-D-prophylaxis can start as early as 11+0 weeks of gestation in singleton and multiple pregnancies.

Use of Orange Data Mining Toolbox for Data Analysis in Clinical Decision Making

2018 ◽  
pp. 143-167
Author(s):  
Musa Peker ◽  
Osman Özkaraca ◽  
Ali Şaşar
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Blood Sugar ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Diabetic Patients ◽  
Proposed Model ◽  
Treatment Of Diabetes

Diabetes is a life-long illness which occurs as a result of lack of insulin hormone or ineffectiveness of insulin hormone. Blood sugar, fructosamine, and hemoglobin A1c (HbA1c) values are widely used for diagnosis of this disease. Although the role of insulin in diagnosing diabetes is great, the HbA1c value is more accurate. This is because HbA1c value gives information about the past two or three months of blood sugar in the treatment of diabetes. This study aims to estimate the HbA1c value with high accuracy. Follow-up data of diabetic patients were used as data. The Orange data mining software is used because it is easy to use in the modeling phase and contains many methods. In this context, the chapter aims to develop an effective prediction model by using a large number of feature selection and classification methods. The results show that the proposed model successfully predicts the HbA1c parameter. In addition, determination of the parameters that are effective in the diagnosis of diabetes has been carried out with the feature selection methods.

scholarly journals Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19

2020 ◽  
Author(s):  
Eliane Röösli ◽  
Brian Rice ◽  
Tina Hernandez-Boussard
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Health Disparities ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Mitigation Strategies ◽  
Reporting Standards ◽  
Regulatory Frameworks ◽  
Rapid Dissemination ◽  
Data Source

Abstract The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection rate, hospitalizations, and mortality. Many believe artificial intelligence (AI) is a solution to guide clinical decision-making for this novel disease, resulting in the rapid dissemination of underdeveloped and potentially biased models, which may exacerbate the disparities gap. We believe there is an urgent need to enforce the systematic use of reporting standards and develop regulatory frameworks for a shared COVID-19 data source to address the challenges of bias in AI during this pandemic. There is hope that AI can help guide treatment decisions within this crisis; yet given the pervasiveness of biases, a failure to proactively develop comprehensive mitigation strategies during the COVID-19 pandemic risks exacerbating existing health disparities.

scholarly journals Diagnostic performance of the noninvasive prenatal FetoGnost RhD assay for the prediction of the fetal RhD blood group status

2021 ◽  
Author(s):  
Tobias J. Legler ◽  
Sandra Lührig ◽  
Irina Korschineck ◽  
Dieter Schwartz
Keyword(s):  
Diagnostic Accuracy ◽  
Diagnostic Performance ◽  
Clinical Decision Making ◽  
False Negative ◽  
Clinical Decision ◽  
Multiple Pregnancies ◽  
Group Status ◽  
Test Kit ◽  
Existing Data

Abstract Purpose To evaluate the diagnostic accuracy of a commercially available test kit for noninvasive prenatal determination of the fetal RhD status (NIPT-RhD) with a focus on early gestation and multiple pregnancies. Methods The FetoGnost RhD assay (Ingenetix, Vienna, Austria) is routinely applied for clinical decision making either in woman with anti-D alloimmunization or to target the application of routine antenatal anti-D prophylaxis (RAADP) to women with a RhD positive fetus. Based on existing data in the laboratory information system the newborn’s serological RhD status was compared with NIPT RhD results. Results Since 2009 NIPT RhD was performed in 2968 pregnant women between weeks 5 + 6 and 40 + 0 of gestation (median 12 + 6) and conclusive results were obtained in 2888 (97.30%) cases. Diagnostic accuracy was calculated from those 2244 (77.70%) cases with the newborn’s serological RhD status reported. The sensitivity of the FetoGnost RhD assay was 99.93% (95% CI 99.61–99.99%) and the specificity was 99.61% (95% CI 98.86–99.87%). No false-positive or false-negative NIPT RhD result was observed in 203 multiple pregnancies. Conclusion NIPT RhD results are reliable when obtained with FetoGnost RhD assay. Targeted routine anti-D-prophylaxis can start as early as 11 + 0 weeks of gestation in singleton and multiple pregnancies.

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