Designing Data Elements and Minimum Data Set (MDS) for Creating the Registry of Patients with Gestational Diabetes Mellitus

Ahmadi, Maryam; Mirbagheri, Esmat

doi:10.25122/jml-2019-0011

Cited by 12 publications

(2 citation statements)

References 7 publications

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“…One of the important factors in developing an MDS is that users must understand the importance of applying it and have the necessary knowledge in this regard 5 . Furthermore, designing an MDS requires a well‐defined framework that covers the information needs of its intended users 6 . Studies have shown that designing a minimum data set helps define standard criteria that can lead to the reduction of medical errors 7 .…”

Section: Introductionmentioning

confidence: 99%

“… 5 Furthermore, designing an MDS requires a well‐defined framework that covers the information needs of its intended users. 6 Studies have shown that designing a minimum data set helps define standard criteria that can lead to the reduction of medical errors. 7 It enhances data collection and facilitates prediction of illnesses and prevents their outbreak Moreover, it can be a strong tool for collecting data to be used at national and regional levels.…”

Section: Introductionmentioning

confidence: 99%

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Designing a minimum data set of laboratory data for the electronic summary sheet of pediatric ward in Iran: A cross‐sectional study

Shahbakhsh

Khajouei

Sabahi

et al. 2023

Health Science Reports

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Background and Aim: Iranian hospitals are provided with hospital information systems (HISs) from different vendors, which make it hardly possible to summarize laboratory data in an consistent manner. Therefore, there is a need to design a minimum data set of laboratory data that will define standard criteria and reduce potential medical errors. The purpose of this study was to design a minimum data set (MDS) of laboratory data for an electronic summary sheet to be used in the pediatric ward of Iranian hospitals.Methods: This study consists of three phases. In the first phase, out of 3997 medical records from the pediatric ward, 604 summary sheets were chosen as sample. The laboratory data of these sheets were examined and the recorded tests were categorized. In the second phase, based on the types of diagnosis we developed a list of tests. Then we asked the physicians of the ward to select which ones should be documented for each patient's diagnosis. In the third phase, the tests that were reported in 21%-80% of the records, and were verified by the same percentage of physicians, were evaluated by the experts' panel.Results: In the first phase, 10,224 laboratory data were extracted. Of these, 144 data elements reported in more than 80% of the records, and more than 80% of experts approved them to be included in the MDS for patients' summary sheet. After data elements were investigated in the experts' panel, 292 items were chosen for the final list of the data set.Conclusions: This MDS was designed such that, if implemented in hospital information systems, it could automatically enable registering data in the summary sheet when patient's diagnosis is registered.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Designing a minimum data set of laboratory data for the electronic summary sheet of pediatric ward in Iran: A cross‐sectional study

Shahbakhsh

Khajouei

Sabahi

et al. 2023

Health Science Reports

View full text Add to dashboard Cite

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RETRACTED ARTICLE: Prediction of gestational diabetes based on explainable deep learning and fog computing

et al. 2022

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Gestational diabetes mellitus (GDM) is one of the pregnancy complications that endangers both mothers and babies. GDM is usually diagnosed at 22–26 weeks of gestation. However, early prediction is preferable because it may decrease the risk. The continuous monitoring of the mother’s vital signs helps in predicting any deterioration during pregnancy. The originality of this research is to provide a comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework consists of three layers, which are: (i) Internet of things (IoT) Layer, (ii) Fog Layer, and (iii) Cloud Layer. The first layer used IoT sensors to aggregate vital signs from pregnancies using invasive and non-invasive sensors. The vital signs are then transmitted to fog nodes to be processed and finally stored in the cloud layer. The main contribution in this research is located in the fog layer producing the GDM module to implement two influential tasks which are as follows: (i) Data Finding Methodology (DFM), and (ii) Explainable Prediction Algorithm (EPM) using DNN. First, the DFM is used to replace the unused data to free up the cache space for new incoming data items. The cache replacement is very important in the case of the healthcare system as the incoming vital signs are frequent and must be replaced continuously. Second, the EPM is used to predict the occurrence of GDM in the second trimester of the pregnancy. To evaluate our model, we extracted data from 16,354 pregnant women from the medical information mart for intensive care (MIMIC III) benchmark dataset. For each woman, vital signs, demographic data, and laboratory tests were aggregated. The results of the prediction model are superior to the state-of-the-art (ACC = 0.957, AUC = 0.942). Regarding explainability, we used Shapley additive explanation (SHAP) framework to provide local and global explanations for the developed models. Overall, the proposed framework is medically intuitive and allows the early prediction of GDM with a cost-effective solution.

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RETRACTED ARTICLE: Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction

El-Rashidy

ElSayed

El-Ghamry

et al. 2022

Neural Comput & Applic

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Gestational diabetes mellitus (GDM) is one of the pregnancy complications that poses a significant risk on mothers and babies as well. GDM usually diagnosed at 22–26 of gestation. However, the early prediction is desirable as it may contribute to decrease the risk. The continuous monitoring for mother’s vital signs helps in predicting any deterioration during pregnancy. The originality of this paper is to provide comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework consists of three layers which are: (i) IoT Layer, (ii) Fog Layer, and (iii) Cloud Layer. The first layer used IOT sensors to aggregate vital sings from pregnancies using invasive and noninvasive sensors. Then the vital signs transmitted to fog nodes to processed and finally stored in the cloud layer. The main contribution in this paper is located in the fog layer producing GDM module to implement two influential tasks which are: (i) Data Finding Methodology (DFM), and (ii) Explainable Prediction Algorithm (EPM) using DNN. First, the DFM is used to replace the unused data to free the cache space for the new incoming data items. The cache replacement is very important in the case of healthcare system as the incoming vital signs are frequent and must be replaced continuously. Second, the EPM is used to predict the incidence of GDM that may occur in the second trimester of the pregnancy. To evaluate our model, we extract data of 16,354 pregnancy women from medical information mart for intensive care (MIMIC III) benchmark dataset. For each woman, vital signs, demographic data and laboratory tests was aggregated. The results of the prediction model superior the state of the art (ACC = 0.957, AUC = 0.942). Regarding to explainability, we utilized Shapley additive explanation framework to provide local and global explanation for the developed models. Overall, the proposed framework is medically intuitive, allow the early prediction of GDM with cost effective solution.

show abstract

Designing Data Elements and Minimum Data Set (MDS) for Creating the Registry of Patients with Gestational Diabetes Mellitus

Cited by 12 publications

References 7 publications

Designing a minimum data set of laboratory data for the electronic summary sheet of pediatric ward in Iran: A cross‐sectional study

Designing a minimum data set of laboratory data for the electronic summary sheet of pediatric ward in Iran: A cross‐sectional study

RETRACTED ARTICLE: Prediction of gestational diabetes based on explainable deep learning and fog computing

RETRACTED ARTICLE: Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction

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