Identification of data elements for blood gas analysis dataset: a base for developing registries and artificial intelligence-based systems

Zare, Sahar; Meidani, Zahra; Ouhadian, Maryam; Akbari, Hosein; Zand, Farid; Fakharian, Esmaeil; Sharifian, Roxana

doi:10.1186/s12913-022-07706-y

Cited by 6 publications

(1 citation statement)

References 33 publications

(33 reference statements)

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“…Neonatal care is an important component of obstetric care. Neonates are physically poor, have immature organs and tissues, are at high risk of care, and are unable to speak, requiring close supervision by caregivers [9]. Failure to identify nursing risks in clinical practice may increase the risk of nursing deficits and increase the efficiency of illnesses such as jaundice and infection.…”

Section: Introductionmentioning

confidence: 99%

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

Zhao

2022

Wireless Communications and Mobile Computing

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Nowadays, people are paying greater attention to the concept of childcare, and the healthy development of children is humanity’s hope for the future. The newborn stage is the most critical stage of a child’s development, and learning and mastering neonatal care is the first important lesson for parents. To meet the needs of today’s learners and to break the traditional teaching model, this paper develops a set of interactive newborn nursing knowledge system that composes the content of trivial nursing knowledge, reconstructs the knowledge structure system, and provides new teaching methods such as text browsing, 3D animation presentation, picture presentation, video presentation, interactive games, and tests for newborns. Text browsing, 3D animation, image demonstration, video demonstration, and interactive games and tests, among other new teaching methods, give a new teaching mode and method for neonatal nursing education. The study included 158 infants from local hospital. The study was divided into two groups, each with 80 cases: a control group (conventional care) and a study group (quality care management). The outcomes were contrasted and compared. The results revealed that the study group had a lower incidence of nursing defects than the control group, and the difference was statistically significant ( P 0.05 ); the study group had a lower incidence of diseases than the control group, and the difference was statistically significant ( P < 0.05 ). The study goes over the complete process of incorporating the instructional display into the interactive newborn care knowledge system. Multimedia techniques and applications were researched throughout the design and production process in order to blend intrinsic knowledge material with digital technology to produce new educational ideas.

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Section: Introductionmentioning

confidence: 99%

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

Zhao

2022

Wireless Communications and Mobile Computing

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Identifying data elements and key features to design a telerehabilitation system for speech and language disorders in children with hearing impairments in Iran: A cross‐sectional study

Ebrahimi,

Zakerabbasali,

Oryadi

et al. 2024

Health Science Reports

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Background and AimsDesigning and implementing telerehabilitation systems would require the identification of data elements to meet disabled people's needs. An in‐depth study on the identification and validation of data elements can help design and implement telerehabilitation systems successfully. Therefore, this study aimed to identify and validate data elements to design a telerehabilitation system for the speech and language disorders of hearing‐impaired children in Iran.MethodsThis descriptive cross‐sectional study was conducted in three steps including literature review, focused group discussion, and Delphi technique implementation to extract and validate data elements. In the first step, the literature on electronic databases was reviewed to extract the data elements of telerehabilitation systems, and nine studies were selected based on the inclusion criteria. In the second step, a focused group discussion was held to review and classify the extracted data elements. Finally, the Delphi technique was employed to validate the drafted data elements.ResultsIn total, 352 data elements were extracted from the literature review. Finally, 102 data elements in 10 categories (Demographic and Clinical information of the disabled person, Clinical history, Demographic information of the provider, Customization of exercises, Reminders, Online and offline counseling, and training, Reporting, Key features of the system, Evaluation of the progress of the disabled person) were classified and validated by experts as essential data elements to design a telerehabilitation system for the speech and language disorders of hearing‐impaired children.ConclusionsThe necessary data elements were proposed as the foundations to design a telerehabilitation system for the speech and language disorders of hearing‐impaired children. These data elements help design and implement telerehabilitation systems successfully so that such systems can easily be provided for children with hearing disabilities.

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Interpretation of acid–base metabolism on arterial blood gas samples via machine learning algorithms

Ozdemir,

Sasmaz,

Guven

et al. 2024

Ir J Med Sci

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Background Arterial blood gas evaluation is crucial for critically ill patients, as it provides essential information about acid–base metabolism and respiratory balance, but evaluation can be complex and time-consuming. Artificial intelligence can perform tasks that require human intelligence, and it is revolutionizing healthcare through technological advancements. Aim This study aims to assess arterial blood gas evaluation using artificial intelligence algorithms. Methods The study included 21.541 retrospective arterial blood gas samples, categorized into 15 different classes by experts for evaluating acid–base metabolism status. Six machine learning algorithms were utilized; accuracy, balanced accuracy, sensitivity, specificity, precision, and F1 values of the models were determined; and ROC curves were drawn to assess areas under the curve for each class. Evaluation of which sample was estimated in which class was conducted using the confusion matrices of the models. Results The bagging classifier (BC) model achieved the highest balanced accuracy with 99.24%, whereas the XGBoost model reached the highest accuracy with 99.66%. The BC model shows 100% sensitivity for nine classes and 100% specificity for 10 classes, and the model correctly predicted 6438 of 6463 test samples and achieved an accuracy of 99.61%, with an area under the curve > 0.9 in all classes on a class basis. Conclusion The machine learning models developed exhibited remarkable accuracy, sensitivity, and specificity in predicting the status of acid–base metabolism. However, implementing these models can aid clinicians, freeing up their time for more intricate tasks.

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Identification of data elements for blood gas analysis dataset: a base for developing registries and artificial intelligence-based systems

Cited by 6 publications

References 33 publications

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

Identifying data elements and key features to design a telerehabilitation system for speech and language disorders in children with hearing impairments in Iran: A cross‐sectional study

Interpretation of acid–base metabolism on arterial blood gas samples via machine learning algorithms

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