A long short-term memory-fully connected (LSTM-FC) neural network for predicting the incidence of bronchopneumonia in children

Zhao, Dongzhe; Chen, Min; Shi, Kaifang; Ma, Mingguo; Huang, Yang; Shen, Jingwei

doi:10.1007/s11356-021-14632-9

Cited by 12 publications

(4 citation statements)

References 50 publications

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“…Selama proses implematasi keperawatan terdapat beberapa beberapa faktor pendukung yang mempengaruhi proses keberhasilan dari strategi pelaksanaan yang dilakukan yaitu pasien tidak terlalu banyak bergerak dan kooperatif (Zhang, 2022). Evaluasi Keperawatan dilakukan setiap hari untuk mngetahui perkembangan keadaan pasien setiap hari secara berkala (Zhao, 2021). Intervensi dan implementasi yang dilakukan memiliki tujuan, pasien 1 dan pasien 2 dapat tercapai setelah dilakukan asukan keperawatan selama tiga hari dengan kriteria hasil SpO2 ≥95%, pola napas membaik, tidak ada sekret pada jalan napas.…”

Section: Hasil Dan Pembahasanunclassified

Asuhan Keperawatan Bersihan Jalan Napas tidak Efektif pada Pasien Anak dengan Bronkopneumonia di RSD Balung dan Kalisat Jember

Cahya Putra,

Utami

2023

phms

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Bersihan jalan nafas yang tidak efektif pada anak dengan bronkopneumonia dapat menjadi permasalahan serius apabila tidak ditangani dengan tepat. Penatalaksanaan bersihan jalan nafas yang tidak efektif pada anak dengan bronkopneumonia memerlukan ketersediaan perawatan berkualitas. Studi kasus ini bertujuan merepresentasikan pe-natalaksanaan asuhan keperawatan anak dengan masalah keperawatan bersihan jalan nafas yang tidak efektif pada kasus bronkopneumonia di ruang keperawatan anak (Dahlia) RSUD Balung dan ruang keperawatan anak (Manyar) RSUD Kalisat Jember. Metode: rancangan penelitian merupakan studi kasus. Waktu penelitian dilakukan pada tanggal 27 Juni 2023 sampai 10 Juli 2023 di Ruangan keperawatan anak (Dahlia) RSUD Balung dan ruang keperawatan anak (Manyar) RSUD Kalisat Jember. Populasi adalah semua Anak yang menderita Bronkopneumonia dengan 2 sampel yang diambil secara purposive sampling. Instrumen pengumpulan data yang digunakan adalah format pengkajian sampai evaluasi keperawatan anak. Cara pengumpulan data dilakukan melalui wawancara, pengukuran, observasi dan studi doku-mentasi. Analisis dilakukan pada semua temuan di tahapan proses keperawatan pada anak. Hasil: Setelah tindakan asuhan keperawatan meunjukkan bahwa pasien An. R (10 bulan) lebih cepat mengalami perbaikan kondisi lebih cepat dari pada pasien An. S (18 bulan) tetapi hal tersebut juga berkaitan dengan faktor lain. Pasien An. R (10 bulan) lebih kooperatif dari pada pasien An. S (18 bulan)

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Section: Hasil Dan Pembahasanunclassified

Asuhan Keperawatan Bersihan Jalan Napas tidak Efektif pada Pasien Anak dengan Bronkopneumonia di RSD Balung dan Kalisat Jember

Cahya Putra,

Utami

2023

phms

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“…The clinical symptoms of bronchopneumonia mainly include cough, fever, and shortness of breath. When not being treated timely, it can induce cardiovascular and digestive system dysfunction, or even multiple organ failure (MOF), thus severely damaging the life heath of the affected children (Celis et al, 2021;Zhao et al, 2021). In recent years, factors like environmental pollution and climate changes have resulted in an increasing number of bronchopneumonia children, which has brought a heavy burden on the family (Celis et al, 2021;Ye et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Remimazolam attenuates inflammation in bronchopneumonia through the inhibition of NLRP3 activity by PDPK1 ubiquitination

Yang,

2024

Chem Biol Drug Des

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Bronchopneumonia is the most common pneumonia in childhood. Therefore, we tested the effects of Remimazolam presented Bronchopneumonia and its possible mechanisms. Phillygenin increased survival rate, reduced W/D ratio, and lung injury score, and inhibited IL‐1β, IL‐6, TNF‐α, and INF‐γ levels in mice model of bronchopneumonia. Remimazolam induced PDPK1 and p‐AKT protein expressions, and suppressed NLRP3 protein expression in lung tissue of mice model. In vitro model, Remimazolam also induced PDPK1 and p‐AKT protein expressions, and suppressed NLRP3 protein expression. Remimazolam also inhibited inflammation levels in vitro model. PDPK1 inhibitor, PHT‐427 (100 mg/kg) reduced survival rate, increased W/D ratio and lung injury score, and promoted inflammation levels in mice model of bronchopneumonia by treated with Remimazolam. PHT‐427 suppressed PDPK1 and p‐AKT protein expressions and induced NLRP3 protein expression in mice model of bronchopneumonia by treated with Remimazolam. Remimazolam interlinked PDPK1 protein. Remimazolam increased the expressions of PDPK1 and p‐AKT in vitro model. Remimazolam reduced PDPK1 ubiquitination in vitro model.

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“…The application of artificial intelligence in the medical field is gradually maturing. BP neural network (BPNN) model [ 15 ] and support vector machine (SVM) model [ 16 ] have no special requirements for data distribution and have certain fault tolerance. In addition, they are widely used in dealing with complex relationships between data and can seek the optimal solution under the current information [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Bronchopneumonia Inpatients’ Total Hospitalization Expenses Based on BP Neural Network and Support Vector Machine Models

Zha

Gao

2022

Computational and Mathematical Methods in Medicine

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Objective. BP neural network (BPNN) model and support vector machine (SVM) model were used to predict the total hospitalization expenses of patients with bronchopneumonia. Methods. A total of 355 patients with bronchopneumonia from January 2018 to December 2020 were collected and sorted out. The data set was randomly divided into a training set ( n = 249 ) and a test set ( n = 106 ) according to 7 : 3. The BPNN model and SVM model were constructed to analyze the predictors of total hospitalization expenses. The effectiveness was compared between these two prediction models. Results. The top three influencing factors and their importance for predicting total hospitalization cost by the BPNN model were hospitalization days (0.477), age (0.154), and discharge department (0.083). The top 3 factors predicted by the SVM model were hospitalization days (0.215), age (0.196), and marital status (0.172). The area under the curve of these two models is 0.838 (95% CI: 0.755~0.921) and 0.889 (95% CI: 0.819~0.959), respectively. Conclusion. Both the BPNN model and SVM model can predict the total hospitalization expenses of patients with bronchopneumonia, but the prediction effect of the SVM model is better than the BPNN model.

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A long short-term memory-fully connected (LSTM-FC) neural network for predicting the incidence of bronchopneumonia in children

Cited by 12 publications

References 50 publications

Asuhan Keperawatan Bersihan Jalan Napas tidak Efektif pada Pasien Anak dengan Bronkopneumonia di RSD Balung dan Kalisat Jember

Asuhan Keperawatan Bersihan Jalan Napas tidak Efektif pada Pasien Anak dengan Bronkopneumonia di RSD Balung dan Kalisat Jember

Remimazolam attenuates inflammation in bronchopneumonia through the inhibition of NLRP3 activity by PDPK1 ubiquitination

Prediction of Bronchopneumonia Inpatients’ Total Hospitalization Expenses Based on BP Neural Network and Support Vector Machine Models

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