Probabilistic glycemic control decision support in icu: proof of concept using Bayesian network
Glycemic control in critically ill patients is complex in terms of patients’ response to care and treatment. The variability and the search for improved insulin therapy outcomes have led to the use of human physiology model based on per-patient metabolic condition to provide automated recommendation...
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Penerbit UTM Press
2019
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| オンライン･アクセス: | http://irep.iium.edu.my/68312/13/68312%20Probabilistic%20glycemic%20control%20decision%20support%20in%20ICU.pdf http://irep.iium.edu.my/68312/14/68312%20Probabilistic%20glycemic%20control%20decision%20support%20in%20ICU%20SCOPUS.pdf http://irep.iium.edu.my/68312/25/68312_PROBABILISTIC%20GLYCEMIC%20CONTROL%20DECISION%20_wos.pdf http://irep.iium.edu.my/68312/ https://jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/12721/6501 |
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English English English |
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R Medicine (General) |
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R Medicine (General) Abu-Samah, Asma Abdul Razak, Normy Norfiza Suhaimi, Fatanah M. Jamaludin, Ummu Kulthum Md Ralib, Azrina Probabilistic glycemic control decision support in icu: proof of concept using Bayesian network |
| description |
Glycemic control in critically ill patients is complex in terms of patients’ response to care and treatment. The variability and the search for improved insulin therapy outcomes have led to the use of human physiology model based on per-patient metabolic condition to provide automated recommendations. One of the most promising solution for this is the STAR protocol which is based on a clinically validated ICING insulin and nutrition physiological model, however this approach does not consider demographical background such as age, weight, height and ethnicity. This article presents the extension to their personalized care solution by integrating per-patient demographical and upon admission to intensive care conditions to automate decision support for clinical staffs. In this context, a virtual study was conducted on 210 retrospectives critically ill patients’ data. To provide a ground, the integration concept is presented roughly, but the details are given in terms of a proof of concept using Bayesian Network, linking the admission background and the STAR control’s performance. The proof of concept study shows the feasibility and the clinical potential to employ the probabilistic method as a decision support towards a more personalized care.
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Kawalan glisemik dalam pesakit kritikal di unit rawatan rapi adalah rumit dari segi tindak
balas pesakit terhadap penjagaan dan rawatan. Sifat keberubahan individu dan
pencarian hasil terapi insulin yang lebih baik telah membawa kepada penggunaan
model matematik fisiologi manusia berdasarkan keadaan metabolik pesakit untuk
memberikan cadangan rawatan secara individu. Salah satu penyelesaian yang paling
menjanjikan harapan adalah protokol STAR yang berdasarkan kepada model fisiologi
insulin-nutrisi-glukosa yang telah disahkan secara klinikal. Namun pendekatan ini tidak
mengambil kira latar belakang demografi seperti umur, berat, ketinggian dan etnik.
Artikel ini membentangkan lanjutan kepada penyelesaian rawatan secara peribadi
mereka dengan mengintegrasikan informasi demografi pesakit dan keadaan mereka
semasa dimasukkan ke dalam unit rawatan rapi untuk mengautomasikan sokongan
keputusan untuk kakitangan unit. Dalam konteks ini, satu kajian ‘virtual’ dilakukan pada
data 210 pesaki. Sebagai kajian kes, konsep integrasi dibentangkan secara kasar, tetapi
butiran diberikan dari segi bukti konsep yang menggunakan Rangkaian Bayesian,
menghubungkan latar belakang kemasukan ke unit dan prestasi kawalan STAR. Bukti
kajian kes menunjukkan 71.43% dan 73.90% ketepatan dan kebolehlaksanaan unjuran
masing-masing dengan dataset ujian. Dengan lebih banyak data, rangkaian Bayesian
yang lebih baik dipercayai boleh dihasilkan. Walaubagaimanapun, keputusan ini
menunjukkan kemungkinan rangkaian ini bertindak sebagai pengelas yang berkesan
dengan menggunakan data dari unit rawatan rapi dan prestasi kawalan glisemik untuk
menjadi asas sokongan keputusan bersifat probabilistik, peribadi dan automatic dalam
unit rawatan rapi. |
| format |
Article |
| author |
Abu-Samah, Asma Abdul Razak, Normy Norfiza Suhaimi, Fatanah M. Jamaludin, Ummu Kulthum Md Ralib, Azrina |
| author_facet |
Abu-Samah, Asma Abdul Razak, Normy Norfiza Suhaimi, Fatanah M. Jamaludin, Ummu Kulthum Md Ralib, Azrina |
| author_sort |
Abu-Samah, Asma |
| title |
Probabilistic glycemic control decision support in icu: proof of concept using Bayesian network |
| title_short |
Probabilistic glycemic control decision support in icu: proof of concept using Bayesian network |
| title_full |
Probabilistic glycemic control decision support in icu: proof of concept using Bayesian network |
| title_fullStr |
Probabilistic glycemic control decision support in icu: proof of concept using Bayesian network |
| title_full_unstemmed |
Probabilistic glycemic control decision support in icu: proof of concept using Bayesian network |
| title_sort |
probabilistic glycemic control decision support in icu: proof of concept using bayesian network |
| publisher |
Penerbit UTM Press |
| publishDate |
2019 |
| url |
http://irep.iium.edu.my/68312/13/68312%20Probabilistic%20glycemic%20control%20decision%20support%20in%20ICU.pdf http://irep.iium.edu.my/68312/14/68312%20Probabilistic%20glycemic%20control%20decision%20support%20in%20ICU%20SCOPUS.pdf http://irep.iium.edu.my/68312/25/68312_PROBABILISTIC%20GLYCEMIC%20CONTROL%20DECISION%20_wos.pdf http://irep.iium.edu.my/68312/ https://jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/12721/6501 |
| _version_ |
1672610118908248064 |
| spelling |
my.iium.irep.683122020-07-02T08:27:58Z http://irep.iium.edu.my/68312/ Probabilistic glycemic control decision support in icu: proof of concept using Bayesian network Abu-Samah, Asma Abdul Razak, Normy Norfiza Suhaimi, Fatanah M. Jamaludin, Ummu Kulthum Md Ralib, Azrina R Medicine (General) Glycemic control in critically ill patients is complex in terms of patients’ response to care and treatment. The variability and the search for improved insulin therapy outcomes have led to the use of human physiology model based on per-patient metabolic condition to provide automated recommendations. One of the most promising solution for this is the STAR protocol which is based on a clinically validated ICING insulin and nutrition physiological model, however this approach does not consider demographical background such as age, weight, height and ethnicity. This article presents the extension to their personalized care solution by integrating per-patient demographical and upon admission to intensive care conditions to automate decision support for clinical staffs. In this context, a virtual study was conducted on 210 retrospectives critically ill patients’ data. To provide a ground, the integration concept is presented roughly, but the details are given in terms of a proof of concept using Bayesian Network, linking the admission background and the STAR control’s performance. The proof of concept study shows the feasibility and the clinical potential to employ the probabilistic method as a decision support towards a more personalized care. ************************************************************************************* Kawalan glisemik dalam pesakit kritikal di unit rawatan rapi adalah rumit dari segi tindak balas pesakit terhadap penjagaan dan rawatan. Sifat keberubahan individu dan pencarian hasil terapi insulin yang lebih baik telah membawa kepada penggunaan model matematik fisiologi manusia berdasarkan keadaan metabolik pesakit untuk memberikan cadangan rawatan secara individu. Salah satu penyelesaian yang paling menjanjikan harapan adalah protokol STAR yang berdasarkan kepada model fisiologi insulin-nutrisi-glukosa yang telah disahkan secara klinikal. Namun pendekatan ini tidak mengambil kira latar belakang demografi seperti umur, berat, ketinggian dan etnik. Artikel ini membentangkan lanjutan kepada penyelesaian rawatan secara peribadi mereka dengan mengintegrasikan informasi demografi pesakit dan keadaan mereka semasa dimasukkan ke dalam unit rawatan rapi untuk mengautomasikan sokongan keputusan untuk kakitangan unit. Dalam konteks ini, satu kajian ‘virtual’ dilakukan pada data 210 pesaki. Sebagai kajian kes, konsep integrasi dibentangkan secara kasar, tetapi butiran diberikan dari segi bukti konsep yang menggunakan Rangkaian Bayesian, menghubungkan latar belakang kemasukan ke unit dan prestasi kawalan STAR. Bukti kajian kes menunjukkan 71.43% dan 73.90% ketepatan dan kebolehlaksanaan unjuran masing-masing dengan dataset ujian. Dengan lebih banyak data, rangkaian Bayesian yang lebih baik dipercayai boleh dihasilkan. Walaubagaimanapun, keputusan ini menunjukkan kemungkinan rangkaian ini bertindak sebagai pengelas yang berkesan dengan menggunakan data dari unit rawatan rapi dan prestasi kawalan glisemik untuk menjadi asas sokongan keputusan bersifat probabilistik, peribadi dan automatic dalam unit rawatan rapi. Penerbit UTM Press 2019-03 Article PeerReviewed application/pdf en http://irep.iium.edu.my/68312/13/68312%20Probabilistic%20glycemic%20control%20decision%20support%20in%20ICU.pdf application/pdf en http://irep.iium.edu.my/68312/14/68312%20Probabilistic%20glycemic%20control%20decision%20support%20in%20ICU%20SCOPUS.pdf application/pdf en http://irep.iium.edu.my/68312/25/68312_PROBABILISTIC%20GLYCEMIC%20CONTROL%20DECISION%20_wos.pdf Abu-Samah, Asma and Abdul Razak, Normy Norfiza and Suhaimi, Fatanah M. and Jamaludin, Ummu Kulthum and Md Ralib, Azrina (2019) Probabilistic glycemic control decision support in icu: proof of concept using Bayesian network. Jurnal Teknologi (Sciences and Engineering), 81 (2). pp. 61-69. E-ISSN 2180-3772 https://jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/12721/6501 |
| score |
13.251813 |