Application of the Gradient boosted tree approach for thin film classification based on disintegration time
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Metapodaci
Prikaz svih podataka o dokumentuApstrakt
Thin films are polymeric strips that disintegrate in the
oral cavity and consist of a film-forming agent and an
active pharmaceutical ingredient (API). Generally, thin
films disintegrate within seconds, but their composition
can be modified to allow slower disintegration and release
of the loaded API, depending on the properties of the film.
Research into various aspects of oral thin films is
progressing rapidly, but thin films are also being discussed
in the context of a broader range of other dosage forms,
such as carrier for multiparticulates or nano-based dosage
forms and for the fixed-dose combinations (Turković et al.,
2022). Large amounts of data are being generated over the
years, so integrating machine learning algorithms can be
beneficial to gain more in-depth knowledge about the thin
film properties and interactions between film constituents.
Gradient boosted tree is one of machine learning tools that
perform regression or classification by combining the
out...puts from individual decision trees. This work is aimed
to explore the possibility of integrating a machine learning
approach in evaluation of experimental data obtained by
films characterization. Potential application of Gradient
boosted trees for thin films characterization based on their
disintegration properties as film critical quality attribute
was investigated.
Izvor:
Macedonian Pharmaceutical Bulletin, 2023, 69, Suppl 1, 113-114Izdavač:
- Macedonian Pharmaceutical Association
- Ss. Cyril and Methodius University in Skopje, Faculty of Pharmacy
Finansiranje / projekti:
- Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200161 (Univerzitet u Beogradu, Farmaceutski fakultet) (RS-MESTD-inst-2020-200161)
Napomena:
- 14th Central European Symposium on Pharmaceutical Technology, 28th - 30th September, Ohrid, N. Macedonia, 2023
Institucija/grupa
PharmacyTY - CONF AU - Turković, Erna AU - Vasiljević, Ivana AU - Parojčić, Jelena PY - 2023 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/5064 AB - Thin films are polymeric strips that disintegrate in the oral cavity and consist of a film-forming agent and an active pharmaceutical ingredient (API). Generally, thin films disintegrate within seconds, but their composition can be modified to allow slower disintegration and release of the loaded API, depending on the properties of the film. Research into various aspects of oral thin films is progressing rapidly, but thin films are also being discussed in the context of a broader range of other dosage forms, such as carrier for multiparticulates or nano-based dosage forms and for the fixed-dose combinations (Turković et al., 2022). Large amounts of data are being generated over the years, so integrating machine learning algorithms can be beneficial to gain more in-depth knowledge about the thin film properties and interactions between film constituents. Gradient boosted tree is one of machine learning tools that perform regression or classification by combining the outputs from individual decision trees. This work is aimed to explore the possibility of integrating a machine learning approach in evaluation of experimental data obtained by films characterization. Potential application of Gradient boosted trees for thin films characterization based on their disintegration properties as film critical quality attribute was investigated. PB - Macedonian Pharmaceutical Association PB - Ss. Cyril and Methodius University in Skopje, Faculty of Pharmacy C3 - Macedonian Pharmaceutical Bulletin T1 - Application of the Gradient boosted tree approach for thin film classification based on disintegration time VL - 69 IS - Suppl 1 SP - 113 EP - 114 DO - 10.33320/maced.pharm.bull.2023.69.03.055 ER -
@conference{ author = "Turković, Erna and Vasiljević, Ivana and Parojčić, Jelena", year = "2023", abstract = "Thin films are polymeric strips that disintegrate in the oral cavity and consist of a film-forming agent and an active pharmaceutical ingredient (API). Generally, thin films disintegrate within seconds, but their composition can be modified to allow slower disintegration and release of the loaded API, depending on the properties of the film. Research into various aspects of oral thin films is progressing rapidly, but thin films are also being discussed in the context of a broader range of other dosage forms, such as carrier for multiparticulates or nano-based dosage forms and for the fixed-dose combinations (Turković et al., 2022). Large amounts of data are being generated over the years, so integrating machine learning algorithms can be beneficial to gain more in-depth knowledge about the thin film properties and interactions between film constituents. Gradient boosted tree is one of machine learning tools that perform regression or classification by combining the outputs from individual decision trees. This work is aimed to explore the possibility of integrating a machine learning approach in evaluation of experimental data obtained by films characterization. Potential application of Gradient boosted trees for thin films characterization based on their disintegration properties as film critical quality attribute was investigated.", publisher = "Macedonian Pharmaceutical Association, Ss. Cyril and Methodius University in Skopje, Faculty of Pharmacy", journal = "Macedonian Pharmaceutical Bulletin", title = "Application of the Gradient boosted tree approach for thin film classification based on disintegration time", volume = "69", number = "Suppl 1", pages = "113-114", doi = "10.33320/maced.pharm.bull.2023.69.03.055" }
Turković, E., Vasiljević, I.,& Parojčić, J.. (2023). Application of the Gradient boosted tree approach for thin film classification based on disintegration time. in Macedonian Pharmaceutical Bulletin Macedonian Pharmaceutical Association., 69(Suppl 1), 113-114. https://doi.org/10.33320/maced.pharm.bull.2023.69.03.055
Turković E, Vasiljević I, Parojčić J. Application of the Gradient boosted tree approach for thin film classification based on disintegration time. in Macedonian Pharmaceutical Bulletin. 2023;69(Suppl 1):113-114. doi:10.33320/maced.pharm.bull.2023.69.03.055 .
Turković, Erna, Vasiljević, Ivana, Parojčić, Jelena, "Application of the Gradient boosted tree approach for thin film classification based on disintegration time" in Macedonian Pharmaceutical Bulletin, 69, no. Suppl 1 (2023):113-114, https://doi.org/10.33320/maced.pharm.bull.2023.69.03.055 . .