Derleme
Yıl 2023,
Cilt: 10 Sayı: 1, 36 - 44, 23.06.2023
Öz
Tıp, mühendislik, ziraat başta olmak üzere birçok alanda, yapay zeka temelli çalışmalarına sıklıkla rastlamaktayız. Zaman içerisinde yapay zeka temelli çalışmaların artmasıyla hem yapay zekanın birbirinden farklı sorunlara karşı ürettiği çözüm yeteneği geliştirilmiş, hem de sorunları çözülüp iş akışı kolaylaşan sektörlerin gelişimi giderek artmıştır. Bu çalışmada ise ilaç sektörü ve onun üzerine yapılmış olan yapay zeka çalışmaları incelenmiştir. Zaman içerisinde çalışmaların seyri irdelenmiştir. İncelenen çalışmaların sağlık hizmetleri ve sağlık ekonomisi alanında yoğunlaştığı ve artan oranda Derin Öğrenme yöntemlerine başvurduğu görülmüştür.
Anahtar Kelimeler
Kaynakça
- 1. Wikipedi. Vikipedi, Özgür Ansiklopedi. Erişim tarihi 06.35, Mart 11, 2022; url://tr.wikipedia.org/w/index.php?title=%C4%B0la%C3%A7&oldid=27150084.
- 2. İEİS. 2021 yılı ilaç endüstrisi değerlendirmesi & 2022 yılı beklentileri. Erişim tarihi 09.39, Mart 11, 2022; url://http://ieis.org.tr/ieis/assets/media/files/local/Winally-17-12_Y2nPtU_E7NTe8.pdf.
- 3. KPMG. Küresel ilaç sektörü 1,5 trilyon dolara koşuyor. Erişim tarihi 09.41, Mart 11, 2022; url://https://home.kpmg/tr/tr/home/medya/press-releases/2020/03/kuresel-ilac-sektoru-bir-bucuk-trilyon-dolara-kosuyor.html.
- 4. Şuayip B. Sağlıkta yüksek teknoloji ve yapay zekâ. D (Sağlık Düşüncesi ve Tıp Kültürü) Dergisi. 2019; 50: 32-35.
- 5. Kinjel S. Third Emerging Technologies Set to Transform the Pharma World. Zacks. Erişim tarihi 10.00, Mart 11, 2022; url://https://www.zacks.com/stock/news/298075/3-emerging-technologies-set-to-transform-the-pharma-world.
- 6. Zhu H. Big data and artificial intelligence modeling for drug discovery. Annual review of pharmacology and toxicology. 2020; 60: 573-589.
- 7. Chaikijurajai T, Laffin L J, Tang W H W. Artificial intelligence and hypertension: recent advances and future outlook. American Journal of Hypertension. 2020; 33(11): 967-974.
- 8. Kainz B, Heinrich,MP, Makropoulos A, Oppenheimer J, Mandegaran R, Sankar S, Curry N. Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. NPJ Digital Medicine. 2021; 4(1): 1-18.
- 9. Mohammadi R, Jain S, Namin AT, Heller MS, Palacholla R, Kamarthi S, Wallace B. Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study. JMIR medical informatics. 2020: 8(11): e19761.
- 10. Ramkumar P N, Karnuta J M, Navarro SM, Haeberle HS, Iorio R, Mont MA, Krebs VE. Preoperative prediction of value metrics and a patient-specific payment model for primary total hip arthroplasty: development and validation of a deep learning model. The Journal of Arthroplasty. 2019; 34(10): 2228-2234.
- 11. Ashfaq A, Sant’Anna A, Lingman M, Nowaczyk S. Readmission prediction using deep learning on electronic health records. Journal of biomedical informatics. 2019; 97:103256.
- 12. Zhong Q, Li Z, Wang W, Zhang L, He KIntegrated medical resource consumption stratification in hospitalized patients: an Auto Triage Management model based on accurate risk, cost and length of stay prediction. Science China Life Sciences, 2021; 1: 1-12.
- 13. Hainline AE, Nath V, Parvathaneni P, Schilling KG, Blaber JA, Anderson AW, Landman BA. A deep learning approach to estimation of subject-level bias and variance in high angular resolution diffusion imaging. Magnetic resonance imaging. 2019; 59: 130-136.
- 14. Peine A, Hallawa A, Schöffski O, Dartmann G, Fazlic LB, Schmeink A, Martin L. A deep learning approach for managing medical consumable materials in intensive care units via convolutional neural networks: technical proof-of-concept study. JMIR medical informatics. 2019; 7(4): e14806.
- 15. Xu BY, Chiang M, Pardeshi AA, Moghimi S, Varma R. Deep neural network for scleral spur detection in anterior segment OCT images: The Chinese American eye study. Translational vision science & technology. 2020; 9(2): 18-18.
- 16. Laplante JF, Akhloufi MA. Predicting cancer types from miRNA stem-loops using deep learning. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 5312-5315).
- 17. Letterie G. Three ways of knowing: the integration of clinical expertise, evidence-based medicine, and artificial intelligence in assisted reproductive technologies. Journal of Assisted Reproduction and Genetics. 2021; 38(7): 1617-1625.
- 18. Aksu B, Paradkar A, De Matas M, Özer Ö, Güneri T, York P. Quality by design approach: application of artificial intelligence techniques of tablets manufactured by direct compression. AAPS PharmSciTech. 2012; 13(4): 1138-1146.
- 19. Dongsheng H, Xiaogen L, Jiaying W, Xiaoyang L, Hongmei W, Lu L. A kind of recognition methods and device for condition of medicine treatment for hypertension price sensitivity. Clinical Epidemiology. 2020; 10: 1467- 1478.
- 20. Zhao Y, Zheng K, Guan B, Guo M, Song L, Gao J, Zhang Y. DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network representation. Journal of Translational Medicine. 2020; 18(1): 1-15.
- 21. Klemencic J, Mihelic J. Application of algorithms and machine learning methods in pharmaceutical manufacture. 2019; 1:1-6.
- 22. Chi HM, Moskowitz H, Ersoy OK, Altinkemer K, Gavin PF, Huff BE, Olsen BA. Machine learning and genetic algorithms in pharmaceutical development and manufacturing processes. Decision Support Systems. 2009; 48(1): 69-80.
- 23. Asgari E, Garakani K, McHardy AC, Mofrad MR. MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples. Bioinformatics. 2018; 34(13): 132-142.
- 24. Vde Oliveira MB, Zucchi G, Lippi M, Cordeiro DF, da Silva NR, Iori M. Lead Time Forecasting with Machine Learning Techniques for a Pharmaceutical Supply Chain. In ICEIS. 2021; 1: 634-641.
- 25. Jun C, Xuebin H, Qingyun D, Yan L, Jianzheng LCombination drug pricing method, device and computer readable storage medium. (2019, August 6).
- 26. Zhiying, X. A kind of medicine marketing method based on cloud computing and big data. (2019, October 11).
Yıl 2023,
Cilt: 10 Sayı: 1, 36 - 44, 23.06.2023
Öz
We often come across artificial intelligence-based studies in many fields, especially in agriculture, medicine and engineering. With the increase in artificial intelligence-based studies over time, both the ability of artificial intelligence to solve different problems have been improved, and the development of sectors whose problems have been solved and workflow has increased gradually. In this study, the pharmaceutical industry and artificial intelligence studies on it were examined. The course of the studies over time has been examined. The studies have been concentrated in the field of health services and health economics, and Deep Learning methods are increasingly used.
Anahtar Kelimeler
Artificial intelligence Pharmaceutical industry Pharmaceutical price prediction Pharmacoeconomics
Kaynakça
- 1. Wikipedi. Vikipedi, Özgür Ansiklopedi. Erişim tarihi 06.35, Mart 11, 2022; url://tr.wikipedia.org/w/index.php?title=%C4%B0la%C3%A7&oldid=27150084.
- 2. İEİS. 2021 yılı ilaç endüstrisi değerlendirmesi & 2022 yılı beklentileri. Erişim tarihi 09.39, Mart 11, 2022; url://http://ieis.org.tr/ieis/assets/media/files/local/Winally-17-12_Y2nPtU_E7NTe8.pdf.
- 3. KPMG. Küresel ilaç sektörü 1,5 trilyon dolara koşuyor. Erişim tarihi 09.41, Mart 11, 2022; url://https://home.kpmg/tr/tr/home/medya/press-releases/2020/03/kuresel-ilac-sektoru-bir-bucuk-trilyon-dolara-kosuyor.html.
- 4. Şuayip B. Sağlıkta yüksek teknoloji ve yapay zekâ. D (Sağlık Düşüncesi ve Tıp Kültürü) Dergisi. 2019; 50: 32-35.
- 5. Kinjel S. Third Emerging Technologies Set to Transform the Pharma World. Zacks. Erişim tarihi 10.00, Mart 11, 2022; url://https://www.zacks.com/stock/news/298075/3-emerging-technologies-set-to-transform-the-pharma-world.
- 6. Zhu H. Big data and artificial intelligence modeling for drug discovery. Annual review of pharmacology and toxicology. 2020; 60: 573-589.
- 7. Chaikijurajai T, Laffin L J, Tang W H W. Artificial intelligence and hypertension: recent advances and future outlook. American Journal of Hypertension. 2020; 33(11): 967-974.
- 8. Kainz B, Heinrich,MP, Makropoulos A, Oppenheimer J, Mandegaran R, Sankar S, Curry N. Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. NPJ Digital Medicine. 2021; 4(1): 1-18.
- 9. Mohammadi R, Jain S, Namin AT, Heller MS, Palacholla R, Kamarthi S, Wallace B. Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study. JMIR medical informatics. 2020: 8(11): e19761.
- 10. Ramkumar P N, Karnuta J M, Navarro SM, Haeberle HS, Iorio R, Mont MA, Krebs VE. Preoperative prediction of value metrics and a patient-specific payment model for primary total hip arthroplasty: development and validation of a deep learning model. The Journal of Arthroplasty. 2019; 34(10): 2228-2234.
- 11. Ashfaq A, Sant’Anna A, Lingman M, Nowaczyk S. Readmission prediction using deep learning on electronic health records. Journal of biomedical informatics. 2019; 97:103256.
- 12. Zhong Q, Li Z, Wang W, Zhang L, He KIntegrated medical resource consumption stratification in hospitalized patients: an Auto Triage Management model based on accurate risk, cost and length of stay prediction. Science China Life Sciences, 2021; 1: 1-12.
- 13. Hainline AE, Nath V, Parvathaneni P, Schilling KG, Blaber JA, Anderson AW, Landman BA. A deep learning approach to estimation of subject-level bias and variance in high angular resolution diffusion imaging. Magnetic resonance imaging. 2019; 59: 130-136.
- 14. Peine A, Hallawa A, Schöffski O, Dartmann G, Fazlic LB, Schmeink A, Martin L. A deep learning approach for managing medical consumable materials in intensive care units via convolutional neural networks: technical proof-of-concept study. JMIR medical informatics. 2019; 7(4): e14806.
- 15. Xu BY, Chiang M, Pardeshi AA, Moghimi S, Varma R. Deep neural network for scleral spur detection in anterior segment OCT images: The Chinese American eye study. Translational vision science & technology. 2020; 9(2): 18-18.
- 16. Laplante JF, Akhloufi MA. Predicting cancer types from miRNA stem-loops using deep learning. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 5312-5315).
- 17. Letterie G. Three ways of knowing: the integration of clinical expertise, evidence-based medicine, and artificial intelligence in assisted reproductive technologies. Journal of Assisted Reproduction and Genetics. 2021; 38(7): 1617-1625.
- 18. Aksu B, Paradkar A, De Matas M, Özer Ö, Güneri T, York P. Quality by design approach: application of artificial intelligence techniques of tablets manufactured by direct compression. AAPS PharmSciTech. 2012; 13(4): 1138-1146.
- 19. Dongsheng H, Xiaogen L, Jiaying W, Xiaoyang L, Hongmei W, Lu L. A kind of recognition methods and device for condition of medicine treatment for hypertension price sensitivity. Clinical Epidemiology. 2020; 10: 1467- 1478.
- 20. Zhao Y, Zheng K, Guan B, Guo M, Song L, Gao J, Zhang Y. DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network representation. Journal of Translational Medicine. 2020; 18(1): 1-15.
- 21. Klemencic J, Mihelic J. Application of algorithms and machine learning methods in pharmaceutical manufacture. 2019; 1:1-6.
- 22. Chi HM, Moskowitz H, Ersoy OK, Altinkemer K, Gavin PF, Huff BE, Olsen BA. Machine learning and genetic algorithms in pharmaceutical development and manufacturing processes. Decision Support Systems. 2009; 48(1): 69-80.
- 23. Asgari E, Garakani K, McHardy AC, Mofrad MR. MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples. Bioinformatics. 2018; 34(13): 132-142.
- 24. Vde Oliveira MB, Zucchi G, Lippi M, Cordeiro DF, da Silva NR, Iori M. Lead Time Forecasting with Machine Learning Techniques for a Pharmaceutical Supply Chain. In ICEIS. 2021; 1: 634-641.
- 25. Jun C, Xuebin H, Qingyun D, Yan L, Jianzheng LCombination drug pricing method, device and computer readable storage medium. (2019, August 6).
- 26. Zhiying, X. A kind of medicine marketing method based on cloud computing and big data. (2019, October 11).
Toplam 26 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Sağlık Kurumları Yönetimi |
| Bölüm | Derleme Bölümü |
| Yazarlar | |
| Yayımlanma Tarihi | 23 Haziran 2023 |
| Gönderilme Tarihi | 3 Ekim 2022 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 10 Sayı: 1 |