Machine learning capabilities for the diagnosis of orphan diseases
https://doi.org/10.37489/2782-3784-myrwd-40
EDN: XJXWRQ
Abstract
Rare or orphan diseases belong to one of the most severe groups of diseases. At the same time, early and accurate diagnosis of such diseases is a serious problem for general practitioners, pediatricians and therapists. The article discusses the possibilities of using machine learning methods, including artificial intelligence, to improve the diagnosis of rare diseases. Information is provided on various models developed by both international experts and Russian researchers.
About the Author
N. Y. Dmitrieva
AO "Aston Consulting"
Russian Federation
Russian Federation
Natalia Y. Dmitrieva — Ph.D. Sc., Head of Information Systems Department
Moscow
References
1. Segura-Bedmar I, Camino-Perdones D, Guerrero-Aspizua S. Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts. BMC Bioinformatics. 2022 Jul 6;23(1):263. doi: 10.1186/s12859-022-04810-y. PMID: 35794528; PMCID: PMC9258216.
2. «Такеда» и «Фармимэкс» будут развивать диагностику редких заболеваний в России. PHARMPROM. Интернет-ресурс. https://pharmprom.ru/take-da-i-farmimeks-budut-razvivat-diagnostiku-red-kix-zabolevanij-v-rossii/?ysclid=lf5p34sow569226820. (Режим доступа: 02.09.2023)
3. Мирошниченко И.И., Вальдман Е.А., Кузьмин И.И. Новое предназначение старых лекарств (обзор). Разработка и регистрация лекарственных средств. 2023;12(1):182-190. https://doi.org/10.33380/2305-2066-2023-12-1-182-190 [Miroshnichenko I.I., Valdman E.A., Kuz'min I.I. Old Drugs, New Indications (Review). Drug development & registration. 2023;12(1):182-190. (In Russ.)].
4. Jefferies JL, Spencer AK, Lau HA, Nelson MW, Giuliano JD, Zabinski JW, Boussios C, Curhan G, Gliklich RE, Warnock DG. A new approach to identifying patients with elevated risk for Fabry disease using a machine learning algorithm. Orphanet J Rare Dis. 2021 Dec 20;16(1):518. doi: 10.1186/s13023-021-02150-3. PMID: 34930374; PMCID: PMC8686369.
5. Hersh WR, Cohen AM, Nguyen MM, Bensching KL, Deloughery TG. Clinical study applying machine learning to detect a rare disease: results and lessons learned. JAMIA Open. 2022 Jun 30;5(2):ooac053. doi: 10.1093/jamiaopen/ooac053. PMID: 35783073; PMCID: PMC9243401.
6. Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 2019 Nov 19;11(1):70. doi: 10.1186/s13073-019-0689-8. PMID: 31744524; PMCID: PMC6865045.
7. Song Y, Yin Z, Zhang C, Hao S, Li H, Wang S, Yang X, Li Q, Zhuang D, Zhang X, Cao Z, Ma X. Random forest classifier improving phenylketonuria screening performance in two Chinese populations. Front Mol Biosci. 2022 Oct 11;9:986556. doi: 10.3389/ fmolb.2022.986556. PMID: 36304929; PMCID: PMC9592754
8. Hallowell N, Badger S, Sauerbrei A, Nellåker C, Kerasidou A. "I don't think people are ready to trust these algorithms at face value": trust and the use of machine learning algorithms in the diagnosis of rare disease. BMC Med Ethics.2022 Nov 16;23(1):112. doi: 10.1186/s12910-022-00842-4. PMID: 36384545; PMCID: PMC9670402
9. Выявление редких (генетических) заболеваний с помощью искусственного интеллекта. Интернет-ресурс. https://webiomed.ru/novosti/zapuskaiut-proekt-elektronnaia-dispanserizatsiia/?ysclid=lf5tjhu-fyd561951251 (Режим доступа: 02.09.2023).
10. Орфанные заболевания. Интернет-ресурс. https://aston-health.com/terapevticheskie-oblasti/orfann-yie-zabolevaniya.html (Режим доступа: 02.з09.2023).
11. Диагностика болезней: МПС I, Фабри, Помпе, Ниманна-Пика типа А/В. https://docsfera.ru/dbscontent/ (Режим доступа: 02.09.2023).
12. Программа диагностики НАО и ЛБН. Интернет-ресурс. https://gendisease.aston-health.com/Home/About (Режим доступа: 02.09.2023).
Review
For citations:
Dmitrieva N.Y. Machine learning capabilities for the diagnosis of orphan diseases. Real-World Data & Evidence. 2023;3(3):36-39. (In Russ.) https://doi.org/10.37489/2782-3784-myrwd-40. EDN: XJXWRQ
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