![Důležité termíny](https://cdn.muni.cz/media/3716855/image_2.jpg?mode=crop¢er=0.5,0.5&rnd=133667368190000000&heightratio=0.5&width=278)
Informace o projektu
AIcope - AI support for Clinical Oncology and Patient Empowerment
(AIcope)
- Kód projektu
- MUNI/G/1763/2020
- Období řešení
- 4/2021 - 12/2023
- Investor / Programový rámec / typ projektu
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Masarykova univerzita
- Grantová agentura MU
- INTERDISCIPLINARY - Mezioborové výzkumné projekty
- Fakulta / Pracoviště MU
- Fakulta informatiky
- Další fakulta/pracoviště MU
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Lékařská fakulta
- MUDr. Jana Halámková, Ph.D.
- doc. MUDr. Tomáš Kazda, Ph.D.
- Mgr. Kamila Stančeková
In 2016, over half a million people were registered as oncological patients in the Czech Republic, of which 96,500 were newly diagnosed. 27,261 people died that year (based on data from the Institute of Health Information and Statistics). This clearly shows the substantial impact cancer has not only on the healthcare system, but also on the whole society and economy. The additional problem is that while there are various efficacious treatments available for most cancers, many of them may drastically impact the entire patients’ life, each in their own way. To further reduce the societal burden of cancer, it is therefore crucial to pick the right type of treatment, not only based on the patient's biological profile but also on their preferences and general lifestyle.
Unfortunately, this is seldom feasible. One of the main reasons are the severe time constraints of contemporary clinical practice—it is virtually impossible to review the implications of various available treatments with the patients while truly taking both their biomedical and their lifestyle profiles into account. And this is the very real, pressing clinical need that motivates the AIcope project.
To address the need, we will:
(i) Collect, extract and preprocess data from oncological patient records and relevant public datasets on diseases, interventions and drugs.
(ii) Integrate the preprocessed data in a uniform, semantically-interlinked resource (a knowledge graph) and augment it by inferred links.
(iii) Develop question answering and visual exploration interfaces on top of the knowledge graph in a co-design process with doctors, patients and clinical psychologists.
(iv)Evaluate the resulting decision support prototype by its preliminary deployment in clinical settings and comparison with the current practices in the treatment selection process.
While the project will deliver cutting-edge, internationally-relevant results on its own, it will also provide a platform for an ERC Synergy grant application or a similar follow-up involving leading Irish cancer biologists and biomedical AI researchers with whom the AIcope PI already collaborates. This will let us extend the project research agenda by using advanced AI techniques at the diagnosis stage as well, and thus deliver a comprehensive and highly competitive precision oncology solution.
Cíle udržitelného rozvoje
Masarykova univerzita se hlásí k cílům udržitelného rozvoje OSN, jejichž záměrem je do roku 2030 zlepšit podmínky a kvalitu života na naší planetě.
Publikace
Počet publikací: 10
2024
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Machine learning estimated probability of relapse in early-stage non-small-cell lung cancer patients with aneuploidy imputation scores and knowledge graph embeddings
Expert Systems with Applications, rok: 2024, ročník: 2024, vydání: 235, DOI
2023
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Curing Cancer with Knowledge Graphs (and other Outrageous Ideas)
Rok: 2023, druh: Vyžádané přednášky
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Extraction, labeling, clustering, and semantic mapping of segments from clinical notes
IEEE TRANSACTIONS ON NANOBIOSCIENCE, rok: 2023, ročník: 22, vydání: 4, DOI
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Machine Learning–Assisted Recurrence Prediction for Patients With Early-Stage Non–Small-Cell Lung Cancer
JCO CLINICAL CANCER INFORMATICS, rok: 2023, ročník: 7, vydání: e2200062, DOI
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Understanding Health Records in West Slavic Languages: Available Resources, Case Study in Oncology
Healthcare Transformation with Informatics and Artificial Intelligence, rok: 2023
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Unsupervised extraction, classification and visualization of clinical note segments using the MIMIC-III dataset
Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), rok: 2023
2022
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AI for Cancer Patient Empowerment - Interim Results of the AIcope Project
Rok: 2022, druh: Vyžádané přednášky
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Medical Knowledge Resources for Text-Mining of Health Records in Czech, Polish, and Slovak
Proceedings of the Sixteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2022, rok: 2022
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Predikce průběhu onkologické léčby na základě podobností pacientek vypočítaných z jejich klinických zpráv
Rok: 2022, druh: Vyžádané přednášky
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Unsupervised extraction, labelling and clustering of segments from clinical notes
Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), rok: 2022