Morbidity Detection from Clinical Text Data Using Artificial Intelligence Technique

1. Dessì D, Reforgiato Recupero D, Fenu G, Consoli S (2019) A recommender system of medical reports leveraging cognitive computing and frame semantics. In: Tsihrintzis G, Sotiropoulos D, Jain L (eds) Machine learning paradigms. Intelligent systems reference library, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-319-94030-4_2

2. Rajput K, Chetty G, Davey R (2018) Obesity and co-morbidity detection in clinicaltext using deep learning and machine learning techniques. In: 2018 5th Asia-Pacific World congress on computer science and engineering (APWC on CSE), pp 51–56. https://doi.org/10.1109/APWConCSE.2018.00017

3. Consoli S, Reforgiato Recupero D, Petkovic M (2019) Data science for healthcare: methodologies and applications. https://doi.org/10.1007/978-3-030-05249-2

4. Wyber R et al (2015) Big data in global health: improving health in low- and middle-income countries. Bull World Health Organ 93(3):203–208. https://doi.org/10.2471/BLT.14.139022

   Article  Google Scholar 

5. Gu J, Feng W, Zeng J, Mamitsuka H, Zhu S (2013) Efficient semi-supervised MEDLINE document clustering with MeSH-semantic and global-content constraints. IEEE Trans Cybern 43(4):1265–1276. https://doi.org/10.1109/TSMCB.2012.2227998

   Article  Google Scholar 

6. Thorsen-Meyer HC, Nielsen AB, Nielsen AP, Kaas- Hansen BS, Toft P, Schierbeck J, Strøm T,Chmura PJ, Heimann M, Dybdahl L, Spangsege L, Hulsen P, Belling K, Brunak S, Perner A (2020) Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high- frequency data in electronic patient records. Lancet Digit Health

   Google Scholar 

7. Wei Q, Ji Z, Si Y, Du J, Wang J, Tiryaki F, Wu S, Tao C, Roberts K, Xu H (2020) Relation extraction from clinical narratives using pre-trained language models. AMIA Annu Symp Proc 2019:1236–1245. PMID: 32308921; PMCID: PMC7153059

   Google Scholar 

8. Uijen AA, van de Lisdonk EH (2008) Multimorbidity in primary care: prevalence and trend over the last 20 years. Eur J Gen Pract 14(Suppl 1):28–32. https://doi.org/10.1080/13814780802436093. PMID: 18949641

   Article  Google Scholar 

9. Waheeb S, Khan N (2020) Machine learning based sentiment text classification for evaluating treatment quality of discharge summary. Information (Switzerland) 11:17. https://doi.org/10.3390/info11050281

   Article  Google Scholar 

10. Jain D, Singh V (2018) Feature selection and classification systems for chronic disease prediction: a review. Egyptian Inf J 19(3)

    Google Scholar 

11. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributedrepresentations of words and phrases and their compositionality. In Advance in neural information processing systems, pp 3111–3119

    Google Scholar 

12. Pennington J, Socher R, Manning C (2014) GloVe: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). Doha, Qatar. Association for Computational Linguistics, pp 1532–1543

    Google Scholar 

13. Joulin A, Grave E, Bojanowski P, Mikolov T (2017) Bag of tricks for efficient text classification. In: Proceedings of the 15th conference of the european chapter of the association for computational linguistics: Vol 2, Short Papers. Valencia, Spain. Association for Computational Linguistics, pp 427–431

    Google Scholar 

14. Kumar V, Recupero DR, Riboni D, Helaoui R (2021) Ensembling classical machine learning and deep learning approaches for morbidity identification from clinical notes. IEEE Access 9:7107–7126. https://doi.org/10.1109/ACCESS.2020.3043221

    Article  Google Scholar 

15. Hossin M, Sulaiman MN (2015) A review on evaluation metrics for data classification evaluations. Int J Data Mining Knowl Manage Process (IJDKP) 5(2)

    Google Scholar 

16. Blum RW, Bastos FIPM, Kabiru C, Le LC et al (2012) Adolescent health in the 21st century. The Lancet

    Google Scholar 

17. Sawyer SM, Drew S, Yeo MS, Britto MT (2007) Adolescents with a chronic condition: challenges living, challenges treating. The Lancet 369(9571):1481–1489

    Article  Google Scholar 

18. Mercer S, Furler J, Moffat K, Fischbacher- Smith D, Sanci L (2016) Multimorbidity:technical series on safer primary care. World Health Organ

    Google Scholar 

19. Bromuri S, Zufferey D, Hennebert J, Schumacher M (2014) Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms. J Biomed Inform 51:165–175

    Article  Google Scholar 

20. Ambert KH, Cohen AM (2009) A system for classifying disease comorbidity status from medical discharge summaries using automated hotspot and negated concept detection. J Ams Med Inf Assoc: JAMIA 16(4):590–5. https://doi.org/10.1197/jamia.M3095

Download references