Abstract
Deadly diseases claim millions of life every year and therefore the key focus in Health Research lies in early and accurate detection, better medication, patient centric health policy, post therapy care to combat the disease and increase the quality of life. Modern life has been impacted greatly by big data and machine learning. Machine learning algorithms and statistical methods are enabling revolution in healthcare sector also with algorithms that perform like human physicians. It has been possible to predict diseases from imaging data far more accurately and early phases of disease progression. Big data has been generating in an increasing scale with applications in the Health Science. It has been possible to apply machine leaning tools towards data extraction from Electronic Health Records (EHR). It is also a step towards predicting high risk individuals, prepare well ahead of emergency viral/pathogen attacks that would prevent chances of epidemics, predict wounds, trauma and injury in car accidents, detect Cancer at early stages and also extract accurate information from ECG which forms one of the few major applications of Machine Learning in the Health Sciences. In this chapter we highlight (1) study in diabetes, (2) predicting Cancer using machine learning and deep learning algorithm, (3) interpreting ECG, (4) detection of glaucoma through machine learning algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Abbreviations
- 3D:
-
3 Dimensional
- AD:
-
Alzheimer’s disease
- ADR:
-
Adverse drug reactions
- AI:
-
Artificial intelligence
- CHD:
-
Coronary heart disease
- CNN:
-
Convolutional neural networks
- CT:
-
Computed tomography
- CVD:
-
Cardiovascular disease
- ECG:
-
Echocardiography
- EHR:
-
Electronic Health Records
- LDCT:
-
Low-dose computed tomography
- LUMAS:
-
Lung malignancy scores
- ML:
-
Machine learning
- NLP:
-
Natural language processing
- PAD:
-
Peripheral arterial disease
- PCA:
-
Principal component analysis
- PD:
-
Parkinson’s disease
- ROI:
-
Region of interest
- SL:
-
Supervised learning
- SSL:
-
Semi-supervised learning
- T2D:
-
Type 2 diabetes
- TID:
-
Type I diabetes
- USL:
-
Unsupervised learning
- WHO:
-
World Health Organization
References
An G, Omodaka K, Hashimoto K, Tsuda S, Yukihiro Shiga Y, Takada N, Kikawa T, Yokota H, Akiba M, Nakazawa T (2019) Glaucoma diagnosis with machine learning based on optical coherence tomography and color fundus images. J Healthc Eng 2019:4061313, 9p. https://doi.org/10.1155/2019/4061313
Ardila D, Kiraly AP, Bharadwaj S et al (2019) End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 25:954–961
Ashrafian H, Darzi A (2018) Transforming health policy through machine learning. PLoS Med 15(11):e1002692. https://doi.org/10.1371/journal.pmed.1002692
Cheng L, Hu Y (2018) Human disease system biology. Curr Gene Ther 18(5):255–256
Danaee P, Ghaeini R, Hendrix DA (2017) A deep learning approach for cancer detection and relevant gene identification. Pac Symp Biocomput 22:219–229
Dolley S (2018) Big Data’s role in precision public health. Front Public Health 6:68
Finger F, Genolet T, Mari L, de Magny GC, Manga NM, Rinaldo A et al (2016) Mobile phone data highlights the role of mass gatherings in the spreading of cholera outbreaks. Proc Natl Acad Sci U S A 113(23):6421–6426
Ghorbani A, Ouyang D, Abid A et al (2020) Deep learning interpretation of echocardiograms. NPJ Digit Med 3:10
Hamidi H, Kretzler M (2018) Systems biology approaches to identify disease mechanisms and facilitate targeted therapy in the management of glomerular disease. Curr Opin Nephrol Hypertens 27(6):433–439
Huff A, Allen T, Whiting K, Breit N, Arnold B (2016) FLIRT-ing with Zika: a web application to predict the movement of infected travelers validated against the current Zika virus epidemic. PLoS Curr 8. https://doi.org/10.1371/currents.outbreaks.711379ace737b7c04c89765342a9a8c9
Institute of Medicine (US) Committee on a National Surveillance System for Cardiovascular and Select Chronic Diseases (2011) A nationwide framework for surveillance of cardiovascular and chronic lung diseases, vol 2: Cardiovascular disease. National Academies Press (US), Washington, DC
Isdory A, Mureithi EW, Sumpter DJ (2015) The impact of human mobility on HIV transmission in Kenya. PLoS One 10:e0142805. https://doi.org/10.1371/journal.pone.0142805
Kim SJ, Cho KJ, Oh S (2017) Development of machine learning models for diagnosis of glaucoma. PLoS One 12(5):e0177726
Mari L, Gatto M, Ciddio M, Dia ED, Sokolow SH, De Leo GA et al (2017) Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis. Sci Rep 7:489. https://doi.org/10.1038/s41598-017-00493-1
Mooney SJ, Pejaver V (2018) Big Data in public health: terminology, machine learning, and privacy. Annu Rev Public Health 1(39):95–112
Sidey-Gibbons J, Sidey-Gibbons C (2019) Machine learning in medicine: a practical introduction. BMC Med Res Methodol 19:64
Somvanshi PR, Venkatesh KV (2014) A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics. Syst Synth Biol 8(1):99–116
Tatem AJ (2014) Mapping population and pathogen movements. Int Health 6(1):5–11
Wesolowski A, Buckee CO, Bengtsson L, Wetter E, Lu X, Tatem AJ (2014) Commentary: containing the Ebola outbreak - the potential and challenge of mobile network data. PLoS Curr 6. https://doi.org/10.1371/currents.outbreaks.0177e7fcf52217b8b634376e2f3efc5e
Wesolowski A, Qureshi T, Boni MF, Sundsøy PR, Johansson MA, Rasheed SB et al (2015) Impact of human mobility on the emergence of dengue epidemics in Pakistan. Proc Natl Acad Sci U S A 112:11887–11892
Wesolowski A, Buckee CO, Engø-Monsen K, Metcalf CJE (2016) Connecting mobility to infectious diseases: the promise and limits of mobile phone data. J Infect Dis 214(suppl 4):S414–S420. https://doi.org/10.1093/infdis/jiw273
WHO report on cancer: setting priorities, investing wisely and providing care for all. World Health Organization, Geneva. 2020. Licence: CC BY-NC-SA 3.0 IGO
Global report on diabetes, World Health Organization (2016). https://apps.who.int/iris/bitstream/handle/10665/204871/9789241565257_eng.pdf;jsessionid=05FF75E89AD714779A54F039EA3B38A4?sequence=1
Yehia E, Boshnak H, Gaber SA, Abdo A, Doaa S, Elzanfaly DS (2019) Ontology-based clinical information extraction from physician’s free-text notes. J Biomed Inform 98:103276
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Ghosh, S., Dasgupta, R. (2022). Applications and Software of Machine Learning and Artificial Intelligence (AI) in Medical Knowledge and Health. In: Machine Learning in Biological Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-16-8881-2_17
Download citation
DOI: https://doi.org/10.1007/978-981-16-8881-2_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8880-5
Online ISBN: 978-981-16-8881-2
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)