Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically

References

• Leachman SA, Merlino G. The final frontier in cancer diagnosis. Nature. 2017;542:36. doi:10.1038/nature21492
   PubMedGoogle Scholar
• Webb S. Deep learning for biology. Nature. 2018;554(7693):555–557. doi:10.1038/d41586-018-02174-z
   Google Scholar
• Camacho DM, Collins KM, Powers RK, et al. Next-generation machine learning for biological networks. Cell. 2018;173(7):1581–1592. doi:10.1016/j.cell.2018.05.015
   PubMed Web of Science ®Google Scholar
• Lim E-C, Park JH, Jeon HJ, et al. Developing a diagnostic decision support system for benign paroxysmal positional vertigo using a deep-learning model. J Clin Med. 2019;8(5):633. doi:10.3390/jcm8050633
   PubMedGoogle Scholar
• Kolachalama VB, Garg PS. Machine learning and medical education. Npj Digital Med. 2018;1(1):54. doi:10.1038/s41746-018-0061-1
   PubMedGoogle Scholar
• Choi E, Schuetz A, Stewart WF, Sun J. Using recurrent neural network models for early detection of heart failure onset. J Am Med Inf Assoc Jamia. 2016;24(2):361.
   Web of Science ®Google Scholar
• Rasmy L, Yonghui W, Wang N, et al. A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. J Biomed Inform. 2018;84:11–16. doi:10.1016/j.jbi.2018.06.011
   PubMedGoogle Scholar
• Chilamkurthy S, Ghosh R, Tanamala S, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet. 2018;392(10162):2388–2396. doi:10.1016/S0140-6736(18)31645-3
   PubMed Web of Science ®Google Scholar
• Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172(5):1122–1131.e9. doi:10.1016/j.cell.2018.02.010
   PubMed Web of Science ®Google Scholar
• Schwemmer Michael A, Skomrock Nicholas D, Sederberg Per B, et al. Meeting brain–computer interface user performance expectations using a deep neural network decoding framework. Nat Med. 2018;24:1669–1676. doi:10.1038/s41591-018-0171-y
   PubMedGoogle Scholar
• Kaysen G, Gambertoglio J, Felts J, Hutchison F. Albumin synthesis, albuminuria and hyperlipemia in nephrotic patients. Kidney Int. 1987;31:1368–1376. doi:10.1038/ki.1987.151
   PubMedGoogle Scholar
• Lupattelli G, Pirro M, Siepi D, et al. Non-cholesterol sterols in different forms of primary hyperlipemias. Nutr Metab Cardiovasc Dis. 2012;22(3):231–236. doi:10.1016/j.numecd.2010.05.010
   PubMedGoogle Scholar
• Zhao J, Mao X, Chen L. Speech emotion recognition using deep 1D & 2D CNN LSTM networks. Biomed Signal Process Control. 2019;47:312–323. doi:10.1016/j.bspc.2018.08.035
   Web of Science ®Google Scholar
• Reddy BK, Delen D. Predicting hospital readmission for lupus patients: an RNN-LSTM-based deep-learning methodology. Comput Biol Med. 2018;101:199–209. doi:10.1016/j.compbiomed.2018.08.029
   PubMed Web of Science ®Google Scholar
• Qiu TS, Shi X, Wang JF, et al. Deep learning: a rapid and efficient route to automatic metasurface design. Adv Sci. 2019;6:1900128. doi:10.1002/advs.v6.12
   Google Scholar
• Tanveer MS, Hasan MK. Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based ANN-LSTM network. Biomed Signal Process Control. 2019;51:382–392. doi:10.1016/j.bspc.2019.02.028
   Google Scholar
• Alexander T, Nguyen DS, Greenfield AS, Bhakta JL, Feuer WJ. Detecting glaucoma progression using guided progression analysis with OCT and visual field assessment in eyes classified by international classification of disease severity codes. Ophthalmol Glaucoma. 2019;2(1):36–46. doi:10.1016/j.ogla.2018.11.004
   PubMedGoogle Scholar
• Nicole Gray W, Chunhua WJJOTAMIAJ. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inf Assoc. 2013;20(1):144–151. doi:10.1136/amiajnl-2011-000681
   PubMedGoogle Scholar
• Ben-assuli O, Sagi D, Leshno M, et al. Improving diagnostic accuracy using EHR in emergency departments: a simulation-based study. J Biomed Inform. 2015;55:31–40. doi:10.1016/j.jbi.2015.03.004
   PubMed Web of Science ®Google Scholar
• Liu Y, Zhang Q, Geng Z, et al. Detecting diseases by human-physiological-parameter-based deep learning. IEEE Access. 2019;7:22002–22010. doi:10.1109/ACCESS.2019.2893877
   Google Scholar
• Zhang Q, Liu Y, Liu G, Zhao G, Qu Z, Yang W. An automatic diagnostic system based on deep learning, to diagnose hyperlipidemia. Diabetes Metab Syndrome Obesity. 2019;12:637–645. doi:10.2147/DMSO.S198547
   PubMed Web of Science ®Google Scholar
• Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat Med. 2018;24:1559–1567. doi:10.1038/s41591-018-0177-5
   PubMed Web of Science ®Google Scholar
• Hedge C, Stothart G, Jones JT, Frías PR, Magee KL, Brooks JCW. A frontal attention mechanism in the visual mismatch negativity. Behav Brain Res. 2015;293:173–181. doi:10.1016/j.bbr.2015.07.022
   PubMed Web of Science ®Google Scholar
• Liu G, Guo J. Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing. 2019;337:325–338. doi:10.1016/j.neucom.2019.01.078
   Web of Science ®Google Scholar
• Ståhl N, Mathiason G, Falkman G, Karlsson A. Using recurrent neural networks with attention for detecting problematic slab shapes in steel rolling. Appl Math Model. 2019;70:365–377. doi:10.1016/j.apm.2019.01.027
   Google Scholar
• Tsai F-J, Li T-M, Cheng C-F, et al. Effects of Chinese herbal medicine on hyperlipidemia and the risk of cardiovascular disease in HIV-infected patients in Taiwan. J Ethnopharmacol. 2018;219:71–80. doi:10.1016/j.jep.2018.03.006
   PubMed Web of Science ®Google Scholar
• Lim S-W, Nyam T-TE, Ho C-H, et al. Increased risk of anxiety or depression after traumatic spinal cord injury in patients with preexisting hyperlipidemia: a population-based study. World Neurosurg. 2017;106:402–408. doi:10.1016/j.wneu.2017.06.182
   PubMedGoogle Scholar
• Yang Q, Wang J-H, Huang D-D, et al. Clinical significance of analysis of the level of blood fat, CRP and hemorheological indicators in the diagnosis of elder coronary heart disease. Saudi J Biol Sci. 2018;25(8):1812–1816. doi:10.1016/j.sjbs.2018.09.002
   PubMedGoogle Scholar
• Lupattelli G, Siepi D, De Vuono S, et al. Cholesterol metabolism differs after statin therapy according to the type of hyperlipemia. Life Sci. 2012;90(21–22):846–850. doi:10.1016/j.lfs.2012.03.038
   PubMed Web of Science ®Google Scholar
• Georgescu A, Alexandru N, Constantinescu E, Popov D. Effect of gap junction uncoupler heptanol on resistance arteries reactivity in experimental models of diabetes, hyperlipemia and hyperlipemia-diabetes. Vascul Pharmacol. 2006;44(6):513–518. doi:10.1016/j.vph.2006.03.005
   PubMedGoogle Scholar
• Lan J, Zhao Y, Dong F, et al. Meta-analysis of the effect and safety of berberine in the treatment of type 2 diabetes mellitus, hyperlipemia and hypertension. J Ethnopharmacol. 2015;161:69–81. doi:10.1016/j.jep.2014.09.049
   PubMed Web of Science ®Google Scholar
• Goede V, Stauder R. Multidisciplinary care in the hematology clinic: implementation of geriatric oncology. J Geriatr Oncol. 2018.10(3):497–503.
   PubMedGoogle Scholar
• Navder KP, Baraona E, Lieber CS. Polyenylphosphatidylcholine decreases alcoholic hyperlipemia without affecting the alcohol-induced rise of HDL-cholesterol. Life Sci. 1997;61(19):1907–1914. doi:10.1016/S0024-3205(97)00830-8
   PubMedGoogle Scholar
• Liu Y, Liu G, Zhang Q. Deep learning and medical diagnosis. Lancet. 2019;394(10210):1709–1710. doi:10.1016/S0140-6736(19)32501-2
   PubMed Web of Science ®Google Scholar
• Kingma DP, Ba JL. ADAM: a method for stochastic optimization. arXiv:1412.6980. 2014.
   Google Scholar
• Faust O, Shenfield A, Kareem M, San TR, Fujita H, Rajendra Acharya U. Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Comput Biol Med. 2018;102:327–335. doi:10.1016/j.compbiomed.2018.07.001
   PubMed Web of Science ®Google Scholar
• Seera M, Lim CP, Tan KS, Liew WS. Classification of transcranial Doppler signals using individual and ensemble recurrent neural networks. Neurocomputing. 2017;249:337–344. doi:10.1016/j.neucom.2016.05.117
   Google Scholar
• Stephen W, Liu S, Sohn S, et al. Modeling asynchronous event sequences with RNNs. J Biomed Inform. 2018;83:167–177. doi:10.1016/j.jbi.2018.05.016
   PubMedGoogle Scholar
• Singh S, Pandey SK, Pawar U, Janghel RR. Classification of ECG Arrhythmia using Recurrent Neural Networks. Procedia Comput Sci. 2018;132:1290–1297. doi:10.1016/j.procs.2018.05.045
   Google Scholar
• Rader DJ, Kees Hovingh G. HDL and cardiovascular disease. Lancet. 2014;384(9943):618–625. doi:10.1016/S0140-6736(14)61217-4
   PubMed Web of Science ®Google Scholar
• Sasso FC, Pafundi PC, Gelso A, et al. High HDL cholesterol: a risk factor for diabetic retinopathy? Findings from NO BLIND study. Diabetes Res Clin Pract. 2019;150:236–244. doi:10.1016/j.diabres.2019.03.028
   PubMed Web of Science ®Google Scholar
• Beliaev AM, Booth M, Rowbotham D, Bergin C. Diagnostic inflammatory markers in acute cholangitis. J Surg Res. 2018;228:35–41. doi:10.1016/j.jss.2018.02.048
   PubMed Web of Science ®Google Scholar
• Hua M, Bandos AI, Gur D. Informativeness of diagnostic marker values and the impact of data grouping. Comput Stat Data Anal. 2018;117:76–89. doi:10.1016/j.csda.2017.07.008
   PubMedGoogle Scholar
• Yuki A, Shinkuma S, Hayashi R, et al. CADM1 is a diagnostic marker in early-stage mycosis fungoides: multicenter study of 58 cases. J Am Acad Dermatol. 2018;6(79):1039–1046. doi:10.1016/j.jaad.2018.06.025
   Google Scholar
• Rajendra Acharya U, Faust O, Adib Kadri N, Suri JS, Yu W. Automated identification of normal and diabetes heart rate signals using nonlinear measures. Comput Biol Med. 2013;43(10):1523–1529. doi:10.1016/j.compbiomed.2013.05.024
   PubMedGoogle Scholar