Deep Learning for Predictive Analytics in Healthcare

Despite a recent wealth of data and information, the healthcare sector is lacking in actionable knowledge. The healthcare industry faces challenges in essential areas like electronic record management, data integration, and computer-aided diagnoses and disease predictions. It is necessary to reduce healthcare costs and the movement towards personalized healthcare. The rapidly expanding fields of deep learning and predictive analytics has started to play a pivotal role in the evolution of large volume of healthcare data practices and research. Deep learning offers a wide range of tools, techniques, and frameworks to address these challenges. Health data predictive analytics is emerging as a transformative tool that can enable more proactive and preventative treatment options. In a nutshell, this paper focus on the framework for deep learning data analysis to clinical decision making depicts the study on various deep learning techniques and tools in practice as well as the applications of deep learning in healthcare.

[1]  Richard K. G. Do,et al.  Convolutional neural networks: an overview and application in radiology , 2018, Insights into Imaging.

[2]  Viju Raghupathi,et al.  Big data analytics in healthcare: promise and potential , 2014, Health Information Science and Systems.

[3]  Vasant Dhar,et al.  Editorial - Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research , 2014, Inf. Syst. Res..

[4]  M. Pencina,et al.  General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study , 2008, Circulation.

[5]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[6]  Timothy R Church,et al.  Selection criteria for lung-cancer screening. , 2013, The New England journal of medicine.

[7]  Yu Tsao,et al.  S1 and S2 Heart Sound Recognition Using Deep Neural Networks , 2017, IEEE Trans. Biomed. Eng..

[8]  Parashkev Nachev,et al.  Computer Methods and Programs in Biomedicine NiftyNet: a deep-learning platform for medical imaging , 2022 .

[9]  Yvonne Vergouwe,et al.  Prognosis and prognostic research: what, why, and how? , 2009, BMJ : British Medical Journal.

[10]  Jin He,et al.  Real-Time Multilead Convolutional Neural Network for Myocardial Infarction Detection , 2018, IEEE Journal of Biomedical and Health Informatics.

[11]  Hojjat Adeli,et al.  Imaging and machine learning techniques for diagnosis of Alzheimer’s disease , 2016, Reviews in the neurosciences.

[12]  Thomas Blaschke,et al.  The rise of deep learning in drug discovery. , 2018, Drug discovery today.

[13]  R. Stevens,et al.  UKPDS 60: Risk of Stroke in Type 2 Diabetes Estimated by the UK Prospective Diabetes Study Risk Engine , 2002, Stroke.

[14]  R. Holman,et al.  The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56). , 2001, Clinical science.

[15]  Mohammad Hassan Moradi,et al.  The attractor recurrent neural network based on fuzzy functions: An effective model for the classification of lung abnormalities , 2017, Comput. Biol. Medicine.

[16]  Indranil R. Bardhan,et al.  Predictive Analytics for Readmission of Patients with Congestive Heart Failure , 2015, Inf. Syst. Res..

[17]  Haohan Wang,et al.  Deep Learning for Genomics: A Concise Overview , 2018, ArXiv.

[18]  Xiaowu Sun,et al.  Using electronic health record data to develop inpatient mortality predictive model: Acute Laboratory Risk of Mortality Score (ALaRMS) , 2013, J. Am. Medical Informatics Assoc..

[19]  Luca Ambrogioni,et al.  Generative adversarial networks for reconstructing natural images from brain activity , 2017, NeuroImage.

[20]  Brian J Smith,et al.  An interactive Bayesian model for prediction of lymph node ratio and survival in pancreatic cancer patients. , 2014, Journal of the American Medical Informatics Association : JAMIA.

[21]  Ankica Babic,et al.  Structural Risk Evaluation of a Deep Neural Network and a Markov Model in Extracting Medical Information from Phonocardiography , 2018, ICIMTH.

[22]  Gediminas Adomavicius,et al.  A Machine Learning Approach to Improving Dynamic Decision Making , 2014, Inf. Syst. Res..

[23]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[24]  Y. Vergouwe,et al.  Validation, updating and impact of clinical prediction rules: a review. , 2008, Journal of clinical epidemiology.

[25]  Ming Dong,et al.  Generating synthetic CTs from magnetic resonance images using generative adversarial networks , 2018, Medical physics.

[26]  Charles Elkan,et al.  Learning to Diagnose with LSTM Recurrent Neural Networks , 2015, ICLR.

[27]  Shabnam Ghaffarzadegan,et al.  Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[28]  Hua Xu,et al.  Predict effective drug combination by deep belief network and ontology fingerprints , 2018, J. Biomed. Informatics.

[29]  Hung T. Nguyen,et al.  Deep learning framework for detection of hypoglycemic episodes in children with type 1 diabetes , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[30]  Jimeng Sun,et al.  Using recurrent neural network models for early detection of heart failure onset , 2016, J. Am. Medical Informatics Assoc..

[31]  Jeremy Howard.  The business impact of deep learning , 2013, KDD.