Big Data Analytics in Healthcare: A Review of Opportunities and Challenges

Big data analytics is a rapidly expanding issue in computer engineering, and health informatics is one of the most challenging topics. Big data investigation in healthcare could certainly make improvements to medical study as well as the quality of treatment offered to patients. Machine Learning (ML) algorithms due to powerful and efficient handling of data analytics could gain knowledge from data to discover patterns and trends in the database to make predictive models. Our study aims to review the most recent scholarly publications about big data analytics and its applications which include predictive models in healthcare. A systematic search of articles in the three most significant scientific databases: ScienceDirect, PubMed, and IEEE Xplore was carried out following the PRISMA methodology. This study shows how machine learning algorithms are evolving into a promising field for supporting intelligent decisions by analyzing large data sets and thereby improving treatments while reducing costs. However, there remain challenges to overcome and there is still room for improvement to develop methods and applications. Finally, we outline the unsolved issue and the future perspectives for health sciences in the big data era.

[1]  VelidePhani Kumar,et al.  A DATA MINING APPROACH FOR PREDICTION AND TREATMENT OFDIABETES DISEASE , 2014 .

[2]  Igor Jurisica,et al.  Knowledge Discovery and interactive Data Mining in Bioinformatics - State-of-the-Art, future challenges and research directions , 2014, BMC Bioinformatics.

[3]  Shyam Visweswaran,et al.  The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data , 2011, J. Am. Medical Informatics Assoc..

[4]  Majid Sarrafzadeh,et al.  Effectiveness of Remote Patient Monitoring After Discharge of Hospitalized Patients With Heart Failure: The Better Effectiveness After Transition -- Heart Failure (BEAT-HF) Randomized Clinical Trial. , 2016, JAMA internal medicine.

[5]  Xiaoqian Jiang,et al.  A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences , 2016, JMIR medical informatics.

[6]  Terry Anthony Byrd,et al.  Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations , 2018 .

[7]  M H Cole,et al.  Predictors of future falls in Parkinson disease , 2010, Neurology.

[8]  Igor Jurisica,et al.  Knowledge Discovery and Data Mining in Biomedical Informatics: The Future Is in Integrative, Interactive Machine Learning Solutions , 2014, Interactive Knowledge Discovery and Data Mining in Biomedical Informatics.

[9]  M. Ingber,et al.  Initiative To Reduce Avoidable Hospitalizations Among Nursing Facility Residents Shows Promising Results. , 2017, Health affairs.

[10]  Panagiota Galetsi,et al.  Values, challenges and future directions of big data analytics in healthcare: A systematic review. , 2019, Social science & medicine.

[11]  Divya Tomar,et al.  A survey on Data Mining approaches for Healthcare , 2013, BSBT 2013.

[12]  Yunhao Liu,et al.  Big Data: A Survey , 2014, Mob. Networks Appl..

[13]  Michael J Pencina,et al.  Predictive models in heart failure: who cares? , 2013, Circulation. Heart failure.

[14]  J. Hanley,et al.  Risk of Injury Associated with Opioid Use in Older Adults , 2010, Journal of the American Geriatrics Society.

[15]  Ibrahim Khalil,et al.  ViSiBiD: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data , 2017, Comput. Networks.

[16]  Shahaboddin Shamshirband,et al.  Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine , 2016, Entropy.

[17]  P. Deb,et al.  Reducing Readmissions among Heart Failure Patients Discharged to Home Health Care: Effectiveness of Early and Intensive Nursing Services and Early Physician Follow‐Up , 2017, Health services research.

[18]  Andreas Holzinger,et al.  Interactive machine learning for health informatics: when do we need the human-in-the-loop? , 2016, Brain Informatics.

[19]  Min Chen,et al.  Disease Prediction by Machine Learning Over Big Data From Healthcare Communities , 2017, IEEE Access.

[20]  Donald O. Case,et al.  Looking for Information: A Survey of Research on Information Seeking, Needs and Behavior , 2012 .

[21]  J. McAuley,et al.  Reassurance for patients with non-specific conditions – a user's guide , 2017, Brazilian journal of physical therapy.

[22]  E. Capobianco.  Systems and precision medicine approaches to diabetes heterogeneity: a Big Data perspective , 2017, Clinical and Translational Medicine.

[23]  Uffe Kock Wiil,et al.  The Prediction of Alcohol Use Disorder: A Scoping Review , 2019, 2019 IEEE Symposium on Computers and Communications (ISCC).

[24]  Saurabh Pal,et al.  Data Mining Approach to Detect Heart Diseases , 2014 .

[25]  Stephen J Mooney,et al.  Big Data in Public Health: Terminology, Machine Learning, and Privacy. , 2018, Annual review of public health.

[26]  M. Barras,et al.  Systematic review of predictive risk models for adverse drug events in hospitalized patients , 2018, British journal of clinical pharmacology.

[27]  Cynthia Rudin,et al.  An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis , 2013, AAAI.

[28]  G. Filippatos,et al.  Contemporary management of acute right ventricular failure: a statement from the Heart Failure Association and the Working Group on Pulmonary Circulation and Right Ventricular Function of the European Society of Cardiology , 2016, European journal of heart failure.

[29]  Marjan Mansourvar,et al.  Predicting Dropouts From an Electronic Health Platform for Lifestyle Interventions: Analysis of Methods and Predictors , 2019, Journal of medical Internet research.

[30]  Doina Precup,et al.  Mining Administrative Data to Predict Falls in the Elderly Population , 2012, Canadian Conference on AI.

[31]  Y. Tabak,et al.  An Automated Model to Identify Heart Failure Patients at Risk for 30-Day Readmission or Death Using Electronic Medical Record Data , 2010, Medical care.

[32]  Jihoon Kim,et al.  A patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support , 2012, J. Am. Medical Informatics Assoc..

[33]  D. Mark,et al.  Clinical prediction models: are we building better mousetraps? , 2003, Journal of the American College of Cardiology.

[34]  Bradley Malin,et al.  Biomedical data privacy: problems, perspectives, and recent advances , 2013, J. Am. Medical Informatics Assoc..

[35]  Beant Kaur,et al.  Review on Heart Disease Prediction System using Data Mining Techniques , 2014 .

[36]  Eva Negri,et al.  Risk Factors for Falls in Community-dwelling Older People: A Systematic Review and Meta-analysis , 2010, Epidemiology.

[37]  Christian Nøhr,et al.  A Predictive Model for Acute Admission in Aged Population , 2018, MIE.

[38]  D. Bates,et al.  Big data in health care: using analytics to identify and manage high-risk and high-cost patients. , 2014, Health affairs.

[39]  Jihoon Kim,et al.  Calibrating predictive model estimates to support personalized medicine , 2011, J. Am. Medical Informatics Assoc..

[40]  Karen Andersen-Ranberg,et al.  Increasing primary health-care services are associated with acute short-term hospitalization of Danes aged 70 years and older , 2017 .

[41]  T. Richards,et al.  The ACTA PORT-score for predicting perioperative risk of blood transfusion for adult cardiac surgery , 2017, British journal of anaesthesia.

[42]  D. Bates,et al.  Clinical prediction rule to identify high‐risk inpatients for adverse drug events: the JADE Study , 2012, Pharmacoepidemiology and drug safety.

[43]  Xindong Wu,et al.  Data mining with big data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[44]  Juan Lu,et al.  Predicting Outcome after Traumatic Brain Injury: Development and International Validation of Prognostic Scores Based on Admission Characteristics , 2008, PLoS medicine.

[45]  Robert P Kocher,et al.  Hospital readmissions and the Affordable Care Act: paying for coordinated quality care. , 2011, JAMA.

[46]  Andrew Y. Ng,et al.  Improving palliative care with deep learning , 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[47]  Mark V. Williams,et al.  Rehospitalizations among patients in the Medicare fee-for-service program. , 2009, The New England journal of medicine.

[48]  Ziad Obermeyer,et al.  Lost in Thought - The Limits of the Human Mind and the Future of Medicine. , 2017, The New England journal of medicine.

[49]  Fadimana Alptekin,et al.  Unintentional non-fatal home-related injuries in Central Anatolia, Turkey: frequencies, characteristics, and outcomes. , 2008, Injury.

[50]  Xingchen Peng,et al.  Depressive symptoms in Chinese family caregivers of patients with heart failure , 2017, Medicine.

[51]  S. D. de Rooij,et al.  Fear of falling: measurement strategy, prevalence, risk factors and consequences among older persons. , 2008, Age and ageing.

[52]  He Li,et al.  The promising future of healthcare services: When big data analytics meets wearable technology , 2016, Inf. Manag..

[53]  Ahmed Elragal,et al.  Big Data Analytics: A Literature Review Paper , 2014, ICDM.

[54]  E John Orav,et al.  Readmissions, Observation, and the Hospital Readmissions Reduction Program. , 2016, The New England journal of medicine.

[55]  Ian H. Witten,et al.  Chapter 10 – Deep learning , 2017 .

[56]  R. Eisenberg,et al.  Promoting healthcare innovation on the demand side , 2016, Journal of law and the biosciences.

[57]  H. Krumholz.  Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. , 2014, Health affairs.

[58]  Yichuan Wang,et al.  Exploring the path to big data analytics success in healthcare , 2017 .

[59]  Kamran Sedig,et al.  The Challenge of Big Data in Public Health: An Opportunity for Visual Analytics , 2014, Online journal of public health informatics.

[60]  Vijender Kumar Solanki,et al.  IoT Sensor Data Integration in Healthcare using Semantics and Machine Learning Approaches , 2019, A Handbook of Internet of Things in Biomedical and Cyber Physical System.

[61]  Vicente García-Díaz,et al.  Machine learning classification analysis for a hypertensive population as a function of several risk factors , 2018, Expert Syst. Appl..

[62]  C. Lee,et al.  Medical big data: promise and challenges , 2017, Kidney research and clinical practice.

[63]  Taghi M. Khoshgoftaar,et al.  A review of data mining using big data in health informatics , 2013, Journal Of Big Data.

[64]  P. Austin,et al.  Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community , 2010, Canadian Medical Association Journal.

[65]  A. K. Swain.  Mining big data to support decision making in healthcare , 2016 .

[66]  Gopal K Gupta,et al.  Introduction to Data Mining with Case Studies , 2011 .

[67]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[68]  Sameem Abdul Kareem,et al.  Automated web based system for bone age assessment using historam technique , 2012 .

[69]  M. Speechley,et al.  Use of the Berg Balance Scale for Predicting Multiple Falls in Community-Dwelling Elderly People: A Prospective Study , 2008, Physical Therapy.

[70]  Soni Jyoti,et al.  Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction , 2011 .

[71]  J DeCou,et al.  An introduction to predictive modelling of drug concentration in anaesthesia monitors , 2017, Anaesthesia.

[72]  Karl Swedberg,et al.  Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. , 2013, European heart journal.

[73]  Z. Obermeyer,et al.  Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. , 2016, The New England journal of medicine.

[74]  Peter Groves,et al.  The 'big data' revolution in healthcare: Accelerating value and innovation , 2016 .

[75]  J. Alberto Espinosa,et al.  Big Data: Issues and Challenges Moving Forward , 2013, 2013 46th Hawaii International Conference on System Sciences.

[76]  Theodore Rostow.  What Happens When an Acquaintance Buys Your Data?: A New Privacy Harm in the Age of Data Brokers , 2017 .

[77]  David C. Chan,et al.  Improving safety and eliminating redundant tests: cutting costs in U.S. hospitals. , 2009, Health affairs.

[78]  Zhaohong Deng,et al.  An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines , 2015, PloS one.

[79]  Yasha Hasija,et al.  Applications of Deep Learning in Healthcare and Biomedicine , 2019, Studies in Big Data.

[80]  J. Kofman,et al.  Review of fall risk assessment in geriatric populations using inertial sensors , 2013, Journal of NeuroEngineering and Rehabilitation.

[81]  Nishita Mehta,et al.  Concurrence of big data analytics and healthcare: A systematic review , 2018, Int. J. Medical Informatics.

[82]  Brent C Taylor,et al.  Comparison of 2 frailty indexes for prediction of falls, disability, fractures, and death in older women. , 2008, Archives of internal medicine.