A proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees

Meningitis is an inflammation of the protective membranes covering the brain and the spinal cord. Meningitis can have different causes, and discriminating between meningitis etiologies is still considered a hard task, especially when some specific clinical parameters, mostly derived from blood and cerebrospinal fluid analysis, are not completely available. Although less frequent than its viral version, bacterial meningitis can be fatal, especially when diagnosis is delayed. In addition, often unnecessary antibiotic and/or antiviral treatments are used as a solution, which is not cost or health effective. In this work, we address this issue through the use of machine learning-based methodologies. We consider two distinct cases. In one case, we take into account both blood and cerebrospinal parameters; in the other, we rely exclusively on the blood data. As a result, we have rules and formulas applicable in clinical settings. Both results highlight that a combination of the clinical parameters is required to properly distinguish between the two meningitis etiologies. The results on standard and clinical datasets show high performance. The formulas achieve 100% of sensitivity in detecting a bacterial meningitis.

[1]  Nathan Kuppermann,et al.  Development and validation of a multivariable predictive model to distinguish bacterial from aseptic meningitis in children in the post-Haemophilus influenzae era. , 2002, Pediatrics.

[2]  K. Tyler,et al.  Herpes Simplex Virus Infection as a Cause of Benign Recurrent Lymphocytic Meningitis , 1994, Annals of Internal Medicine.

[3]  C. Choi,et al.  Bacterial meningitis in aging adults. , 2001, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[4]  J. McFadden,et al.  Polymerase chain reaction for diagnosis of meningococcal meningitis , 1992, The Lancet.

[5]  S. Spector,et al.  Virologic markers of human immunodeficiency virus type 1 in cerebrospinal fluid of infected children. , 1996, The Journal of infectious diseases.

[6]  J. Klein,et al.  Diagnosis and management of meningitis , 1992, The Pediatric infectious disease journal.

[7]  S. Hammer,et al.  The acute aseptic meningitis syndrome. , 1990, Infectious disease clinics of North America.

[8]  Allan R Tunkel,et al.  Practice guidelines for the management of bacterial meningitis. , 2004, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[9]  M. Sawyer,et al.  Diagnosis of enteroviral meningitis by using PCR with a colorimetric microwell detection assay , 1994, Journal of clinical microbiology.

[10]  A. Schuchat,et al.  Bacterial Meningitis in the United States in 1995 , 1997 .

[11]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[12]  T. Kleine,et al.  New and old diagnostic markers of meningitis in cerebrospinal fluid (CSF) , 2003, Brain Research Bulletin.

[13]  F. Harrell,et al.  Differential diagnosis of acute meningitis. An analysis of the predictive value of initial observations. , 1989, JAMA.

[14]  Vipin Kumar,et al.  Feature Selection: A literature Review , 2014, Smart Comput. Rev..

[15]  K. Kim.  Acute bacterial meningitis in infants and children. , 2010, The Lancet. Infectious diseases.

[16]  E. Bingen,et al.  Neonatal Bacterial Meningitis: 444 Cases in 7 Years , 2011, The Pediatric infectious disease journal.

[17]  Gianni D'Angelo,et al.  Diagnosis of aerospace structure defects by a HPC implemented soft computing algorithm , 2014, 2014 IEEE Metrology for Aerospace (MetroAeroSpace).

[18]  Dejan B. Popovic,et al.  8th Seminar on Neural Network Applications In Electrical Engineering, NEUREL 2006, Proceedings, 25-27 September 2006, Belgrade, Serbia , 2006 .

[19]  J. Romero.  Diagnosis and management of enteroviral infections of the central nervous system , 2002, Current infectious disease reports.

[20]  B. Hoen,et al.  Validation of a Diagnosis Model for Differentiating Bacterial from Viral Meningitis in Infants and Children under 3.5 Years of Age , 2000, European Journal of Clinical Microbiology and Infectious Diseases.

[21]  L. Hansson,et al.  Serum C-reactive protein in the differential diagnosis of acute meningitis. , 1993, Scandinavian journal of infectious diseases.

[22]  R. Hayden,et al.  More laboratory testing: greater cost but not necessarily better , 2000, The Pediatric infectious disease journal.

[23]  E. Mylonakis,et al.  Central Nervous System Infection with Listeria monocytogenes : 33 Years' Experience at a General Hospital and Review of 776 Episodes from the Literature , 1998, Medicine.

[24]  J. McArthur.  Neurologic Manifestations of AIDS , 1987, Medicine.

[25]  S. Fukuhara,et al.  Diagnostic accuracy of cerebrospinal fluid lactate for differentiating bacterial meningitis from aseptic meningitis: a meta-analysis. , 2011, The Journal of infection.

[26]  P. Parizel,et al.  Tuberculosis of the central nervous system: overview of neuroradiological findings , 2003, European Radiology.

[27]  A. Hightower,et al.  Bacterial Meningitis in the United States, 1978 Through 1981: The National Bacterial Meningitis Surveillance Study , 1985 .

[28]  Dabeeruddin Syed,et al.  Techniques to deal with missing data , 2016, 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA).

[29]  Ayman Taha,et al.  A proposed outliers identification algorithm for categorical data sets , 2010, 2010 The 7th International Conference on Informatics and Systems (INFOS).

[30]  J. Farrar,et al.  Comparison of Conventional Bacteriology with Nucleic Acid Amplification (Amplified Mycobacterium Direct Test) for Diagnosis of Tuberculous Meningitis before and after Inception of Antituberculosis Chemotherapy , 2004, Journal of Clinical Microbiology.

[31]  P. Kragsbjerg,et al.  Detection of bacterial DNA in cerebrospinal fluid by an assay for simultaneous detection of Neisseria meningitidis, Haemophilus influenzae, and streptococci using a seminested PCR strategy , 1994, Journal of clinical microbiology.

[32]  S B Freedman,et al.  Predictors of bacterial meningitis in the era after Haemophilus influenzae. , 2001, Archives of pediatrics & adolescent medicine.

[33]  M. Sawyer,et al.  Impact of a diagnostic cerebrospinal fluid enterovirus polymerase chain reaction test on patient management. , 2000, JAMA.

[34]  Stephan M. Winkler,et al.  Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications , 2009 .

[35]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[36]  M. Sawyer,et al.  Diagnosis of enteroviral central nervous system infection by polymerase chain reaction during a large community outbreak. , 1994, The Pediatric infectious disease journal.

[37]  Dominic P. Searson.  GPTIPS 2: An Open-Source Software Platform for Symbolic Data Mining , 2014, Handbook of Genetic Programming Applications.

[38]  Shailendra Narayan Singh,et al.  Outlier Detection: Applications and techniques in Data Mining , 2016, 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence).

[39]  J. Kremastinou,et al.  Simultaneous single-tube PCR assay for the detection of Neisseria meningitidis, Haemophilus influenzae type b and Streptococcus pneumoniae. , 2005, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.

[40]  Ben Vandermeer,et al.  Clinical Features Suggestive of Meningitis in Children: A Systematic Review of Prospective Data , 2010, Pediatrics.

[41]  P. Martínez-Martín,et al.  Detection of varicella‐zoster virus‐specific DNA sequences in cerebrospinal fluid from patients with acute aseptic meningitis and no cutaneous lesions , 1994, Journal of medical virology.

[42]  Amit Konar,et al.  Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain , 1999 .

[43]  Gianni D'Angelo,et al.  Feature Extraction and Soft Computing Methods for Aerospace Structure Defect Classification , 2016, ArXiv.

[44]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[45]  Nik Bessis,et al.  CS-PSO: chaotic particle swarm optimization algorithm for solving combinatorial optimization problems , 2016, Soft Computing.

[46]  John M Colford,et al.  Diagnostic accuracy of nucleic acid amplification tests for tuberculous meningitis: a systematic review and meta-analysis. , 2003, The Lancet. Infectious diseases.

[47]  D. Gendrel,et al.  Clinical decision rules to distinguish between bacterial and aseptic meningitis , 2006, Archives of Disease in Childhood.

[48]  Amparo Alonso-Betanzos,et al.  Filter Methods for Feature Selection - A Comparative Study , 2007, IDEAL.

[49]  M. Bottai,et al.  Adult bacterial meningitis: earlier treatment and improved outcome following guideline revision promoting prompt lumbar puncture. , 2015, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[50]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[51]  J. Levy,et al.  Neurologic abnormalities and recovery of human immunodeficiency virus from cerebrospinal fluid. , 1987, Annals of internal medicine.

[52]  D. Fedorko,et al.  Laboratory diagnosis of bacterial meningitis , 1992, Clinical Microbiology Reviews.

[53]  L. J. La Scolea,et al.  Quantitation of bacteria in cerebrospinal fluid and blood of children with meningitis and its diagnostic significance , 1984, Journal of clinical microbiology.

[54]  G. Schutze,et al.  Clinical usefulness of cerebrospinal fluid bacterial antigen studies. , 1994, The Journal of pediatrics.

[55]  M. Harper,et al.  Differentiating Acute Bacterial Meningitis From Acute Viral Meningitis Among Children With Cerebrospinal Fluid Pleocytosis: A Multivariable Regression Model , 2004, The Pediatric infectious disease journal.

[56]  Gianni D'Angelo,et al.  Shape-based defect classification for non destructive testing , 2015, 2015 IEEE Metrology for Aerospace (MetroAeroSpace).

[57]  T. Chonmaitree,et al.  Role of the virology laboratory in diagnosis and management of patients with central nervous system disease , 1989, Clinical Microbiology Reviews.

[58]  E. Singer,et al.  Quantifying HIV-1 RNA using the polymerase chain reaction on cerebrospinal fluid and serum of seropositive individuals with and without neurologic abnormalities. , 1995, Journal of acquired immune deficiency syndromes and human retrovirology : official publication of the International Retrovirology Association.

[59]  Erdal Kiliç,et al.  Feature selection in text classification , 2016, 2016 24th Signal Processing and Communication Application Conference (SIU).

[60]  Gianni D'Angelo,et al.  Towards a HPC-oriented parallel implementation of a learning algorithm for bioinformatics applications , 2014, BMC Bioinformatics.

[61]  W. Bonadio.  The cerebrospinal fluid: physiologic aspects and alterations associated with bacterial meningitis , 1992, The Pediatric infectious disease journal.

[62]  Gianni D'Angelo,et al.  Toward a soft computing-based correlation between oxygen toxicity seizures and hyperoxic hyperpnea , 2018, Soft Comput..

[63]  Gianni D'Angelo,et al.  Automated Eddy Current non-destructive testing through low definition lissajous figures , 2016, 2016 IEEE Metrology for Aerospace (MetroAeroSpace).

[64]  A. Viallon,et al.  Clinical decision rules for acute bacterial meningitis: current insights , 2016, Open access emergency medicine : OAEM.

[65]  P. Ruutu,et al.  Etiology of central nervous system infections in the Philippines and the role of serum C-reactive protein in excluding acute bacterial meningitis. , 1998, International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases.

[66]  K. Revett.  A Neural Network Based Classifier for Acute Meningitis , 2006, 2006 8th Seminar on Neural Network Applications in Electrical Engineering.

[67]  S. Shafran,et al.  Acute Bacterial Meningitis in Adults: A 12‐Year Review , 2000, Medicine.

[68]  Scott W. Sinner.  Approach to the Diagnosis and Management of Tuberculous Meningitis , 2002, Current infectious disease reports.

[69]  L. Saravolatz,et al.  Broad-range bacterial polymerase chain reaction for early detection of bacterial meningitis. , 2003, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[70]  David D. Lewis,et al.  Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.

[71]  J. Zamora,et al.  Cerebrospinal fluid lactate concentration to distinguish bacterial from aseptic meningitis: a systemic review and meta-analysis , 2010, Critical care.

[72]  D. Cook,et al.  Does this adult patient have acute meningitis , 1999 .

[73]  Martijn Weisfelt,et al.  Clinical features and prognostic factors in adults with bacterial meningitis. , 2004, The New England journal of medicine.

[74]  H. Hollander,et al.  Human immunodeficiency virus-associated meningitis. Clinical course and correlations. , 1987, The American journal of medicine.

[75]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[76]  V. Quagliarello,et al.  Computed tomography of the head before lumbar puncture in adults with suspected meningitis. , 2001, The New England journal of medicine.

[77]  M. Täuber,et al.  Lactate and glucose concentrations in brain interstitial fluid, cerebrospinal fluid, and serum during experimental pneumococcal meningitis. , 1992, The Journal of infectious diseases.

[78]  R. Hondo,et al.  Detection of varicella-zoster virus DNA by polymerase chain reaction in cerebrospinal fluid of patients with herpes zoster meningitis , 1992, Journal of Neurology.

[79]  Hyun Kang.  The prevention and handling of the missing data , 2013, Korean journal of anesthesiology.

[80]  A. Julián-Jiménez,et al.  Capacidad de la procalcitonina para predecir meningitis bacterianas en el servicio de urgencias , 2016 .

[81]  Francesco Palmieri,et al.  An Artificial Intelligence-Based Trust Model for Pervasive Computing , 2015, 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC).

[82]  J. Duffy,et al.  Using Symbolic Regression to Infer Strategies from Experimental Data , 2002 .

[83]  V. Caviness,et al.  Acute bacterial meningitis in adults. A review of 493 episodes. , 1993, The New England journal of medicine.

[84]  Olivier François,et al.  Generation of Incompliete Test-Data usinng Bayesinan Networks , 2007, 2007 International Joint Conference on Neural Networks.

[85]  S. Rao,et al.  Lack of sensitivity of the latex agglutination test to detect bacterial antigen in the cerebrospinal fluid of patients with culture-negative meningitis. , 2001, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[86]  B. Aprill,et al.  Cerebrospinal fluid and human immunodeficiency virus. Findings in healthy, asymptomatic, seropositive men. , 1990, Archives of internal medicine.

[87]  Francesco Palmieri,et al.  Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification , 2016, Soft Computing.

[88]  K. Nelson,et al.  Community-acquired purulent meningitis: a review of 1,316 cases during the antibiotic era, 1954-1976. , 1980, Reviews of infectious diseases.

[89]  A. Hightower,et al.  Bacterial meningitis in the United States, 1986: report of a multistate surveillance study. The Bacterial Meningitis Study Group. , 1990, The Journal of infectious diseases.

[90]  Pasi Fränti,et al.  Outlier detection using k-nearest neighbour graph , 2004, ICPR 2004.

[91]  Kenneth Revett,et al.  A Machine Learning Approach to Differentiating Bacterial From Viral Meningitis , 2006, IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA'06).

[92]  Francesco Palmieri,et al.  An uncertainty-managing batch relevance-based approach to network anomaly detection , 2015, Appl. Soft Comput..

[93]  W. Schaffner,et al.  Bacterial meningitis in the United States, 1998-2007. , 2005, The New England journal of medicine.

[94]  A. Julián-Jiménez,et al.  Ability of procalcitonin to predict bacterial meningitis in the emergency department. , 2016, Neurologia.

[95]  N. H. Duc,et al.  Effect of antituberculosis drug resistance on response to treatment and outcome in adults with tuberculous meningitis. , 2005, The Journal of infectious diseases.

[96]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .