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Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9122486] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.
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Tacconelli E, Górska A, De Angelis G, Lammens C, Restuccia G, Schrenzel J, Huson DH, Carević B, Preoţescu L, Carmeli Y, Kazma M, Spanu T, Carrara E, Malhotra-Kumar S, Gladstone BP. Estimating the association between antibiotic exposure and colonization with extended-spectrum β-lactamase-producing Gram-negative bacteria using machine learning methods: a multicentre, prospective cohort study. Clin Microbiol Infect 2019; 26:87-94. [PMID: 31128285 DOI: 10.1016/j.cmi.2019.05.013] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 04/20/2019] [Accepted: 05/13/2019] [Indexed: 10/26/2022]
Abstract
OBJECTIVES The aim of the study was to measure the impact of antibiotic exposure on the acquisition of colonization with extended-spectrum β-lactamase-producing Gram-negative bacteria (ESBL-GNB) accounting for individual- and group-level confounding using machine-learning methods. METHODS Patients hospitalized between September 2010 and June 2013 at six medical and six surgical wards in Italy, Serbia and Romania were screened for ESBL-GNB at hospital admission, discharge, antibiotic start, and after 3, 7, 15 and 30 days. Primary outcomes were the incidence rate and predictive factors of new ESBL-GNB colonization. Random forest algorithm was used to rank antibiotics according to the risk of selection of ESBL-GNB colonization in patients not colonized before starting antibiotics. RESULTS We screened 10 034 patients collecting 28 322 rectal swab samples. New ESBL-GNB colonization incidence with and without antibiotic treatment was 22/1000 and 9/1000 exposure-days, respectively. In the adjusted regression analyses, antibiotic exposure (hazard ratio (HR) 2.38; 95% CI 1.29-4.40), age 60-69 years (HR 1.19; 95% CI 1.05-1.34), and spring season (HR 1.25; 95% CI 1.14-1.38) were independently associated with new colonization. Monotherapy ranked higher als combination therapy in promoting ESBL-GNB colonization. Among monotherapy, cephalosporins ranked first followed by tetracycline (second), macrolide (fourth) and cotrimoxazole (seventh). Overall the ranking of cephalosporins was lower when used in combination. Among combinations not including cephalosporins, quinolones plus carbapenems ranked highest (eighth). Among sequential therapies, quinolones ranked highest (tenth) when prescribed within 30 days of therapy with cephalosporins. CONCLUSIONS Impact of antibiotics on selecting ESBL-GNB at intestinal level varies if used in monotherapy or combination and according to previous antibiotic exposure. These finding should be explored in future clinical trials on antibiotic stewardship interventions. CLINICAL TRIAL REGISTRATION NCT01208519.
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Affiliation(s)
- E Tacconelli
- Division of Infectious Disease, Department of Internal Medicine I, Tübingen University Hospital, Tübingen, Germany; Division of Infectious Diseases, Department of Diagnostic and Public Health, University of Verona, Italy.
| | - A Górska
- Algorithms in Bioinformatics, University of Tübingen and International Max Planck Research School, Tübingen, Germany
| | - G De Angelis
- Institute of Microbiology, Fondazione Policlinico Universitario A. Gemelli IRCCS - Università Cattolica del Sacro Cuore, Rome, Italy
| | - C Lammens
- Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - G Restuccia
- Department of Anaesthesiology and Intensive Care Medicine, University of Catania, Catania, Italy
| | - J Schrenzel
- Bacteriology Laboratory, Service of Infectious Diseases, University of Geneva Hospitals and Medical Faculty, Geneva, Switzerland
| | - D H Huson
- Algorithms in Bioinformatics, University of Tübingen and International Max Planck Research School, Tübingen, Germany
| | - B Carević
- Department for Hospital Epidemiology, Clinical Centre of Serbia, Belgrade, Serbia
| | - L Preoţescu
- National Institute for Infectious Diseases, University of Medicine 'Carol Davila', Bucharest, Romania
| | - Y Carmeli
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; National Centre for Infection Control, Israel Ministry of Health, Tel Aviv, Israel
| | - M Kazma
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; National Centre for Infection Control, Israel Ministry of Health, Tel Aviv, Israel
| | - T Spanu
- Institute of Microbiology, Fondazione Policlinico Universitario A. Gemelli IRCCS - Università Cattolica del Sacro Cuore, Rome, Italy
| | - E Carrara
- Division of Infectious Diseases, Department of Diagnostic and Public Health, University of Verona, Italy
| | - S Malhotra-Kumar
- Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - B P Gladstone
- Division of Infectious Disease, Department of Internal Medicine I, Tübingen University Hospital, Tübingen, Germany
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A Deep Neural Network-Based Method for Early Detection of Osteoarthritis Using Statistical Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16071281. [PMID: 30974803 PMCID: PMC6480580 DOI: 10.3390/ijerph16071281] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 03/29/2019] [Accepted: 04/07/2019] [Indexed: 11/25/2022]
Abstract
A large number of people suffer from certain types of osteoarthritis, such as knee, hip, and spine osteoarthritis. A correct prediction of osteoarthritis is an essential step to effectively diagnose and prevent severe osteoarthritis. Osteoarthritis is commonly diagnosed by experts through manual inspection of patients’ medical images, which are usually collected in hospitals. Checking the occurrence of osteoarthritis is somewhat time-consuming for patients. In addition, the current studies are focused on automatically detecting osteoarthritis through image-based deep learning algorithms. This needs patients’ medical images, which requires patients to visit the hospital. However, medical utilization and health behavior information as statistical data are easier to collect and access than medical images. Using indirect statistical data without any medical images to predict the occurrence of diverse forms of OA can have significant impacts on pro-active and preventive medical care. In this study, we used a deep neural network for detecting the occurrence of osteoarthritis using patient’s statistical data of medical utilization and health behavior information. The study was based on 5749 subjects. Principal component analysis with quantile transformer scaling was employed to generate features from the patients’ simple background medical records and identify the occurrence of osteoarthritis. Our experiments showed that the proposed method using deep neural network with scaled PCA resulted in 76.8% of area under the curve (AUC) and minimized the effort to generate features. Hence, this methos can be a promising tool for patients and doctors to prescreen for possible osteoarthritis to reduce health costs and patients’ time in hospitals.
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Alcalá-Rmz V, Zanella-Calzada LA, Galván-Tejada CE, García-Hernández A, Cruz M, Valladares-Salgado A, Galván-Tejada JI, Gamboa-Rosales H. Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E381. [PMID: 30700010 PMCID: PMC6388177 DOI: 10.3390/ijerph16030381] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Revised: 01/20/2019] [Accepted: 01/21/2019] [Indexed: 01/16/2023]
Abstract
Diabetes is a chronic and noncommunicable but preventable disease that is affecting the Mexican population at worrying levels, being the first place in prevalence worldwide. Early diabetes detection has become important to prevent other health conditions that involve low organ yield until the patient death. Based on this problem, this work proposes the architecture of an Artificial Neural Network (ANN) for the automated classification of healthy patients from diabetics patients. The analysis was performed used a set of 19 para-clinical features to determine the health status of the patients. The developed model was evaluated through a statistical analysis based on the calculation of the loss function, accuracy, area under the curve (AUC) and receiving operating characteristics (ROC) curve. The results obtained present statistically significant values, with accuracy of 0.94 and AUC values of 0.98. Based on these results, it is possible to conclude that the ANN implemented in this work can classify patients with presence of diabetes from controls with significant accuracy, presenting preliminary results for the development of a diagnostic tool that can be supportive for health specialists.
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Affiliation(s)
- Vanessa Alcalá-Rmz
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico.
| | - Laura A Zanella-Calzada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico.
| | - Carlos E Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico.
| | - Alejandra García-Hernández
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico.
| | - Miguel Cruz
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Av. Cuauhtémoc 330, Col. Doctores, Del. Cuauhtémoc, Ciudad de México CP 06720, Mexico.
| | - Adan Valladares-Salgado
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Av. Cuauhtémoc 330, Col. Doctores, Del. Cuauhtémoc, Ciudad de México CP 06720, Mexico.
| | - Jorge I Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico.
| | - Hamurabi Gamboa-Rosales
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico.
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Toledo Jr A. The use of meta-analysis in infectious disease surveillance: Is it a relevant tool? Rev Soc Bras Med Trop 2019; 52:e20190010. [DOI: 10.1590/0037-8682-0010-2019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 01/11/2019] [Indexed: 11/21/2022] Open
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