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Cai T, Anceschi U, Prata F, Collini L, Brugnolli A, Migno S, Rizzo M, Liguori G, Gallelli L, Wagenlehner FME, Johansen TEB, Montanari L, Palmieri A, Tascini C. Artificial Intelligence Can Guide Antibiotic Choice in Recurrent UTIs and Become an Important Aid to Improve Antimicrobial Stewardship. Antibiotics (Basel) 2023; 12:antibiotics12020375. [PMID: 36830285 PMCID: PMC9952599 DOI: 10.3390/antibiotics12020375] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 01/29/2023] [Accepted: 02/06/2023] [Indexed: 02/15/2023] Open
Abstract
BACKGROUND A correct approach to recurrent urinary tract infections (rUTIs) is an important pillar of antimicrobial stewardship. We aim to define an Artificial Neural Network (ANN) for predicting the clinical efficacy of the empiric antimicrobial treatment in women with rUTIs. METHODS We extracted clinical and microbiological data from 1043 women. We trained an ANN on 725 patients and validated it on 318. RESULTS The ANN showed a sensitivity of 87.8% and specificity of 97.3% in predicting the clinical efficacy of empirical therapy. The previous use of fluoroquinolones (HR = 4.23; p = 0.008) and cephalosporins (HR = 2.81; p = 0.003) as well as the presence of Escherichia coli with resistance against cotrimoxazole (HR = 3.54; p = 0.001) have been identified as the most important variables affecting the ANN output decision predicting the fluoroquinolones-based therapy failure. A previous isolation of Escherichia coli with resistance against fosfomycin (HR = 2.67; p = 0.001) and amoxicillin-clavulanic acid (HR = 1.94; p = 0.001) seems to be the most influential variable affecting the output decision predicting the cephalosporins- and cotrimoxazole-based therapy failure. The previously mentioned Escherichia coli with resistance against cotrimoxazole (HR = 2.35; p < 0.001) and amoxicillin-clavulanic acid (HR = 3.41; p = 0.007) seems to be the most influential variable affecting the output decision predicting the fosfomycin-based therapy failure. CONCLUSIONS ANNs seem to be an interesting tool to guide the antimicrobial choice in the management of rUTIs at the point of care.
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Affiliation(s)
- Tommaso Cai
- Department of Urology, Santa Chiara Regional Hospital, 38123 Trento, Italy
- Institute of Clinical Medicine, University of Oslo, 0315 Oslo, Norway
- Correspondence:
| | - Umberto Anceschi
- IRCCS “Regina Elena” National Cancer Institute, 00144 Rome, Italy
| | - Francesco Prata
- Department of Urology, Campus Bio-Medico University of Rome, 00128 Rome, Italy
| | - Lucia Collini
- Department of Microbiology, Santa Chiara Regional Hospital, 38123 Trento, Italy
| | - Anna Brugnolli
- Centre of Higher Education for Health Sciences, 38122 Trento, Italy
| | - Serena Migno
- Department of Gynecology and Obstetrics, Santa Chiara Regional Hospital, 38123 Trento, Italy
| | - Michele Rizzo
- Department of Urology, University of Trieste, 34127 Trieste, Italy
| | - Giovanni Liguori
- Department of Urology, University of Trieste, 34127 Trieste, Italy
| | - Luca Gallelli
- Department of Health Science, School of Medicine, University of Catanzaro, 88100 Catanzaro, Italy
| | - Florian M. E. Wagenlehner
- Clinic for Urology, Pediatric Urology and Andrology, Justus Liebig University, 35390 Giessen, Germany
| | - Truls E. Bjerklund Johansen
- Institute of Clinical Medicine, University of Oslo, 0315 Oslo, Norway
- Department of Urology, Oslo University Hospital, 0315 Oslo, Norway
- Institute of Clinical Medicine, University of Aarhus, 8000 Aarhus, Denmark
| | - Luca Montanari
- Department of Medicine (DAME), Infectious Diseases Clinic, University of Udine, 33100 Udine, Italy
| | - Alessandro Palmieri
- Department of Urology, University of Naples Federico II, 80138 Naples, Italy
| | - Carlo Tascini
- Department of Medicine (DAME), Infectious Diseases Clinic, University of Udine, 33100 Udine, Italy
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A Personalized Compression Method for Steady-State Visual Evoked Potential EEG Signals. INFORMATION 2022. [DOI: 10.3390/info13040186] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data form, so it is necessary to compress the signal. This paper proposes a personalized EEG compression and reconstruction algorithm for the SSVEP application. In the algorithm, to realize personalization, a primary artificial neural network (ANN) model is first pre-trained with the open benchmark database towards BCI application (BETA). Then, an adaptive ANN model is generated with incremental learning for each subject to compress their individual data. Additionally, a personalized, non-uniform quantization method is proposed to reduce the errors caused by compression. The recognition accuracy only decreases by 3.79% when the compression rate is 12.7 times, and is tested on BETA. The proposed algorithm can reduce signal loss by from 50.43% to 81.08% in the accuracy test compared to the case without ANN and uniform quantization.
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Real-time non-uniform EEG sampling. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Bent B, Lu B, Kim J, Dunn JP. Biosignal Compression Toolbox for Digital Biomarker Discovery. SENSORS (BASEL, SWITZERLAND) 2021; 21:E516. [PMID: 33450898 PMCID: PMC7828339 DOI: 10.3390/s21020516] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/07/2021] [Accepted: 01/11/2021] [Indexed: 12/26/2022]
Abstract
A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare "data deluge," leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the "Biosignal Data Compression Toolbox," an open-source, accessible software platform for compressing biosignal data.
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Affiliation(s)
- Brinnae Bent
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA; (B.B.); (B.L.); (J.K.)
| | - Baiying Lu
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA; (B.B.); (B.L.); (J.K.)
| | - Juseong Kim
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA; (B.B.); (B.L.); (J.K.)
| | - Jessilyn P. Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA; (B.B.); (B.L.); (J.K.)
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA
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Hosny KM, Khalid AM, Mohamed ER. Efficient compression of bio-signals by using Tchebichef moments and Artificial Bee Colony. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.02.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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