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

D’Angelo, Gianni; Pilla, Raffaele; Tascini, Carlo; Rampone, Salvatore

doi:10.1007/s00500-018-03729-y

Cited by 80 publications

(60 citation statements)

References 87 publications

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“…Some CDSS have been found for meningitis diagnosis. Fuzzy cognitive maps [23], Case-Based Reasoning (CBR) [24], rough sets [25], [26], neural networks [27], genetic programming [28] or decision trees [29] are some of the techniques they use. A decision support tool to predict the likelihood of meningitis in younger infants and children (2 months to 7 years) of semi-urban areas of India is described in [23].…”

Section: Related Workmentioning

confidence: 99%

Non-Invasive Meningitis Diagnosis Using Decision Trees

2020

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Meningitis is one of the pandemic diseases that many less developed countries suffer, primarily due to the lack of economic resources to face it. The more severe types of meningitis, Meningococcal Disease, MD, demand immediate medical attention since delays increase the risk of mortality. This paper presents an open and integrated Clinical Decision Support System to assist physicians in the different stages of meningitis diagnostics through observable symptoms. Our system integrates three intelligent components which try to give support to physicians in early diagnostics of meningitis. These components are based on interpretable tree-based machine learning models and knowledge-engineering techniques. A dataset of 26,228 records of patients with a meningitis diagnosis in Brazil was used to construct and evaluate the system. The performance indicators of the decision models exhibit an outstanding classification performance for MD meningitis with a classification accuracy of 94.3%. In order to test the correct diagnosis of the system, an evaluation study with real patients' data was performed. The experimental results concluded that excluding meningitis cases based only on observable symptoms is much more complicated than diagnosing it. However, the system properly diagnosed 88% of meningitis cases from the real database. INDEX TERMS Medical diagnosis, meningitis diagnostic models, tree-based machine learning.

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Section: Related Workmentioning

confidence: 99%

Non-Invasive Meningitis Diagnosis Using Decision Trees

2020

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“…Indeed, computer-based techniques have already proved to be very effective in dealing with modeling and processing of several kind of biological data. 19,20…”

Section: The Sars-cov-2 Structure and Defeating Strategiesmentioning

confidence: 99%

Discovering genomic patterns in SARS‐CoV‐2 variants

D’Angelo

Palmieri

2020

Int J Intell Syst

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SARS‐CoV‐2 is a novel severe acute respiratory syndrome‐like coronavirus (SARS‐CoV), which is responsible of the ongoing world pandemic of COVID‐19 disease. Although many approaches are being investigated to address this issue, nowaday there are no vaccines available and there is little evidence supporting the efficiency of potential therapeutic agents. Moreover, the high mutation rate of this virus heavily affects the understanding of its evolution and diffusion mechanisms, and, in turn, the development of effective solutions. In this study, two novel algorithms are provided for finding out recurrent patterns of nucleotide subsequences of different SARS‐CoV‐2 genomes as a unique signature capable of identifying the most peculiar features of the pathogen. In particular, we provide several subsequence patterns related to the Spike glycoprotein, which is believed to be the main target for developing effective drugs and vaccines against the COVID‐19 disease because of its role in the entrance of coronaviruses into host cells. The experimental results, obtained by analyzing 5000 genomes of SARS‐CoV‐2, have shown that the extracted patterns are able to recognize the Spyke protein in the 99.35% of the considered genomes. In addition, such patterns have proven to be highly discriminating with respect to other pathogenic genomes, such as SARS, Middle East respiratory syndrome, Nipah, and the streptococcus bacteria. We hope that the findings presented in this study can help specialists in speeding up the design of more accurate drugs or vaccines against SARS‐CoV‐2.

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“…For example, Gianni D'Angelo et al presented a robust approach to the optimal policy of autonomous space systems modeled via Markov decision process (MDP) from the values assigned to its transition probability matrix [34]. In the medical field, Gianni D'Angelo et al presented a proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees [35]. Machine learning methods are also widely used in prediction problems, and have achieved good results in problems such as weather forecasting and ocean wave prediction.…”

Section: Related Workmentioning

confidence: 99%

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

et al. 2020

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Timely and accurate depth estimation of a shallow waterway can improve shipping efficiency and reduce the danger of waterway transport accidents. However, waterway depth data measured during actual maritime navigation is limited, and the depth values can have large variability. Big data collected in real time by automatic identification systems (AIS) might provide a way to estimate accurate waterway depths, although these data include no direct channel depth information. We suggest a deep neural network (DNN) based model, called DDTree, for using the real-time AIS data and the data from Global Mapper to predict waterway depth for ships in an accurate and timely way. The model combines a decision tree and DNN, which is trained and tested on the AIS and Global Mapper data from the Nantong and Fangcheng ports on the southeastern and southwestern coast of China. The actual waterway depth data were used together with the AIS data as the input to DDTree. The latest data on waterway depths from the Chinese maritime agency were used to verify the results. The experiments show that the DDTree model has a prediction accuracy of 91.15%. Therefore, the DDTree model can provide an accurate prediction of waterway depth and compensate for the shortage of waterway depth monitoring means. The proposed hybrid DDTree model could improve marine situational awareness, navigation safety, and shipping efficiency, and contribute to smart navigation.

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

Cited by 80 publications

References 87 publications

Non-Invasive Meningitis Diagnosis Using Decision Trees

Non-Invasive Meningitis Diagnosis Using Decision Trees

Discovering genomic patterns in SARS‐CoV‐2 variants

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

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