Machine learning based predictive model and systems-level network of host-microbe interactions in post-COVID-19 mucormycosis

Verma, Ajay; Rathi, Brijesh

doi:10.1016/j.micpath.2021.105324

Cited by 7 publications

(7 citation statements)

References 37 publications

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“…Lastly, he showed the sign "abdominal pain", whose sequelae were: prostration, diarrhoea, and inability to drink water and eat properly. The research theme has shown outstanding interest according to Gupta et al ( 2021 ); Verma and Rathi 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Hybrid model for early identification post-Covid-19 sequelae

Andrade

Pinheiro

et al. 2023

J Ambient Intell Human Comput

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Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation.

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Section: Discussionmentioning

confidence: 99%

Hybrid model for early identification post-Covid-19 sequelae

Andrade

Pinheiro

et al. 2023

J Ambient Intell Human Comput

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“…47 Another predictive model was developed to analyse mucormycosis developing in patients after COVID-19. 48 The model was made to understand the characteristics of mucormycosis so that a host-microbe interaction network could be created comprising microbial interactions, proteins and markers involved in such interaction to look for potential drug targets and biomarkers.…”

Section: Mucormycosismentioning

confidence: 99%

“…Basic local alignment search tool was employed for homology at E value of 0.0001 (identity ≥25%). R1A (Replicase polyprotein 1a) was identified as a potential drug target and RPS6 (Ribosomal Protein S6) as a biomarker that can have early diagnostic and prognostication value and have a therapeutic connotation in post‐COVID‐19 mucormycosis 48 . However, further validation by more extensive experiments is required.…”

Section: Appraisal Of Studies On the Application Of Ai In The Detecti...mentioning

confidence: 99%

Artificial Intelligence: Exploring utility in detection and typing of fungus with futuristic application in fungal cytology

Singla,

Kundu,

Dey

2023

Cytopathology

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Artificial Intelligence (AI) is an emerging, transforming and revolutionary technology that has captured attention worldwide. It is translating research into precision oncology treatments. AI can analyse large or big data sets requiring high‐speed specialized computing solutions. The data are big in terms of volume and multimodal with the amalgamation of images, text and structure. Machine learning has identified antifungal drug targets, and taxonomic and phylogenetic classification of fungi based on sequence analysis is now available. Real‐time identification tools and user‐friendly mobile applications for identifying fungi have been discovered. Akin to histopathology, AI can be applied to fungal cytology. AI has been fruitful in cytopathology of the thyroid gland, breast, urine and uterine cervical lesions. AI has a huge scope in fungal cytology and would certainly bear fruit with its accuracy, reproducibility and capacity for handling big data. The purpose of this systematic review was to highlight the AI's utility in detecting fungus and its typing with a special focus on future application in fungal cytology. We also touch upon the basics of AI in brief.

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“…Thus, a very impressive work done by the authors in [18] have proposed potential IoT and web technologies in the health sector for these scenarios. [45]have proposed a method that classi-fies cough and is known as multi-criteria decision making (MCDM) method that uses ensemble learning techniques for COVID-19 cough classification. The Cambridge, Coswara, Virufy, and NoCoCo cough databases have been used for the training of the proposed method.…”

Section: Iot-based Technologies the Authors Inmentioning

confidence: 99%

“…Gardians, doctors Both Parker et.al [39] Wearables Generic Covid Hussain et.al [40] Wearables, Data analytics, Random Forest. Generic Covid Hemamalini et.al [41] Bio Medical Sensors.wearables, AI Generic Covid Rida et.al [45] Wireless network. Generic Both 4.…”

Section: Generic Bothmentioning

confidence: 99%

Post-Covid Remote Patient Monitoring using Medical Internet of Things and Machine Learning Analytics

Rahman

Parveen

Sofi

et al. 2023

SCPE

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The Covid-19 pandemic disturbed the smooth functioning of healthcare services throughout the world. New practices such as masking, social distancing and so on were followed to prevent the spread. Further, the severity of the problem increases for the elderly people and people having co-morbidities as proper medical care was not possible and as a result many deaths were recorded. Even for those patients who recovered from Covid could not get proper health monitoring in the Post-Covid phase as a result many deaths and severity in health conditions were reported after the Covid recovery i.e., the Post-Covid era. Technical interventions like the Internet of Things (IoT) based remote patient monitoring using Medical Internet of Things (M-IoT) wearables is one of the solutions that could help in the Post-Covid scenarios. The paper discusses a proposed framework where in a variety of IoT sensing devices along with ML algorithms are used for patient monitoring by utilizing aggregated data acquired from the registered Post-Covid patients. Thus, by using M-IoT along with Machine Learning (ML) approaches could help us in monitoring Post-Covid patients with co-morbidities for and immediate medical help.

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Machine learning based predictive model and systems-level network of host-microbe interactions in post-COVID-19 mucormycosis

Cited by 7 publications

References 37 publications

Hybrid model for early identification post-Covid-19 sequelae

Hybrid model for early identification post-Covid-19 sequelae

Artificial Intelligence: Exploring utility in detection and typing of fungus with futuristic application in fungal cytology

Post-Covid Remote Patient Monitoring using Medical Internet of Things and Machine Learning Analytics

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