A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

Pandya, Sharnil; Thakur, Aanchal; Saxena, S.C.; Jassal, Nandita; Patel, Chirag; Modi, Kirit; Shah, Pooja; Joshi, Rahul; Gonge, Sudhanshu; Kadam, Kalyani; Kadam, Prachi

doi:10.3390/s21237786

Cited by 21 publications

(6 citation statements)

References 128 publications

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“…In obstetrics, fetal heart rate data analysis by a random forest algorithm provides accurate information on the health state of the fetus [ 12 ]. In addition, immunology research was carried out on disease detection, including autoimmune diseases, immunological deficiency syndromes, cancer, mental health, bacterial infection, and many more [ 13 ]. In cardiology, Shah et al used a machine learning model to recognize cardiac arrest risk and survival probability [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis

Chen

Lin

Fang

et al. 2022

JPM

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The ejection fraction (EF) provides critical information about heart failure (HF) and its management. Electrocardiography (ECG) is a noninvasive screening tool for cardiac electrophysiological activities that has been used to detect patients with low EF based on a deep learning model (DLM) trained via large amounts of data. However, no studies have widely investigated its clinical impacts. OBJECTIVE: This study developed a DLM to estimate EF via ECG (ECG-EF). We further investigated the relationship between ECG-EF and echo-based EF (ECHO-EF) and explored their contributions to future cardiovascular adverse events. METHODS: There were 57,206 ECGs with corresponding echocardiograms used to train our DLM. We compared a series of training strategies and selected the best DLM. The architecture of the DLM was based on ECG12Net, developed previously. Next, 10,762 ECGs were used for validation, and another 20,629 ECGs were employed to conduct the accuracy test. The changes between ECG-EF and ECHO-EF were evaluated. The primary follow-up adverse events included future ECHO-EF changes and major adverse cardiovascular events (MACEs). RESULTS: The sex-/age-matching strategy-trained DLM achieved the best area under the curve (AUC) of 0.9472 with a sensitivity of 86.9% and specificity of 89.6% in the follow-up cohort, with a correlation of 0.603 and a mean absolute error of 7.436. In patients with accurate prediction (initial difference < 10%), the change traces of ECG-EF and ECHO-EF were more consistent (R-square = 0.351) than in all patients (R-square = 0.115). Patients with lower ECG-EF (≤35%) exhibited a greater risk of cardiovascular (CV) complications, delayed ECHO-EF recovery, and earlier ECHO-EF deterioration than patients with normal ECG-EF (>50%). Importantly, ECG-EF demonstrated an independent impact on MACEs and all CV adverse outcomes, with better prediction of CV outcomes than ECHO-EF. CONCLUSIONS: The ECG-EF could be used to initially screen asymptomatic left ventricular dysfunction (LVD) and it could also independently contribute to the predictions of future CV adverse events. Although further large-scale studies are warranted, DLM-based ECG-EF could serve as a promising diagnostic supportive and management-guided tool for CV disease prediction and the care of patients with LVD.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis

Chen

Lin

Fang

et al. 2022

JPM

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“…Since the 1970s, interest in applying AI-ML in the health sector has grown [12]. In the medical field, a significant amount of patient data is managed, including sociodemographic and epidemiological information, results from physical examinations, diagnostic support test outcomes, and procedures performed, among others [10,.…”

Section: Data Setmentioning

confidence: 99%

Supervised Machine Learning Methods for Seasonal Influenza Diagnosis

Marquez,

Barrón-Palma,

Rodríguez

et al. 2023

Diagnostics

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Influenza has been a stationary disease in Mexico since 2009, and this causes a high cost for the national public health system, including its detection using RT-qPCR tests, treatments, and absenteeism in the workplace. Despite influenza’s relevance, the main clinical features to detect the disease defined by international institutions like the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention (CDC) do not follow the same pattern in all populations. The aim of this work is to find a machine learning method to facilitate decision making in the clinical differentiation between positive and negative influenza patients, based on their symptoms and demographic features. The research sample consisted of 15480 records, including clinical and demographic data of patients with a positive/negative RT-qPCR influenza tests, from 2010 to 2020 in the public healthcare institutions of Mexico City. The performance of the methods for classifying influenza cases were evaluated with indices like accuracy, specificity, sensitivity, precision, the f1-measure and the area under the curve (AUC). Results indicate that random forest and bagging classifiers were the best supervised methods; they showed promise in supporting clinical diagnosis, especially in places where performing molecular tests might be challenging or not feasible.

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“…The immune system is very complex and the more we learn about it, the more we realise how little we know [25]. What is certain, however, is that the immune system is energy-dependent and any reduction in energy and resources can cause a significant decrease in immune response [26].…”

Section: Immune Systemmentioning

confidence: 99%

Halalopathy: Stimulation of the Immune System Through Enrichment of Potential Energy

Alzeer¹

2022

RGM

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The human mind is highly exposed to different sources of information. According to halalopathy, supportive information enriches potential, while distracting information increases entropy. Potential energy and entropy are inversely proportional. The enrichment of potential energy creates favourable circumstances for prevention and recovery, while the accumulation of entropy accelerates aging and the development of diseases. The availability of potential energy plays a crucial role in how the immune system can exist and behave. Fight mode i.e., high potential energy mode where errors and defects are detected and corrected simultaneously. Fright mode in which potential energy is dispersed and entropy is accelerated, causing the immune system to respond randomly and attack indiscriminately. The flight mode, in which potential energy is suppressed, leads to a slowing down of the immune response, thereby bypassing many defects and mutations. The main objective of halalopathy is to develop methods and strategies to enrich the potential, keep the fight mode active and thus stimulate the immune system for better prevention and recovery.

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A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

Cited by 21 publications

References 128 publications

Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis

Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis

Supervised Machine Learning Methods for Seasonal Influenza Diagnosis

Halalopathy: Stimulation of the Immune System Through Enrichment of Potential Energy

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