Machine Learning Approaches in COVID-19 Diagnosis, Mortality, and Severity Risk Prediction: A Review.

This paper proposes a novel grey spatiotemporal model and quantitatively analyzes the spillover and momentum effects of the COVID-19 lockdown policy on the concentration of PM2.5 (particulate matter of diameter less than 2.5 μm) in Wuhan during the COVID-19 pandemic lockdown from 23 January to 8 April 2020 inclusive, and the post-pandemic period from 9 April 2020 to 17 October 2020 inclusive. The results suggest that the stringent lockdowns lead to a reduction in PM2.5 emissions arising from a momentum effect (9.57-18.67%) and a spillover effect (7.07-27.60%). 

https://doi.org/10.1016/j.seps.2022.101228

COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review

More than a year has passed since the report of the first case of coronavirus disease 2019 (COVID), and increasing deaths continue to occur. Minimizing the time required for resource allocation and clinical decision making, such as triage, choice of ventilation modes and admission to the intensive care unit is important. Machine learning techniques are acquiring an increasingly sought-after role in predicting the outcome of COVID patients. Particularly, the use of baseline machine learning techniques is rapidly developing in COVID mortality prediction, since a mortality prediction model could rapidly and effectively help clinical decision-making for COVID patients at imminent risk of death. Recent studies reviewed predictive models for SARS-CoV-2 diagnosis, severity, length of hospital stay, intensive care unit admission or mechanical ventilation modes outcomes; however, systematic reviews focused on prediction of COVID mortality outcome with machine learning methods are lacking in the literature. The present review looked into the studies that implemented machine learning, including deep learning, methods in COVID mortality prediction thus trying to present the existing published literature and to provide possible explanations of the best results that the studies obtained. The study also discussed challenging aspects of current studies, providing suggestions for future developments.

COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal.

This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman's correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.

/pdf/a-novel-deep-learning-based-quantification-of-serial-chest-4hgwudc1nx.pdf

A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19).

The global covid-19 pandemic puts great pressure on medical resources worldwide and leads healthcare professionals to question which individuals are in imminent need of care. With appropriate data of each patient, hospitals can heuristically predict whether or not a patient requires immediate care. We adopted a deep learning model to predict fatality of individuals tested positive given the patient's underlying health conditions, age, sex, and other factors. As the allocation of resources toward a vulnerable patient could mean the difference between life and death, a fatality prediction model serves as a valuable tool to healthcare workers in prioritizing resources and hospital space. The models adopted were evaluated and refined using the metrics of accuracy, specificity, and sensitivity. After data preprocessing and training, our model is able to predict whether a covid-19 confirmed patient is likely to be dead or not, given their information and disposition. The metrics between the different models are compared. Results indicate that the deep learning model outperforms other machine learning models to solve this rare event prediction problem.

/pdf/individual-level-fatality-prediction-of-covid-19-patients-4eyh6yjrvu.pdf

Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods

Since epilepsy has become one of the biggest problems worried by the world, especially affecting thousands of children every year, many scholars have developed some therapies to try to save those families. One of the reasons why some people suffer from such illness is the genetic factors, which changes the function of the corresponding protein and causes a seizure. Among them, two representative diseases are Ohtahara syndrome and Childhood Absence Epilepsy. In the last century, as the mutated genes and the mechanisms of these two syndromes were still largely unknown, general antiepileptic drugs for them weren’t performing well in some conditions. Therefore, the demands for the mechanisms for these two diseases were increasingly higher though the complexity of human genes and genetic mutation still remains a challenging problem to the current technology. Luckily, extensive preclinical studies have shown that new drugs have promising therapeutic effects on these two syndromes. This article introduces the therapies for Ohtahara syndrome and Childhood Absence Epilepsy. There are going to collect the factors resulting from Childhood Absence Epilepsy and Ohtahara syndrome. Concluding past treatments and comparing them with the new therapies to find the medical progress in these two illnesses and whether Allopregnanolone, antisense oligonucleotides (ASOs) can help to treat Ohtahara syndrome, Zonisamide (ZSN), Levetiracetam (LEV), Topiramate (TPM) can treat Therapy-Resistant CAE.

https://drpress.org/ojs/index.php/HSET/article/download/1122/1055

Genetic Mutations That Lead to Ohtahara Syndrome and Childhood Absence Epilepsy

Alzheimer's disease (AD), an incurable disease, poses a major health problem. It is important to identify patients with mild cognitive impairment (MCI) and early AD. Clock rendering test (CDT) is an effect way to screen AD patients quickly in the community. However, the current CDT methods require specific equipment to collect features, and the existing prediction models are inefficient in early warning of MCI. To solve the problem, this paper replaces digital pen with fingertip interaction, and proposes an early warning model for AD early dCDT images based on ResNet50. The dCDT tests were carried out on normal cognitive elderly, MCI patients and mild AD patients, and the results were used to verify the analysis and classification ability of the ResNet50-based early AD prediction model, in contrast to the clock score-based early AD prediction model. The comparison shows that the ResNet50-based early AD prediction model is efficient in early warning than the other model, and is suitable for large-scale screening of AD patients in the community, in the absence of doctors.

/pdf/a-new-early-warning-method-for-human-computer-interaction-of-mzjdrypt.pdf

A New Early Warning Method for Human-Computer Interaction of Alzheimer's Disease Patients Based on Deep Learning

The early identification of mild cognitive impairment (MCI) due to Alzheimer’s disease (AD), in an early stage of AD can expand the AD warning window. We propose a new capability index evaluating the spatial execution process (SEP), which can dynamically evaluate the execution process in the space navigation task. The hypothesis is proposed that there are neurobehavioral differences between normal cognitive (NC) elderly and AD patients with MCI reflected in digital biomarkers captured during SEP. According to this, we designed a new smart 2-min mobile alerting method for MCI due to AD, for community screening. Two digital biomarkers, total mission execution distance (METRtotal) and execution distance above the transverse obstacle (EDabove), were selected by step-up regression analysis. For the participants with more than 9 years of education, the alerting efficiency of the combination of the two digital biomarkers for MCI due to AD could reach 0.83. This method has the advantages of fast speed, high alerting efficiency, low cost and high intelligence and thus has a high application value for community screening in developing countries. It also provides a new intelligent alerting approach based on the human–computer interaction (HCI) paradigm for MCI due to AD in community screening.

https://www.mdpi.com/2076-3425/13/2/244/pdf?version=1675218083

Jiakang Liu

Papers

Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods

Genetic Mutations That Lead to Ohtahara Syndrome and Childhood Absence Epilepsy

A New Early Warning Method for Human-Computer Interaction of Alzheimer's Disease Patients Based on Deep Learning

A New Smart 2-Min Mobile Alerting Method for Mild Cognitive Impairment Due to Alzheimer’s Disease in the Community