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Fulltext Time series predicting of COVID-19 based on deep learning

Alassafi MO, Jarrah M, Alotaibi R

Neurocomputing, 2022 Jan 11;468:335-344.
PMID: 34690432 DOI: 10.1016/j.neucom.2021.10.035

COVID-19 was declared a global pandemic by the World Health Organisation (WHO) on 11th March 2020. Many researchers have, in the past, attempted to predict a COVID outbreak and its effect. Some have regarded time-series variables as primary factors which can affect the onset of infectious diseases like influenza and severe acute respiratory syndrome (SARS). In this study, we have used public datasets provided by the European Centre for Disease Prevention and Control for developing a prediction model for the spread of the COVID-19 outbreak to and throughout Malaysia, Morocco and Saudi Arabia. We have made use of certain effective deep learning (DL) models for this purpose. We assessed some specific major features for predicting the trend of the existing COVID-19 outbreak in these three countries. In this study, we also proposed a DL approach that includes recurrent neural network (RNN) and long short-term memory (LSTM) networks for predicting the probable numbers of COVID-19 cases. The LSTM models showed a 98.58% precision accuracy while the RNN models showed a 93.45% precision accuracy. Also, this study compared the number of coronavirus cases and the number of resulting deaths in Malaysia, Morocco and Saudi Arabia. Thereafter, we predicted the number of confirmed COVID-19 cases and deaths for a subsequent seven days. In this study, we presented their predictions using the data that was available up to December 3rd, 2020.
Fulltext The ability of digital breast tomosynthesis to reduce additional examinations in older women

Gharaibeh M, Alfwares AA, Elobeid E, Khasawneh R, Rousan L, El-Heis M, et al.

Front Med (Lausanne), 2023;10:1276434.
PMID: 38076239 DOI: 10.3389/fmed.2023.1276434

AIMS: To assess the diagnostic performance of digital breast tomosynthesis (DBT) in older women across varying breast densities and to compare its effectiveness for cancer detection with 2D mammography and ultrasound (U/S) for different breast density categories. Furthermore, our study aimed to predict the potential reduction in unnecessary additional examinations among older women due to DBT.
METHODS: This study encompassed a cohort of 224 older women. Each participant underwent both 2D mammography and digital breast tomosynthesis examinations. Supplementary views were conducted when necessary, including spot compression and magnification, ultrasound, and recommended biopsies. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were calculated for 2D mammography, DBT, and ultrasound. The impact of DBT on diminishing the need for supplementary imaging procedures was predicted through binary logistic regression.
RESULTS: In dense breast tissue, DBT exhibited notably heightened sensitivity and NPV for lesion detection compared to non-dense breasts (61.9% vs. 49.3%, p 0.05) between DBT and the four dependent variables.
CONCLUSION: Our findings indicate that among older women, DBT does not significantly decrease the requirement for further medical examinations.
Clinical Features, Socioeconomic Status, Management, and Outcomes of Acute Heart Failure: PEACE MENA Registry Phase I Results

AlBackr H, Alhabib KF, Sulaiman K, Jamee A, Sobhy M, Benkhedda S, et al.

Curr Vasc Pharmacol, 2023;21(4):257-267.
PMID: 37231723 DOI: 10.2174/1570161121666230525111259

INTRODUCTION: PEACE MENA (Program for the Evaluation and Management of Cardiac Events in the Middle East and North Africa) is a prospective registry in Arab countries for in-patients with acute myocardial infarction (AMI) or acute heart failure (AHF). Here, we report the baseline characteristics and outcomes of in-patients with AHF who were enrolled during the first 14 months of the recruitment phase.
METHODS: A prospective, multi-centre, multi-country study including patients hospitalized with AHF was conducted. Clinical characteristics, echocardiogram, BNP (B-type natriuretic peptide), socioeconomic status, management, 1-month, and 1-year outcomes are reported.
RESULTS: Between April 2019 and June 2020, a total of 1258 adults with AHF from 16 Arab countries were recruited. Their mean age was 63.3 (±15) years, 56.8% were men, 65% had monthly income ≤US$ 500, and 56% had limited education. Furthermore, 55% had diabetes mellitus, 67% had hypertension; 55% had HFrEF (heart failure with reduced ejection fraction), and 19% had HFpEF (heart failure with preserved ejection fraction). At 1 year, 3.6% had a heart failure-related device (0-22%) and 7.3% used an angiotensin receptor neprilysin inhibitor (0-43%). Mortality was 4.4% per 1 month and 11.77% per 1-year post-discharge. Compared with higher-income patients, lower-income patients had a higher 1-year total heart failure hospitalization rate (45.6 vs 29.9%, p=0.001), and the 1-year mortality difference was not statistically significant (13.2 vs 8.8%, p=0.059).
CONCLUSION: Most of the patients with AHF in Arab countries had a high burden of cardiac risk factors, low income, and low education status with great heterogeneity in key performance indicators of AHF management among Arab countries.

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