NLP Text Classification for COVID-19 Automatic Detection from Radiology Report in Indonesian Language

Radiology is used as an important assessment for patients with pulmonary disease. The radiology images are usually accompanied by a written report from a radiologist to be passed to the other referring physicians. These radiology reports are written in a natural language where they can have different systematic structures based on the language used. In our study, the radiology reports were collected from an Indonesian hospital and written in Bahasa Indonesia. We performed an automatic text classification to differentiate the information written in the radiology reports into two classes, COVID-19 and non COVID-19. To find the best model, we evaluated several embedding techniques available for Bahasa and five Machine Learning (ML) models, namely (1) XGBoost, (2) fastText, (3) LSTM, (4) Bi-LSTM and (5) IndoBERT. The result shows that IndoBERT outperformed the others with an accuracy of 98%. In terms of training speed, the shallow neural network architecture implemented with the fastText library can train the model in under one second and still result in a reasonably good accuracy of 86%. © 2022 IEEE.

NLP Analysis of COVID-19 Radiology Reports in Indonesian using IndoBERT

The presence of COVID-19, a respiratory disease, can be detected through medical imaging, such as Chest X-Ray (CXR) and Computed Tomography (CT) scans. These radiology images can also show how the patient's condition progresses. Radiologists need to provide a written report for each image, so that other clinicians can use it in their decision making. In this study, we applied one of the Natural Language Processing (NLP) models called IndoBERT to analyze radiology reports of COVID-19 patients written in Indonesian. We performed two tasks, clustering to group reports by meaning and understand their content, and text classification to predict one of the five possible outcomes for each patient. We show the most frequent topics in radiology reports, and word scores in each topic. The IndoBERT model was fine tuned on a medical text, 'Kamus Kedokteran Dorland' in an attempt to further improve it. This proved unnecessary: on one hand, there were no additional benefits, on the other, the standard model alone achieved a very satisfactory classification accuracy of over 90 %. © 2022 IEEE.

An XGBoost Model for Age Prediction from COVID-19 Blood Test

COVID-19 was declared a pandemic by the World Health Organization (WHO) in January 2020. Many studies found that some specific age groups of people have a higher risk of contracting the disease. The gold standard test for the disease is a condition-specific test based on Reverse-Transcriptase Polymerase Chain Reaction (RT-PCR). We have previously shown that the results of a standard suite of non-specific blood tests can be used to indicate the presence of a COVID-19 infection with a high likelihood. We continue our research in this area with a study of the connection between the patients' routine blood test results and their age. Predicting a person's age from blood chemistry is not new in health science. Most often, such results are used to detect the signs of diseases associated with aging and develop new medications. The experiment described here shows that the XGBoost algorithm can be used to predict the patients' age from their routine blood tests. The performance evaluation is very satisfactory, with R

Respiratory support in COVID-19 patients in Kommunarka hospital: a single-centered, retrospective study

Introduction. During the SARS-CoV-2 pandemic, worldwide healthcare system faced a new, insufficiently investigated, fast-spreading disease with multisystem failure and relatively high amount of severe diseased. Existing evidence base needs to be frequently revisited after data accumulation and analysis. Experience of dedicated COVID-19 centers should be summarized and implicated in clinical practice according to evidence-based principles, extensive clinical trial initiation. Objectives. To investigate baseline characteristics and treatment outcomes of patients with severe SARS-CoV-2 infection course, requiring respiratory support in the critical care settings of dedicated hospital. Materials and methods. In single-center retrospective study retrospective data collection of 451 respiratory support for COVID-19 related acute respiratory distress syndrome cases (noninvasive ventilation, mechanical ventilation) in intensive care unit patients for a 5-month period performed. The analysis aimed on demographic, clinical data, disease severity scores, respiratory support parameters and modality, continuous renal replacement therapy utilization and interleukin-6 receptor blockers administration, survival rates. Results. Respiratory support required 48.8 % of intensive care unit patients, the population was demographically balanced, Charlson Comorbidity Index was 4.46 ± 2.6 and was higher in the mechanically ventilated group. 30-day survival rate (all respiratory support cases) was 33.7 %, mortality structure analysis performed. The disease severity scores, respiratory mechanics among patients in dependence of respiratory support mode and during the period of case registration analysed as well. Median static respiratory compliance at the point of initiation of invasive mechanically ventilation was 43 (IQR 35–51). Mortality in the volume controlled mechanically ventilated group was higher. Conclusions. The patients, requiring respiratory support, during intensive care unit stay have high comorbidity levels. Indications for non-invasive ventilation may be extended on patients with lower Charlson index and initial SOFA score, however, early recognition of high risk of noninvasive ventilation failure required. Volume control invasive ventilation associated with higher mortality levels despite comparable disease severity scores. Further investigation required. © 2021, Practical Medicine Publishing House LLC. All rights reserved.

A Tree-based Mortality Prediction Model of COVID-19 from Routine Blood Samples

COVID-19 has been declared by The World Health Organization (WHO) a global pandemic in January, 2020. Researchers have been working on formulating the best approach and solutions to cure the disease and help to prevent such pandemics in the future. A lot of efforts have been made to develop a fast and accurate early clinical assessment of the disease. Machine Learning (ML) has proven helpful for research and applications in the health domain as a way to understand real-world phenomena through data analysis. In our experiment, we collected the retrospective blood samples data set from 1,000 COVID-19 patients in Jakarta, Indonesia for the period of March to December 2020. We report our preliminary findings on the use of common blood test biomarkers in predicting COVID-19 patient mortality. This study took advantage of explainable machine learning to examine the data set. The contribution of this paper is to explain our findings on predicting COVID-19 mortality, including the role of the top 11 biomarkers found in our dataset. These findings can be generalized, especially in Indonesia, which is now at its highest peak of the epidemic. We show that tree-based AI models performed well on predicting COVID-19 mortality, while also making it easy to interpret the findings, as they lend themselves to human scrutiny and allow clinicians to interpret them and comment on their viability. © 2021 IEEE.

Replacement renal therapy in the treatment of patients with a severe course of the new coronavirus infection

Objective: To study the use of RRT methods and their influence on the results of treatment of patients with severe COVID-19. Subjects and methods. We retrospectively analyzed the data of 283 patients with COVID-19 in the intensive care units of Moscow City Hospital no. 40 in 2020 who had received RRT as one of the treatment methods. Results. Frequency of RRT in COVID-19 patients in ICU of Moscow City Hospital no. 40 for 2020 made 5.7% (504 out of 8.711 patients treated in ICU received RRT). In 86% of cases, RRT was performed for renal indications. At the time of initiation of RRT, the studied groups did not differ according to SOFA score. The frequency of using dialysis units with high and low cut-off point in the groups of survived and deceased patients differed significantly. The surgery itself started at relatively the same time from the onset and statistically significantly earlier in the group of survivors from the beginning of tracheal intubation (4.9 0.5 vs 6.8 0.3 days, p = 0.0013). Against the background of ongoing therapy, overall severity of the state progressed in the group of deceased patients to 9.9 0.2 SOFA scores, while in the group of survivors there was an improvement to 6.1 0.4 scores. © 2020 Geocarrefour. All rights reserved.