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| Content Provider | frontiers |
|---|---|
| Author | BuHamra, Sana S. Almutairi, Abdullah N. Buhamrah, Abdullah K. Almadani, Sabah H. Alibrahim, Yusuf A. |
| Description | Automating the process of extracting information from unstructured text data, such as physician notes, is becoming increasingly popular through the use of Natural Language Processing (NLP). There are numerous ways in which healthcare providers can use NLP to enhance their services. We investigated COVID-19 mortality data from the intensive care unit (ICU) in Kuwait over the first 18 months of the pandemic using NLP. A key goal is to extract and classify the primary and intermediate causes of death from electronic health records (EHRs) in a timely way. Comorbid conditions, which refers to concurrent diseases or medical disorders, were also retrieved and studied in relation to various causes of mortality. A wide range of medical systems around the world have difficulties in dealing with this pandemic of COVID-19, due to its mutating feature. The primary cause of death for 54.8% of the 1691 ICU patients we investigated was septic shock or sepsis-related multiorgan failure. About three-quarters of patients die from acute respiratory distress syndrome (ARDS), a common intermediate cause of death. An arrhythmia (AF) disorder was determined to be the strongest predictor of intermediate cause of death, whether caused by ARDS or another cause (ARDS/Other) using machine learning decision trees. |
| Abstract | BackgroundThe high infection rate, severe symptoms, and evolving aspects of the COVID-19 pandemic provide challenges for a variety of medical systems around the world. Automatic information retrieval from unstructured text is greatly aided by Natural Language Processing (NLP), the primary approach taken in this field. This study addresses COVID-19 mortality data from the intensive care unit (ICU) in Kuwait during the first 18 months of the pandemic. A key goal is to extract and classify the primary and intermediate causes of death from electronic health records (EHRs) in a timely way. In addition, comorbid conditions or concurrent diseases were retrieved and analyzed in relation to a variety of causes of mortality.MethodAn NLP system using the Python programming language is constructed to automate the process of extracting primary and secondary causes of death, as well as comorbidities. The system is capable of handling inaccurate and messy data, this includes inadequate formats, spelling mistakes and mispositioned information. A machine learning decision trees method is used to classify the causes of death.ResultsFor 54.8% of the 1691 ICU patients we studied, septic shock or sepsis-related multiorgan failure was the leading cause of mortality. About three-quarters of patients die from acute respiratory distress syndrome (ARDS), a common intermediate cause of death. An arrhythmia (AF) disorder was determined to be the strongest predictor of intermediate cause of death, whe... |
| ISSN | 22962565 |
| DOI | 10.3389/fpubh.2022.1070870 |
| Volume Number | 10 |
| Journal | Frontiers in Public Health |
| Language | English |
| Publisher Date | 2022-12-01 |
| Access Restriction | Open |
| Subject Keyword | Prediction Natural Language Processing Mortality SARS-CoV-2 Decision tree Text mining Information Extraction |
| Content Type | Text |
| Resource Type | Article |
| Subject | Public Health, Environmental and Occupational Health |
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