Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU

Elhazmi, Alyaa; Al‐Omari, Awad; Sallam, Hend; Mufti, Hani N.; Rabie, Ahmed A.; Alshahrani, Mohammed; Mady, Ahmed; Alghamdi, Adnan; Altalaq, Ali; Azzam, Mohamed H.; Sindi, Anees; Kharaba, Ayman; Aseri, Zohair Al; Almekhlafi, Ghaleb; Tashkandi, Wail; Alajmi, Saud A.; Faqihi, Fahad; Alharthy, Abdulrahman; Al‐Tawfiq, Jaffar A.; Melibari, Rami Ghazi; Alhazzani, Waleed; Arabi, Yaseen M.

doi:10.1016/j.jiph.2022.06.008

Cited by 52 publications

(43 citation statements)

References 41 publications

(38 reference statements)

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“…Clinical notes and electronic medical records were the most common primary data sources, used in 57 studies (69.5%). 21,23–27,29,30,33–36,40,42–46,48–52,54,55,57–64,66–68,70–73,75,79–93,95 Other primary sources included audio recordings ( n = 6, 7.3%), 6,28,32,38,39,65 administrative data ( n = 5, 6.1%), 37,47,53,77,...…”

Section: Resultsmentioning

confidence: 99%

“…Clinical notes and electronic medical records were the most common primary data sources, used in 57 studies (69.5%). 21,[23][24][25][26][27]29,30,[33][34][35][36]40,[42][43][44][45][46][48][49][50][51][52]54,55,[57][58][59][60][61][62][63][64][66][67][68][70][71][72][73]75,[79][80][81][82][83][84][85][86][87][88][89]…”

Section: Sources Considered For Data Processingmentioning

confidence: 99%

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The use of natural language processing in palliative care research: A scoping review

et al. 2022

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Background: Natural language processing has been increasingly used in palliative care research over the last 5 years for its versatility and accuracy. Aim: To evaluate and characterize natural language processing use in palliative care research, including the most commonly used natural language processing software and computational methods, data sources, trends in natural language processing use over time, and palliative care topics addressed. Design: A scoping review using the framework by Arksey and O’Malley and the updated recommendations proposed by Levac et al. was conducted. Sources: PubMed, Web of Science, Embase, Scopus, and IEEE Xplore databases were searched for palliative care studies that utilized natural language processing tools. Data on study characteristics and natural language processing instruments used were collected and relevant palliative care topics were identified. Results: 197 relevant references were identified. Of these, 82 were included after full-text review. Studies were published in 48 different journals from 2007 to 2022. The average sample size was 21,541 (median 435). Thirty-two different natural language processing software and 33 machine-learning methods were identified. Nine main sources for data processing and 15 main palliative care topics across the included studies were identified. The most frequent topic was mortality and prognosis prediction. We also identified a trend where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings. Conclusions: We found 82 papers on palliative care using natural language processing methods for a wide-range of topics and sources of data that could expand the use of this methodology. We encourage researchers to consider incorporating this cutting-edge research methodology in future studies to improve published palliative care data.

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Section: Resultsmentioning

confidence: 99%

Section: Sources Considered For Data Processingmentioning

confidence: 99%

The use of natural language processing in palliative care research: A scoping review

et al. 2022

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“…After that, 300 articles were evaluated for full-text eligibility, and 197 articles with available data but not Asian were excluded. Ultimately, 103 studies conducted in Asia were enrolled in this meta-analysis [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ,…”

Section: Resultsmentioning

confidence: 99%

Comorbid Asthma Increased the Risk for COVID-19 Mortality in Asia: A Meta-Analysis

et al. 2022

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We aimed to explore the influence of comorbid asthma on the risk for mortality among patients with coronavirus disease 2019 (COVID-19) in Asia by using a meta-analysis. Electronic databases were systematically searched for eligible studies. The pooled odds ratio (OR) with 95% confidence interval (CI) was estimated by using a random-effect model. An inconsistency index (I2) was utilized to assess the statistical heterogeneity. A total of 103 eligible studies with 198,078 COVID-19 patients were enrolled in the meta-analysis; our results demonstrated that comorbid asthma was significantly related to an increased risk for COVID-19 mortality in Asia (pooled OR = 1.42, 95% CI: 1.20–1.68; I2 = 70%, p < 0.01). Subgroup analyses by the proportion of males, setting, and sample sizes generated consistent findings. Meta-regression indicated that male proportion might be the possible sources of heterogeneity. A sensitivity analysis exhibited the reliability and stability of the overall results. Both Begg’s analysis (p = 0.835) and Egger’s analysis (p = 0.847) revealed that publication bias might not exist. In conclusion, COVID-19 patients with comorbid asthma might bear a higher risk for mortality in Asia, at least among non-elderly individuals.

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“…Moghadam et al [ 4 ] designs a machine learning algorithm to predict hypotension up to 30 min in advance based on the data from only 5 min of patient physiological history. Elhazmi et al [ 5 ] uses the decision tree algorithm to predict mortality in critically ill adult COVID-19 patients admitted to the ICU. Rayan et al [ 6 ] uses the support vector machine algorithm for sepsis prediction with good performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Status Forecasting Based on the Baseline Information Using Logistic Regression

Zhao

Nie

2022

Entropy

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In the status forecasting problem, classification models such as logistic regression with input variables such as physiological, diagnostic, and treatment variables are typical ways of modeling. However, the parameter value and model performance differ among individuals with different baseline information. To cope with these difficulties, a subgroup analysis is conducted, in which models’ ANOVA and rpart are proposed to explore the influence of baseline information on the parameters and model performance. The results show that the logistic regression model achieves satisfactory performance, which is generally higher than 0.95 in AUC and around 0.9 in F1 and balanced accuracy. The subgroup analysis presents the prior parameter values for monitoring variables including SpO2, milrinone, non-opioid analgesics and dobutamine. The proposed method can be used to explore variables that are and are not medically related to the baseline variables.

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Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU

Cited by 52 publications

References 41 publications

The use of natural language processing in palliative care research: A scoping review

The use of natural language processing in palliative care research: A scoping review

Comorbid Asthma Increased the Risk for COVID-19 Mortality in Asia: A Meta-Analysis

Status Forecasting Based on the Baseline Information Using Logistic Regression

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