Discriminative Features Generation for Mortality Prediction in ICU

2020 ◽  
pp. 324-338
Author(s):  
Suresh Pokharel ◽  
Zhenkun Shi ◽  
Guido Zuccon ◽  
Yu Li
Keyword(s):  
Mortality Prediction

Using artificial neural network condensation to facilitate adaption of machine learning in medical settings by reducing computational burden (Preprint)

2020 ◽  
Author(s):  
Dianbo Liu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Third World ◽  
Mortality Prediction ◽  
Neural Net ◽  
Medical Settings ◽  
Hidden Layer ◽  
Applications Of Machine Learning ◽  
Computational Resources ◽  
Developed Nations

BACKGROUND Applications of machine learning (ML) on health care can have a great impact on people’s lives. At the same time, medical data is usually big, requiring a significant amount of computational resources. Although it might not be a problem for wide-adoption of ML tools in developed nations, availability of computational resource can very well be limited in third-world nations and on mobile devices. This can prevent many people from benefiting of the advancement in ML applications for healthcare. OBJECTIVE In this paper we explored three methods to increase computational efficiency of either recurrent neural net-work(RNN) or feedforward (deep) neural network (DNN) while not compromising its accuracy. We used in-patient mortality prediction as our case analysis upon intensive care dataset. METHODS We reduced the size of RNN and DNN by applying pruning of “unused” neurons. Additionally, we modified the RNN structure by adding a hidden-layer to the RNN cell but reduce the total number of recurrent layers to accomplish a reduction of total parameters in the network. Finally, we implemented quantization on DNN—forcing the weights to be 8-bits instead of 32-bits. RESULTS We found that all methods increased implementation efficiency–including training speed, memory size and inference speed–without reducing the accuracy of mortality prediction. CONCLUSIONS This improvements allow the implementation of sophisticated NN algorithms on devices with lower computational resources.

Risk Stratification in Acute Pulmonary Embolism: The Latest Algorithms

2021 ◽  
Vol 42 (02) ◽  
pp. 183-198
Author(s):  
Georgios A. Triantafyllou ◽  
Oisin O'Corragain ◽  
Belinda Rivera-Lebron ◽  
Parth Rali
Keyword(s):  
Pulmonary Embolism ◽  
Risk Stratification ◽  
Prediction Models ◽  
Bleeding Risk ◽  
Hemodynamic Instability ◽  
Reperfusion Therapy ◽  
Mortality Prediction ◽  
Risk Scores ◽  
Short Term Mortality ◽  
Urgent Patient

AbstractPulmonary embolism (PE) is a common clinical entity, which most clinicians will encounter. Appropriate risk stratification of patients is key to identify those who may benefit from reperfusion therapy. The first step in risk assessment should be the identification of hemodynamic instability and, if present, urgent patient consideration for systemic thrombolytics. In the absence of shock, there is a plethora of imaging studies, biochemical markers, and clinical scores that can be used to further assess the patients' short-term mortality risk. Integrated prediction models incorporate more information toward an individualized and precise mortality prediction. Additionally, bleeding risk scores should be utilized prior to initiation of anticoagulation and/or reperfusion therapy administration. Here, we review the latest algorithms for a comprehensive risk stratification of the patient with acute PE.

scholarly journals Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Hospital Mortality ◽  
Patient Population ◽  
Generalized Additive Models ◽  
Routine Data ◽  
Predictive Performance ◽  
Population Based ◽  
Mortality Prediction ◽  
Additive Models ◽  
General Hospitals ◽  
Net Reclassification Improvement

Abstract Background Understanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weights in an adult in-patient population-based cohort of general hospitals. Methods Retrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012–2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weights were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types. Results Overall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI, 0.865–0.868) and van Walraven’s weights (0.863, 95% CI, 0.862–0.864) had substantial advantage over Charlson’s weights (0.850, 95% CI, 0.849–0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weights. Conclusions All weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

Comparison of mortality prediction scores in elderly patients with ICD for heart failure with reduced ejection fraction

2021 ◽  
Author(s):  
Mert İlker Hayıroğlu ◽  
Tufan Çınar ◽  
Göksel Çinier ◽  
Levent Pay ◽  
Ahmet Çağdaş Yumurtaş ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Elderly Patients ◽  
Mortality Prediction ◽  
Reduced Ejection Fraction

scholarly journals Prognostic biomarkers in predicting mortality in respiratory patients with ventilator-associated pneumonia

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Nermeen A. Abdelaleem ◽  
Hoda A. Makhlouf ◽  
Eman M. Nagiub ◽  
Hassan A. Bayoumi
Keyword(s):  
Sofa Score ◽  
Diagnostic Performance ◽  
Mortality Prediction ◽  
Ventilator Associated Pneumonia ◽  
Distribution Width ◽  
Mechanically Ventilated ◽  
Neutrophil Lymphocyte Ratio ◽  
Prognostic Accuracy ◽  
Severity Scores ◽  
Critical Patients

Abstract Background Ventilator-associated pneumonia (VAP) is the most common nosocomial infection. Red cell distribution width (RDW) and neutrophil-lymphocyte ratio (NLR) are prognostic factors to mortality in different diseases. The aim of this study is to evaluate prognostic efficiency RDW, NLR, and the Sequential Organ Failure Assessment (SOFA) score for mortality prediction in respiratory patients with VAP. Results One hundred thirty-six patients mechanically ventilated and developed VAP were included. Clinical characteristics and SOFA score on the day of admission and at diagnosis of VAP, RDW, and NLR were assessed and correlated to mortality. The average age of patients was 58.80 ± 10.53. These variables had a good diagnostic performance for mortality prediction AUC 0.811 for SOFA at diagnosis of VAP, 0.777 for RDW, 0.728 for NLR, and 0.840 for combined of NLR and RDW. The combination of the three parameters demonstrated excellent diagnostic performance (AUC 0.889). A positive correlation was found between SOFA at diagnosis of VAP and RDW (r = 0.446, P < 0.000) and with NLR (r = 0.220, P < 0.010). Conclusions NLR and RDW are non-specific inflammatory markers that could be calculated quickly and easily via routine hemogram examination. These markers have comparable prognostic accuracy to severity scores. Consequently, RDW and NLR are simple, yet promising markers for ICU physicians in monitoring the clinical course, assessment of organ dysfunction, and predicting mortality in mechanically ventilated patients. Therefore, this study recommends the use of blood biomarkers with the one of the simplest ICU score (SOFA score) in the rapid diagnosis of critical patients as a daily works in ICU.

Prognostic role of Krebs von den Lungen-6 (KL-6) measurement in idiopathic pulmonary fibrosis: a systematic review and meta-analysis

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Elena Aloisio ◽  
Federica Braga ◽  
Chiara Puricelli ◽  
Mauro Panteghini
Keyword(s):  
Systematic Review ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Binary Data ◽  
Meta Analysis ◽  
Mortality Prediction ◽  
Asian Populations ◽  
Increased Risk ◽  
Using Data ◽  
Patient Prognosis

Abstract Objectives Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial disease with limited therapeutic options. The measurement of Krebs von den Lungen-6 (KL-6) glycoprotein has been proposed for evaluating the risk of IPF progression and predicting patient prognosis, but the robustness of available evidence is unclear. Methods We searched Medline and Embase databases for peer-reviewed literature from inception to April 2020. Original articles investigating KL-6 as prognostic marker for IPF were retrieved. Considered outcomes were the risk of developing acute exacerbation (AE) and patient survival. Meta-analysis of selected studies was conducted, and quantitative data were uniformed as odds ratio (OR) or hazard ratio (HR) estimates, with corresponding 95% confidence intervals (CI). Results Twenty-six studies were included in the systematic review and 14 were finally meta-analysed. For AE development, the pooled OR (seven studies) for KL-6 was 2.72 (CI 1.22–6.06; p=0.015). However, a high degree of heterogeneity (I2=85.6%) was found among selected studies. Using data from three studies reporting binary data, a pooled sensitivity of 72% (CI 60–82%) and a specificity of 60% (CI 52–68%) were found for KL-6 measurement in detecting insurgence of AE in IPF patients. Pooled HR (seven studies) for mortality prediction was 1.009 (CI 0.983–1.036; p=0.505). Conclusions Although our meta-analysis suggested that IPF patients with increased KL-6 concentrations had a significant increased risk of developing AE, the detection power of the evaluated biomarker is limited. Furthermore, no relationship between biomarker concentrations and mortality was found. Caution is also needed when extending obtained results to non-Asian populations.

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