Mortality Prediction in the ICU: The Daunting Task of Predicting the Unpredictable

Abstract At first, grant writing may look like a daunting task. You may ask yourself, “Is it really worth the time and effort?” With today's economic situation, teachers and therapists need ways to supplement their programs and grants provide such an opportunity. However, many of us do not know how to get started. After a few experiences and many lessons learned, I have come to enjoy researching and writing grants to supplement my students' learning. It is well worth the time and effort. This article provides information about a personal journey, lessons learned, and resources to get you started.

Download Full-text

The logistic cardiac surgery score: A new postoperative scoring system for mortality prediction in cardiac surgery

The Thoracic and Cardiovascular Surgeon ◽

10.1055/s-0030-1269316 ◽

2011 ◽

Vol 59 (S 01) ◽

Author(s):

F Dörr ◽

A Badreldin ◽

M Heldwein ◽

T Doenst ◽

K Hekmat

Keyword(s):

Cardiac Surgery ◽

Scoring System ◽

Mortality Prediction

Download Full-text

Mortality prediction with transient elastography in recurrent-HCV infection after liver transplantation

Ultraschall in der Medizin - European Journal of Ultrasound ◽

10.1055/s-0033-1354826 ◽

2013 ◽

Vol 34 (S 01) ◽

Author(s):

A Pecorelli ◽

F Piscaglia ◽

V Salvatore ◽

F Corradi ◽

S Flori ◽

...

Keyword(s):

Liver Transplantation ◽

Transient Elastography ◽

Mortality Prediction ◽

Hcv Infection

Download Full-text

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

10.2196/preprints.20767 ◽

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.

Download Full-text

Modern Organic Synthesis in the Laboratory

10.1093/oso/9780195187984.001.0001 ◽

2008 ◽

Cited By ~ 1

Author(s):

Jie Jack Li ◽

Chris Limberakis ◽

Derek A. Pflum

Keyword(s):

Organic Chemistry ◽

Graduate Students ◽

Organic Synthesis ◽

Functional Group ◽

Bond Formation ◽

Reaction Conditions ◽

Carbon Bond ◽

Oxidation Reduction ◽

Carbon Carbon Bond ◽

Daunting Task

Searching for reaction in organic synthesis has been made much easier in the current age of computer databases. However, the dilemma now is which procedure one selects among the ocean of choices. Especially for novices in the laboratory, it becomes a daunting task to decide what reaction conditions to experiment with first in order to have the best chance of success. This collection intends to serve as an "older and wiser lab-mate" one could have by compiling many of the most commonly used experimental procedures in organic synthesis. With chapters that cover such topics as functional group manipulations, oxidation, reduction, and carbon-carbon bond formation, Modern Organic Synthesis in the Laboratory will be useful for both graduate students and professors in organic chemistry and medicinal chemists in the pharmaceutical and agrochemical industries.

Download Full-text

‘Only a Poet Can Translate True Poetry’

10.1093/oso/9780198810810.003.0021 ◽

2018 ◽

Author(s):

Giampiero Scafoglio

Keyword(s):

Translation Practice ◽

Daunting Task

This chapter’s exploration of Giacomo Leopardi’s translation of the Aeneid tackles one of the most debated dilemmas in translation practice: whether or not one has to be a poet in order to translate poetry. Having undertaken the daunting task of translating the Aeneid, Leopardi shows himself to be a good philologist and, at the same time, also comes into his own poetic vocation as his translation progresses. The result of his translation is an impressive achievement, Scafoglio argues, a work that combines literary faithfulness to the original with the rendering of the expressive musicality and elusive fascination of Virgilian verse in Italian.

Download Full-text

Mortality prediction in systemic lupus erythematosus patients with pulmonary infection

International Journal of Rheumatic Diseases ◽

10.1111/1756-185x.13555 ◽

2019 ◽

Author(s):

Zhimin Lu ◽

Jing Li ◽

Juan Ji ◽

Zhifeng Gu ◽

Zhanyun Da

Keyword(s):

Systemic Lupus Erythematosus ◽

Lupus Erythematosus ◽

Pulmonary Infection ◽

Mortality Prediction ◽

Systemic Lupus

Download Full-text

Risk Stratification in Acute Pulmonary Embolism: The Latest Algorithms

Seminars in Respiratory and Critical Care Medicine ◽

10.1055/s-0041-1722898 ◽

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.

Download Full-text

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

BMC Health Services Research ◽

10.1186/s12913-020-05999-5 ◽

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.

Download Full-text