Publications by authors named "Christoph Bergmeir"

12 Publications

  • Page 1 of 1

Association Between Urine Output and Mortality in Critically Ill Patients: A Machine Learning Approach.

Crit Care Med 2021 Sep 22. Epub 2021 Sep 22.

School of Medicine, Griffith University, Southport, QLD, Australia. Department of Intensive Care, Alfred Hospital, Melbourne, VIC, Australia. Centre for Transformative Innovation, Faculty of Business and Law, Swinburne University of Technology, Hawthorn, VIC, Australia. Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia. School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia. Australian and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation, Melbourne, VIC, Australia. Department of Cardiology, Alfred Hospital, Melbourne, VIC, Australia. Heart Failure Research Group, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia. Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.

Objectives: Current definitions of acute kidney injury use a urine output threshold of less than 0.5 mL/kg/hr, which have not been validated in the modern era. We aimed to determine the prognostic importance of urine output within the first 24 hours of admission to the ICU and to evaluate for variance between different admission diagnoses.

Design: Retrospective cohort study.

Setting: One-hundred eighty-three ICUs throughout Australia and New Zealand from 2006 to 2016.

Patients: Patients greater than or equal to 16 years old who were admitted with curative intent who did not regularly receive dialysis. ICU readmissions during the same hospital admission and patients transferred from an external ICU were excluded.

Measurements And Main Results: One hundred and sixty-one thousand nine hundred forty patients were included with a mean urine output of 1.05 mL/kg/hr and an overall in-hospital mortality of 7.8%. A urine output less than 0.47 mL/kg/hr was associated with increased unadjusted in-hospital mortality, which varied with admission diagnosis. A machine learning model (extreme gradient boosting) was trained to predict in-hospital mortality and examine interactions between urine output and survival. Low urine output was most strongly associated with mortality in postoperative cardiovascular patients, nonoperative gastrointestinal admissions, nonoperative renal/genitourinary admissions, and patients with sepsis.

Conclusions: Consistent with current definitions of acute kidney injury, a urine output threshold of less than 0.5 mL/kg/hr is modestly predictive of mortality in patients admitted to the ICU. The relative importance of urine output for predicting survival varies with admission diagnosis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/CCM.0000000000005310DOI Listing
September 2021

Time series extrinsic regression: Predicting numeric values from time series data.

Data Min Knowl Discov 2021 Mar 11:1-29. Epub 2021 Mar 11.

Faculty of Information Technology, Monash University, 25 Exhibition Walk, Melbourne, VIC 3800 Australia.

This paper studies time series extrinsic regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable; a task closely related to time series classification (TSC), which aims to learn the relationship between a time series and a categorical class label. This task generalizes time series forecasting, relaxing the requirement that the value predicted be a future value of the input series or primarily depend on more recent values. In this paper, we motivate and study this task, and benchmark existing solutions and adaptations of TSC algorithms on a novel archive of 19 TSER datasets which we have assembled. Our results show that the state-of-the-art TSC algorithm Rocket, when adapted for regression, achieves the highest overall accuracy compared to adaptations of other TSC algorithms and state-of-the-art machine learning (ML) algorithms such as XGBoost, Random Forest and Support Vector Regression. More importantly, we show that much research is needed in this field to improve the accuracy of ML models. We also find evidence that further research has excellent prospects of improving upon these straightforward baselines.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10618-021-00745-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7951134PMC
March 2021

Machine Learning Algorithms for Predicting and Risk Profiling of Cardiac Surgery-Associated Acute Kidney Injury.

Semin Thorac Cardiovasc Surg 2021 24;33(3):735-745. Epub 2020 Sep 24.

Department of Surgery, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia.

Using a large national database of cardiac surgical procedures, we applied machine learning (ML) to risk stratification and profiling for cardiac surgery-associated acute kidney injury. We compared performance of ML to established scoring tools. Four ML algorithms were used, including logistic regression (LR), gradient boosted machine (GBM), K-nearest neighbor, and neural networks (NN). These were compared to the Cleveland Clinic score, and a risk score developed on the same database. Five-fold cross-validation repeated 20 times was used to measure the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Risk profiles from GBM and NN were generated using Shapley additive values. A total of 97,964 surgery events in 96,653 patients were included. For predicting postoperative renal replacement therapy using pre- and intraoperative data, LR, GBM, and NN achieved an AUC (standard deviation) of 0.84 (0.01), 0.85 (0.01), 0.84 (0.01) respectively outperforming the highest performing scoring tool with 0.81 (0.004). For predicting cardiac surgery-associated acute kidney injury, LR, GBM, and NN each achieved 0.77 (0.01), 0.78 (0.01), 0.77 (0.01) respectively outperforming the scoring tool with 0.75 (0.004). Compared to scores and LR, shapely additive values analysis of black box model predictions was able to generate patient-level explanations for each prediction. ML algorithms provide state-of-the-art approaches to risk stratification. Explanatory modeling can exploit complex decision boundaries to aid the clinician in understanding the risks specific to individual patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1053/j.semtcvs.2020.09.028DOI Listing
October 2021

LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series With Multiple Seasonal Patterns.

IEEE Trans Neural Netw Learn Syst 2021 Apr 2;32(4):1586-1599. Epub 2021 Apr 2.

Generating forecasts for time series with multiple seasonal cycles is an important use case for many industries nowadays. Accounting for the multiseasonal patterns becomes necessary to generate more accurate and meaningful forecasts in these contexts. In this article, we propose long short-term memory multiseasonal net (LSTM-MSNet), a decomposition-based unified prediction framework to forecast time series with multiple seasonal patterns. The current state of the art in this space is typically univariate methods, in which the model parameters of each time series are estimated independently. Consequently, these models are unable to include key patterns and structures that may be shared by a collection of time series. In contrast, LSTM-MSNet is a globally trained LSTM network, where a single prediction model is built across all the available time series to exploit the cross-series knowledge in a group of related time series. Furthermore, our methodology combines a series of state-of-the-art multiseasonal decomposition techniques to supplement the LSTM learning procedure. In our experiments, we are able to show that on data sets from disparate data sources, e.g., the popular M4 forecasting competition, a decomposition step is beneficial, whereas, in the common real-world situation of homogeneous series from a single application, exogenous seasonal variables or no seasonal preprocessing at all are better choices. All options are readily included in the framework and allow us to achieve competitive results for both cases, outperforming many state-of-the-art multiseasonal forecasting methods.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/TNNLS.2020.2985720DOI Listing
April 2021

Closing the Gap in Surveillance and Audit of Invasive Mold Diseases for Antifungal Stewardship Using Machine Learning.

J Clin Med 2019 Sep 5;8(9). Epub 2019 Sep 5.

Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne 3004, VIC, Australia.

Clinical audit of invasive mold disease (IMD) in hematology patients is inefficient due to the difficulties of case finding. This results in antifungal stewardship (AFS) programs preferentially reporting drug cost and consumption rather than measures that actually reflect quality of care. We used machine learning-based natural language processing (NLP) to non-selectively screen chest tomography (CT) reports for pulmonary IMD, verified by clinical review against international definitions and benchmarked against key AFS measures. NLP screened 3014 reports from 1 September 2008 to 31 December 2017, generating 784 positives that after review, identified 205 IMD episodes (44% probable-proven) in 185 patients from 50,303 admissions. Breakthrough-probable/proven-IMD on antifungal prophylaxis accounted for 60% of episodes with serum monitoring of voriconazole or posaconazole in the 2 weeks prior performed in only 53% and 69% of episodes, respectively. Fiberoptic bronchoscopy within 2 days of CT scan occurred in only 54% of episodes. The average turnaround of send-away bronchoalveolar galactomannan of 12 days (range 7-22) was associated with high empiric liposomal amphotericin consumption. A random audit of 10% negative reports revealed two clinically significant misses (0.9%, 2/223). This is the first successful use of applied machine learning for institutional IMD surveillance across an entire hematology population describing process and outcome measures relevant to AFS. Compared to current methods of clinical audit, semi-automated surveillance using NLP is more efficient and inclusive by avoiding restrictions based on any underlying hematologic condition, and has the added advantage of being potentially scalable.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/jcm8091390DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6780614PMC
September 2019

A comparison of characteristics and outcomes of patients admitted to the ICU with asthma in Australia and New Zealand and United states.

J Asthma 2020 04 31;57(4):398-404. Epub 2019 Jan 31.

Centre for Outcome and Resource Evaluation, Australian and New Zealand Intensive Care Society, Melbourne, Australia.

: To compare the characteristics, use of invasive ventilation and outcomes of patients admitted with critical asthma syndrome (CAS) to ICUs in Australia and New Zealand (ANZ), and a large cohort of ICUs in the United States (US). : We examined two large databases of ICU for patients admitted with CAS in 2014 and 2015. We obtained, analyzed, and compared information on demographic and physiological characteristics, use of invasive mechanical ventilation, and clinical outcome and derived predictive models. : Overall, 2202 and 762 patients were admitted with a primary diagnosis of CAS in the ANZ and US databases respectively (0.73% vs. 0.46% of all ICU admissions,  < 0.001). A similar percentage of patients received invasive mechanical ventilation in the first 24 h (24.7% vs. 24.4%,  = 0.87) but ANZ patients had lower respiratory rates and higher PaCO levels. Overall mortality was low (1.23 for ANZ and 1.71 for USA;  = 0.36) and even among invasively ventilated patients (2.4% for ANZ vs. 1.1% for USA;  = 0.38). However, ANZ patients also had longer length of stay in ICU (43 vs. 37 h,  = 0.001) and hospital (105 vs. 78 h,  = 0.003). : Patients admitted to ANZ and USA ICU with CAS are broadly similar and have a low and similar rate of invasive ventilation and mortality. However, ANZ patients made up a greater proportion of ICU patients and had longer ICU and hospital stays. These findings provide a modern invasive ventilation and mortality rates benchmark for future studies of CAS.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/02770903.2019.1571082DOI Listing
April 2020

Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study.

PLoS Med 2018 11 30;15(11):e1002709. Epub 2018 Nov 30.

Department of Cardiology, Alfred Hospital, Melbourne, Victoria, Australia.

Background: Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admission, we aimed to develop more accurate models of risk prediction using both logistic regression (LR) and machine learning (ML) techniques, with a combination of demographic, physiologic, and biochemical information.

Methods And Findings: Patient-level data were extracted from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for patients who had experienced a cardiac arrest within 24 hours prior to admission to an intensive care unit (ICU) during the period January 2006 to December 2016. The primary outcome was in-hospital mortality. The models were trained and tested on a dataset (split 90:10) including age, lowest and highest physiologic variables during the first 24 hours, and key past medical history. LR and 5 ML approaches (gradient boosting machine [GBM], support vector classifier [SVC], random forest [RF], artificial neural network [ANN], and an ensemble) were compared to the APACHE III and Australian and New Zealand Risk of Death (ANZROD) predictions. In all, 39,566 patients from 186 ICUs were analysed. Mean (±SD) age was 61 ± 17 years; 65% were male. Overall in-hospital mortality was 45.5%. Models were evaluated in the test set. The APACHE III and ANZROD scores demonstrated good discrimination (area under the receiver operating characteristic curve [AUROC] = 0.80 [95% CI 0.79-0.82] and 0.81 [95% CI 0.8-0.82], respectively) and modest calibration (Brier score 0.19 for both), which was slightly improved by LR (AUROC = 0.82 [95% CI 0.81-0.83], DeLong test, p < 0.001). Discrimination was significantly improved using ML models (ensemble and GBM AUROCs = 0.87 [95% CI 0.86-0.88], DeLong test, p < 0.001), with an improvement in performance (Brier score reduction of 22%). Explainability models were created to assist in identifying the physiologic features that most contributed to an individual patient's survival. Key limitations include the absence of pre-hospital data and absence of external validation.

Conclusions: ML approaches significantly enhance predictive discrimination for mortality following cardiac arrest compared to existing illness severity scores and LR, without the use of pre-hospital data. The discriminative ability of these ML models requires validation in external cohorts to establish generalisability.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pmed.1002709DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6267953PMC
November 2018

Designing a more efficient, effective and safe Medical Emergency Team (MET) service using data analysis.

PLoS One 2017 27;12(12):e0188688. Epub 2017 Dec 27.

Department of Intensive Care Medicine, Commercial Road, The Alfred Hospital, Prahran, Vic, Australia.

Introduction: Hospitals have seen a rise in Medical Emergency Team (MET) reviews. We hypothesised that the commonest MET calls result in similar treatments. Our aim was to design a pre-emptive management algorithm that allowed direct institution of treatment to patients without having to wait for attendance of the MET team and to model its potential impact on MET call incidence and patient outcomes.

Methods: Data was extracted for all MET calls from the hospital database. Association rule data mining techniques were used to identify the most common combinations of MET call causes, outcomes and therapies.

Results: There were 13,656 MET calls during the 34-month study period in 7936 patients. The most common MET call was for hypotension [31%, (2459/7936)]. These MET calls were strongly associated with the immediate administration of intra-venous fluid (70% [1714/2459] v 13% [739/5477] p<0.001), unless the patient was located on a respiratory ward (adjusted OR 0.41 [95%CI 0.25-0.67] p<0.001), had a cardiac cause for admission (adjusted OR 0.61 [95%CI 0.50-0.75] p<0.001) or was under the care of the heart failure team (adjusted OR 0.29 [95%CI 0.19-0.42] p<0.001). Modelling the effect of a pre-emptive management algorithm for immediate fluid administration without MET activation on data from a test period of 24 months following the study period, suggested it would lead to a 68.7% (2541/3697) reduction in MET calls for hypotension and a 19.6% (2541/12938) reduction in total METs without adverse effects on patients.

Conclusion: Routinely collected data and analytic techniques can be used to develop a pre-emptive management algorithm to administer intravenous fluid therapy to a specific group of hypotensive patients without the need to initiate a MET call. This could both lead to earlier treatment for the patient and less total MET calls.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0188688PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5744916PMC
January 2018

Toward Electronic Surveillance of Invasive Mold Diseases in Hematology-Oncology Patients: An Expert System Combining Natural Language Processing of Chest Computed Tomography Reports, Microbiology, and Antifungal Drug Data.

JCO Clin Cancer Inform 2017 11;1:1-10

Michelle R. Ananda-Rajah, Alfred Health; Michelle R. Ananda-Rajah, Christoph Bergmeir, François Petitjean, and Geoffrey I. Webb, Monash University; and Monica A. Slavin and Karin A. Thursky, Peter Doherty Centre for Infection and Immunity; University of Melbourne, Melbourne, Victoria, Australia.

Purpose: Prospective epidemiologic surveillance of invasive mold disease (IMD) in hematology patients is hampered by the absence of a reliable laboratory prompt. This study develops an expert system for electronic surveillance of IMD that combines probabilities using natural language processing (NLP) of computed tomography (CT) reports with microbiology and antifungal drug data to improve prediction of IMD.

Methods: Microbiology indicators and antifungal drug-dispensing data were extracted from hospital information systems at three tertiary hospitals for 123 hematology-oncology patients. Of this group, 64 case patients had 26 probable/proven IMD according to international definitions, and 59 patients were uninfected controls. Derived probabilities from NLP combined with medical expertise identified patients at high likelihood of IMD, with remaining patients processed by a machine-learning classifier trained on all available features.

Results: Compared with the baseline text classifier, the expert system that incorporated the best performing algorithm (naïve Bayes) improved specificity from 50.8% (95% CI, 37.5% to 64.1%) to 74.6% (95% CI, 61.6% to 85.0%), reducing false positives by 48% from 29 to 15; improved sensitivity slightly from 96.9% (95% CI, 89.2% to 99.6%) to 98.4% (95% CI, 91.6% to 100%); and improved receiver operating characteristic area from 73.9% (95% CI, 67.1% to 80.6%) to 92.8% (95% CI, 88% to 97.5%).

Conclusion: An expert system that uses multiple sources of data (CT reports, microbiology, antifungal drug dispensing) is a promising approach to continuous prospective surveillance of IMD in the hospital, and demonstrates reduced false notifications (positives) compared with NLP of CT reports alone. Our expert system could provide decision support for IMD surveillance, which is critical to antifungal stewardship and improving supportive care in cancer.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1200/CCI.17.00011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873951PMC
November 2017

Modelling risk-adjusted variation in length of stay among Australian and New Zealand ICUs.

PLoS One 2017 2;12(5):e0176570. Epub 2017 May 2.

School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.

Purpose: Comparisons between institutions of intensive care unit (ICU) length of stay (LOS) are significantly confounded by individual patient characteristics, and currently there is a paucity of methods available to calculate risk-adjusted metrics.

Methods: We extracted de-identified data from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for admissions between January 1 2011 and December 31 2015. We used a mixed-effects log-normal regression model to predict LOS using patient and admission characteristics. We calculated a risk-adjusted LOS ratio (RALOSR) by dividing the geometric mean observed LOS by the exponent of the expected Ln-LOS for each site and year. The RALOSR is scaled such that values <1 indicate a LOS shorter than expected, while values >1 indicate a LOS longer than expected. Secondary mixed effects regression modelling was used to assess the stability of the estimate in units over time.

Results: During the study there were a total of 662,525 admissions to 168 units (median annual admissions = 767, IQR:426-1121). The mean observed LOS was 3.21 days (median = 1.79 IQR = 0.92-3.52) over the entire period, and declined on average 1.97 hours per year (95%CI:1.76-2.18) from 2011 to 2015. The RALOSR varied considerably between units, ranging from 0.35 to 2.34 indicating large differences after accounting for case-mix.

Conclusions: There are large disparities in risk-adjusted LOS among Australian and New Zealand ICUs which may reflect differences in resource utilization.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176570PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5413040PMC
September 2017

Time series modeling and forecasting using memetic algorithms for regime-switching models.

IEEE Trans Neural Netw Learn Syst 2012 Nov;23(11):1841-7

In this brief, we present a novel model fitting procedure for the neuro-coefficient smooth transition autoregressive model (NCSTAR), as presented by Medeiros and Veiga. The model is endowed with a statistically founded iterative building procedure and can be interpreted in terms of fuzzy rule-based systems. The interpretability of the generated models and a mathematically sound building procedure are two very important properties of forecasting models. The model fitting procedure employed by the original NCSTAR is a combination of initial parameter estimation by a grid search procedure with a traditional local search algorithm. We propose a different fitting procedure, using a memetic algorithm, in order to obtain more accurate models. An empirical evaluation of the method is performed, applying it to various real-world time series originating from three forecasting competitions. The results indicate that we can significantly enhance the accuracy of the models, making them competitive to models commonly used in the field.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/TNNLS.2012.2216898DOI Listing
November 2012

Segmentation of cervical cell nuclei in high-resolution microscopic images: A new algorithm and a web-based software framework.

Comput Methods Programs Biomed 2012 Sep 10;107(3):497-512. Epub 2012 Feb 10.

Department of Computer Science and Artificial Intelligence, E.T.S. de Ingenierías Informática y de Telecomunicación, University of Granada, Granada, Spain.

In order to automate cervical cancer screening tests, one of the most important and longstanding challenges is the segmentation of cell nuclei in the stained specimens. Though nuclei of isolated cells in high-quality acquisitions often are easy to segment, the problem lies in the segmentation of large numbers of nuclei with various characteristics under differing acquisition conditions in high-resolution scans of the complete microscope slides. We implemented a system that enables processing of full resolution images, and proposes a new algorithm for segmenting the nuclei under adequate control of the expert user. The system can work automatically or interactively guided, to allow for segmentation within the whole range of slide and image characteristics. It facilitates data storage and interaction of technical and medical experts, especially with its web-based architecture. The proposed algorithm localizes cell nuclei using a voting scheme and prior knowledge, before it determines the exact shape of the nuclei by means of an elastic segmentation algorithm. After noise removal with a mean-shift and a median filtering takes place, edges are extracted with a Canny edge detection algorithm. Motivated by the observation that cell nuclei are surrounded by cytoplasm and their shape is roughly elliptical, edges adjacent to the background are removed. A randomized Hough transform for ellipses finds candidate nuclei, which are then processed by a level set algorithm. The algorithm is tested and compared to other algorithms on a database containing 207 images acquired from two different microscope slides, with promising results.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cmpb.2011.09.017DOI Listing
September 2012
-->