10 results on '"Machine Learning Algorithms"'
Search Results
2. Advancing the beneficial use of machine learning in health care and medicine: Toward a community understanding.
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Nevin, Linda, null, null, and PLOS Medicine Editors
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MACHINE learning , *MEDICAL innovations , *PNEUMONIA diagnosis - Abstract
An introduction to the journal is presented in which the editor discusses the various articles published within the issue on such topics as machine learning (ML) in health care and medicine, ML in coronary ischemia diagnosis, and ML in pneumonia diagnosis via chest radiography.
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- 2018
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3. Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study.
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Nanayakkara, Shane, Fogarty, Sam, Tremeer, Michael, Ross, Kelvin, Richards, Brent, Bergmeir, Christoph, Xu, Sheng, Stub, Dion, Smith, Karen, Tacey, Mark, Liew, Danny, Pilcher, David, and Kaye, David M.
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CARDIAC arrest , *MACHINE learning , *LOGISTIC regression analysis , *HOSPITAL mortality , *INTENSIVE care patients , *INTENSIVE care units - Abstract
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. [ABSTRACT FROM AUTHOR]- Published
- 2018
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4. Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study.
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Taylor, Andrew G., Mielke, Clinton, and Mongan, John
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PNEUMOTHORAX , *CHEST X rays , *ARTIFICIAL neural networks , *MACHINE learning , *MEDICAL technology - Abstract
Background: Pneumothorax can precipitate a life-threatening emergency due to lung collapse and respiratory or circulatory distress. Pneumothorax is typically detected on chest X-ray; however, treatment is reliant on timely review of radiographs. Since current imaging volumes may result in long worklists of radiographs awaiting review, an automated method of prioritizing X-rays with pneumothorax may reduce time to treatment. Our objective was to create a large human-annotated dataset of chest X-rays containing pneumothorax and to train deep convolutional networks to screen for potentially emergent moderate or large pneumothorax at the time of image acquisition.Methods and Findings: In all, 13,292 frontal chest X-rays (3,107 with pneumothorax) were visually annotated by radiologists. This dataset was used to train and evaluate multiple network architectures. Images showing large- or moderate-sized pneumothorax were considered positive, and those with trace or no pneumothorax were considered negative. Images showing small pneumothorax were excluded from training. Using an internal validation set (n = 1,993), we selected the 2 top-performing models; these models were then evaluated on a held-out internal test set based on area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV). The final internal test was performed initially on a subset with small pneumothorax excluded (as in training; n = 1,701), then on the full test set (n = 1,990), with small pneumothorax included as positive. External evaluation was performed using the National Institutes of Health (NIH) ChestX-ray14 set, a public dataset labeled for chest pathology based on text reports. All images labeled with pneumothorax were considered positive, because the NIH set does not classify pneumothorax by size. In internal testing, our "high sensitivity model" produced a sensitivity of 0.84 (95% CI 0.78-0.90), specificity of 0.90 (95% CI 0.89-0.92), and AUC of 0.94 for the test subset with small pneumothorax excluded. Our "high specificity model" showed sensitivity of 0.80 (95% CI 0.72-0.86), specificity of 0.97 (95% CI 0.96-0.98), and AUC of 0.96 for this set. PPVs were 0.45 (95% CI 0.39-0.51) and 0.71 (95% CI 0.63-0.77), respectively. Internal testing on the full set showed expected decreased performance (sensitivity 0.55, specificity 0.90, and AUC 0.82 for high sensitivity model and sensitivity 0.45, specificity 0.97, and AUC 0.86 for high specificity model). External testing using the NIH dataset showed some further performance decline (sensitivity 0.28-0.49, specificity 0.85-0.97, and AUC 0.75 for both). Due to labeling differences between internal and external datasets, these findings represent a preliminary step towards external validation.Conclusions: We trained automated classifiers to detect moderate and large pneumothorax in frontal chest X-rays at high levels of performance on held-out test data. These models may provide a high specificity screening solution to detect moderate or large pneumothorax on images collected when human review might be delayed, such as overnight. They are not intended for unsupervised diagnosis of all pneumothoraces, as many small pneumothoraces (and some larger ones) are not detected by the algorithm. Implementation studies are warranted to develop appropriate, effective clinician alerts for the potentially critical finding of pneumothorax, and to assess their impact on reducing time to treatment. [ABSTRACT FROM AUTHOR]- Published
- 2018
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5. Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation.
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Hae, Hyeonyong, Kang, Soo-Jin, Kim, Won-Jang, Choi, So-Yeon, Lee, June-Goo, Bae, Youngoh, Cho, Hyungjoo, Yang, Dong Hyun, Kang, Joon-Won, Lim, Tae-Hwan, Lee, Cheol Hyun, Kang, Do-Yoon, Lee, Pil Hyung, Ahn, Jung-Min, Park, Duk-Woo, Lee, Seung-Whan, Kim, Young-Hak, Lee, Cheol Whan, Park, Seong-Wook, and Park, Seung-Jung
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MACHINE learning , *CORONARY disease , *CORONARY angiography , *RETROSPECTIVE studies , *COHORT analysis , *ANGINA pectoris - Abstract
Background: Invasive fractional flow reserve (FFR) is a standard tool for identifying ischemia-producing coronary stenosis. However, in clinical practice, over 70% of treatment decisions still rely on visual estimation of angiographic stenosis, which has limited accuracy (about 60%-65%) for the prediction of FFR < 0.80. One of the reasons for the visual-functional mismatch is that myocardial ischemia can be affected by the supplied myocardial size, which is not always evident by coronary angiography. The aims of this study were to develop an angiography-based machine learning (ML) algorithm for predicting the supplied myocardial volume for a stenosis, as measured using coronary computed tomography angiography (CCTA), and then to build an angiography-based classifier for the lesions with an FFR < 0.80 versus ≥ 0.80.Methods and Findings: A retrospective study was conducted using data from 1,132 stable and unstable angina patients with 1,132 intermediate lesions who underwent invasive coronary angiography, FFR, and CCTA at the Asan Medical Center, Seoul, Korea, between 1 May 2012 and 30 November 2015. The mean age was 63 ± 10 years, 76% were men, and 72% of the patients presented with stable angina. Of these, 932 patients (assessed before 31 January 2015) constituted the training set for the algorithm, and 200 patients (assessed after 1 February 2015) served as a test cohort to validate its diagnostic performance. Additionally, external validation with 79 patients from two centers (CHA University, Seongnam, Korea, and Ajou University, Suwon, Korea) was conducted. After automatic contour calibration using the caliber of guiding catheter, quantitative coronary angiography was performed using the edge-detection algorithms (CAAS-5, Pie-Medical). Clinical information was provided by the Asan BiomedicaL Research Environment (ABLE) system. The CCTA-based myocardial segmentation (CAMS)-derived myocardial volume supplied by each vessel (right coronary artery [RCA], left anterior descending [LAD], left circumflex [LCX]) and the myocardial volume subtended to a stenotic segment (CAMS-%Vsub) were measured for labeling. The ML for (1) predicting vessel territories (CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA) and CAMS-%Vsub and (2) identifying the lesions with an FFR < 0.80 was constructed. Angiography-based ML, employing a light gradient boosting machine (GBM), showed mean absolute errors (MAEs) of 5.42%, 8.57%, and 4.54% for predicting CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA, respectively. The percent myocardial volumes predicted by ML were used to predict the CAMS-%Vsub. With 5-fold cross validation, the MAEs between ML-predicted percent myocardial volume subtended to a stenotic segment (ML-%Vsub) and CAMS-%Vsub were minimized by the elastic net (6.26% ± 0.55% for LAD, 5.79% ± 0.68% for LCX, and 2.95% ± 0.14% for RCA lesions). Using all attributes (age, sex, involved vessel segment, and angiographic features affecting the myocardial territory and stenosis degree), the ML classifiers (L2 penalized logistic regression, support vector machine, and random forest) predicted an FFR < 0.80 with an accuracy of approximately 80% (area under the curve [AUC] = 0.84-0.87, 95% confidence intervals 0.71-0.94) in the test set, which was greater than that of diameter stenosis (DS) > 53% (66%, AUC = 0.71, 95% confidence intervals 0.65-0.78). The external validation showed 84% accuracy (AUC = 0.89, 95% confidence intervals 0.83-0.95). The retrospective design, single ethnicity, and the lack of clinical outcomes may limit this prediction model's generalized application.Conclusion: We found that angiography-based ML is useful to predict subtended myocardial territories and ischemia-producing lesions by mitigating the visual-functional mismatch between angiographic and FFR. Assessment of clinical utility requires further validation in a large, prospective cohort study. [ABSTRACT FROM AUTHOR]- Published
- 2018
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6. Machine learning to identify pairwise interactions between specific IgE antibodies and their association with asthma: A cross-sectional analysis within a population-based birth cohort.
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Fontanella, Sara, Frainay, Clément, Murray, Clare S., Simpson, Angela, and Custovic, Adnan
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ASTHMA , *MACHINE learning , *CROSS-sectional method , *IMMUNOGLOBULIN E , *COHORT analysis - Abstract
Background: The relationship between allergic sensitisation and asthma is complex; the data about the strength of this association are conflicting. We propose that the discrepancies arise in part because allergic sensitisation may not be a single entity (as considered conventionally) but a collection of several different classes of sensitisation. We hypothesise that pairings between immunoglobulin E (IgE) antibodies to individual allergenic molecules (components), rather than IgE responses to 'informative' molecules, are associated with increased risk of asthma.Methods and Findings: In a cross-sectional analysis among 461 children aged 11 years participating in a population-based birth cohort, we measured serum-specific IgE responses to 112 allergen components using a multiplex array (ImmunoCAP Immuno‑Solid phase Allergy Chip [ISAC]). We characterised sensitivity to 44 active components (specific immunoglobulin E [sIgE] > 0.30 units in at least 5% of children) among the 213 (46.2%) participants sensitised to at least one of these 44 components. We adopted several machine learning methodologies that offer a powerful framework to investigate the highly complex sIgE-asthma relationship. Firstly, we applied network analysis and hierarchical clustering (HC) to explore the connectivity structure of component-specific IgEs and identify clusters of component-specific sensitisation ('component clusters'). Of the 44 components included in the model, 33 grouped in seven clusters (C.sIgE-1-7), and the remaining 11 formed singleton clusters. Cluster membership mapped closely to the structural homology of proteins and/or their biological source. Components in the pathogenesis-related (PR)-10 proteins cluster (C.sIgE-5) were central to the network and mediated connections between components from grass (C.sIgE-4), trees (C.sIgE-6), and profilin clusters (C.sIgE-7) with those in mite (C.sIgE-1), lipocalins (C.sIgE-3), and peanut clusters (C.sIgE-2). We then used HC to identify four common 'sensitisation clusters' among study participants: (1) multiple sensitisation (sIgE to multiple components across all seven component clusters and singleton components), (2) predominantly dust mite sensitisation (IgE responses mainly to components from C.sIgE-1), (3) predominantly grass and tree sensitisation (sIgE to multiple components across C.sIgE-4-7), and (4) lower-grade sensitisation. We used a bipartite network to explore the relationship between component clusters, sensitisation clusters, and asthma, and the joint density-based nonparametric differential interaction network analysis and classification (JDINAC) to test whether pairwise interactions of component-specific IgEs are associated with asthma. JDINAC with pairwise interactions provided a good balance between sensitivity (0.84) and specificity (0.87), and outperformed penalised logistic regression with individual sIgE components in predicting asthma, with an area under the curve (AUC) of 0.94, compared with 0.73. We then inferred the differential network of pairwise component-specific IgE interactions, which demonstrated that 18 pairs of components predicted asthma. These findings were confirmed in an independent sample of children aged 8 years who participated in the same birth cohort but did not have component-resolved diagnostics (CRD) data at age 11 years. The main limitation of our study was the exclusion of potentially important allergens caused by both the ISAC chip resolution as well as the filtering step. Clustering and the network analyses might have provided different solutions if additional components had been available.Conclusions: Interactions between pairs of sIgE components are associated with increased risk of asthma and may provide the basis for designing diagnostic tools for asthma. [ABSTRACT FROM AUTHOR]- Published
- 2018
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7. Machine learning in medicine: Addressing ethical challenges.
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Vayena, Effy, Blasimme, Alessandro, and Cohen, I. Glenn
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ARTIFICIAL intelligence in medicine , *MACHINE learning , *DATA protection laws , *BIOENGINEERING , *MEDICAL ethics , *MEDICAL equipment - Abstract
Effy Vayena and colleagues argue that machine learning in medicine must offer data protection, algorithmic transparency, and accountability to earn the trust of patients and clinicians. [ABSTRACT FROM AUTHOR]
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- 2018
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8. Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study.
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Lin, Haotian, Long, Erping, Ding, Xiaohu, Diao, Hongxing, Chen, Zicong, Liu, Runzhong, Huang, Jialing, Cai, Jingheng, Xu, Shuangjuan, Zhang, Xiayin, Wang, Dongni, Chen, Kexin, Yu, Tongyong, Wu, Dongxuan, Zhao, Xutu, Liu, Zhenzhen, Wu, Xiaohang, Jiang, Yuzhen, Yang, Xiao, and Cui, Dongmei
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MYOPIA , *VISION disorders , *ELECTRONIC health records , *OPHTHALMOLOGY , *CHILDREN - Abstract
Background: Electronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children. Methods and findings: Real-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ≤ −6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records (≥3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered. Conclusions: To our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia. [ABSTRACT FROM AUTHOR]
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- 2018
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9. Machine learning in medicine: Addressing ethical challenges
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Effy Vayena, I. Glenn Cohen, and Alessandro Blasimme
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020205 medical informatics ,Medical Doctors ,Health Care Providers ,lcsh:Medicine ,02 engineering and technology ,Public opinion ,computer.software_genre ,Medical Records ,Machine Learning ,0302 clinical medicine ,Computer software ,0202 electrical engineering, electronic engineering, information engineering ,Medicine and Health Sciences ,Data Protection Act 1998 ,Data Mining ,Confidentiality ,030212 general & internal medicine ,Medical Personnel ,Allied Health Care Professionals ,Data Processing ,Attitude to Computers ,Applied Mathematics ,Simulation and Modeling ,Software Development ,Software Engineering ,General Medicine ,3. Good health ,Professions ,Accountability ,Perspective ,Physical Sciences ,Engineering and Technology ,Psychology ,Information Technology ,Algorithms ,Biotechnology ,Medical Ethics ,Computer and Information Sciences ,Attitude of Health Personnel ,MEDLINE ,Bioengineering ,Machine learning ,Research and Analysis Methods ,Trust ,03 medical and health sciences ,Machine Learning Algorithms ,Artificial Intelligence ,Humans ,Computer Security ,Research ethics ,business.industry ,lcsh:R ,Biology and Life Sciences ,Transparency (behavior) ,Health Care ,Self Care ,Public Opinion ,People and Places ,Population Groupings ,Medical Devices and Equipment ,Artificial intelligence ,business ,computer ,Delivery of Health Care ,Mathematics - Abstract
Effy Vayena and colleagues argue that machine learning in medicine must offer data protection, algorithmic transparency, and accountability to earn the trust of patients and clinicians.
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- 2018
10. Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study
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Danny Liew, Sheng Xu, Michael Tremeer, David M. Kaye, Christoph Bergmeir, Shane Nanayakkara, Mark Tacey, Karen Smith, Dion Stub, Kelvin Ross, Brent Richards, Sam Fogarty, and David Pilcher
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Male ,Time Factors ,Databases, Factual ,medicine.medical_treatment ,Health Status ,030204 cardiovascular system & hematology ,computer.software_genre ,Logistic regression ,law.invention ,Machine Learning ,0302 clinical medicine ,Mathematical and Statistical Techniques ,law ,Risk Factors ,Medicine and Health Sciences ,Cardiac Arrest ,Medicine ,030212 general & internal medicine ,Hospital Mortality ,Registries ,Applied Mathematics ,Simulation and Modeling ,Statistics ,General Medicine ,Middle Aged ,Intensive care unit ,Hospitals ,Intensive Care Units ,Treatment Outcome ,Brier score ,Physical Sciences ,Female ,Risk assessment ,Algorithms ,Research Article ,Computer and Information Sciences ,Clinical Decision-Making ,Cardiology ,Machine learning ,Research and Analysis Methods ,Risk Assessment ,Decision Support Techniques ,03 medical and health sciences ,Machine Learning Algorithms ,Artificial Intelligence ,Intensive care ,Support Vector Machines ,Humans ,Cardiopulmonary resuscitation ,Statistical Methods ,Artificial Neural Networks ,Aged ,Retrospective Studies ,Computational Neuroscience ,Receiver operating characteristic ,business.industry ,Australia ,Biology and Life Sciences ,Computational Biology ,Retrospective cohort study ,Cardiopulmonary Resuscitation ,Heart Arrest ,Health Care ,Health Care Facilities ,Artificial intelligence ,business ,computer ,Mathematics ,Neuroscience ,Forecasting ,New Zealand - Abstract
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., In their study, Shane Nanayakkara and colleagues find that machine learning-based models enhance predictive discrimination for mortality following cardiac arrest compared to existing approaches., Author summary Why was this study done? Cardiac arrest is a frequent cause of admission to the intensive care unit and has a low survival rate following admission to hospital. Current illness severity scores perform poorly in regard to predicting survival for this specific group of patients. Machine learning involves the creation of algorithms that can learn from large datasets to improve risk estimation, but can be biased by the data used. We aimed to use machine learning to predict death after admission to an intensive care unit with a cardiac arrest, and then to use an ‘explainer’ model to make the decision-making process transparent. What did the researchers do and find? We analysed one of the largest international datasets of patients admitted to the intensive care unit, comprising 1.5 million patients. We studied the data of patients admitted with cardiac arrest and developed several machine learning algorithms to predict death, and then compared these with existing scores. We found that the machine learning models were more accurate at estimating the risk of death, and were able to use another algorithm to explain the reasoning behind the risk estimate given for a particular patient. What do these findings mean? Using machine learning can increase the accuracy of estimating survival for intensive care patients after a cardiac arrest. These raw estimates can then be further resolved on a per-patient basis to provide a breakdown to understand the reasoning behind the algorithm’s decision, which could help clinicians decide whether to trust the algorithm on a per-patient basis. These findings have only been assessed in a single large group of patients, and should be validated in another separate group, with other predictors added.
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- 2018
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