Dataset Information

Can Machine Learning Methods Predict Extubation Outcome in Premature Infants as well as Clinicians?

ABSTRACT: RATIONALE:Though treatment of the prematurely born infant breathing with assistance of a mechanical ventilator has much advanced in the past decades, predicting extubation outcome at a given point in time remains challenging. Numerous studies have been conducted to identify predictors for extubation outcome; however, the rate of infants failing extubation attempts has not declined. OBJECTIVE:To develop a decision-support tool for the prediction of extubation outcome in premature infants using a set of machine learning algorithms. METHODS:A dataset assembled from 486 premature infants on mechanical ventilation was used to develop predictive models using machine learning algorithms such as artificial neural networks (ANN), support vector machine (SVM), naïve Bayesian classifier (NBC), boosted decision trees (BDT), and multivariable logistic regression (MLR). Performance of all models was evaluated using area under the curve (AUC). RESULTS:For some of the models (ANN, MLR and NBC) results were satisfactory (AUC: 0.63-0.76); however, two algorithms (SVM and BDT) showed poor performance with AUCs of ~0.5. CONCLUSION:Clinician's predictions still outperform machine learning due to the complexity of the data and contextual information that may not be captured in clinical data used as input for the development of the machine learning algorithms. Inclusion of preprocessing steps in future studies may improve the performance of prediction models.

SUBMITTER: Mueller M

PROVIDER: S-EPMC4238927 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Can Machine Learning Methods Predict Extubation Outcome in Premature Infants as well as Clinicians?

Mueller Martina M Almeida Jonas S JS Stanislaus Romesh R Wagner Carol L CL

Journal of neonatal biology 20130101

<h4>Rationale</h4>Though treatment of the prematurely born infant breathing with assistance of a mechanical ventilator has much advanced in the past decades, predicting extubation outcome at a given point in time remains challenging. Numerous studies have been conducted to identify predictors for extubation outcome; however, the rate of infants failing extubation attempts has not declined.<h4>Objective</h4>To develop a decision-support tool for the prediction of extubation outcome in premature i ...[more]

PMID: 25419493

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Project description:BackgroundApproximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census.MethodsA five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN.ResultsCEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0-1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool.ConclusionState-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide.

Project description:BackgroundICU-acquired weakness (ICUAW) has been shown to be associated with prolonged duration of mechanical ventilation and extubation failure. It is usually assessed through Medical Research Council (MRC) score, a time-consuming score performed by physiotherapists. Handgrip strength (HG) can be monitored very easily at the bedside. It has been shown to be a reproducible and reliable marker of global muscular strength in critical care patients. We sought to test if muscular weakness, as assessed by handgrip strength, was associated with extubation outcome.MethodsProspective multicenter trial over an 18 months period in six mixed ICUs. Adults receiving mechanical ventilation for at least 48 h were eligible. Just before weaning trial, HG, Maximal Inspiratory Pressure (MIP), Peak Cough Expiratory Flow (PCEF) and Medical Research Council (MRC) score were registered. The attending physicians were unaware of the tests results and weaning procedures were conducted according to guidelines. Occurrence of unscheduled reintubation, non-invasive ventilation (NIV) or high-flow nasal continuous oxygen (HFNC) because of respiratory failure within 7 days after extubation defined extubation failure. The main outcome was the link between HG and extubation outcome.Results233 patients were included. Extubation failure occurred in 51 (22.5%) patients, 39 (17.2%) required reintubation. Handgrip strength was 12 [6-20] kg and 12 [8-20] kg, respectively, in extubation success and failure (p = 0.85). There was no association between extubation outcome and MRC score, MIP or PCEF. Handgrip strength was well correlated with MRC score (r = 0.718, p < 0.0001). ICU and hospital length of stay were significantly higher in the subset of patients harboring muscular weakness as defined by handgrip performed at the first weaning trial (respectively, 15 [10-25] days vs. 11 [7-17] days, p = 0.001 and 34 [19-66] days vs. 22 [15-43] days, p = 0.002).ConclusionNo association was found between handgrip strength and extubation outcome. Whether this was explained by the appropriateness of the tool in this specific setting, or by the precise impact of ICUAW on extubation outcome deserves to be further evaluated. Trial registration Clinical Trials; NCT02946502, 10/27/2016, URL: https://clinicaltrials.gov/ct2/results?cond=&term=gripwean&cntry=&state=&city=&dist=.

Project description:BackgroundThe care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods - as applied in other fields - produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified - from the aforementioned predictive classification models - with putative causal relations to PTSD.MethodsML predictive classification methods - with causal discovery feature selection - were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains.ResultsSeven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable.ConclusionsIn this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to replicate, refine, and extend the results produced in this study.

Project description:Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determine if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach to develop a classification model to predict individual patient outcome after surgery for DCM. Patients undergoing surgery for DCM as a part of the AOSpine CSM-NA or CSM-I prospective, multi-centre studies were included in the analysis. Out of 757 patients 605, 583, and 539 patients had complete follow-up information at 6, 12, and 24 months respectively and were included in the analysis. The primary outcome was improvement in the SF-6D quality of life indicator score by the minimum clinically important difference (MCID). The secondary outcome was improvement in the modified Japanese Orthopedic Association (mJOA) score by the MCID. Predictor variables reflected information about pre-operative disease severity, disease presentation, patient demographics, and comorbidities. A machine learning approach of feature engineering, data pre-processing, and model optimization was used to create the most accurate predictive model of outcome after surgery for DCM. Following data pre-processing 48, 108, and 101 features were chosen for model training at 6, 12, and 24 months respectively. The best performing predictive model used a random forest structure and had an average area under the curve (AUC) of 0.70, classification accuracy of 77%, and sensitivity of 78% when evaluated on a testing cohort that was not used for model training. Worse pre-operative disease severity, longer duration of DCM symptoms, older age, higher body weight, and current smoking status were associated with worse surgical outcomes. We developed a model that predicted positive surgical outcome for DCM with good accuracy at the individual patient level on an independent testing cohort. Our analysis demonstrates the applicability of machine-learning to predictive modeling in spine surgery.

Dataset Information

Can Machine Learning Methods Predict Extubation Outcome in Premature Infants as well as Clinicians?

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Can Machine Learning Methods Predict Extubation Outcome in Premature Infants as well as Clinicians?

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