Effect of early nutritional initiation on post‐cerebral infarction discharge destination: A propensity‐matched analysis using machine learning

2021 ◽  
Author(s):  
Kazuto Ikezawa ◽  
Mitsuaki Hirose ◽  
Tsunehiko Maruyama ◽  
Koichiro Yuji ◽  
Yoshito Yabe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Infarction ◽  
Discharge Destination

scholarly journals Machine learning models to predict length of stay and discharge destination in complex head and neck surgery

Head & Neck ◽  
2020 ◽  
Author(s):  
Khodayar Goshtasbi ◽  
Tyler M. Yasaka ◽  
Mehdi Zandi‐Toghani ◽  
Hamid R. Djalilian ◽  
William B. Armstrong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Head And Neck ◽  
Neck Surgery ◽  
Head And Neck Surgery ◽  
Discharge Destination ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records

2021 ◽  
Vol 12 ◽  
Author(s):  
Santu Rana ◽  
Wei Luo ◽  
Truyen Tran ◽  
Svetha Venkatesh ◽  
Paul Talman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Ischemic Stroke ◽  
Machine Learning Techniques ◽  
Discharge Destination ◽  
Clinical Factors ◽  
Administrative Records ◽  
Factors Associated ◽  
Learning Techniques ◽  
Discharge Outcomes

Aim: To use available electronic administrative records to identify data reliability, predict discharge destination, and identify risk factors associated with specific outcomes following hospital admission with stroke, compared to stroke specific clinical factors, using machine learning techniques.Method: The study included 2,531 patients having at least one admission with a confirmed diagnosis of stroke, collected from a regional hospital in Australia within 2009–2013. Using machine learning (penalized regression with Lasso) techniques, patients having their index admission between June 2009 and July 2012 were used to derive predictive models, and patients having their index admission between July 2012 and June 2013 were used for validation. Three different stroke types [intracerebral hemorrhage (ICH), ischemic stroke, transient ischemic attack (TIA)] were considered and five different comparison outcome settings were considered. Our electronic administrative record based predictive model was compared with a predictive model composed of “baseline” clinical features, more specific for stroke, such as age, gender, smoking habits, co-morbidities (high cholesterol, hypertension, atrial fibrillation, and ischemic heart disease), types of imaging done (CT scan, MRI, etc.), and occurrence of in-hospital pneumonia. Risk factors associated with likelihood of negative outcomes were identified.Results: The data was highly reliable at predicting discharge to rehabilitation and all other outcomes vs. death for ICH (AUC 0.85 and 0.825, respectively), all discharge outcomes except home vs. rehabilitation for ischemic stroke, and discharge home vs. others and home vs. rehabilitation for TIA (AUC 0.948 and 0.873, respectively). Electronic health record data appeared to provide improved prediction of outcomes over stroke specific clinical factors from the machine learning models. Common risk factors associated with a negative impact on expected outcomes appeared clinically intuitive, and included older age groups, prior ventilatory support, urinary incontinence, need for imaging, and need for allied health input.Conclusion: Electronic administrative records from this cohort produced reliable outcome prediction and identified clinically appropriate factors negatively impacting most outcome variables following hospital admission with stroke. This presents a means of future identification of modifiable factors associated with patient discharge destination. This may potentially aid in patient selection for certain interventions and aid in better patient and clinician education regarding expected discharge outcomes.

scholarly journals Machine learning modeling for predicting hospital readmission following lumbar laminectomy

2019 ◽  
Vol 30 (3) ◽  
pp. 344-352 ◽  
Author(s):  
Saisanjana Kalagara ◽  
Adam E. M. Eltorai ◽  
Wesley M. Durand ◽  
J. Mason DePasse ◽  
Alan H. Daniels
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Hospital Readmission ◽  
Hospital Readmissions ◽  
Influential Factors ◽  
Gradient Boosting ◽  
Unplanned Readmission ◽  
Discharge Destination ◽  
Improvement Program ◽  
Patient Variables

OBJECTIVEHospital readmission contributes substantial costs to the healthcare system. The purpose of this investigation was to create a predictive machine learning model to identify lumbar laminectomy patients at risk for postoperative hospital readmission.METHODSPatients who had undergone a lumbar laminectomy procedure in the period from 2011 to 2014 were isolated from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database. Demographic characteristics and clinical factors, including complications, comorbidities, length of stay, age, and body mass index, were analyzed in relation to whether or not the patients had been readmitted to the hospital within 30 days after their procedure by utilizing independent-samples t-tests. Supervised gradient boosting machine learning was then used to create two models to predict readmission—one with all collected patient variables and one with only the variables known prior to hospital discharge.RESULTSA total of 26,869 patients were evaluated, 5.59% (1501 patients) of whom had an unplanned readmission to the hospital within 30 days of their procedure. Readmitted patients were older and had a greater number of complications and comorbidities, longer operative time, longer hospital stay, higher BMI, and higher work relative value unit (RVU) operation score (p < 0.01). They also had a worse health status prior to surgery (p < 0.01) and were more likely to be sent to a skilled discharge destination postoperatively (p < 0.01). The model with all patient variables accurately identified 49.6% of readmissions with an overall accuracy of 95.33% (area under the curve [AUC] = 0.8059), with postdischarge complications and comorbidities as the most important predictors. The predictive model built with only clinical information known predischarge identified 40.5% of readmitted patients with an accuracy of 79.55% (AUC = 0.6901), with discharge destination, comorbidities, and American Society of Anesthesiologists (ASA) classification as the most influential factors in identifying readmitted patients.CONCLUSIONSIn this study, the authors analyzed hospital readmissions following laminectomy and developed predictive models to identify readmitted patients with an accuracy of over 95% using all variables and over 79% when using only predischarge variables. Using only the variables available predischarge, the authors created a model capable of predicting 40% of the readmitted patients. This study provides data that will assist in the development of predictive models for readmission and the creation of interventions to prevent readmission in high-risk patients.

scholarly journals Prediction of post-stroke epilepsy using machine learning method

2020 ◽  
Author(s):  
Anjiao Peng ◽  
Xiaorong Yang ◽  
Zhining Wen ◽  
Wanling Li ◽  
Yusha Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Cerebral Infarction ◽  
Cerebral Hemorrhage ◽  
Characteristic Curve ◽  
Support Vector ◽  
Learning Methods ◽  
Post Stroke ◽  
Machine Learning Methods ◽  
One Year

Abstract Background : Stroke is one of the most important causes of epilepsy and we aimed to find if it is possible to predict patients with high risk of developing post-stroke epilepsy (PSE) at the time of discharge using machine learning methods. Methods : Patients with stroke were enrolled and followed at least one year. Machine learning methods including support vector machine (SVM), random forest (RF) and logistic regression (LR) were used to learn the data. Results : A total of 2730 patients with cerebral infarction and 844 patients with cerebral hemorrhage were enrolled and the risk of PSE was 2.8% after cerebral infarction and 7.8% after cerebral hemorrhage in one year. Machine learning methods showed good performance in predicting PSE. The area under the receiver operating characteristic curve (AUC) for SVM and RF in predicting PSE after cerebral infarction was close to 1 and it was 0.92 for LR. When predicting PSE after cerebral hemorrhage, the performance of SVM was best with AUC being close to 1, followed by RF ( AUC = 0.99) and LR (AUC = 0.85) . Conclusion : Machine learning methods could be used to predict patients with high risk of developing PSE, which will help to stratify patients with high risk and start treatment earlier. Nevertheless, more work is needed before the application of thus intelligent predictive model in clinical practice.

scholarly journals Discharge prediction of critical patients with spinal cord injury: a machine learning study with 1485 cases

2021 ◽  
Author(s):  
Guoxin Fan ◽  
Huaqing Liu ◽  
Sheng Yang ◽  
Libo Luo ◽  
Lunji Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Intensive Care ◽  
Care Home ◽  
Area Under The Curve ◽  
Discharge Destination ◽  
Predictive Tool ◽  
Cord Injury ◽  
Critical Patients

Abstract Objectives: Prognostication of spinal cord injury (SCI) is vital, especially for critical patients who need intensive care. The study aims to develop machine-learning (ML) classifiers for discharge prediction of SCI patients in the intensive care unit (ICU). Methods: Clinical data of patients diagnosed with SCI were extracted from the publicly available ICU database. A total of 105 ML classifiers were initially developed to predict the discharge destination (dead, further medical care, home), and then the top 3 classifiers with the best performance were stacked into an ensemble classifier (Esb-Clf). To balance the accuracy and the feasibility, the complete Esb-Clf was finally simplified with top 10 features (simplified Esb-Clf). The micro-average area under the curve (AUC) was used to compare the prediction performance of difference ML classifiers and 6 doctors' artificial prediction. Results: A total of 1485 SCI patients were used for the early and the recent prediction of discharge destination. In the early prediction, the micro-average AUC of the Esb-Clf and the simplified Esb-Clf was 0.846 and 0.835 during the independent testing, respectively. While in the recent prediction, the micro-average AUC of the Esb-Clf and the simplified Esb-Clf was 0.898 and 0.892. Performance of both the Esb-Clf and the simplified Esb-Clf were superior to the doctors' in the early and the recent prediction. Conclusions: ML classifiers can discriminate the discharge destination of SCI patients with high accuracy, feasibility and interpretability. Whether the simplified Esb-Clf as an online predictive tool is applicable to guiding clinical management needs further verification.

scholarly journals Beta Blockade and Clinical Outcomes in Aneurysmal Subarachnoid Hemorrhage

2016 ◽  
Vol 10 (1) ◽  
pp. 155-163 ◽  
Author(s):  
Melody M. Chang ◽  
Ronak N. Raval ◽  
Jessie J. Southerland ◽  
Dare A. Adewumi ◽  
Khaled A. Bahjri ◽  
...  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Cerebral Infarction ◽  
Long Term Care ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Beta Blockers ◽  
Retrospective Chart Review ◽  
Beta Blockade ◽  
Discharge Destination ◽  
Term Care

Background: Aneurysmal subarachnoid hemorrhages are frequently complicated by hypertension and neurogenic myocardial stunning. Beta blockers may be used for management of these complications. We sought to investigate sympathetic nervous system modulation by beta blockers and their effect on radiographic vasospasm, delayed cerebral infarction, discharge destination and death. Methods: Retrospective chart review of 218 adults admitted to the ICU between 8/2004 and 9/2010 was performed. Groups were identified relevant to beta blockade: 77 were never beta blocked (No/No), 123 received post-admission beta blockers (No/Yes), and 18 were continued on their home beta blockers (Yes/Yes). Records were analyzed for baseline characteristics and the development of vasospasm, delayed cerebral infarction, discharge destination and death, expressed as adjusted odds ratio. Results: Of the 218 patients 145 patients developed vasospasm, 47 consequently infarcted, and 53 died or required care in a long-term facility. When compared to No/No patients, No/Yes patients had significantly increased vasospasm (OR 2.11 (1.06-4.16)). However, these patients also had significantly fewer deaths or need for long term care (OR 0.17 (0.05-0.64)), with decreased tendency for infarcts (OR 0.70 (0.32-1.55)). When compared to No/No patients, Yes/Yes patients demonstrated a trend toward increased vasospasm (OR 1.61 (0.50-5.29)) that led to infarction (OR 1.51 (0.44-5.13)), but with decreased mortality or need for long term care in a facility (OR 0.13 (0.01-1.30)). Conclusion: Post-admission beta blockade in aneurysmal subarachnoid hemorrhage patients was associated with increased incidence of vasospasm. However, despite the increased occurrence of vasospasm, beta blockers were associated with improved discharge characteristics and fewer deaths.

scholarly journals Lesion Detection of Cerebral Infarction by a Machine Learning Model that Learned ‘Normal’ [Presidential Award Proceedings]

2020 ◽  
Vol 40 (1) ◽  
pp. 26-29
Author(s):  
Akihiko WADA ◽  
Yuya SAITO ◽  
Shinpei KATO ◽  
Akifumi HAGIWARA ◽  
Shohei FUJITA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Infarction ◽  
Learning Model ◽  
Lesion Detection ◽  
Machine Learning Model

scholarly journals Incorporating Ensemble approach into Automated Machine Learning in an attempt to predict Cerebral Infarction in patients presenting with Subarachnoid Hemorrhage : Secondary analysis of a novel exploration

2021 ◽  
Author(s):  
Ali Haider Bangash ◽  
Tauseef Ullah ◽  
Inayat Ullah Khan ◽  
Arshiya Fatima ◽  
Saiqa Zehra
Keyword(s):  
Machine Learning ◽  
Subarachnoid Hemorrhage ◽  
Cerebral Infarction ◽  
Subarachnoid Haemorrhage ◽  
Secondary Analysis ◽  
Ensemble Approach ◽  
Automated Machine Learning ◽  
Risk Predictor

Automated machine learning is explored to develop a sensitive risk predictor for cerebral infarction in patients presenting with subarachnoid haemorrhage.

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