scholarly journals MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model

2020 ◽  
Vol 9 (2) ◽  
pp. 343 ◽  
Author(s):  
Arash Kia ◽  
Prem Timsina ◽  
Himanshu N. Joshi ◽  
Eyal Klang ◽  
Rohit R. Gupta ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Area Under The Curve ◽  
Learning Model ◽  
Clinical Deterioration ◽  
Early Warning Score ◽  
Support Vector ◽  
Adult Age ◽  
Machine Learning Model ◽  
Patients At Risk

Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models’ performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions.

Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis

2019 ◽  
Vol 73 (4) ◽  
pp. 334-344 ◽  
Author(s):  
Ryan J. Delahanty ◽  
JoAnn Alvarez ◽  
Lisa M. Flynn ◽  
Robert L. Sherwin ◽  
Spencer S. Jones
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Early Identification ◽  
Learning Model ◽  
Machine Learning Model ◽  
Patients At Risk

P-040. Development of a machine-learning model to predict adverse outcomes in patients at risk for preeclampsia

2021 ◽  
Vol 25 ◽  
pp. e41-e42
Author(s):  
Leon J. Schmidt ◽  
Oliver Rieger ◽  
Mark Neznansky ◽  
Max Hackelöer ◽  
Lisa A. Dröge ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Model ◽  
Adverse Outcomes ◽  
Machine Learning Model ◽  
Patients At Risk

scholarly journals A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ahsan Huda ◽  
Adam Castaño ◽  
Anindita Niyogi ◽  
Jennifer Schumacher ◽  
Michelle Stewart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
At Risk ◽  
Cardiac Amyloidosis ◽  
Learning Model ◽  
Wild Type ◽  
Derivation Cohort ◽  
Machine Learning Model ◽  
Patients At Risk ◽  
Amyloid Cardiomyopathy

AbstractTransthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. Here we show that a random forest machine learning model can identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data. We derive a machine learning model in 1071 cases and 1071 non-amyloid heart failure controls and validate the model in three nationally representative cohorts (9412 cases, 9412 matched controls), and a large, single-center electronic health record-based cohort (261 cases, 39393 controls). We show that the machine learning model performs well in identifying patients with cardiac amyloidosis in the derivation cohort and all four validation cohorts, thereby providing a systematic framework to increase the suspicion of transthyretin cardiac amyloidosis in patients with heart failure.

A HIGHLY PREDICTIVE MACHINE LEARNING MODEL TO IDENTIFY HOSPITALIZED PATIENTS AT RISK FOR 30-DAY READMISSION OR MORTALITY

2018 ◽  
Vol 71 (11) ◽  
pp. A1734
Author(s):  
Zhaohui Su ◽  
Gregory Donadio ◽  
Tom Brecht ◽  
Francis O'Donovan ◽  
Costas Boussios ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Model ◽  
Hospitalized Patients ◽  
Machine Learning Model ◽  
Patients At Risk

17: MEWS++: PREDICTING CLINICAL DETERIORATION IN ADMITTED PATIENTS USING A NOVEL MACHINE LEARNING MODEL

2020 ◽  
Vol 48 (1) ◽  
pp. 9-9
Author(s):  
Rohit Gupta ◽  
Arash Kia ◽  
Eyal Klang ◽  
Robert Freeman ◽  
Prem Timsina ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Clinical Deterioration ◽  
Machine Learning Model

scholarly journals Machine Learning Prediction for Complete Response to Hypomethylating Agents with or without Additional Agents in Patients with Newly Diagnosed Myelodysplastic Syndrome

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1720-1720
Author(s):  
Koji Sasaki ◽  
Guillermo Montalban Bravo ◽  
Rashmi Kanagal-Shamanna ◽  
Elias Jabbour ◽  
Farhad Ravandi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Board Of Directors ◽  
Research Funding ◽  
Area Under The Curve ◽  
Complete Response ◽  
Learning Model ◽  
Hypomethylating Agents ◽  
Newly Diagnosed ◽  
Advisory Committees ◽  
Machine Learning Model

Background: Myelodysplastic syndrome (MDS) is a heterogeneous malignant myeloid neoplasm of hematopoietic stem cells due to cytogenetic alterations and somatic mutations in genes (DNA methylation, DNA repair, chromatin regulation, RNA splicing, transcription regulation, and signal transduction). Hypomethylating agents (HMA) are the standard of care for MDS, and 40-60% of patients achieved response to HMA. However, the prediction for response is difficult due to the nature of heterogeneity and the context of clinical conditions such as the degree of cytopenias and the dependency on transfusion. Machine learning outperforms conventional statistical models for prediction in statistical competitions. Prediction with machine learning models may predict response in patients with MDS. The aim of this study is to develop a machine learning model for the prediction of complete response (CR) to HMA with or without additional therapeutic agents in patients with newly diagnosed MDS. Methods: From November 2012 to August 2017, we analyzed 435 patients with newly diagnosed MDS who received frontline therapy as follows; azacitidine (AZA) (3-day, 5-day, or 7-day) ± vorinostat ± ipilimumab ± nivolumab; decitabine (DAC) (3-day or 5-day) ± vorinostat; 5-day guadecitabine. Clinical variables, cytogenetic abnormalities, and the presence of genetic mutations by next generation sequencing (NGS) were included for variable selection. The whole cohort was randomly divided into training/validation and test cohorts at an 8:2 ratio. The training/validation cohort was used for 4-fold cross validation. Hyperparameter optimization was performed with Stampede2, which was ranked as the 15th fastest supercomputer at Texas Advanced Computing Center in June 2018. A gradient boosting decision tree-based framework with the LightGBM Python module was used after hyperparameter tuning for the development of the machine learning model with training/validation cohorts. The performance of prediction was assessed with an independent test dataset with the area under the curve. Results: We identified 435 patients with newly diagnosed MDS who enrolled on clinical trials as follows: 33 patients, 5-day AZA; 23, 5-day AZA + vorinostat; 43, 3-day AZA; 20, 5-day AZA + ipilimumab; 19 patients, AZA + nivolumab; 7, AZA + ipilumumab + nivolumab; 114, 5-day DAC; 74, 3-day DAC; 4, DAC + vorinostat; 97, 5-day guadecitabine. In the whole cohort, the median age at diagnosis was 68 years (range, 13.0-90.3); 117 (27%) patients had a history of prior radiation or cytotoxic chemotherapy; the median white blood cell count was 2.9 (×109/L) (range, 0.5-102); median absolute neutrophil count, 1.1 (×109/L) (range, 0.0-55.1); median hemoglobin count, 9.5 (g/dL) (range, 4.7-15.4); median platelet count, 63 (×109/L) (range, 2-881); and median blasts in bone marrow, 8% (range, 0-20). Among 411 evaluable patients for the revised international prognostic scoring system, 15 (4%) had very low risk disease; 42 (10%), low risk; 68 (17%), intermediate risk; 124 (30%), high risk; and 162 (39%), very high risk. Overall, 153 patients (53%) achieved CR. Hyperparameter tuning identified the optimal hyperparameters with colsample by tree of 0.175, learning rate of 0.262, the maximal depth of 2, minimal data in leaf of 29, number of leaves of 11, alpha regularization of 0.010, lambda regularization of 2.085, and subsample of 0.639. On the test cohort with 87 patients, the machine learning model accurately predicted response in 65 patients (75%); 53 non-CR among 56 non-CR (95% accuracy); and 12 CR among 31 CR (39% accuracy). The trend of accuracy improvement by iteration (i.e., the number of decision trees) is shown in Figure 1. The area under the curve was 0.761521 in the test cohort. Conclusion: Our machine learning model with clinical, cytogenetic, and NGS data can predict CR to HMA in patients with newly diagnosed MDS. This approach can identify patients who may benefit from HMA therapy with and without additional agents for response, and can optimize the timing of allogeneic stem cell transplant. Disclosures Sasaki: Otsuka: Honoraria; Pfizer: Consultancy. Jabbour:Takeda: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Cyclacel LTD: Research Funding. Ravandi:Cyclacel LTD: Research Funding; Selvita: Research Funding; Menarini Ricerche: Research Funding; Macrogenix: Consultancy, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Xencor: Consultancy, Research Funding. Kadia:Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Bioline RX: Research Funding; Jazz: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Research Funding; BMS: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Takahashi:Symbio Pharmaceuticals: Consultancy. DiNardo:syros: Honoraria; jazz: Honoraria; agios: Consultancy, Honoraria; celgene: Consultancy, Honoraria; notable labs: Membership on an entity's Board of Directors or advisory committees; medimmune: Honoraria; abbvie: Consultancy, Honoraria; daiichi sankyo: Honoraria. Cortes:Novartis: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Immunogen: Consultancy, Honoraria, Research Funding; Sun Pharma: Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Astellas Pharma: Consultancy, Honoraria, Research Funding; Jazz Pharmaceuticals: Consultancy, Research Funding; Merus: Consultancy, Honoraria, Research Funding; Forma Therapeutics: Consultancy, Honoraria, Research Funding; Daiichi Sankyo: Consultancy, Honoraria, Research Funding; BiolineRx: Consultancy; Biopath Holdings: Consultancy, Honoraria; Takeda: Consultancy, Research Funding. Kantarjian:AbbVie: Honoraria, Research Funding; Cyclacel: Research Funding; Pfizer: Honoraria, Research Funding; Astex: Research Funding; Agios: Honoraria, Research Funding; Jazz Pharma: Research Funding; Daiichi-Sankyo: Research Funding; Novartis: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Immunogen: Research Funding; Takeda: Honoraria; BMS: Research Funding; Ariad: Research Funding; Amgen: Honoraria, Research Funding. Garcia-Manero:Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Merck: Research Funding.

scholarly journals Preoperative prediction of complicated appendicitis using machine learning method

2020 ◽  
Author(s):  
Chunbo Kang ◽  
Xubin Li ◽  
Xiaoqian Chi ◽  
Yabin Yang ◽  
Haifeng Shan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Symptoms ◽  
Laboratory Data ◽  
Learning Model ◽  
Complicated Appendicitis ◽  
Support Vector ◽  
Training Set ◽  
Preoperative Prediction ◽  
Machine Learning Model ◽  
Testing Set

Abstract BACKGROUND Accurate preoperative prediction of complicated appendicitis (CA) could help selecting optimal treatment and reducing risks of postoperative complications. The study aimed to develop a machine learning model based on clinical symptoms and laboratory data for preoperatively predicting CA.METHODS 136 patients with clinicopathological diagnosis of acute appendicitis were retrospectively included in the study. The dataset was randomly divided (94: 42) into training and testing set. Predictive models using individual and combined selected clinical and laboratory data features were built separately. Three combined models were constructed using logistic regression (LR), support vector machine (SVM) and random forest (RF) algorithms. The CA prediction performance was evaluated with Receiver Operating Characteristic (ROC) analysis, using the area under the curve (AUC), sensitivity, specificity and accuracy factors.RESULTS The features of the abdominal pain time, nausea and vomiting, the highest temperature, high sensitivity-CRP (hs-CRP) and procalcitonin (PCT) had significant differences in the CA prediction (P<0.001). The ability to predict CA by individual feature was low (AUC<0.8). The prediction by combined features was significantly improved. The AUC of the three models (LR, SVM and RF) in the training set and the testing set were 0.805, 0.888, 0.908 and 0.794, 0.895, 0.761, respectively. The SVM-based model showed a better performance for CA prediction. RF had a higher AUC in the training set, but its poor efficiency in the testing set indicated a poor generalization ability.CONCLUSIONS The SVM machine learning model applying clinical and laboratory data can well predict CA preoperatively which could assist diagnosis in resource limited settings.

scholarly journals Moderate to severe OSA screening based on support vector machine of the Chinese population faciocervical measurements dataset: a cross-sectional study

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e048482
Author(s):  
Liu Zhang ◽  
Ya Ru Yan ◽  
Shi Qi Li ◽  
Hong Peng Li ◽  
Ying Ni Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chinese Population ◽  
Learning Model ◽  
Cross Sectional Study ◽  
Neck Circumference ◽  
Secondary Outcome ◽  
Support Vector ◽  
Sectional Study ◽  
Cross Sectional ◽  
Machine Learning Model

ObjectivesObstructive sleep apnoea (OSA) has received much attention as a risk factor for perioperative complications and 68.5% of OSA patients remain undiagnosed before surgery. Faciocervical characteristics may screen OSA for Asians due to smaller upper airways compared with Caucasians. Thus, our study aimed to explore a machine-learning model to screen moderate to severe OSA based on faciocervical and anthropometric measurements.DesignA cross-sectional study.SettingData were collected from the Shanghai Jiao Tong University School of Medicine affiliated Ruijin Hospital between February 2019 and August 2020.ParticipantsA total of 481 Chinese participants were included in the study.Primary and secondary outcome(1) Identification of moderate to severe OSA with apnoea–hypopnoea index 15 events/hour and (2) Verification of the machine-learning model.ResultsSex-Age-Body mass index (BMI)-maximum Interincisal distance-ratio of Height to thyrosternum distance-neck Circumference-waist Circumference (SABIHC2) model was set up. The SABIHC2 model could screen moderate to severe OSA with an area under the curve (AUC)=0.832, the sensitivity of 0.916 and specificity of 0.749, and performed better than the STOP-BANG (snoring, tiredness, observed apnea, high blood pressure, BMI, age, neck circumference, and male gender) questionnaire, which showed AUC=0.631, the sensitivity of 0.487 and specificity of 0.772. Especially for asymptomatic patients (Epworth Sleepiness Scale <10), the SABIHC2 model demonstrated better predictive ability compared with the STOP-BANG questionnaire, with AUC (0.824 vs 0.530), sensitivity (0.892 vs 0.348) and specificity (0.755 vs 0.809).ConclusionThe SABIHC2 machine-learning model provides a simple and accurate assessment of moderate to severe OSA in the Chinese population, especially for those without significant daytime sleepiness.

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