Machine Learning Based on Clinical Variables and Chest CT Quantitative Measurements for Early Prediction of Progression Risk in COVID-19 Patients: A Multicentre Study

2020 ◽  
Author(s):  
Zhichao Feng ◽  
Hui Shen ◽  
Kai Gao ◽  
Jianpo Su ◽  
Shanhu Yao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multicentre Study ◽  
Chest Ct ◽  
Early Prediction ◽  
Clinical Variables ◽  
Quantitative Measurements ◽  
Progression Risk

5228Identification of reliable clinical variables for early prediction of outcome after out-of-hospital cardiac arrest using algorithm-based machine learning statistical methods

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
J Johnsson ◽  
S Hoerberg ◽  
A Holm ◽  
S Gustafzelius ◽  
J Dankiewicz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Low Flow ◽  
Temperature Management ◽  
Target Temperature Management ◽  
Extended Model ◽  
Early Prediction ◽  
Prediction Of Outcome ◽  
Clinical Variables ◽  
Hospital Cardiac Arrest

Abstract Background Several factors are known to influence both survival and long-term neurologic function after out-of-hospital cardiac arrest (OHCA). Previous studies have indicated that both pre-hospital circumstances as well as patients' history and clinical status on hospital admission are variables strongly associated with later outcome. This study aimed to identify and evaluate clinical variables for early prediction of outcome for unconscious survivors after OHCA using machine learning statistics analysis. Methods The Target Temperature Management (TTM) trial randomized 939 international patients with OHCA of presumed cardiac cause to TTM at 33°C or 36 °C for 24 h in intensive care units (ICUs). Patient outcome were survival and neurological function defined by the Cerebral Performance Category (CPC) scale. This multicentre cohort was used for a post hoc analysis using machine learning statistical analysis. A Conditional Interference decision forest algorithm was designed for training on the TTM-dataset to perform early prediction of outcome at 180 days. Results After ranking all available variables in the TTM-dataset based on their importance for the algorithm to make predictions, we could identify a slimmed list with eleven clinical predictors of a poor outcome including older age, low motor score on Glasgow Coma Scale (GCS), increasing doses of adrenaline, first monitored rhythm not shockable, longer duration of low flow, longer time from cardiac arrest to advanced life support, high BMI (Body Mass Index), low pH, bilateral absence of corneal and pupillary reflex, low initial body temperature and cardiac arrest location at home. Age was overall the most important variable for prediction. Our slimmed prediction model performed slightly worse with an AUC of 0.813 (0.741–0.916) compared to an extended model with all available variables included, AUC = 0.839 (0.778 – 0.886). When using all variables in a comparing logistic regression analysis the mean AUC was a corresponding 0.830 (0.792–0.882). Conclusion This algorithm with eleven clinical variables predicted outcome almost as good as a corresponding large model with cardiac arrest patients from the TTM-trial and could be a powerful clinical decision tool for early prediction of outcome after cardiac arrest.

scholarly journals An Explainable Machine Learning Model for Early Prediction of Sepsis Using ICU Data

2021 ◽  
Author(s):  
Naimahmed Nesaragi ◽  
Shivnarayan Patidar
Keyword(s):  
Machine Learning ◽  
Utility Score ◽  
Learning Model ◽  
Training Data ◽  
Early Prediction ◽  
Clinical Variables ◽  
Machine Learning Model ◽  
Hospital Systems ◽  
Prediction Risk ◽  
The Given

Early identification of individuals with sepsis is very useful in assisting clinical triage and decision-making, resulting in early intervention and improved outcomes. This study aims to develop an explainable machine learning model with the clinical interpretability to predict sepsis onset before 6 hours and validate with improved prediction risk power for every time interval since admission to the ICU. The retrospective observational cohort study is carried out using PhysioNet Challenge 2019 ICU data from three distinct hospital systems, viz. A, B, and C. Data from A and B were shared publicly for training and validation while sequestered data from all three cohorts were used for scoring. However, this study is limited only to publicly available training data. Training data contains 15,52,210 patient records of 40,336 ICU patients with up to 40 clinical variables (sourced for each hour of their ICU stay) divided into two datasets, based on hospital systems A and B. The clinical feature exploration and interpretation for early prediction of sepsis is achieved using the proposed framework, viz. the explainable Machine Learning model for Early Prediction of Sepsis (xMLEPS). A total of 85 features comprising the given 40 clinical variables augmented with 10 derived physiological features and 35 time-lag difference features are fed to xMLEPS for the said prediction task of sepsis onset. A ten-fold cross-validation scheme is employed wherein an optimal prediction risk threshold is searched for each of the 10 LightGBM models. These optimum threshold values are later used by the corresponding models to refine the predictive power in terms of utility score for the prediction of labels in each fold. The entire framework is designed via Bayesian optimization and trained with the resultant feature set of 85 features, yielding an average normalized utility score of 0.4214 and area under receiver operating characteristic curve of 0.8591 on publicly available training data. This study establish a practical and explainable sepsis onset prediction model for ICU data using applied ML approach, mainly gradient boosting. The study highlights the clinical significance of physiological inter-relations among the given and proposed clinical signs via feature importance and SHapley Additive exPlanations (SHAP) plots for visualized interpretation.

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

scholarly journals A machine learning approach to predict healthcare cost of breast cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pratyusha Rakshit ◽  
Onintze Zaballa ◽  
Aritz Pérez ◽  
Elisa Gómez-Inhiesto ◽  
Maria T. Acaiturri-Ayesta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Healthcare Cost ◽  
Percentage Error ◽  
Learning Approach ◽  
Early Prediction ◽  
Breast Cancer Patients ◽  
Machine Learning Approach ◽  
Clinical Records

AbstractThis paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: (1) first, the healthcare cost of a new patient’s treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost. Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.

scholarly journals Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

Life ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 122
Author(s):  
Ruggiero Seccia ◽  
Silvia Romano ◽  
Marco Salvetti ◽  
Andrea Crisanti ◽  
Laura Palagi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
High Impact ◽  
Prognostic Models ◽  
Early Prediction ◽  
Disease Course ◽  
Slow Progression ◽  
Relapsing Remitting ◽  
Long Time ◽  
Effective Drugs

The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.

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