Machine learning models predict total charges and drivers of cost for transcatheter aortic valve replacement.

Background: Given the increasing healthcare costs, there is an interest in developing machine learning (ML) prediction models for estimating hospitalization charges. We use ML algorithms to predict hospitalization charges for patients undergoing transfemoral transcatheter aortic valve replacement (TF-TAVR) utilizing the National Inpatient Sample (NIS) database. Methods: Patients who underwent TF-TAVR from 2012 to 2016 were included in the study. The primary outcome was total hospitalization charges. Study dataset was divided into 80% training and 20% testing sets. We used following ML regression algorithms: random forest, gradient boosting, k-nearest neighbors (KNN), multi-layer perceptron and linear regression. ML algorithms were built for for 3 stages: Stage 1, including variables that were known pre-procedurally (prior to TF-TAVR); Stage 2, including variables that were known post-procedurally; Stage 3, including length of stay (LOS) in addition to the stage 2 variables. Results: A total of 18,793 hospitalization for TF-TAVR were analyzed. The mean and median adjusted hospitalization charges were $220,725.2 ($137,675.1) and $187,212.0 ($137,971.0-264,824.8) respectively. Random forest regression algorithm outperformed other ML algorithms at all stages with higher R2 score and lower mean absolute error (MAE), root mean squared area (RMSE) and root mean squared logarithmic error (RMSLE) (Stage 1: MAE 79,979.11, R2 0.157; Stage 2: MAE 76,200.09, R2 0.256; Stage 3: MAE 69,350.09, R2 0.453). LOS was the most important predictor of hospitalization charges. Conclusions: We built ML algorithms that predict hospitalization charges with good accuracy in patients undergoing TF-TAVR at different stages of hospitalization and that can be used by healthcare providers to better understand the drivers of charges.

Use of Machine Learning to Develop Prediction Models for Mortality and Stroke in Patients Undergoing Balloon Aortic Valvuloplasty.

OBJECTIVE: To develop an artificial intelligence, machine learning prediction model for estimating in-hospital mortality and stroke in patients undergoing balloon aortic valvuloplasty (BAV). METHODS: The National Inpatient Sample (NIS) database was used to identify patients who underwent BAV from 2005 to 2017. Outcomes analyzed were in-hospital all-cause mortality and stroke after BAV. Predictors of mortality and stroke were selected using LASSO regularization. A conventional logistic regression and a random forest machine learning algorithm were used to train the models for predicting outcomes. The performance of all the modeling algorithms for predicting in-hospital mortality and stroke was compared between models using c-statistic, F1 score, brier score loss, diagnostic accuracy, and Kolmogorov-Smirnov plots. RESULTS: A total of 6962 patients with severe aortic stenosis who underwent BAV were identified. The performance of random forest classifier was comparable with logistic regression for predicting in-hospital mortality for all measures of performance (F1 score 0.422 vs 0.409, ROC-AUC 0.822 [95 % CI 0.787-0.855] vs 0.815 [95 % CI 0.779-0.849], diagnostic accuracy 70.42 % vs 70.93 %, KS-statistic 0.513 vs 0.494 and brier score loss 0.295 vs 0.291). The random forest algorithm significantly outperformed logistic regression in predicting in-hospital stroke with respect to all performance metrics: F1 score 0.225 vs 0.095, AUC 0.767 [0.662-0.858] vs 0.637 [0.499-0.754], brier score loss [0.399 vs 0.407], and KS-statistic [0.465 vs 0.254]. CONCLUSIONS: The good discrimination of machine learning models reveal the potential of artificial intelligence to improve patient risk stratification for BAV.

Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review.

The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls.

Selfies: A boon or bane?

BACKGROUND: Selfie deaths have become an emerging problem and we performed this study to assess the epidemiology of selfie-related deaths across the globe. SUBJECT AND METHODS: We performed a comprehensive search for keywords such as "selfie deaths; selfie accidents; selfie mortality; self photography deaths; koolfie deaths; mobile death/accidents" from news reports to gather information regarding selfie deaths. RESULTS: From October 2011 to November 2017, there have been 259 deaths while clicking selfies in 137 incidents. The mean age was 22.94 years. About 72.5% of the total deaths occurred in males and 27.5% in females. The highest number of incidents and selfie-deaths has been reported in India followed by Russia, United States, and Pakistan. Drowning, transport, and fall form the topmost reasons for deaths caused by selfies. We also classified reasons for deaths due to selfie as risky behavior or non-risky behavior. Risky behavior caused more deaths and incidents due to selfies than non-risky behavior. The number of deaths in females is less due to risky behavior than non-risky behavior while it is approximately three times in males. CONCLUSION: "No selfie zones" areas should be declared across tourist areas especially places such as water bodies, mountain peaks, and over tall buildings to decrease the incidence of selfie-related deaths.