SCREENING OF PATIENTS AT RISK FOR WILD TYPE ATTR-CM USING A COMPUTATIONAL MACHINE LEARNING ALGORITHM

2021 ◽  
Vol 37 (10) ◽  
pp. S65
Author(s):  
C Willis ◽  
K Kawamoto ◽  
A Watanabe ◽  
J Biskupiak ◽  
K Nolen ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Wild Type ◽  
Patients At Risk

scholarly journals SCREENING OF PATIENTS AT RISK FOR WILD TYPE ATTR-CM USING A COMPUTATIONAL MACHINE LEARNING ALGORITHM

2021 ◽  
Vol 77 (18) ◽  
pp. 677
Author(s):  
Connor Willis ◽  
Kensaku Kawamoto ◽  
Alexandre Watanabe ◽  
Joseph Biskupiak ◽  
Kim Nolen ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Wild Type ◽  
Patients At Risk

IMPLEMENTING MACHINE LEARNING ALGORITHM TO IDENTIFY PATIENTS AT RISK OF DEVELOPING ELEVATED LEFT SIDED CARDIAC FILLING PRESSURES

2018 ◽  
Vol 71 (11) ◽  
pp. A727
Author(s):  
Kinjan Patel ◽  
Marton Tokodi ◽  
Partho P. Sengupta ◽  
Ashok Runkana ◽  
Sirish Shrestha ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Patients At Risk ◽  
Cardiac Filling

Abstract 15783: A Simplified Machine Learning Algorithm for Identification of Patients At-risk for Wild-type Transthyretin Amyloid Cardiomyopathy

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Stephen B Heitner ◽  
Ahmad Masri ◽  
Miriam R. Elman ◽  
Birol Emir ◽  
Kim D. Nolen ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Algorithm ◽  
High Sensitivity ◽  
Recursive Feature Elimination ◽  
Wild Type ◽  
Snomed Ct ◽  
Retrospective Case ◽  
Referral Center ◽  
Amyloid Cardiomyopathy

Introduction: Wild-type transthyretin amyloid cardiomyopathy (wtATTR-CM) is a progressive, life-threatening, increasingly recognized but underdiagnosed cause of heart failure (HF). A previously validated machine learning (ML) model trained on medical claims data from 300 million US patients predicted wtATTR-CM among individuals diagnosed with HF with high sensitivity and specificity. Here, we simplified the ML model by reducing the number of predictive variables and tested it in a cohort of patients with confirmed wtATTR-CM at a large academic amyloid referral center. Methods: A retrospective, case-control study was conducted using electronic health records (EHR) from a random 1:1 sample of patients diagnosed with wtATTR-CM (cases) and non-amyloid HF (controls) at OHSU (Jul 2005-Nov 2019). Inclusion criteria were age ≥50 years; HF diagnosis (based on ICD-10 codes/SNOMED CT); and ≥1 of the following: ≥12 months of medical history, ≥5 clinical visits, or ≥10 documented diagnosis codes. The original 1,871 variables were systematically reduced to 15 based on recursive feature elimination and clinical relevance to ATTR-CM. After confirmation of the full ML model algorithm performance, the simplified model, validated against Optum EHR, was applied to the OHSU cohort of patients with wtATTR-CM. Results: Of 25,233 patients who met study criteria, 38 (0.2%) had wtATTR-CM and were evaluated along with 38 patients with non-amyloid HF. Performance of the simplified ML model was consistent with the previously validated model, with an ROC AUC of 0.812 and 0.804, respectively, and improved at lower thresholds (Table). Conclusions: A simplified ML algorithm to estimate the empirical probability of wtATTR-CM in patients with HF performed well at an academic amyloid referral center. This may serve as a practical approach to aid physicians in identifying HF patients who may be at-risk for wtATTR-CM. Additional studies are needed to confirm these findings in larger cohorts.

Machine Learning Algorithm Based Bandwidth Prediction for Internet Usage

2020 ◽  
Vol 17 (8) ◽  
pp. 3749-3753
Author(s):  
J. Rajaram ◽  
M. Nalini ◽  
N. Vadivelan
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Algorithm ◽  
Arima Model ◽  
Internet Usage ◽  
Machine Learning Algorithm ◽  
Framework Structure ◽  
The Past ◽  
Basic Activity ◽  
Time Plan

The applicability of framework structure and affiliation arranging recognize a basic activity in the bandwidth prediction. The procedure for predicting the framework use is to see the basic transmission limit with respect to future periods. This prediction helps with utilizing the techniques workplaces in the saint way. Thinking about the fundamental cost of bandwidth, at top hours of a framework traffic we can follow an amazing sort of plan to purchase. In this paper, the past use data of FWDR organize centers is at risk to univariate direct time plan ARIMA model after precise change is used to calculate necessary bandwidth limit concerning future needs. The anticipated data is veered from the obvious data gained from a for all intents and purposes indistinguishable framework and the foreseen data has been viewed as inside ten percent MAPE. This design reduction the MAPE by eleven point seventy-one percentage and fifteen point forty-two percent of self-rulingly when stood separated from the non-able changed ARIMA model at ninety-nine percent CI. The outcome show that the suitably changed ARIMA design has improved show when meandered from non-intentionally changed ARIMA model. Increasingly significant dataset can be passed on with season alterations and thought of expanded length groupings, for dynamically unequivocal and longer term needs.

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.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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