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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

BIOTRONIK Introduces AI-Powered Cardiac Monitor Implants

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 14 Jun, 2023
4 min read

BIOTRONIK, a renowned leader in implantable medical device technology, has successfully implemented its revolutionary BIOMONITOR IV implantable cardiac monitor (ICM). This cutting-edge device combines BIOTRONIK’s SmartECG technology with the power of artificial intelligence (AI), revolutionizing how cardiac arrhythmias are monitored. With its exceptional accuracy and advanced features, the BIOMONITOR IV aims to deliver unparalleled efficiency in detecting & diagnosing cardiac irregularities. Let’s explore this innovation and its potential to transform patient care.

Also Read: Machine Learning & AI for Healthcare in 2023

BIOTRONIK: Pioneering Medical Technology for a Healthier Future

BIOTRONIK is a globally recognized medical technology company committed to saving and improving the lives of millions affected by heart and blood vessel diseases and chronic pain. The company develops innovative cardiovascular, endovascular, and neuromodulation solutions, ensuring optimal patient outcomes. It operates with a mission to seamlessly integrate technology with the human body.

BIOTRONIK launches BIOMONITOR IV, an AI-powered implantable cardiac monitor for enhanced cardiac arrhythmia monitoring.

The BIOMONITOR IV: An Advanced Solution for Cardiac Monitoring

The BIOMONITOR IV represents a significant leap forward in cardiac monitoring technology. Equipped with BIOTRONIK’s SmartECG technology & powered by AI, this state-of-the-art implantable cardiac monitor offers unmatched precision in detecting cardiac arrhythmias. By leveraging the latest advancements in AI, the BIOMONITOR IV minimizes false positive detections by an impressive 86%. At the same time, it maintains a 98% accuracy in capturing true episodes. Notably, it stands as the only ICM capable of distinguishing between premature atrial contractions (PACs) and premature ventricular contractions (PVCs). Moreover, it provides healthcare professionals with invaluable tools for risk stratification and diagnosis.

Also Read: Breaking Down Social Bias in Artificial Intelligence Algorithms for Cardiovascular Risk Assessment

BIOMONITOR IV - an AI-powered implantable cardiac monitor for cardiac arrhythmia monitoring.

Harnessing Artificial Intelligence for Improved Diagnosis

The integration of artificial intelligence within the BIOMONITOR IV sets a new standard in cardiac arrhythmia monitoring. By analyzing vast amounts of data, the AI algorithm learns to identify genuine cardiac irregularities accurately. This feature allows healthcare professionals to focus on actionable events, ensuring more efficient and targeted patient care. Dr. Di Biase, a prominent cardiologist, expresses enthusiasm for the BIOMONITOR IV’s AI capabilities, highlighting the potential to reduce false positive alerts and enhance patient care.

Also Read: From Trial and Error to Precision: AI’s Answer to Hypertension Treatment

Expert Testimony: Dr. Di Biase Shares Enthusiasm for the BIOMONITOR IV

Dr. Di Biase, a respected figure in the field of cardiology, shares his excitement about the BIOMONITOR IV’s innovative algorithm and artificial intelligence capabilities. Having examined the data, Dr. Di Biase emphasizes the potential of these features to significantly reduce false positive alerts, allowing healthcare professionals to concentrate on actionable events and deliver more efficient care. This testimonial underscores the transformative impact of the BIOMONITOR IV on patient outcomes and the medical community’s response to this breakthrough innovation.

Dr. Di Biase speaks highly of BIOTRONIK's BIOMONITOR IV cardiac monitor.

Advantages for Patients and Healthcare Professionals Alike

Aside from its remarkable AI capabilities, the BIOMONITOR IV offers numerous benefits to both patients and healthcare professionals:

  • Long battery life: The device’s extended battery life ensures continuous monitoring without the need for frequent replacements.
  • Wireless connectivity: Seamless data transmission enables healthcare professionals to remotely monitor patients’ cardiac data, ensuring real-time analysis and intervention.
  • Remote monitoring: Patients can experience the convenience of remote monitoring, reducing the need for frequent in-person clinic visits while maintaining proactive care.
  • Enhanced diagnostic accuracy: The BIOMONITOR IV’s advanced features empower healthcare professionals with improved diagnostic tools, enabling better risk stratification and personalized treatment plans.

Streamlining Workflow with Electronic Health Record Integration

BIOTRONIK recognizes the importance of seamless integration with electronic health records (EHR) for healthcare providers. The BIOMONITOR IV offers the easiest EHR data integration capability, enhancing clinics’ workflow efficiency. This seamless integration enables healthcare professionals to access and analyze patient data effortlessly, enabling informed decision-making and comprehensive patient care.

Also Read: Carbon Health Revolutionizes Healthcare with AI Charting in Its EHR

AI helps to streamline diagnosis and healthcare services.

A Testament to Innovation: BIOTRONIK’s Commitment to Patient Care

The launch of the BIOMONITOR IV exemplifies BIOTRONIK’s unwavering commitment to innovation and enhancing patient care. By consistently pushing the boundaries of medical advancements, BIOTRONIK empowers healthcare professionals worldwide, enabling them to provide the best possible care for their patients. The BIOMONITOR IV’s introduction solidifies BIOTRONIK’s position as a leading player in cardiac monitoring devices, driving forward the field of cardiovascular medicine.

Also Read: ChatGPT Outperforms Doctors in Providing Quality Medical Advice

Leading the Way in Cardiovascular Medicine

As a frontrunner in the medical technology industry, BIOTRONIK continues to revolutionize cardiovascular medicine. Through devices like the BIOMONITOR IV, BIOTRONIK empowers healthcare professionals and improves patients’ lives worldwide. The company’s relentless pursuit of excellence and dedication to technological advancements reaffirms its position as a trailblazer in the field of cardiac monitoring devices.

Also Read: Anomaly Detection in ECG Signals: Identifying Abnormal Heart Patterns Using Deep Learning

Our Say

With the introduction of the BIOMONITOR IV, BIOTRONIK has paved the way for a new era of cardiac arrhythmia monitoring. Combining advanced AI technology with their unparalleled expertise, BIOTRONIK delivers a solution that significantly enhances patient care and healthcare professionals’ diagnostic capabilities. The BIOMONITOR IV’s exceptional accuracy, extended battery life, and seamless integration with electronic health records make it a game-changer in the field of cardiac monitoring. As BIOTRONIK continues to innovate and lead the way, patients worldwide can look forward to improved outcomes and a healthier future.

AIAI in healthcareArtificial Intelligencehealthcareintegrationmedical devicenewsTechnology
K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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