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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

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

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 07 Jun, 2023
3 min read

Hypertension, a silent killer affecting millions of Americans, could see a major breakthrough in treatment thanks to an innovative artificial intelligence (AI) program. With hypertension being a leading cause of death, stroke, and chronic heart failure, finding the most effective medication for each patient has been challenging. However, a new data-driven machine learning model developed by Boston University scientists and physicians aims to provide real-time treatment recommendations based on patient-specific characteristics. This AI program offers personalized solutions, enhances transparency, and builds trust in AI-generated results. Let’s delve into the details of this remarkable development and its potential impact on hypertension treatment.

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Boston University has developed a machine learning model that suggests personalized treatment for hypertension based on AI-generated results.

A Game-Changing Approach to Hypertension Treatment

Conventional methods of treating hypertension often involve a trial-and-error approach with various medications, each with its own pros and cons. However, the new AI program developed by Boston University offers a revolutionary approach to personalized treatment. By analyzing patient-specific data, including demographics, vital signs, medical history, and clinical test records, the program generates tailored hypertension prescriptions for individual patients.

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Advancing Patient Care with Data-Driven Recommendations

Published in BMC Medical Informatics and Decision Making, the study describes how the AI program outperforms the current standard of care in reducing systolic blood pressure. Leveraging machine learning algorithms, the program provides physicians with a list of suggested medications & the probability of success for each. The goal is to maximize the effectiveness of hypertensive medications at the individual level, enabling a personalized approach to treatment.

ML & AI can now suggest personalized treatment for hypertension.

Building Trust in AI-Generated Results

One of the critical aspects of this AI program is its emphasis on transparency. The research team aimed to ensure clinicians, including those without technical expertise, could understand and trust the algorithm’s recommendations. The program clearly explains how the model works and why specific therapeutic recommendations are proposed. By doing so, it aims to overcome the low levels of trust typically associated with AI in healthcare.

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

Data-Driven Validation and Impressive Results

To develop the model, the researchers utilized deidentified data from over 42,000 hypertensive patients at Boston Medical Center. The model sorted patients into affinity groups based on similar characteristics and compared its effectiveness to the current standard of care and other predictive algorithms. The results showed the AI program achieved a 70.3% larger reduction in systolic blood pressure than the standard of care. This means that it outperformed alternative models by 7.08%.

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Boston University has developed a data-driven machine learning model to treat hypertension.

Unveiling New Insights and Deprescribing Opportunities

Beyond its success in personalized treatment recommendations, the AI program also demonstrated the benefits of deprescribing—reducing or stopping prescriptions for certain patients taking multiple medications. The program’s ability to suggest optimal therapies offers valuable insights in situations where the medical community debates the effectiveness of different drugs, known as clinical equipoise.

Embracing AI’s Potential in Healthcare

While the adoption of machine learning in healthcare has been limited, the potential impact is widely recognized. The transparency and accuracy of this AI program address key concerns, such as interpreting results and trust in artificial intelligence. By effectively handling large amounts of patient data, uncovering patterns, and providing personalized recommendations, this AI program has the potential to revolutionize hypertension treatment and improve patient outcomes.

Also Read: AI Discovers Antibiotic to Combat Deadly Bacteria

Our Say

Boston University’s new AI program for hypertension treatment represents a significant breakthrough in healthcare. Physicians can receive real-time personalized hypertension prescription recommendations by leveraging patient-specific data and advanced machine learning algorithms. This innovative approach outperforms the current standard of care and also builds trust in AI-generated results by ensuring transparency & understanding. As this AI program continues to evolve, it holds the promise of transforming treatment strategies for hypertension and improving the lives of millions of patients worldwide.

AIAI in healthcarealgorithmsdata drivenhealthcarehypertensionmachine learningnews
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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