AI for Healthcare

Artificial Intelligence Master Course Algorithms and Applications

Artificial Intelligence Master Course

Algorithms and Applications 

A 15-Weeks Guided Online Course 

Sri Amit Ray Compassionate AI Lab

In this 15-week online course, you will learn and understand the main algorithms and approaches to artificial intelligence and deep learning. You will learn the techniques to improve your AI and machine learning model building skills and algorithm development skills. It includes total 15 online one-on-one classes, for real-world case studies, and hands-on practices and exercises.

The aim of the course is to:

  • be able to apply the algorithms for different applications and interpret the results
  • be able to build models and algorithms and adjust parameters
  • understand the applicability of the algorithms to different types of data and problems along with their strengths and limitations

The case studies share real world stories from teams who have designed AI-driven products using human-centered AI based practices. Primarily you can use R programming or Python programming for practicing the examples and projects.

The Course Contents

The course structure varies, depending on individuals’ experiences, needs, and interests. However, there will be a total of 15 classes. The general content of the modules of the course are as follows:

Module 1: Artificial Intelligence Fundamentals

  1. Human Intelligence vs Artificial Intelligence
  2. Human brain vs Artificial Intelligence
  3. General Artificial Intelligence vs Ethical Artificial Intelligence
  4. Ethical Artificial Intelligence vs Compassionate Artificial Intelligence
  5. Difference between Artificial Intelligence, Machine Learning, and Deep Learning
  6. Recent trends of AI from robots to humanoids
  7. Machine Learning Frameworks
  8. Future challenges of Artificial Intelligence 

Module 2: Basic Machine Learning Algorithms and Practices

  1. Hypothesis Testing
  2. Linear Regression
  3. Logistic Regression
  4. Clustering
  5. Analysis of Variance (ANOVA)
  6. Principal Component Analysis
  7. Naive Bayes
  8. Decision Tree
  9. Random Forest
  10. Support Vector Machines
  11. K Nearest Neighbors
  12. Gradient Boosting algorithms 
  13. Neural Networks
  14. Ensemble Methods

Module 3: Feature Engineering and Model Building

  1. Data preparation
  2. Min-max Scaling, Standardization, Log Transformation, One hot Encoding, etc.
… Read more..
Key Artificial Intelligence Projects to Fight Against COVID-19https://amitray.com/artificial-intelligence-to-fight-against-covid-19/

Artificial Intelligence to Fight Against COVID-19

Key Artificial Intelligence Projects to Fight Against COVID-19

Dr. Amit Ray 
Compassionate  AI Lab 

Prevention and early healing are the primary requirements for the present COVID crisis. In our Compassionate AI Lab, we broadly classified our fight against COVID 19 Artificial intelligence (AI) based research projects into six groups. They are AI for COVID vaccine development, AI for COVID drug discovery, AI for COVID diagnosis, AI for COVID testing, AI for COVID growth rate forecasting, and AI for social robots.

AI to Fight COVID 19 Researches Sri Amit Ray Compassionate AI Lab

AI to Fight COVID 19 Researches Sri Amit Ray Compassionate AI Lab

Read More »Artificial Intelligence to Fight Against COVID-19

Machine Learning to Fight Antimicrobial Resistance

Machine Learning to Fight Antimicrobial Resistance

The seven top machine learning projects to fight against antimicrobial resistance are explained. Antimicrobial resistance  is one of the key reasons of human sufferings in modern hospitals. Preventing microbes from developing resistance to drugs has become as important issue for treating illnesses across the world. Artificial Intelligence, machine learning, genomics and multi-omics data integration are the fast-growing emerging technologies to counter antimicrobial resistance problems. Here,  Dr. Amit Ray explains how these technologies can be used in seven key areas to counter antimicrobial resistance issues.

What’s Holding Back Machine Learning in Healthcare

What is holding back the large scale implementation of machine learning systems in healthcare and precision medicine? In this article Dr. Amit Ray, explains the key obstacles and challenges of  implementing large-scale machine learning systems in healthcare.   Dr. Ray argued that lack of deeper integration, incomplete understanding of the underlying molecular processes of disease it is intended to treat, may limit the progress of implementing large-scale machine learning based reliable systems in healthcare. Here, nine obstacles of present day machine learning systems in healthcare are discussed. 

What Holding Back Machine Learning in Healthcare

Machine Learning in Healthcare

Recently, machine learning algorithms, especially deep learning has shown impressive performance in many areas of medical science, especially in classifying imaging data in different clinical domains. In academic environment, Deep learning and Reinforcement learning methods of Artificial Intelligence (AI) has shown tremendous success in numerous clinical areas such as: Omics data integration (such as genomics, proteomics or metabolomics), prediction of drug-disease correlation based on gene expression, and finding combinations of drugs that should not be taken together. Deep learning is very successful in predicting cancer outcome based on tumour tissue images. Machine learning are used for medical decision support systems for ICU and critical care. Artificial Intelligence in Healthcare Current Trends discusses the current status of AI in healthcare. Read More »What’s Holding Back Machine Learning in Healthcare

7 Limitations of Molecular Docking & Computer Aided Drug Design and Discovery

7 Limitations of Molecular Docking & Computer Aided Drug Design and Discovery

Over the past decades, molecular docking has become an important element for drug design and discovery.  Many novel computational drug design methods were developed to aid researchers in discovering promising drug candidates. In the recent years, with the rapid development of faster architectures of  Graphics Processing Unit (GPU)-based clusters and better machine algorithms for high-level computations, much progress has been made in areas such as scoring functions,  search methods and ligand-receptor interaction for living cells and other approaches for drug design and discovery.  

A large number of successful applications have been reported using a variety of docking techniques. However, despite their success in academic environment for concept validation, their real life application is very limited. There are many obstacles and number of issues remain unsolved. In this article Dr. Amit Ray, explains the key obstacles and challenges of molecular docking methods for developing efficient computer aided drug design and discovery (CADD) methods.   Dr. Ray argued incomplete understanding of the underlying molecular processes of the disease it is intended to treat may limit the progress of drug discovery. Here, the seven limitations of present CADD methods are discussed. 

 7 obstacles of Molecular docking & Computer aided drug design

In vivo, In vitro and In silico:  Experimentation for Drug Discovery 

Experimentation for Drug Discovery pathways are classified into three groups: in vivo, in vitro and in silico. … Read more..

AI for Balance-Control Fall Detection of Elderly People

Artificial Intelligence for Balance Control and Fall Detection of Elderly People

Balance control in elderly people is one of the key issues of old age. Artificial Intelligence can play a big role to solve this issue. In this research work, we demonstrate the application of machine learning techniques for posture alignments and the control of the body center of mass for disable people.

Designing automated balance control system for elderly people is one of the key project  of our Compassionate AI Lab. Here, Dr. Amit Ray discuses about one of the recent project of AI using deep learning algorithms for automatic balance control of elderly people. He explains how machine learning algorithms can be used to study and improve the dynamical properties of postural stability of elderly people. The project focuses on how image recognition, human-body joint dynamics, and path navigation methods of artificial intelligence can be used  to eliminate the imbalance, fall and injury of elderly people or for physically challenged people.

AI for Balance-Control Fall Detection of Elderly People

Compassionate Artificial Intelligence can be used for helping elderly people in many ways. Here, we discuss about one of our recent project of using AI & deep learning techniques for automatic balance control. The machine learning algorithms are used to improve dynamical properties of postural stability. In this project AI based machine learning algorithms are used to find the insights into the person specific postural strategies for older adults in order to adapt to the postural challenges during sleeping, standing, turning and walking. To study the body movement behavior of elderly people accurately, it is necessary to observe and record their movement trajectory and joint movements quantitatively and precisely in three dimensions.Read More »Artificial Intelligence for Balance Control and Fall Detection of Elderly People

Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Artificial Intelligence to Combat Antibiotic Resistant Bacteria – tools, techniques, models, scopes and challenges are discussed. Antibiotic resistance bacteria is one of the key research area of our Compassionate AI Lab. Dr. Amit Ray explains how artificial intelligence can be used in combating these superbugs. Antibiotic resistance bacteria is becoming world’s biggest health crisis. We discussed here multi-agent deep reinforcement learning models for predicting behavior of bacteria and phages in multi-drug environments.  We call this model as DeepCombat. 

Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Antibiotic resistant bacteria are bacteria that are not controlled or killed by antibiotics. They are able to survive and even multiply in the presence of an antibiotic.  These bacteria currently kill an estimated 700,000 people globally each year – a death toll which could rise to 10 million a year by 2050 if we don’t act [1]. The main difficulty is that the bacteria are changing fast. They changing faster than we can change the drugs in response.

 Artificial intelligence is showing alternative means of fighting these deadly infections and killer bacteria. Multi-drug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, thousands of lives lost. Artificial Intelligence for healthcare is progressing at an exponential rate.  We are evaluating here, the role of artificial intelligence in fighting these superbugs.  Especially, the use of AI for intelligent Phage therapy.Read More »Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Artificial Intelligence and Blockchain for Precision Medicine

Here, we discussed the scopes and implementation issues of artificial intelligence and blockchain for precision medicine. Evidence-based medicine is gradually shifting from therapy to prevention and towards individually tailored precision medicine systems. Where, artificial intelligence can be used to automatically detect problems and threats to patient safety, such as patterns of sub-optimal care or outbreaks of hospital-acquired illness. Artificial intelligence can be used to prevent the issues like drug-interaction, over-diagnosis, over-treatment and under-treatment. It can be used more effectively to solve the problems of antibiotic resistant bacteria.

Artificial Intelligence in Precision Medicine

Artificial Intelligence in Precision Medicine

Artificial intelligence in precision medicine is a revolutionary new approach advancing health and wellness, knowledge, and health care delivery to maximize the quality of life for all over a lifetime. The main concept of precision medicine is providing health care which  is individually tailored on the basis of a person’s genes, lifestyle and environment. With the advances in genetics,  artificial intelligence and the growing availability of health data, present an opportunity to make precise personalized patient care a clinical reality. 

It is like cricket. No two cricket ball deliveries, players, — or patients — are exactly alike. No two games or diseases are exactly the same. To win the game every ball, every delivery needs unique strategy. Precision medicine is like that. No two diseases are same, so the treatments will be different and unique.  

AI with precision medicine is a part of artificial intelligence in health care. It brings together innovations in genomics, metabolomics, mobile health, biomedical data sciences, imaging, social engagement and networking, communication, and environmental sciences to make diagnostics, therapeutics, and prevention more individualized, proactive, predictive, and precise. 

Precision medicine often involves the application of panomic analysis and systems biology to analyze the cause of an individual patient’s disease at the molecular level and then to utilize targeted treatments (possibly in combination) to address that individual patient’s disease process.… Read more..

Artificial Intelligence in Radiology for X-Ray and CT-Scan

Artificial Intelligence in Radiology for X-Ray and CT-Scan Image Analysis 

Dr. Amit Ray
Compassionate AI Lab, Radiology Division

With artificial intelligence it is possible to analyze and interpret large amounts of radiological images efficiently. Late detection of disease significantly increases treatment costs and reduces survival rates. Often visual human interpretation of an isolated image are time-consuming, difficult and expensive. Artificial intelligence in healthcare especially in radiology has tremendous scope. Recently, AI for radiology uses deep learning, reinforcement learning, and other machine learning algorithms to systematically assess x-ray, CT Scan and MRI images of and instantly provide detailed reports on their findings.

Artificial Intelligence in Radiology

AI algorithms read medical images like a radiologist, they identify the hidden patterns in the image and relate them with medical data. The AI systems are trained using vast numbers of images like CT scans, magnetic resonance imaging (MRI), ultrasound or nuclear imaging. New AI tools that excel at medical image analysis can automatically detect complex anomalous patterns in radiological images and provide quantitative information on disease [4].

Studies have shown that computer aided screening can decrease false negatives by ~45% [1]. The AI machines reading radiology studies correctly, reaching around 95 percent accuracy [2]. The AI tools can rapidly review a number of images, prior images, patient history and other medical data, and then extract the most meaningful insights, which can then be verified by the radiologist.… Read more..