This masterclass AI and Machine Learning for healthcare training provides in depth knowledge for healthcare specialists and IT specialists who want to start with Artificial intelligence (AI) and Machine Learning (ML).

Especially for Healthcare, Machine Learning is one of the most promising, innovative and disruptive developments in digital technology. Artificial intelligence has seen rapid innovations over the last few years. If used properly, it can empower care providers to make better diagnoses, provide better patient care, and improve access to healthcare.

AI and ML is an excellent example where close cooperation between the domain specialist (the person knowing and understanding the data, for example the healthcare specialist), the data scientist and IT is crucial for a successful implementation. Our AI and ML courses are meant specifically for the domain specialists and IT specialists.

One of the goals is to enhance the care provider’s decision-making. It’s very important to remember that the AI is not making the decision: AI empowers physicians with additional information that can help them more quickly and accurately diagnose particular conditions. Despite vendor enthusiasm, AI is still very much in its infancy, and providers should think of these tools as clinical support capabilities rather than machines that can make medical decisions completely on their own: keep the human in the loop.

AI and ML implemented in healthcare is maybe the best example for Friendly AI: taken from transcript of the Global Health Privacy Summit ‘Artificial intelligence and Ethics’:

There are between 400 million and 2 billion people who don’t have access to healthcare or sanitized facilities. Whether it’s to lower the costs of healthcare or whether it’s to literally make healthcare ubiquitous so that all of humanity can participate in the opportunity to receive care, machine learning is somehow essential to this.

Just some of the use cases covered in this masterclass AI and Machine Learning for healthcare:

• Clinical Decision Support and Predictive Analytics: Identifying and addressing risks, generate patient-specific assessments or recommendations.
• Medical Imaging: Machine learning can supplement the skills of human radiologists by identifying subtler changes in imaging scans more quickly, potentially leading to earlier and more accurate diagnoses.
• Voice-to-Text Transcription/NLP:  Natural language processing (NLP) can ease the transfer of information into electronic health records (EHR) and can make sure that we have the most accurate data fed into the EHR system
• Cybersecurity: Trying to keep up with cyber-issues is a never-ending battle. Patient privacy are critical concerns for every healthcare provider and intelligent algorithms may be the best way to tackle cyber threats, for example reduce the rising threat of ransomware.

In this training you will learn the essential principles of Machine Learning and Data Science. You will learn about the latest tools and means available which will enable you start a ML project and setup a framework within your organization. You will learn about the specific concerns and opportunities in the healthcare industry.

This masterclass AI and Machine Learning for healthcare will answer questions like:

• What is AI, ML and Deep Learning?
• Why should I start now with an AI and ML project?
• What are the first steps to start an AI/ML project?
• What is data governance in healthcare, how can I secure and use my data efficiently?
• How can I make my Big data Smart data in the healthcare industry?
• What are the most popular algorithmes and how can I evaluate and score a ML model?
• Which tools are available ?
• What are the dangers and pitfalls of AI and ML?
  • What is Bias?
  • How to avoid the “black box”
• What are the ethical concerns?

After attending this masterclass AI and Machine Learning for healthcare  you you can start directly with AI / ML and you will have the knowledge to start a Machine Learning project in the healthcare industry. You will receive references (books, courses, online resources) that enable you to increase your knowledge of AI and Machine Learning within the healthcare industry.

You can find a detailed agenda at the end of this page.

Impatient? Read the excellent report from the British NHSx:  Artificial Intelligence: How to get it right. Putting policy into practice for safe data-driven innovation in health and care.

Course delivery

This masterclass AI and Machine Learning for healthcare training is available in the following formats:

• Classroom sessions (to be planned in the Netherlands and Belgium or on location).
• Webinar: Online classroom / virtual classroom: you can join live from anywhere in the world using Skype or Zoom.

An E-learning module (as is available for our IT Essentials for non-IT) is under development.

Course duration

Two and a half days.

Price

The Web Infra Academy uses two price models: per student or per training. For an on-site classroom training and a quotation please contact us or use the form at the end of this page.

• Classroom:           1495,00 Euro per student
• Remote/online:  1095.00 Euro per student (using Skype or Zoom)
• E-learning:            under development
• Blended:                under development

Course dates classroom training

Series 1

Startdate 15-06-2020 – end date 17-06-2020, location Nieuwegein, The Netherlands

Series 2

Startdate 23-09-2020 – enddate 25-09-2020, location Nieuwegein, The Netherlands

Course dates remote/online

Series 1

Startdate 29-06-2020 – end date 01-07-2020, location: online (Skype or Zoom)

Time: in consultation with participants

Series 2

Startdate 30-09-2020 – end date 02-10-2020, location: online (Skype or Zoom)

Time: in consultation with participants

Please register using the form at the end of this page.

Prerequisites

• None.

Target audience

Healthcare specialists, healthcare managers, healthcare IT specialists

Agenda masterclass AI and Machine Learning for healthcare

Day 1

• Examples of applied Machine Learning in healthcare
• What is AI en Machine Learning?
• History of AI and Machine Learning
• Terminology AI and Machine Learning (Deep learning, General AI, Narrow AI, Friendly AI, Singularity etc.)
• Why now: the 7 “key enablers”
• Relationship Big Data, IoT and AI/Machine Learning
• Exercise one: doing your first prediction using Dataiku
• Essentials of Data Science:
  • the bell curve, standard deviation, Z-score etc.
  • Data governance: bringing data silo’s together, data formats healthcare (HL7 FHIR etc.)
  • Visualization
  • Feature engineering
  • Simpson’s paradox
• Exercise two: data selection and feature engineering using Azure ML
• The potential of AI and Machine Learning in healthcare
• Basic methods for Machine Learning (supervised learning, unsupervised learning, deep learning etc.)

Day 2

• The most popular Machine Learning algorithms
• Exercise three: binomial classification
• Training your model (K-fold cross test)
• confusion matrix, Accuracy, precision, recall, ROC, UAC
• Exercise four: predicting a value
• R2 and scatter plots
• Overfitting and regularization
• Exercise five: clustering
• Silhouette
• Variable importance: RFM
• Natural Language Processing (NLP)
• Types of data: matrix, images-tensors, text-corpus
• Word frequency, Topic modelling, sentiment analysis

Day 3

• Neural networks
• Exercise six: image recognition
• Convolutional neural networks
• The most popular tools for Machine Learning
• AutoML
• Risks and ethics of AI and Machine Learning (Bias)
• Data security: GDPR, depersonalization/masking, encryption and MFA
• How do I start a successful Machine Learning project
• How do I incorporate Machine Learning in my organization
• Resources for learning

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If you have any questions or are interested in this course, please contact us or use the form below:

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