Artificial Intelligence for Medical Data with Python

Subject Category: ICT - Information & Communication Technologies

Start date: 06/10/2024

Program Information

PROGRAM CODE: 1533
REGISTRATION PERIOD: 10/07/2024 - 18/10/2024
COURSE PERIOD: 06/10/2024 - 20/10/2024
DURATION: 15 days
TRAINING HOURS: 100.00
CREDITS ECTS: 4.00

Scientific Responsibility: SYMEONIDIS PANAGIOTIS

Academic Responsibility: SYMEONIDIS PANAGIOTIS

INITIAL COST: 120,00 € (Program Discounts)

DESCRIPTION

OUTLINE

CONTACT

DISCOUNTS - TERMS OF USE

Program Description

Subject & purpose of the training program:

This educational program presents the important algorithms from the main scientific fields of Information Retrieval, Machine Learning, Data Mining and Artificial Intelligence using as case study the Health domain and Medical data. The programme is addressed to professionals in the field of Information Technology, as it covers the advanced topics that shape the field
of Medical Intelligent Systems with python. It additionally provides developers with a comprehensive overview of the fundamental concepts of Medical Intelligent Systems, and presents all new methods and techniques through realistic and modern examples using the Python programming language. Accompanying material of the training programme: http://www.panagiotissymeonidis.com/aihealth/index.php

Learning outcomes:

Upon completion of the programme the trainee will be able to:
1. Understand and implement Intelligent Systems for Alerting Doctors for Patients' Health using for Electronic Health Records
2. Understand and implement Prompt Engineering (chatGPT) for GenAI using Electronic Health Records and Text data
3. Understand and implement Machine Learning algorithms for Predicting Drug Combinations for Patients,
4. Understand and implement classic algorithms (linear regression, etc.) for Predicting Drug-Drug unwanted side Effects
5. Understand and implement Artificial Intelligence algorithms (Reinforcement Learning) for Predicting Optimal Dose of a Drug,
6. Understand and implement Deep Leaning (CNNs) for image recommendation
7. Manage and Secure Big Medical data with Central and/or Local Differential Privacy
8. Ethics in Big Medical data (e.g. MIMIC data set)

Implementation Methology:

[Online Distance Learning]
Blended Learning (Modern Distance Learning)

Evaluation Methology:

The evaluation will be with the delivery of the python code for one programming exercise

Minimum requirements of the candidates:

High School Degree

Maximun number of candidates:

100

Program Description

Subject & purpose of the training program:

Learning outcomes:

[Online Distance Learning]
Blended Learning (Modern Distance Learning)

Evaluation Methology:

The evaluation will be with the delivery of the python code for one programming exercise

Minimum requirements of the candidates:

High School Degree

Maximun number of candidates:

100

Program outline

Teaching Unit 1: Health Data Management

Description	a. Introduction to the fundamentals of Data Science. b. Health data types (vital signs, ΜRI, ECG, CT scans/images, waveforms/signals, etc.) c. Electronic Health Record (EHR) (Demographics, Diagnoses, Medical Interventions, Prescriptions, Medications/Drugs, Side effects, Lab/Microbiology Tests, Progress notes) d. MIMIC Data set i. Description ii. Data Types iii. Data Preprocessing (missing values, etc.) iv. Data base schema e. Programming Exercise with python for Analysis and visualization of medical data using libraries such as Pandas, NumPy, Matplotlib, etc
Start date	07/10/2024
End date	07/10/2024
Hours	10.00
Academic Responsibility	PANAGIOTIS SYMEONIDIS
Sessions	1 Health Data Management

Teaching Unit 2: Privacy Protection of Sensitive Medical Data

Description	a. Anonymization Techniques b. Date shifting c. Format Conversion d. Generalization of data interactions e. K-Anonymity f. Adding Noise in Data e. Loss of Information g. Differential Privacy (Central and Local DP) for sensitive health data.
Start date	08/10/2024
End date	08/10/2024
Hours	10.00
Academic Responsibility	PANAGIOTIS SYMEONIDIS
Sessions	1 Privacy Protection of Sensitive Medical Data

Teaching Unit 3: Classification algorithms for Static Data and Time Series

Description	a. Linear Regression b. Logistic Regression c. Decision Tree d. Random Forest e. Naïve Bayes Classifier f. Feature Selection i. Gini index ii. Entropy iii. x^2 statistic .
Start date	09/10/2024
End date	09/10/2024
Hours	10.00
Academic Responsibility	PANAGIOTIS SYMEONIDIS
Sessions	1 Classification algorithms for Static Data and Time Series.

Teaching Unit 4: Clustering of Medical Data and Genetic Algorithms.

Description	a. Clustering Algorithms i. K-means ii. Hierarchical Clustering iii. DBSCAN iv. BFR algorithm (Bradley, Fayyad, and Reina) b. Genetic Algorithms i. Genetic Operations ii. The Architecture of a Genetic Algorithm iii. The Genetic Algorithm in pseudocode form iv. Step-by-Step Execution of the Genetic Algorithm c. Programming Exercise with python for clustering with k-means algorithm the patients into groups of diabetes disease or not
Start date	10/10/2024
End date	10/10/2024
Hours	10.00
Academic Responsibility	PANAGIOTIS SYMEONIDIS
Sessions	1 Clustering of Medical Data and Genetic Algorithms.

Teaching Unit 5: Matrix Factorization for Health Data

Description	a. PCA decomposition b. Singular Value Decomposition c. UV-decomposition d. CUR-decomposition e. Tensor Decomposition f. Programming Exercise with python to apply PCA to perform dimensionality reduction and bring into surface latent associations between drugs and unwanted side effects.
Start date	11/10/2024
End date	11/10/2024
Hours	10.00
Academic Responsibility	PANAGIOTIS SYMEONIDIS
Sessions	1 Matrix Factorization for Health Data

Teaching Unit 6: Machine learning algorithms for Image Processing and other complex medical signals

Description	a. Convolution Neural Networks I. CNN architecture II. Pooling Layers III. ResNet b. Applications over different medical signals i. Electrocardiogram ECG ii. Magnetic Resonance Imaging (MRI) iii. Computed Tomography Scan c. Support Vector machines I. Finding Optimal Separators with Gradient Descent II. Hard and Soft SVMs d. Programming Exercise with python to apply image processing over CT scans and classify patients into COVID and non-COVID disease.
Start date	12/10/2024
End date	12/10/2024
Hours	10.00
Academic Responsibility	PANAGIOTIS SYMEONIDIS
Sessions	1 Machine learning algorithms for Image Processing and other complex medical signals

Teaching Unit 7: Reinforcement Learning, and Deep Neural Nets

Description	a. Markov Chain b. Q-learning Algorithm c. Deep Reinforcement Learning i. Deep Q-Network ii. Double Deep Q-Network/A2C iii. Optimal Insulin Dose Prediction for Diabetes Patients c. Multi-layer perceptron i. Activation Functions ii. Loss Functions iii. Regression Loss iv. Classification Loss d. Recurrent Neural Networks i. Vanishing and Exploding Gradients ii. Long Sort Term Memory LSTM e. Programming Exercise with Python to predict the optimal insulin dose for patient with diabetes using tabular Q-learning algorithm.
Start date	13/10/2024
End date	13/10/2024
Hours	10.00
Academic Responsibility	PANAGIOTIS SYMEONIDIS
Sessions	1 Reinforcement Learning, and Deep Neural Nets

Teaching Unit 8: 8. Graph algorithms

Description	a. Local based similarity algorithms (Shortest Path i. Common Neighbors, Jaccard similarity index, Salton similarity index, Adamic & Adar similarity index, Preferential Attachement. b. Global-based algorithms i. Random Walk with Restart ii. SimRank iii. PathSim c. Graph Convolution networks d. Graph Embeddings e. Programming Exercise with Python to predict drug combinations to patients and explain the predictions using graph data (patient nodes, treatment node, drug node).
Start date	14/10/2024
End date	14/10/2024
Hours	10.00
Academic Responsibility	PANAGIOTIS SYMEONIDIS
Sessions	1 Medical Systems based on graph algorithms

Teaching Unit 9: Evaluation Metrics of Prediction Models in Health

Description	a. Confusion Matrix b. Precision, Recall, and NDCG c. Precision-Recall and ROC curves d. MAE, RMSE for Optimal Drug Dose Prediction e. Beyond Accuracy Metrics i. Explainability in Health f. Programming Exercise with Python to recommend drug combinations to patients and measure quantitatively the predictions along with the unwanted side effects they have.
Start date	15/10/2024
End date	15/10/2024
Hours	10.00
Academic Responsibility	PANAGIOTIS SYMEONIDIS
Sessions	1 New Trends and Apps in Health Care

Teaching Unit 10: Large Language Models, ChatGPT and Prompt Engineering

Description	i. Smart Medical Watches ii. Smart Medical Devices. b. Large Language Models, and Prompt Engineering i. Application of AI algorithms over medical progress notes. ii. Vector space model and TF-IDF iii ChatGPT and RAG c. Programming Exercise with Python to send the electronic health record of a patient to ChatGPT API using a prompt request (prompt engineering) and get a possible therapeutic prediction based on the given vital signs.
Start date	16/10/2024
End date	16/10/2024
Hours	10.00
Academic Responsibility	PANAGIOTIS SYMEONIDIS
Sessions	1 New Trends and Apps in Health Care

Contact:

Contact Details: PANAGIOTIS SYMEONIDIS [psymeon(at)aegean.gr - Phone 00306973882408]

WEBSITE: https://www.linkedin.com/company/92841918/admin/feed/posts/

Discounts - Terms of use:

Discount Policy
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Early registration discount: 10%

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