🚀AI Deep Dive: 🤖Algorithm Essentials, 🏥ML in ER, 💡LinkedIn Magic with ChatGPT, and More!
Educational Supplement to The 'Med AI' Capsule Newsletter 🤖🩺🚀
Dear Med AI Enthusiast,
Welcome to this exclusive weekly feature, a carefully curated blend of valuable educational insights, complimenting your journey through The ‘Med AI’ Capsule - your essential weekly news digest 📰 on the dynamic intersection of artificial intelligence and other emerging technologies shaping the future of medicine. ⚕️
In today’s supplement:
1 Concept Made Easy
1 Knowledge Resource
1 Research Deep Dive
Industry Showcase - 3 Innovative Startups/ Companies
GenAI Corner
Test Your Knowledge
Reading Time: 5-7 minutes
Concept Made Easy 👨🏫
ALGORITHM 👨💻
Algorithms are sets of rules or instructions given to machines, like AI and neural networks, to help them learn and make decisions on their own. 🤖
Think of algorithms as step-by-step guides that tell machines what to do in different situations, allowing them to learn and perform tasks intelligently. 🪜
There are different types of algorithms, such as classification, clustering, recommendation, and regression. 🍀
1. Classification:
Classification algorithms help machines categorize or group data into different classes or categories based on specific characteristics or features. 📊
For example, a classification algorithm can be used to identify whether an email is spam or not based on its content. 📩
It looks at the content and applies certain rules to determine if it falls into the "spam" or "not spam" category. 📥📤
2. Clustering:
Clustering algorithms help machines group similar data points together based on their similarities or patterns.
It helps to identify clusters or groups within a larger dataset.
For example, a clustering algorithm can be used to group customers based on their purchasing behavior. 🛍️
It looks for patterns in the data and creates groups of customers who have similar preferences or buying habits. 🛒
3. Recommendation:
Recommendation algorithms are used to suggest or recommend items, products, or content to users based on their preferences or past behavior. 🎦
These algorithms analyze user data and patterns to make personalized recommendations. 👨🦱
For example, a recommendation algorithm can suggest movies or music based on what you have previously liked or watched. 🎶
It looks at your past behavior and finds patterns to suggest items you might enjoy. 🥳
4. Regression:
Regression algorithms are used for predicting numerical values or estimating relationships between variables. 📈📉
They help to find a mathematical equation that best fits a given set of data.
Regression algorithms are commonly used in fields like finance and economics for forecasting or predicting future values. 🔢
For example, a regression algorithm can predict the future price of a stock based on historical data and market trends. 💹
Knowledge Resource 📚
ROC and AUC are two common metrics for evaluating the performance of binary classifiers, e.g. logistic regression.
ROC stands for receiver operating characteristic, and it plots the true positive rate (TPR) against the false positive rate (FPR) at different threshold values.
AUC stands for area under the curve, and it measures the overall quality of the classifier by calculating the area under the ROC curve. A higher AUC value indicates a better classifier that can distinguish between positive and negative classes.
ROC (Receiver Operator Characteristic) graphs and AUC (the area under the curve), are useful for consolidating the information from a ton of confusion matrices into a single, easy-to-interpret graph.
This video walks you through how to create and interpret ROC graphs step-by-step.
It then shows how the AUC can be used to compare classification methods, and, lastly, it talks about what to do when your data isn't as warm and fuzzy as it should be.
Key takeaways:
1. ROC and AUC can be used to evaluate the effectiveness of logistic regression models (binary classifiers).
2. A classification threshold can be set to determine if a sample is classified as positive or negative.
3. ROC graphs provide a simple way to summarize all of the information.
4. Lowering the classification threshold may result in more false positives.
5. Raising the classification threshold may result in fewer false positives but also fewer correctly classified samples.
Research Deep Dive 🔬
Park SW, Yeo NY, Kang S, Ha T, Kim TH, Lee D, Kim D, Choi S, Kim M, Lee D, Kim D, Kim WJ, Lee SJ, Heo YJ, Moon DH, Han SS, Kim Y, Choi HS, Oh DK, Lee SY, Park M, Lim CM, Heo J; Korean Sepsis Alliance (KSA) Investigators. Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study. J Korean Med Sci. 2024 Feb 5;39(5):e53. doi: 10.3346/jkms.2024.39.e53. PMID: 38317451; PMCID: PMC10843974.
The paper presents the construction of machine learning models to predict the mortality of patients with sepsis in hospital emergency departments, utilizing nationwide data from an ongoing multicenter cohort study.
Key Findings:
Machine learning models, including logistic regression, support vector machine, random forest, XGBoost, light gradient boosting machine, and CatBoost, were employed to predict mortality in patients with sepsis.
Clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components to improve prediction accuracy.
CatBoost demonstrated the highest area under the curve (AUC) of 0.800 for mortality prediction.
Shapley's additive explanations (SHAP) were used to interpret the results and provide insights into the importance of different variables in predicting mortality.
Conclusion: Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.
Limitations:
The study only used data that was already being collected and didn't include things like bio-signals and images. In the future, they need to mix different types of data together.
This study just looked at one point in time. They need to do studies over time to better understand how to treat people.
They tried to predict how likely someone with sepsis is to die early using only information from when they first came to the emergency room. In the future, they should use more types of information to make better predictions.
The way they figured out which information was most important has some problems. They plan to use a better method in future studies to get more accurate results.
Industry Showcase 👨🏭
Neodocs - has developed an innovative approach to conducting lab tests using a smartphone. Their platform allows users to perform instant lab tests on their smartphones, catering to health optimizers and athletes who wish to improve key nutritional markers for better performance. The process involves collecting a sample, taking a picture of it, and then receiving instant results through the app.
Antidote - a digital platform that connects patients to clinical trials, making medical research more accessible and transparent. It offers tools like Antidote Match for patients to find suitable trials and Antidote Bridge for researchers to share study information.
AIdoc - offers AI-powered solutions in healthcare, focusing on radiology. Their technology integrates with existing medical systems to assist in image analysis, facilitating rapid detection of abnormalities and supporting efficient decision-making in patient care.
GenAI Corner 🤖
Tutorial: Using ChatGPT to Craft an Engaging LinkedIn Post
Step 1: Define the Purpose of Your Post
Start by identifying the main goal of your LinkedIn post. Are you sharing medical insights, discussing industry trends, promoting a health-related event, or providing health tips? Clarifying your objective will guide the content and tone of your post.
Step 2: Gather Relevant Information
Collect the necessary information related to your topic. This could include recent research findings, health statistics, professional experiences, or updates on medical technology. Ensure that the data is accurate and from reliable sources.
Step 3: Decide on the Structure and Style
Determine the structure of your post. Will it be a brief update, a detailed article, or a visual post with infographics? Consider your audience on LinkedIn and choose a style that is professional, engaging, and accessible.
Step 4: Create a Detailed Prompt for ChatGPT
Compose a comprehensive prompt for ChatGPT. Include your post's objective, key points, target audience, and any specific style or formatting preferences. For example:
"Create an informative and professional LinkedIn post for a cardiologist. The post should highlight the latest advancements in heart disease treatment and their implications for patient care. Aim for a concise, engaging format with a call-to-action for colleagues to share their experiences or insights."
Step 5: Generate the LinkedIn Post Draft
Use ChatGPT to draft your LinkedIn post. Ensure that it aligns with your initial objectives, conveys the information clearly, and is appropriate for a professional audience.
Step 6: Personalize and Refine the Content
Review the generated post, adding personal insights, professional experiences, or specific anecdotes to make it more engaging and authentic. Ensure that the tone remains professional and the content provides value to your LinkedIn network.
Step 7: Incorporate Visual Elements (Optional)
If applicable, add visual elements such as images, infographics, or videos to enhance the post's appeal. Visual aids can make complex medical information more digestible and engaging.
Step 8: Add a Call-to-Action
Conclude your post with a call-to-action. Encourage your audience to comment, share their thoughts, ask questions, or share the post with their network. This fosters engagement and community building.
Step 9: Review Compliance and Guidelines
Before posting, ensure that your content complies with HIPAA or other relevant confidentiality guidelines, and does not share sensitive patient information.
Test Your Knowledge 🧩
Your feedback is crucial to me, as it helps me understand your interests and improve my offerings. I would appreciate it if you could take a few minutes to share your thoughts about what you've enjoyed and what you think I could do better - https://forms.gle/fUJZhjUBJe9ojySg6
Stay tuned for our upcoming editions as we explore the latest breakthroughs and dive deep into the transformative power of artificial intelligence and emerging technologies, shaping a healthier future. 🚀
Warm regards,
