Medicine, Software and everything in between.

Discovering the perfect resource to quickly familiarize oneself with the ever-evolving world of AI can be a challenge, especially if you haven't been diligently tracking the field...

Unraveling AI Lingo: Dancing Through the Dizzying World of Artificial Intelligence Jargon! (Part 2)

![Robot teaching students](/images/ai-robot-teaching-students.jpg "Robot teaching students")
Robot teaching students (created using Stable Diffusion)

Discovering the perfect resource to quickly familiarize oneself with the ever-evolving world of AI can be a challenge, especially if you haven't been diligently tracking the field. Fear not, for I have stumbled upon a comprehensive glossary of terms that will effortlessly catapult you into the realm of AI expertise in no time!

## Large Language Model (LLM)
An LLM represents a category of neural networks that acquire capabilities such as creating written content, engaging in dialogues, and crafting computer code by examining extensive textual data from the internet. The fundamental purpose is to anticipate the subsequent word in a series; however, these models have astonished specialists by mastering novel skills.

## Generative AI
Generative AI refers to technology that produces various types of content, such as text, images, videos, and computer code, by recognizing patterns in extensive training data and subsequently generating unique, original material with comparable attributes. Instances of such technology encompass ChatGPT for textual content and <a href='https://openai.com/product/dall-e-2' target='__blank'>DALL-E</a> and <a href='https://www.midjourney.com/home/?callbackUrl=%2Fapp%2F' target='__blank'>Midjourney</a> for visual elements.

## Transformer Model
The Transformer model constitutes a neural network design that excels in language comprehension by simultaneously examining an entire sentence rather than processing words individually. Through a method known as self-attention, the model concentrates on specific words that hold significance in grasping the essence of the sentence.

## Parameters
Parameters represent numeric values that outline the structure and conduct of a large language model, acting as hints that aid in predicting subsequent words. Contemporary systems, such as <a href='https://openai.com/research/gpt-4' target='__blank'>GPT-4</a>, are believed to possess hundreds of billions of these parameters.

## Reinforcement Learning
Reinforcement learning is a method that instructs an AI model to discover optimal outcomes through a process of experimentation, obtaining rewards or penalties from an algorithm contingent on its performance. This approach can be improved with the aid of human feedback on the model's effectiveness.

## Hallucination
Hallucination is a widely recognized occurrence in large language models, wherein the system delivers a response that is factually inaccurate, unrelated, or illogical, due to constraints in its training data and structural design.

## Bias
Bias represents a form of mistake that may emerge in a large language model when its output is influenced by the training data. As an illustration, a model might link particular characteristics or occupations with a specific race or gender, resulting in erroneous forecasts and potentially offensive replies.

## Anthropomorphism
Anthropomorphism refers to the inclination of individuals to ascribe human-like traits or features to an AI chatbot. For instance, one might deduce that it possesses kindness or malevolence based on its responses, despite its inability to experience emotions, or one might perceive the AI as conscious due to its proficiency in imitating human speech.

## Natural Language Processing
Natural language processing encompasses methods employed by large language models to comprehend and produce human language, involving tasks such as text categorization and sentiment assessment. These approaches typically utilize a mix of machine learning algorithms, statistical models, and linguistic principles.

## Emergent behavior
Emergent behavior refers to unanticipated or unintended capabilities in a large language model, facilitated by the patterns and regulations the model acquires from its training data. Instances of this phenomenon include LLMs trained on programming and coding resources being able to generate new code, as well as creative proficiencies like crafting poetry, music, and fictional narratives.

## Alignment
Efforts by AI scientists and ethicists focus on making certain that artificial intelligences operate in harmony with the principles and objectives established by their creators.

## Multimodal systems
Artificial intelligences akin to ChatGPT with the capability to handle various non-textual inputs and outputs, such as images, videos, audio, and more.

## Artificial General Intelligence (AGI)
AGI represents a form of artificial intelligence that equates to human intelligence, possessing the capacity to perform any task that the human mind can achieve.

 

---


## References
- _Artifical Intelligence Glossary_ - https://www.nytimes.com/article/ai-artificial-intelligence-glossary.html

Artificial intelligence (AI) may seem like sorcery, but it's not. Grasping some essential concepts and terminology can enhance our comprehension and utilization of AI...

Unraveling AI Lingo: No-Blush Guide to the Terms You've Been Secretly Curious About (Part 1)

![Robot Teacher](/images/robot-teacher.jpg "Robot teacher")
Robot teaching students (created using Stable Diffusion)

Artificial intelligence (AI) may seem like sorcery, but it's not. Grasping some essential concepts and terminology can enhance our comprehension and utilization of AI in our day to day conversations.

![AI Hierarchy Diagram](/images/ai-hierarchy-diagram.jpg "Terminology hierarchy")
A simplified AI hierarchy diagram (created using Pixelmator Pro)

## What is Artificial Intelligence?

![Doctor analyzing an x-ray](/images/female-doctor-analyzing-xray-with-ai.jpg "Doctor analyzing x-ray wit ai")
Radiologist using AI to help review x-ray (created using Stable Diffusion)

Artificial intelligence (AI) refers to the replication of human cognitive abilities in computer systems. AI allows machines to analyze situations and execute actions or generate predictions that optimize the chances of reaching a specific goal.
For instance, AI can be applied in medical diagnosis, such as the detection of diseases through medical imaging. A computer is programmed to identify patterns in images, like X-rays or MRIs, with the aim of accurately diagnosing a patient's condition.
The AI _algorithm_ enables the computer to discern subtle anomalies in the images, determine if the irregularities are indicative of a specific illness, and then provide a diagnostic suggestion to the medical professional for further evaluation and treatment.
While the terms AI and machine learning are frequently used interchangeably, they are not entirely synonymous. Machine learning is the driving force behind many AI advancements, but AI can exist without machine learning.

## What is an algorithm?

An algorithm is a series of systematic instructions aimed at accomplishing a specific objective, such as diagnosing a medical condition from a patient's symptoms or predicting the effectiveness of a treatment. Algorithms form the basis of AI (and computers in general), and can be utilized to resolve issues, discern patterns, and automate tasks in the medical field. At the moment, the most prevalent techniques and algorithms for implementing AI in medicine involve machine learning approaches.

## What is Machine Learning (ML)?

![Machine learning](/images/machine-learning.jpg "Robot learning in front of computer")
Illustration of a machine [robot] learning (created using Stable Diffusion)

A machine learning model comprises a collection of data and methods that facilitate deriving outcomes from given input data.
For instance, a diagnostic model in medicine might utilize data from patient symptoms and lab results to predict the likelihood of a specific disease. The model serves as a blueprint of the procedure through which input data is processed to generate output.
Taking a pharmaceutical production model as an example, raw materials would be the input, and the final medication would be the output. The model would encompass the necessary steps to be executed on the inputs (measure, mix, compress, coat) along with parameters (explanation to follow) for each step (e.g., compress at 1000 psi for 5 minutes).
The primary distinction between machine learning and other AI approaches lies in the fact that machine learning models are not directly programmed by humans. Instead, humans establish the process, provide training data (explanation to follow), and train the model (definition to follow) so that it can accomplish its intended task.
When a model, such as Stable Diffusion or GPT3, is released, it represents the product resulting from the application of machine learning algorithms on training data. The model embodies the rules, values, and frameworks needed to process inputs and generate outputs.

## What is training data?

Training data consists of numerous pairs of elements. Each training data element contains two components: 
- Input
- Anticipated output

For instance, a collection of training data for medical images could comprise numerous images, each labeled with the corresponding diagnosis. To develop a model capable of identifying lung cancer in chest x-ray images, we would assemble a set of training data containing images of both cancerous and non-cancerous lungs. The model would then be trained (I explain this below) using this training data.

## What is training in machine learning?

Training refers to the procedure of inputting labeled data into an algorithm, obtaining the output, and then guiding the model on how well its output aligns with the expected or desired outcome. This process is repeated continuously, with the computer performing calculations to determine how to modify internal settings or values. These settings or internal values are known as weights or parameters (explained below).
The computer adjusts parameters in an attempt to get as close as possible to a specific goal.

![Machines training](/images/robot-training.jpg "Robot training another robot")
Illustration of one robot training another robot (created using Stable Diffusion)

Suppose our objective is to accurately identify 99% of pneumonia cases in chest x-ray images. We configure our pneumonia detection model and then run all our training data through it. The model predicts which images represent pneumonia cases and which do not. We employ a mathematical function to evaluate the model's performance. A straightforward example is the number of correctly identified pneumonia cases divided by the total number of pneumonia images. Assume it correctly identified 50% of the pneumonia cases.
The model then performs numerous calculations to determine how to tweak the parameters to enhance that result and trains again, processing all the training data through the model, attempting to identify pneumonia cases, and using the same mathematical function to assess its performance. Let's say it now correctly identifies 60% of the pneumonia cases. It has improved! Most importantly, no human was explicitly programming it to get better. The machine made adjustments and improved without direct human intervention.
The machine learns to fine-tune the parameters of its model to become increasingly proficient at detecting pneumonia cases.
When the model achieves 99% accuracy, it is considered trained, meaning it consists of various parameters that are integrated into numerous algorithms. We can input our data (an X-ray image) into these algorithms, and the output will indicate whether the image shows pneumonia, with a 99% accuracy rate.

## What is a parameter?

Parameters, also known as weights and biases, represent the internal values within a machine learning model or neural network (this is explained below) that can be adjusted to modify the model's behavior.
Think of a model as a medical device. Parameters are akin to the device's controls, which are fine-tuned to accomplish a particular function, such as accurately monitoring vital signs. Parameters are not established by the model's developer; instead, these values are automatically determined or learned through the training procedure.

## What is a neural network?

Many distinct machine learning training techniques exist, with Neural Networks being a prevalent choice. Neural networks derive their name and architecture from the human brain, imitating the manner in which biological neurons communicate.
A neural network consists of several layers, including an input layer, one or more hidden layers, and an output layer. Each artificial neuron, or node, connects to every neuron in the subsequent layer and has an associated weight/parameter and threshold/bias.

![Neural network](/images/neural-network.jpg "Neural network")
An artistic illustration of a neural network (created using Stable Diffusion)

The parameters stored in the neurons are altered during the training process to improve performance on a specific task.
If the output of a single neuron exceeds a predetermined value, the neuron is activated, transmitting data to the following layer in the network. If not, no data is forwarded to the next layer. The neurons in the subsequent layer compute the weighted sum of input values and provide it as input to a mathematical function known as the activation function. If the resulting number surpasses a certain value, the data is passed on once more.
Incorporating layers of neurons allows neural networks to incorporate more parameters into the model, which permits the model to fit more intricate functions and accomplish more interesting tasks.

## What is deep learning?

Deep learning refers to artificial intelligence methods which rely on so-called 'deep' neural nets -- multi-layered systems inspired by how our brains process information. 
These nets can detect complex underlying patterns in the massive amounts of data they consume during training to make precise forecasts or automate various functions, making them an essential component of contemporary AI innovations.

## What is a generative model?

A generative model refers to a computational framework that creates novel data by learning from a set of pre-existing training data. This model is frequently employed to produce new images or videos using a collection of training data.
Various generative models exist, such as Generative Adversarial Networks (GAN), Variational Autoencoders (VAE), Flow-based models, and Diffusion models.
Recently, Diffusion models have gained significant attention and popularity. To illustrate this concept, consider a medical example: using a diffusion model, researchers can generate realistic 3D models of human organs based on existing medical images. 

![Human heart](/images/human-heart.jpg "Human heart")
Realistic 3D model of a human heart (created using Stable Diffusion)

This enables better visualization and understanding of complex anatomical structures, which can aid in surgical planning and medical education.

## What is a diffusion model?

Diffusion Models represent a specific category within generative models. The functioning of Diffusion Models involves corrupting the training data by introducing Gaussian noise, followed by learning to restore the original data by reversing the noise application process.
Once trained, Diffusion Models can generate novel data by inputting random noise and a fresh description of the desired outcome into the model.

![Diffusion model illustration](/images/diffusion-model-illustration.jpg "Diffusion model illustration")
Explaining how diffusion models work (Source: https://www.leewayhertz.com/how-to-train-a-diffusion-model/)

An example of diffusion models are text-to-image models. In a medical context, a text-to-image diffusion model could be utilized to generate accurate images of anatomical structures based on textual descriptions, such as "human heart with an enlarged left ventricle." This can assist medical professionals in visualizing various conditions and enhance their understanding for diagnosis and treatment purposes.

If you want to gain a greater understanding of diffusion models (with lots of math) checkout this link: <a href='https://medium.com/@steinsfu/diffusion-model-clearly-explained-cd331bd41166' target='__blank'>diffusion model clearly explained</a>

## What is GPT3?

Reviewing our understanding of GPT3:
- GPT3 is an AI, imitating human behavior to achieve specific objectives.
- GPT3 is a generative model, producing new text based on its training data.
- GPT3 employs a deep learning model, utilizing multilayered neural networks.
- GPT3 can generate text for various contexts, even those it hasn't been trained for, enabling it to create content for specific purposes without needing extensive labeled training data or parameter adjustments for a particular task.

GPT3's impressive capabilities:
- Instead of being a single-task model, GPT3 is trained for a vast range of tasks, allowing it to generate text for multiple scenarios without retraining.
- Researchers can use GPT3 for different purposes, such as writing blog posts or poems, without needing separate models for each task.

## What is Stable Diffusion?

The description given by Wikipedia is:

‘Stable Diffusion is a deep learning, text-to-image model released in 2022. It is primarily used to generate detailed images conditioned on text descriptions, though it can also be applied to other tasks such as inpainting, outpainting, and generating image-to-image translations guided by a text prompt.’


- **Stable Diffusion** is an AI system that simulates human intelligence to accomplish tasks, specifically converting text prompts into visually appealing images.

- Instead of explicit programming, **Stable Diffusion** employs *machine learning techniques*, allowing the computer to learn from vast amounts of training data and develop its own programming.

- The AI utilizes a *deep neural network* (with more than two layers), consisting of interconnected nodes that perform mathematical operations on inputs and generate outputs for subsequent layers of nodes.

- As a *generative model*, **Stable Diffusion** incorporates data sets and procedures to produce new outputs based on given inputs. It uses algorithms and data to process text prompts and generate new images.

- **Stable Diffusion** functions as a *diffusion model* by adding and subtracting noise to create images.

Now that you're armed with an understanding of these terms go wow your friends 😀.

 

---


### References
- _A crash course in AI terms_ - https://mythicalai.substack.com/p/a-crash-course-in-ai-terms-machine
- _Diffusion model clearly explained_ - https://medium.com/@steinsfu/diffusion-model-clearly-explained-cd331bd41166

Commotio cordis is a rare but serious condition that occurs when the heart is unexpectedly stunned or jarred as a result of a blunt impact to the chest. It is most common in young athletes participating in contact sports....

Heart Stopped in a Heartbeat: The Dangers of Commotio Cordis

![AI Cover Image](/images/commotio-cordis.jpg "AI cover image")
(Source: New England Journal Of Medicine (NEJM))

### What is Commotio Cordis?
Commotio cordis is a rare but serious condition that occurs when the heart is unexpectedly stunned or jarred as a result of a blunt impact to the chest. It is most common in young athletes participating in contact sports such as American football, hockey, and lacrosse, but it can affect anyone.

The impact does not have to be very hard to cause commotio cordis, and it can occur even if there is no visible injury to the chest. It is caused by a sudden, blunt impact to the chest that occurs during a narrow window of vulnerability in the heart's electrical cycle, which can cause the heart to stop beating effectively and lead to cardiac arrest.

### How is this condition managed?
The management of commotio cordis involves providing cardiopulmonary resuscitation (CPR) and defibrillation as soon as possible to restore normal heart function. It is important to act quickly, as survival rates decrease significantly with each passing minute. Other treatment options may include medications to support blood flow to the heart and body, and supportive care such as oxygen therapy and monitoring of vital signs.

### Prevention
Prevention of commotio cordis may involve using protective equipment such as chest protectors or padding in contact sports, and ensuring that individuals who are at risk of commotio cordis are trained in CPR and have access to defibrillation equipment. It is also important for coaches, athletes, and other individuals to be aware of the signs and symptoms of commotio cordis and to seek medical attention immediately if they suspect that someone may be experiencing this condition.

In conclusion, commotio cordis is a rare but serious condition that requires immediate medical attention. By being aware of the risks and knowing how to respond, we can work to prevent and manage this condition to ensure the safety and well-being of all individuals.

### Further reading
Here is a detailed blog article written by a colleague and friend Collins A. Kwarteng MD, FACC, titled <a href="https://telegra.ph/The-near-tragedy-during-Monday-Night-Football---The-Damar-Hamlin-Incident-Was-It-Commotio-Cordis-01-04" target="_blank" title="The near tragedy during Monday Night Football - The Damar Hamlin Incident. Was It Commotio Cordis?">"The near tragedy during Monday Night Football - The Damar Hamlin Incident. Was It Commotio Cordis?"</a>.

GPT, or Generative Pre-trained Transformer, is a state-of-the-art language generation model developed by OpenAI. It has been making waves in the natural language processing (NLP) community since its release in 2018....

Why you should be excited about GPT: The ultimate AI language model

![AI Cover Image](/images/ai-cover.jpg "AI cover image")
(Source: btlaw.com)

GPT, or Generative Pre-trained Transformer, is a state-of-the-art language generation model developed by <a href="https://openai.com" target="_blank" title="Open AI">Open AI</a>. It has been making waves in the natural language processing (NLP) community since its release in 2018 and has been continually improved upon, with the latest version being GPT-6.

## So, what exactly is GPT and why should you be excited about it?

At a high level, GPT is a machine learning model that is trained to generate human-like text. It does this by predicting the next word in a sequence based on the context provided by the previous words. For example, if the model is given the prompt "The cat sat on the" it might generate the next word as "mat".

What sets GPT apart from previous language generation models is its ability to generate high-quality text that is coherent and reads like it was written by a human. This is due to its use of the transformer architecture, which was first introduced in the paper <a href="https://doi.org/10.48550/arXiv.1706.03762" target="_blank" title="Attention Is All You Need">"Attention is All You Need"</a> and has since become the foundation of many state-of-the-art NLP models.

The transformer architecture allows GPT to process input and output sequences of variable length and to attend to different parts of the input sequence at different times, allowing it to generate more contextually accurate text. In addition, GPT is pre-trained on a massive dataset, which allows it to capture the nuances and subtleties of human language.

## So why should you be excited about GPT? There are several reasons.

First and foremost, GPT has the potential to revolutionize the field of NLP and the way we interact with computers. Imagine being able to have a conversation with your computer as if it were a human, or being able to generate entire articles or even books with a few prompts. This could have a huge impact on tasks such as translation, summarization, and content creation.

In addition, GPT has the potential to improve the accuracy and efficiency of many NLP tasks. For example, it could be used to automatically generate responses to customer service inquiries, freeing up human employees to handle more complex tasks. It could also be used to improve machine translation by generating high-quality translations that are closer to how a human would translate the text.

One of the most exciting potential applications of GPT is in the realm of artificial intelligence (AI) assistants. With GPT's ability to generate human-like text and carry on a conversation, it could be used to build smarter and more natural AI assistants that are able to understand and respond to more complex queries and requests.

Overall, GPT is an exciting development in the field of NLP and has the potential to revolutionize the way we interact with computers and carry out a wide range of tasks. It will be interesting to see how it continues to evolve and what new applications it will enable in the future.

As a software engineer, I was always interested in finding new ways to solve problems and improve my skills. When I came across R, I was immediately drawn to the language for several reasons. First and foremost, R is a powerful tool...

Discover the power of R: Why I decided to learn this versatile programming language and why you should too!

![R Programming Language](/images/r-header.jpg "R Programming Language")
(Source: Thinkstock)

As a software engineer, I was always interested in finding new ways to solve problems and improve my skills. When I came across R, I was immediately drawn to the language for several reasons.

First and foremost, R is a powerful tool for data analysis and visualization. With a wide range of packages and libraries available, it's easy to work with data in R and create visually appealing charts and graphs. I was particularly interested in using R for data analysis because it allows me to easily explore and manipulate data, which is a crucial part of my job as a engineer.

In addition to its capabilities in data analysis, I also learned that R is a popular language for statistical analysis. With a number of packages available for tasks such as regression, ANOVA, and hypothesis testing, R is a great choice for anyone interested in using statistics in their work.

Another reason I decided to learn R is because it is widely used in academia and industry. This means that there are many opportunities for me to use R in my work and potentially collaborate with others who are also familiar with the language.

Finally, I was attracted to R because it is often used for reproducible research. This means that I can easily share my data, code, and results with others, which is especially useful if I am working on research projects or need to collaborate with others on data analysis tasks.

Overall, I decided to learn R because it is a powerful and widely used language for data analysis, statistical analysis, and reproducible research. If you are interested in any of these areas and want to improve your skills as a software engineer, I highly recommend giving R a try.

### Why you should learn R programming:

There are many good reasons to learn R, whether you are a software engineer, data scientist, or simply interested in working with data. Here are a few reasons why you should consider learning R:

1. Data analysis and visualization: R is a powerful language for working with data, with a wide range of packages and libraries available for tasks such as importing, cleaning, and visualizing data. If you are interested in data analysis and visualization, R is a great choice.
2. Statistical analysis: R is also a popular language for statistical analysis, with a number of packages available for tasks such as regression, ANOVA, and hypothesis testing. If you are interested in using statistics in your work, learning R can be a great way to do so.
3. Reproducible research: R is often used for reproducible research, which means that you can easily share your data, code, and results with others. This can be particularly useful if you are working on research projects or need to collaborate with others on data analysis tasks.
4. Widely used in academia and industry: R is widely used in academia and industry, so learning the language can open up new opportunities for you in terms of employment and collaboration.

Overall, if you are interested in data analysis, statistical analysis, or reproducible research, or simply want to improve your skills as a software engineer, learning R can be a valuable and rewarding experience.

During this year's Cognizant Softvision Programmers' Week conference, I heard a very inspiring presentation from an Argentine software engineer about a project he had started to assist in calculating the dosage of insulin for his son, who had recently been diagnosed with Type 1 Diabetes mellitus.....

Contributing to the Pancreas Digital Open Source Insulin Bolus Calculator

During this year's <a href="https://www.cognizantsoftvision.com/news-posts/cognizant-softvision-kicks-off-programmers-week-2022/" target="_blank" title="CSV Programmers' Week">Cognizant Softvision Programmers' Week conference</a>, I listened to a very inspiring presentation from an Argentine software engineer about a project he had started to assist in calculating the dosage of insulin for his son, who had recently been diagnosed with Type 1 Diabetes mellitus (T1DM).

### About the Pancreas Digital 
<a href="https://www.youtube.com/PancreasDigital" target="_blank" title="Pancreas Digital YT Channel">Pancreas Digital</a> is a non-profit organization based in Buenos Aires, Argentina, that focuses on diabetes technology, manages an open-source insulin bolus calculator, and plans online workshops hosted on YouTube. 

### About the Open Source Insulin Bolus Calculator
The Pancreas Digital Insulin Bolus Calculator uses a standard formula that matches the meal insulin to the patient's carbohydrate intake (carbohydrate counting). A deployed version (online version) of this calculator can be found <a href="https://calculadora.pancreas.digital/en-US" target="_blank" title="Bolus Calculators">here</a>.

### My first contribution
My first contribution was to implement Internationalization (i18n) in the project (i.e. adding support for local languages and cultural settings) in order to allow for more broader personal use. You can check out my <a href="https://github.com/Pancreas-Digital/bolus-calculator/pull/7" target="_blank" title="pull request">pull request (PR) here</a>. 

#### Tasks completed
- Added i18n which is <a href="https://nextjs.org/docs/advanced-features/i18n-routing" target="_blank" title="i18n support">supported out-of-the-box by Next.js</a>.
- Added support for the english language `en-US`.
- Translated the Disclaimer text (which runs when the app is first run).FNext
- Translated the labels & input fields on the calculator page.

![Bolus calculator (Spanish)](/images/pancreas-digital-es.jpg "Bolus Calculator - Spanish")
Spanish version of the Pancreas Digital Insulin Bolus Calculator

![Bolus calculator (English)](/images/pancreas-digital-en.jpg "Bolus Calculator - English")
Translated version of the Pancreas Digital Insulin Bolus Calculator

- Changes made to `next.config.js` file to enable the out-of-the-box `i18n` support in Next.js
~~~js
const { PHASE_DEVELOPMENT_SERVER } = require('next/constants');
module.exports = (phase) => {
 return {
 env: {
 siteUrl: phase === PHASE_DEVELOPMENT_SERVER ? 'http://localhost:3000' : 'https://pancreas.digital',
 siteTitle: 'Pancreas Digital - Calculadora de bolos',
 siteDescription: 'Calculadora de bolos de insulina para el control de la diabetes.',
 siteLogo: 'Vertical.jpg',
 siteName: 'Pancreas Digital',
 twitterCard: 'summary_large_image',
 twitterImageAlt: 'Pancreas Digital Logo',
 twitterSite: '@fedemotta_',
 },
 reactStrictMode: true,
 i18n: {
 locales: ['es-AR', 'en-US'],
 localeDetection: true,
 defaultLocale: 'es-AR',
 },
 };
};
~~~

- All the other code changes can be found in the Pull Request (PR) <a href="https://github.com/Pancreas-Digital/bolus-calculator/pull/7" target="_blank" title="Feature/6 Implement i18n in the insulin bolus calculator #7">Feature/6 Implement i18n in the insulin bolus calculator #7</a>.
### Future contributions
- Improving input field validations.
- Investigating further the effect of dietary fat and protein on the rise in basal glucose levels, as well as the possibility of enhancing the algorithm for insulin bolus calculation to include all three variables (carbohydrate, fat and protein).
- Translating the application into other languages, e.g. French, German, Ukrainian, Swahili, etc.
- <a href="https://nightscout.github.io" target="_blank" title="Nightscout">Nightscout</a> integration. Nightscout is an open-source cloud application used by people with diabetes and parents of kids with diabetes to visualize, store and share the data from their Continuous Glucose Monitoring (CGM) sensors in real-time.

### References
1. Schmidt, S., & Nørgaard, K. (2014). Bolus Calculators. Journal of Diabetes Science and Technology, 8(5), 1035–1041. <a href="https://drive.google.com/file/d/1oGir3lOpkCqtBOUywWmORLEfikX2ghTD/view?usp=sharing" target="_blank" title="Bolus Calculators">(View paper)</a> 
2. Pesl, P., Herrero, P., Reddy, et al. (2016). An Advanced Bolus Calculator for Type 1 Diabetes: System Architecture and Usability Results. IEEE Journal of Biomedical and Health Informatics, 20(1), 11–17. <a href="https://drive.google.com/file/d/1wgcFZM0TtbZO7weaLsTuiDfJ5q7Qlg9H/view?usp=sharing" target="_blank" title="Advanced Bolus Calculator for T1DM">(View paper)</a>

As a foreign medical doctor who has worked in Ghana for over a decade, I have had the opportunity to witness firsthand the vital role that primary care plays in ensuring that individuals have access to quality healthcare...

The Importance of Primary Care: A Personal Perspective

![Ampain Refugee Camp](/images/ampain.jpg "Ampain Refugee Camp")
(At the Ampain Refugee camp in the Western Region of Ghana)

As a foreign medical doctor who has worked in Ghana for over a decade, I have had the opportunity to witness firsthand the vital role that primary care plays in ensuring that individuals have access to quality healthcare. 

## The Importance of Primary Care

Primary care is an essential component of any healthcare system, as it is typically the first point of contact that individuals have with the healthcare system. In many countries, primary care is provided by general practitioners or family medicine doctors, who are trained to handle a wide range of health issues and to refer patients to specialists as needed.

One of the biggest advantages of primary care is that it can help to reduce the burden on hospitals and other specialized healthcare facilities, as many health issues can be managed at the primary care level. This is especially important in countries like Ghana, where there may be limited access to specialized healthcare services.

Primary care doctors are also crucial for promoting preventative care and early detection of health issues. By providing regular check-ups and screenings, primary care doctors can help individuals identify and address potential health issues before they become more serious. This can help to reduce the overall burden on the healthcare system and improve the overall health of the population.

In addition to the clinical aspects of primary care, there are also social and economic considerations to consider. For example, primary care doctors may play a role in connecting patients with social services or other community resources that can help them address non-medical issues that may be impacting their health.

## My Experience Working in Primary Care in Ghana

As a primary care doctor working in Ghana, I have had the opportunity to work with individuals from a wide range of backgrounds and with a variety of health issues. One of the things that has struck me most about my experience in Ghana is the importance of building relationships with patients.

In many cases, patients in Ghana may only have limited access to healthcare and may rely on primary care doctors for all of their healthcare needs. As a result, it is important for primary care doctors to be able to build trust and rapport with patients, so that they feel comfortable discussing their health concerns and seeking help when needed.

One of the ways that I have tried to build relationships with my patients in Ghana is by taking the time to listen to their concerns and providing them with education about their health. I have found that many patients are eager to learn more about how to maintain their health and prevent illnesses, and by providing them with this knowledge, I have been able to help them make more informed decisions about their health.

In addition to providing primary care services, I have also had the opportunity to work on public health initiatives in Ghana. These have included working on vaccination campaigns, providing education about infectious diseases, and promoting healthy behaviors such as handwashing and proper nutrition.

One of the challenges that I have faced while working in primary care in Ghana is the limited resources that are often available. This can include a lack of access to specialized medical equipment or medications, which can make it more difficult to provide optimal care to patients.

Despite these challenges, I have found my work in primary care in Ghana to be incredibly rewarding. I have had the opportunity to make a difference in the lives of my patients and to be a part of a healthcare system that is focused on improving the health and well-being of the entire community.

In this short tutorial I'll show you how to go about creating a React application and deploying it to Github Pages (a free web hosting service provided by Github)...

How to deploy a React App to Github Pages

In this short tutorial I will show you how I went about creating a React app and deployed it to <a href="https://docs.github.com/en/pages/getting-started-with-github-pages/about-github-pages" target="_blank" title="Github Pages">Github Pages</a> (a free web hosting service provided by Github). I used 
 <a href="https://create-react-app.dev" target="_blank" title="create-react-app">Create React App</a> (a tool used to build React applications from scratch) and <a href="https://www.npmjs.com/package/gh-pages" target="_blank" title="gh-pages">gh-pages</a> (an `npm` package that makes deployment to Github Pages pure bliss). 

## Prerequisites
---
1. You need to have <a href="https://nodejs.org/en/" target="_blank" title="Node.js">Node.js</a> and <a href="https://docs.npmjs.com/about-npm" target="_blank" title="npm">npm</a> installed on your computer. These are the versions I will be using for this tutorial (you don't have to have the same versions btw). 

~~~js
$ node --version
v16.11.1

$ npm --version
8.0.0
~~~

Personally, I use <a href="https://github.com/nvm-sh/nvm" target="_blank" title="nvm">nvm</a> (a version manager for node.js that allows you to quickly install and use different version of node via the command line).

2. You should also have `Git` installed. The version I will be using is:
~~~js
$ git --version
git version 2.37.0 (Apple Git-136)
~~~

3. You should have a Github account or <a href="https://docs.github.com/en/get-started/onboarding/getting-started-with-your-github-account" target="_blank" title="Setting up a Github account">set one up</a>.

## Steps
---
### 1. Create an empty Github repository
1. Sign into your Github account.
2. Create a `new repository` (you can find the
<a href="https://github.com/new" target="_blank" title="Create a new repository">create a new repository</a> here. 
*Screenshot of the form shown below*

![Create New Repository](/images/create-new-repository.jpg 'Create New Repository')

3. Fill out sections of the form as follows:
- **Repository name:** You can use any name you want, e.g. *portfolio (that's what I chose)*, my-personal-page, etc. 
- **Repository privacy:** Select *Public*. *NB:* For <a href="https://docs.github.com/en/get-started/learning-about-github/githubs-products#github-free-for-user-accounts" target="_blank" title="Github Free">Github Free</a> users, the only type of repository that can be used with GitHub Pages is *Public*. For
<a href="https://docs.github.com/en/get-started/learning-about-github/githubs-products#github-pro" target="_blank" title="Github Pro">Github Pro</a> users (and other paying users), both *Public* and *Private* repositories can be used with GitHub Pages.
- **Initialize the repository:** Just leave this as is (don't check the box or make any drop down selections). Doing this will create an empty Github repository rather than pre-populate it with a `README.md`, `.gitignore` and/or a `LICENSE` file.

4. Submit the form. 
Your GitHub account should now contain an empty repository with the name and privacy setting you requested.

### 2. Creating the React App
1. Create a React App and name it `sedusa-githubio` (this is the name I chose but you can choose whatever name you want. Just make sure you replace `sedusa-githubio` with whatever name you choose in your project) by running the following terminal command:

~~~js
$ npx create-react-app sedusa-githubio
~~~

This command will create a new folder named `sedusa-githubio`, which will contain the source code for the React app.


2. Enter the folder for `sedusa-githubio`:

~~~js
$ cd sedusa-githubio
~~~

3. Install the `gh-pages` npm package
- Install the `gh-pages` npm package as a <a href="https://nodejs.dev/en/learn/npm-dependencies-and-devdependencies/" target="_blank" title="development dependency">development dependency</a>

~~~js
$ npm install gh-pages --save-dev
~~~

The `gh-pages` npm package is now installed on your computer and the React app's dependence upon it is documented in the React app's `package.json` file (seen as a property of `devDependencies`).

~~~js
{
 ...
 "devDependencies": {
 "gh-pages": "^4.0.0"
 }
}
~~~

4. Add a `homepage` property to the `package.json` file.

- Open the `package.json` in a text editor (I use <a href="https://code.visualstudio.com/" target="_blank" title="Visual Studio Code">Visual Studio Code</a>.

- Add a `homepage` property in the following format: 
~~~js
https://{username}.github.io/{repo-name}
~~~

*NB:* For a <a href="https://pages.github.com/#project-site" target="_blank" title="project site">project site</a> that's the format. For a <a href="https://pages.github.com/#user-site" target="_blank" title="user site">user site</a>, the format is: `https://{username}.github.io.` You can read more about the homepage property in the <a href="https://create-react-app.dev/docs/deployment/#github-pages" target="_blank" title="https://create-react-app.dev/docs/deployment/#github-pages">https://create-react-app.dev/docs/deployment/#github-pages</a> section of the `create-react-app` documentation.

~~~js
{
 "name": "sedusa-githubio",
 "version": "0.1.0",
 "private": true,
 "homepage": "https://sedusa.github.io/portfolio",
}
~~~

5. Add the deployment scripts to the `package.json`

- Open the `package.json` file in your text editor.
- Add the `predeply` and `deploy` properties to the scripts object.
*NB:* The `predeploy` script is used to bundle the React application and the `deploy` script deploys the bundled file.

~~~js
{
 "scripts": {
 "predeploy" : "npm run build",
 "deploy" : "gh-pages -d build",
 "start": "react-scripts start",
 "build": "react-scripts build",
 "test": "react-scripts test",
 "eject": "react-scripts eject"
 },
}
~~~

6. Add a `remote` that points to the Github repository

- Add a 
<a href="https://git-scm.com/docs/git-remote" target="_blank" title="https://git-scm.com/docs/git-remote">remote</a> to the local Git repository by doing:

~~~js
$ git remote add origin https://github.com/{username}/{repo-name}.git
~~~

*NB:* Replace `{username}` with your GitHub username and `{repo-name}` with the name of the GitHub repository created in Step 1, e.g.

~~~js
$ git remote add origin https://github.com/sedusa/portfolio.git
~~~

7. Deploy the React app to Github Pages

- Deploy the app to Github Pages by running the command:

~~~js
$ npm run deploy
~~~

This command will cause the `predeploy` and `deploy` scripts defined in `package.json` to run. Under the hood, the `predeploy` script will build a distributable version of the React app and store it in a folder named build. Then, the deploy script will push the contents of that folder to a new commit on the `gh-pages` branch of the GitHub repository, creating that branch if it doesn't already exist.
By default, the new commit on the `gh-pages` branch will have a commit message of `"Updates"`. You can specify a custom commit message via the `-m` option:

~~~js
$ npm run deploy -- -m "Deploy my awesome app to Github Pages"
~~~

GitHub Pages will automatically detect that a new commit has been added to the `gh-pages` branch of the GitHub repository. Once it detects that, it will begin serving the files that make up that commit — in this case, the distributable version of the React app — to anyone that visits the `homepage` URL you specified in the `package.json` (Step 4).
At this point, the React app is accessible to anyone who visits the `homepage` URL you specified in Step 4. For example, this React app is accessible at <a href="https://sedusa.github.io/portfolio" target="_blank" title="https://sedusa.github.io/portfolio">https://sedusa.github.io/portfolio</a>.

## Time to celebrate! The React App is now live on Github Pages 🙌🏽
![Celebrate](https://media.giphy.com/media/3ornkdtVzQfIRpwfug/giphy.gif)

## Code
---
Here is a <a href="https://github.com/sedusa/portfolio" target="_blank" title="Code in Github for project">link to the code</a> for this deployed app.

## References
---
1. <a href="https://create-react-app.dev/docs/deployment/#github-pages" target="_blank" title="The create-react-app deployment guide">The create-react-app deployment guide</a>
2. <a href="https://github.blog/changelog/2020-09-03-build-and-deploy-github-pages-from-any-branch/" target="_blank" title="Build and deploy GitHub Pages from any branch">Build and deploy GitHub Pages from any branch</a>
3. <a href="https://github.com/gitname/react-gh-pages" target="_blank" title="Gitname: Deploying a React App to Github Pages">Gitname: Deploying a React App to Github Pages</a>
4. <a href="https://docs.github.com/en/pages/getting-started-with-github-pages/" target="_blank" title="Getting started with Github Pages">Getting started with Github Pages</a>

Monkeypox has recently been the subject of several news reports. Monkeypox has been prevalent in Central and West Africa since the early 1970s, but in early May 2022, the illness began to spread outside of that region.

Monkeypox: A spreading danger

Monkeypox has recently been the subject of several news reports. It has been prevalent in Central and West Africa since the early 1970s, but in early May 2022, the illness began to spread outside of that region. Over 300 monkeypox cases have been confirmed in 20 non-African countries and six US states as of May 26, 2022. The scenario is still fast changing.

### Background
Monkeypox is an orthopoxvirus that has a clinical appearance similar to smallpox.  The first epidemic in humans was in the Democratic Republic of Congo (DRC) in the early 1970s, despite the fact that it was originally detected in monkeys in 1958.

![Monkeypox virus](/images/monkey-pox-virus.jpg "Monkeypox virus")
(Image from the CDC Cynthia S Goldsmith, Russell Regnery. [Public domain])

During the 2003 US epidemic, monkeypox virion was recovered from a human skin sample, as shown in the electron microscope picture above. On the left are mature, oval-shaped viral particles, while on the right are immature, spherical virus particles.

### Transmission
Monkeypox is a zoonotic disease, meaning it may be transmitted to people by contact with or eating of small animals, especially rodents. Several dozen human cases of monkeypox were discovered in the United States in 2003, and the disease was traced back to pet prairie dogs and, eventually, rats imported from Ghana. Human-to-human transmission has also been documented, with the virus entering the body via damaged skin, respiratory droplets, or mucous membrane contact. A proposed transmission method is indirect contact with lesion material, such as through infected garments or linens. 

Several documented cases in the 2022 outbreak are in males who have intercourse with men, raising questions about the specific human-to-human transmission pathways. The transmission of monkeypox is still hampered by exposure to human bodily fluids, respiratory droplets, and direct contact with sores.

### Signs and Symptoms
Fever and lymphadenopathy are common early signs of monkeypox infection, which are commonly accompanied by headache, myalgias, chills, weariness, and cough. Lymphadenopathy, notably swollen submental, submandibular, cervical, and inguinal lymph nodes, is the most consistent clinical symptom that distinguishes monkeypox from smallpox and chickenpox.

![Monkeypox virus](/images/monkey-pox-pustules-face.jpg "Pustules on face")
Caucasian male with monkey pox pustules on his face (Source: nairobitimes.co.ke)

Monkeypox takes 7-14 days to incubate, although it can take anything from 5 to 21 days. The rash appears 1-3 days after the commencement of the fever, and it usually starts on the face/mouth before quickly spreading to the rest of the body, including the palms and soles. The rash resembles chickenpox in appearance. Before resolving, monkeypox lesions usually go through the macular, papular, vesicular, and pustular phases. The sickness lasts 2-4 weeks, including ulcerative lesions in some cases.

### Evaluation
Droplet precautions, such as a N95 mask, gown, gloves, and eye protection, should be used while examining a patient with probable monkeypox. Investigate your travel and exposure history: Maintain a track of all clinical teammates who have been exposed to patients after this has been established. After the patient has left, disinfect the patient room surfaces as needed.

![Clinicians wearing personal protective equipment](/images/monkey-pox-ppe.jpg "Clinicians wearing personal protective equipment")
Image of clinicians wearing personal protective equipment (PPE) from Wikimedia Commons | Javed Anees, District Hospital, Tirur.

Using an oropharyngeal or nasopharyngeal swab, get a viral culture. A vesiculopustular rash skin biopsy or a sample of the ceiling of an intact vesiculopustule should be examined. Polymerase chain reaction (PCR), electron microscope examination (in the absence of other orthopoxviruses), or immunohistochemistry testing can all be used to confirm monkeypox. 
In the differential diagnosis, a Tzanck smear can assist distinguish monkeypox from other nonviral illnesses. A Tzanck smear, on the other hand, cannot distinguish a monkeypox infection from smallpox or herpetic infections.

![Pustules on hand](/images/monkey-pox-pustules-hand.jpg "Pustules on the hand")
Image courtesy of Medscape. The dorsum of a patient's hand has a monkeypox vesiculopustular rash. Some lesions have a central umbilication.

### Treatment
Monkeypox infection is mostly treated with supportive and symptomatic medicine. To avoid the spread of the disease, it is critical to segregate patients and take proper measures. Until all scabs have dried and dropped off, the patient is infectious. Vaccines for monkeypox are available, and in certain circumstances, immunization after high-risk exposure has been demonstrated to prevent illness or reduce severity.

### Mortality and Complications
In Africa, mortality rates range from 1.0 percent to 10.6 percent, with high-risk individuals (such as those who are immunocompromised or malnourished) having the worst outcomes. Bacterial infections can also develop as a result of the initial infection.

I spent a lot of time researching the best combination of complementary frameworks to build this website, and eventually settled on using NextJS to build it, hosting it in a private Bitbucket repository, and deploying...

How I built this website

I spent a lot of time researching the best combination of complementary frameworks to build this website, and eventually settled on using NextJS to build it, hosting it in a private Bitbucket repository, and deploying it with Netlify. These are the steps you can take to deploy a similar project.

## Prerequisites
1. Node.js (>= 12.0.0)
2. Yarn or npm
3. Git
4. Bitbucket account
5. Netlify account

## Step 1: Create a Next.js Project

You can find the <a href="https://nextjs.org/learn/basics/create-nextjs-app" target="_blank">official docs on how to create a next-js app</a> here.

First, let's create a new Next.js project using the following command:

~~~js
 npx create-next-app my-nextjs-project
 cd my-nextjs-project
~~~

Replace "my-nextjs-project" with your desired project name.

## Step 2: Add Typescript to the project

Next, let's add TypeScript to the project:

~~~js
 yarn add --dev typescript @types/react @types/node
~~~

or

~~~js
 npm install --save-dev typescript @types/react @types/node
~~~

Create a `tsconfig.json` file in the project root with the following contents:

~~~js
{
 "compilerOptions": {
 "target": "esnext",
 "module": "esnext",
 "jsx": "preserve",
 "lib": ["dom", "dom.iterable", "esnext"],
 "allowJs": true,
 "skipLibCheck": true,
 "strict": true,
 "forceConsistentCasingInFileNames": true,
 "noEmit": true,
 "esModuleInterop": true,
 "moduleResolution": "node",
 "resolveJsonModule": true,
 "isolatedModules": true
 },
 "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx"],
 "exclude": ["node_modules"]
}
~~~

Rename `.js` files to `.tsx` or `.ts`, and update the import statements accordingly.

## Step 3: Add Styled Components

Install <a href='https://styled-components.com/' target='_blank'>Styled Components</a> and its TypeScript definitions:

~~~js
 yarn add styled-components @types/styled-components
~~~

or

~~~js
 npm install styled-components @types/styled-components
~~~

## Step 4: Push to a Private Bitbucket Repository

Create a new private repository on Bitbucket.

In your local project, initialize a Git repository and push to Bitbucket:

~~~js
 git init
 git add .
 git commit -m "Initial commit"
 git remote add origin YOUR_BITBUCKET_REPOSITORY_URL
 git push -u origin master
~~~

Replace `YOUR_BITBUCKET_REPOSITORY_URL` with your Bitbucket repository's URL.

## Step 5: Deploy to Netlify

Log in to your Netlify account and create a new site from Git. Choose Bitbucket as the Git provider and authorize access to your private repository.

Configure the build settings as follows:
- Build command: `yarn build` or `npm run build`
- Publish directory: `out`

Click **Deploy Site** to deploy your project.

## Conclusion
Congratulations! you've successfully set up a Next.js project with TypeScript and Styled Components, stored it in a private Bitbucket repository, and deployed it to Netlify 🚀.

![Noice](https://media.giphy.com/media/Od0QRnzwRBYmDU3eEO/giphy.gif)

Samuel Edusa MD

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