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DBSCAN Clustering Algorithm in Machine Learning
An introduction to the DBSCAN algorithm and its Implementation in Python.
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Data Transformation: Standardization vs Normalization
Increasing accuracy in your models is often obtained through the first steps of data transformations. This guide explains the difference between the key feature scaling methods of standardization and normalization, and demonstrates when and how to apply each approach.
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Why Conversational AI is the New UI
Conversational AI is sidestepping traditional user interfaces and offering customers a more natural and streamlined way to interact and…
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A Brief introduction to Perceptron
This article explains a very brief overview of the perceptron algorithm
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The Future of Healthcare with AI
In 2020, the impact of Artificial Intelligence on healthcare became more evident than ever. With coronavirus threats and medical understaffing all over the world, it’s obvious that we need help. Artificial Intelligence is the most prominent solution right now, and its role will likely grow even after the pandemic.
Actual medical cases have already shown the payoff from Artificial Intelligence investment. For many years, China has been on the front-line on building AI-based healthcare software — and during the COVID-19 epidemic, it paid off in full. AI software helped with diagnosis, treatment, organization, and there are a lot many aspects to come.
How AI technology is transforming the future of healthcare
Reports are highly optimistic about the future of healthcare and Artificial Intelligence. According to Accenture, AI can help save more than 150 billion dollars of US healthcare expenses. It’s likely that now healthcare and technology will become even more prominent fields of investment, and we can expect higher growth.
Where can we see the change already, and what fields are starting to get revolutionized? We summarized 7 main aspects of healthcare, changed by AI.
Networked hospitals and connected care
Healthcare institutions need to select their workload in order to be more efficient. When there is a crisis situation, doctors shouldn’t be forced to choose who lives and dies from the patient’s clinical history and vital signs. An AI system can help predict the progress of a case and distribute work in a way that allows all patients to get sufficient care.
AI can turn hospitals into connected systems. Each patient case will be stored in a smart database, while the software will distinguish the bigger picture and update the work plan. Moreover, AI can predict bottlenecks, distribute resources, allocate same-day surgeries, and prevent a shortage of medical staff. This is especially important in crisis situations, such as the one that hit hospitals all over the world due to the COVID-19 pandemic.
Better patient and staff experiences
AI can make diagnoses and clinical exams faster by analyzing medical data and drawing informed conclusions. Such software will spare doctors the necessity to go through pages of data and overburden patients with an unnecessary test. On top of that, AI can control documentation storage, making sure that neither scanned medical records nor prescriptions are lost.
The Duke Institute for Health Innovation developed Sepsis Watch, smart patient diagnosis and monitoring system that analyzes personal data to evaluate patients’ state. The system establishes a personalized threshold that defines normal and critical states. As soon as the data hits the limit, a doctor receives an immediate call-to-action. This way, the hospital response team can get alerts in time and quickly address the patient’s potential complications.
A more accurate diagnostic process
According to the research of the University Hospitals Birmingham NHS Foundation Trust, Artificial Intelligence is at least as good in determining medical diagnosis as humans are. During the study, the research team examined the sensitivity (the probability of recognizing a disease) and specificity of diagnosis (the evaluation if the diagnosis was correct).
The team collected more than 20,500 research articles about AI diagnosis, selected the best-researched ones, and compared the performance of AI and certified medical professionals. The research showed that AI in all the analyzed studies performed as well as doctors — although not much better.
So, while traditional diagnosing methods are not yet replaced by AI, it’s likely that with the help of machine learning, smart algorithms can potentially outperform human specialists.
AI-based predictive care
Artificial Intelligence can create treatment plans for patients who are on the early and late stages of their illnesses, either helping to stop the spread of the disease, or dealing with a serious health crisis. This is already done by IBM Watson — the system creates treatment plans for cancer patients. Watson analyzes medical records, patients’ clinical histories, clinical trials, and journals all over the world.
Medical professionals and patients, there, get access to innovative treatment options all over the world — as the innovations are being published and discovered. This could help deliver higher-standard care and promote innovation adoption.
Top 4 Most Popular Ai Articles:
AI-powered healthcare assistants
Artificial Intelligence, with its superior data processing and analytical capacities, holds huge potential for increasing the accessibility of healthcare. People who don’t have access to healthcare institutions or don’t have proper medical insurance can get informed consultations with AI, and if needed, be connected to medical professionals through a telemedicine application that brings mutual benefits. Moreover, AI-powered healthcare assistants offer personalized experiences and help drive better patient outcomes. Additionally, they can do the following:
- encourage a patient to talk about symptoms;
- provide verified information about diseases, symptoms, and treatments;
- connect a patient to a medical assistant;
- connect a user to a community: a patient will be able to share the experience with people who have the same problem;
- monitor treatment: healthcare assistants can remind users to take medications, control nutrition, exercise;
- control vital signs: a healthcare assistant can process biometric data and alert a user if the data is above the threshold.
Obviously, AI healthcare assistants can’t be proper substitutes for actual medical care, but they can become a bridge between a patient and a healthcare institution.
Robotic surgeries
AI is used in operating rooms (ORs) for a while. For instance, energy delivery robots like Programmable Automata used to calculate the position of equipment during cancer treatment. Dental robots use digital mapping to get doctors a more profound insight into a patient’s anatomy. Robots can pre-plan the procedure, calculate angles and orientations, and analyze risks of the surgery. Another example is Motorized Laparoscopic — a tool that allows getting better visibility of laparoscopic cameras by picking the most vital angles.
Such tools will likely become more widespread in the future, helping doctors to improve the efficiency of their working process, shortening surgery time, and leading to better recovery.
Medical education
AI-based learning systems can help medical students define the most important information for their course, track knowledge, and performance, and plan curriculum. When it comes to getting practical skills, combining AI and virtual reality can be used to create a smart interactive training center, where medical interns and residents can go through surgical procedures, receive advice, and get alerts if there was a mistake.
The challenges of AI in healthcare
The adoption of AI is currently yet held back by several ethical and practical aspects. Let’s take a closer look at them below:
- Education: to create safe and impactful AI tools, hospitals need to cooperate with highly-skilled developers; ideally, those who have a deep understanding of a given medical field.
- Resources: the creation and testing of AI healthcare tools take time and money, and hospitals often prefer to invest in more immediate solutions.
- Ethical concerns: trusting people’s lives to algorithms has been a subject of discussions for many years, and the scientific community is yet to find common ground on the topic.
- Privacy: securing information systems and AI software from cyber attacks is a costly challenge for hospitals and tech firms. Existing regulations like HIPAA might not be enough to prevent safety risks.
- Lack of control: using AI on a regular basis could lead to the loss of some human skills, causing doctors to rely increasingly more on AI-based decision making.
The bottom line
Artificial Intelligence in healthcare can solve many problems that the industry is struggling with: understaffing, the lack of time for research and informed decision making, limited prediction capacities, and poor patient experiences. Additionally, AI has the potential to make healthcare more accessible to everyone — and not just in the US but all over the world.
Obviously, there are many ethical and practical challenges to overcome, but it’s likely that the adoption of AI in healthcare is now only a matter of time. Embracing innovation will ultimately lead to a better doctor and patient experiences, and this result is well worth investing time and resources.
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How To Scrape Data Locked Behind A Login?
In our last tutorial, we looked into request headers and cookies and their role when you scrape data.
So, what’s the next problem you could encounter when scraping?
Yes, it’s login screens.
Sometimes, you might set your sights on scraping data you can access only after you log into an account. It could be your channel analytics, your user history, or any other type of information you need.
In this case, first check if the company provides an API for the purpose. If it does, that should always be your first choice. If it doesn’t, however, don’t despair. There is still hope. After all, the browser has access to the same tools when it comes to a request as we do.
How to Scrape Data That Requires a Login – Important Disclaimer
Information that requires a login to access is generally not public. This means that distributing it or using it for commercial purposes without permission may be a legal violation. So, always make sure to check the legality of your actions first.
With that out of the way, let’s walk through the steps to get past the login and scrape data.
Depending on the popularity and security measures of the website you are trying to access, signing in can be anywhere between ‘relatively easy’ and ‘very hard’.
In most cases, though, it exhibits the following flow.
First, when you press the ‘sign in’ button, you are redirected to a log-in page. This page contains a simple HTML form to prompt for ‘username’ (or ‘email’) and ‘password’.
When filled out, a POST request, containing the form data, is sent to some URL. The server then processes the data and checks its validity. In case the credentials are correct, most of the time a couple of redirects are chained to finally lead us to some account homepage of sorts.
There are a couple of hidden details here, though.
First, although the user is asked to fill out only the email and password, the form sends additional data to the server.
This data often includes some “authenticity token” which signals that this login attempt is legitimate and it may or may not be required for successful login.
The other detail is related to the cookies we mentioned last time.
If we successfully signed into our account, client-side cookies are set. Those should be included in each subsequent request we submit. That way, the server knows that we are still logged in and can send us the correct page containing sensitive info.
So, how can you do this in practice?
The first piece of the puzzle is to find out where the ‘post’ request is sent to and the format of the data. There are a couple of ways to do that. You can either infer that information from the HTML or intercept the requests our browser submits.
The majority of login forms are written using the HTML tag ‘form’:
The URL of the request can be found in an attribute called ‘action’, whereas the parameter fields are contained in the ‘input’ tags. This is important because the hidden parameters will also be placed in input tags and thus can be obtained.
Another important piece of information is the name of the input field.
As trivial as it may seem, we don’t have that knowledge a priori.
For example, think about the username. What should that parameter be called? Well, it might be simply ‘userName’, or it could be called ‘email’, maybe ‘user[email]’. There are many different options, so we should check the one employed by the developers through the ‘name’ attribute.
This information can also be obtained by intercepting the browser requests and inspecting them.
We do that with the help of the Developer tools. Specifically, in the Chrome developers’ tools, there is a ‘Network’ tab that records all requests and responses.
Thus, all we need to do is fill our details and log in while the Network tab is open. The original request should be there somewhere with all request and response headers visible, as well as the form data.
However, bear in mind that it could be buried in a list of many other requests, because of all the redirects and the subsequent fetching of all resources on the page.
Now that we’ve got the URL and form details, we can make the POST request.
The data can be sent as a regular dictionary. Don’t worry about the subsequent redirects – the requests library deals with that automatically, by default. Of course, this behavior can be changed.
But what if we want to then open another page while logged in?
Well, we need to set our cookies in advance first. That means we have to take advantage of requests’ sessions.
Summarizing all this, a sample code for a simple login may look like this:
Here we define all the form details, then we create the session and submit the POST request for authentication. Note that the request is made through the session variable, in this case, ‘s’.
Some websites employ more complex login mechanisms, but this should suffice for most.
Now you know how to tackle a login when scraping data.
I hope this tutorial will help you with your tasks and web scraping projects.
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