AI applications are becoming the one-stop solution for diagnosing critical diseases

Artificial intelligence and machine learning, are dominating every aspect of our lives. AI is used in various areas like healthcare, education, and defense. With the advancement of technology, better computing power, and the availability of large datasets containing valuable information, the use of AI and ML models has increased. The healthcare sector generates enormous amounts of data in terms of images, and patient data, which helps the healthcare companies to understand the patterns and make predictions.

Artificial intelligence is capable of predicting acute critical illness with greater accuracy than the traditional early warning system (EWS), primarily used by healthcare providers. Even though AI is used in healthcare companies for various purposes but predicting critical diseases and their risks beforehand have been one of its greatest contributions.

Recently, researchers and healthcare providers have been using machine learning algorithms to automate the diagnosis of critical diseases like cancer, and other cardiovascular complexities, which has caused a paradigm shift in healthcare facilities. They are using ML models for the real-time diagnosis of disease by developing mobile applications. Some mobile apps can even predict the risk of a certain disease in the future and recommend a diagnosis based on the individual’s medical history and other habits.

Even though machine learning and artificial intelligence have brought a revolutionary change in medical facilities, efficient early detections and diagnosis are still a problem.

Transparency and explainability, are of absolute importance when it comes to the widespread introduction of AI models into clinical practices. Incorrect predictions carry serious consequences. Healthcare providers must understand the underlying reasoning and technical patterns followed by the application to understand potential cases where it might end up with false or incorrect predictions. AI-based early warning systems carry robust and accurate models to predict acute critical diseases.

• Acceleration Of Artificial Intelligence In The Healthcare Industry
• A Surge In The Adoption Of AI By The Healthcare Sector
• Analytics Insight Predicts Healthcare Sector To Touch US$68 Billion In Revenue By 2025

Using Electronic Health Records for Efficient Prediction of Critical Diseases

Electronic health records contain information for both medical providers and patients. These records also contain information that could interfere with the machine’s ability to make correct predictions. Researchers are aiming to eliminate the unnecessary data that can hinder the model’s capability by deploying a machine learning algorithm, called LSAN.

LSAN, is a deep neural network that uses the two-pronged approach to scan electronic health records and identify information that could predict if the patient is facing a risk of developing a deadly disease in the future.

Electronic records use a double-level hierarchical structure to interpret the medical journey of a patient using the International Classification of Diseases (ICD) codes. It begins with the patient’s current situation and follows through the chronological sequence of visits made by the patient. It records the symptoms and the patient’s condition from the last visit to the current state.

Researchers conducted experiments on patients with symptoms of health failure, kidney disease, and dementia and determined that this newly developed machine learning model called LSAN has outperformed the traditional and the current medical technologies and deep learning models.

These models and tools can be effectively used to predict cardiovascular diseases using the patient’s age, cholesterol, weight, blood pressure, and several other factors, and the potential risks that might occur in the next ten years. Hospitals are also increasing the use of business analytics in transportation, patient retention, and other areas to provide the patients a wholesome experience and cost-effective treatments.

The application of AI in this diagnostic process can be of immense support to healthcare providers and patients. The implementation of AI in the medical infrastructure speeds up the identification of relevant medical data from multiple sources, which saves time and resources for the patients and medical practitioners.

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Today, the role of artificial intelligence in an enterprise has become so important that it has touched every facet of business. Its role will become more critical in the years to come.

For the purposes of this article, let us define A.I. as follows. Human intelligence is learnt from experience. Machines so far have been primarily used to follow instructions, i.e. programmed, hence machines have provided automation based on rules.

A.I. is not programmed to follow rules, it is like human intelligence, learns from “experience”, i.e. data.

A.I. application in businesses today can be divided into 5 key areas:

1. A.I. in data cleansing and streamlining.

2. A.I. in BI—i.e. AI to replace business analysts in preliminary analysis on dashboards.

3. A.I. in cognitive intelligence such as voice recognition, video analytics, face recognition.

4. A.I. in natural interaction – chat bots, NLP, Natural-Language Generation (NLG).

5. A.I. in expert systems—learning from myriad data sets and crystallizing an insight or action. This can be applied in classification of future unknowns e.g. fraud prevention, preventive maintenance. This can be applied also in forecasting quantities e.g. demand forecasting, supply shortage prediction. And can be applied in real time dynamic operations e.g. self-driving cars, dynamic digital marketing.

A.I. in data cleansing

There are A.I. applications today that can weed out errors in master data. In fact, a business was able to reduce their data errors in incoming data by 94% through A.I. application in data error correction. Humans used to correct such errors before and the correction was based on knowledge about the product, data on similar products and so on. If a product is a liquid and the units of measure are missing, the human planner used to correct that gap looking at the product being replaced by this new product or other liquid products in the same price range and so on, or the image of the product itself. AI now does the same and has been seen to be able to correct 94% of data errors without human help.

A.I. in BI

We know when reports and dashboards are delivered, junior analysts typically do a pre-analysis to ascertain exceptions, and explanations for those exceptions. These analysts circle these exceptions and comment on the components contributing to the exception. They also may mark something as high priority if an urgent action is needed. This saves the decision maker’s time and presents them with distilled information. Today, A.I. is able to do this – augmenting junior analysts using anomaly detection and auto-drill down, pattern recognition and clustering.

A.I. in cognitive intelligence

The usual examples people think of when talking of AI applications is in cognitive intelligence. As an example, in this pandemic, cognitive systems are tracking masks wearing compliance in closed spaces, distancing norms compliance in factories and warehouses. Cognitive intelligence is also used in less known forms, there is a recent MIT report on an AI model that can detect Covid-19 infection from your cough, being incorporated into an FDA approved cell phone app.

A.I. in natural interaction

Chat bots are here to stay. If we haven’t interacted with one yet, we are probably not clicking on that chat button on most online shopping sites. It is interesting that A.I. learning is not being achieved in most chat bots from chat histories of those businesses alone, but using innovative data sources such as Q&A dialogues in published interviews, consumer panel discussions, even published plays, and so on.

The way personalisation is achieved is even more interesting. If A.I. was simply replacing a human agent the chat would begin with “how may I help you?”. However, A.I. is much more than that. There are call intent prediction models which learn from the behavior of customers who had a similar interaction, purchase history and profile. Gleaning from data, what did such cohorts mostly call about when they did call? So, the conversation starts with “Are you calling about your oximeter ordered yesterday?”

A.I. in expert systems

This is the area impacted most post pandemic. For example, demand forecasting of CPG products using traditional techniques became impossible. However, applying A.I. made a difference.

For example, loyalty card identifiers in transaction data helped A.I. learn that there are some products which saw an upswing in sales that would continue (because the same shoppers were re-purchasing high quantities). Products like soap, sanitizers, home cleaners, etc. were identified for continued upswing from such analysis. Other items that did see an upswing but were unlikely to sustain (because the same shopper was not repurchasing high quantities) were items like paper towels, pet food, baby food, etc. This kind of ability in the first two months of dynamic demand helped many firms to plan better for the supply and allocation across locations.

With digital interactions rising, application of A.I. for a cookie less world has led to Federated learning of cohorts (FLOCs). FLOCs use models learning from customer cohort behavior through distributed data, without transferring data to a central server, thus protecting privacy.

What has been true of times of upheaval, applies to this one as well. It is a time of opportunity for those who leverage available tools including A.I. to transform their business to gain competitive advantage. Others who delay, cannot afford to miss this revolution. They will be late and may struggle, if they sustain at all.

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The work marks a beginning in using machine learning techniques to optimize the architecture of chips.

This month, Google unveiled to the world one of those research projects, called Apollo, in a paper posted on the arXiv file server, “Apollo: Transferable Architecture Exploration,” and a companion blog post by lead author Amir Yazdanbakhsh. 

Apollo represents an intriguing development that moves past what Dean hinted at in his formal address a year ago at the International Solid State Circuits Conference, and in his remarks to ZDNet.

In the example Dean gave at the time, machine learning could be used for some low-level design decisions, known as “place and route.” In place and route, chip designers use software to determine the layout of the circuits that form the chip’s operations, analogous to designing the floor plan of a building.

In Apollo, by contrast, rather than a floor plan, the program is performing what Yazdanbakhsh and colleagues call “architecture exploration.” 

The architecture for a chip is the design of the functional elements of a chip, how they interact, and how software programmers should gain access to those functional elements. 

For example, a classic Intel x86 processor has a certain amount of on-chip memory, a dedicated arithmetic-logic unit, and a number of registers, among other things. The way those parts are put together gives the so-called Intel architecture its meaning.

Asked about Dean’s description, Yazdanbakhsh told ZDNet in email, “I would see our work and place-and-route project orthogonal and complementary.

“Architecture exploration is much higher-level than place-and-route in the computing stack,” explained Yazdanbakhsh, referring to a presentation by Cornell University’s Christopher Batten. 

“I believe it [architecture exploration] is where a higher margin for performance improvement exists,” said Yazdanbakhsh.

Yazdanbakhsh and colleagues call Apollo the “first transferable architecture exploration infrastructure,” the first program that gets better at exploring possible chip architectures the more it works on different chips, thus transferring what is learned to each new task.

The chips that Yazdanbakhsh and the team are developing are themselves chips for AI, known as accelerators. This is the same class of chips as the Nvidia A100 “Ampere” GPUs, the Cerebras Systems WSE chip, and many other startup parts currently hitting the market. Hence, a nice symmetry, using AI to design chips to run AI.

Given that the task is to design an AI chip, the architectures that the Apollo program is exploring are architectures suited to running neural networks. And that means lots of linear algebra, lots of simple mathematical units that perform matrix multiplications and sum the results.

The team define the challenge as one of finding the right mix of those math blocks to suit a given AI task. They chose a fairly simple AI task, a convolutional neural network called MobileNet, which is a resource-efficient network designed in 2017 by Andrew G. Howard and colleagues at Google. In addition, they tested workloads using several internally-designed networks for tasks such as object detection and semantic segmentation. 

In this way, the goal becomes, What are the right parameters for the architecture of a chip such that for a given neural network task, the chip meets certain criteria such as speed?

The search involved sorting through over 452 million parameters, including how many of the math units, called processor elements, would be used, and how much parameter memory and activation memory would be optimal for a given model. 

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While companies continue to realize the importance of data science and its ability to positively impact revenue, scaling it across an organization continues to be a challenge. A new survey released today reveals a new leading factor to success — creating a positive, model-driven business culture among employees. This insight is one of the findings from a survey of data and analytics professionals sponsored by Domino Data Lab, provider of the leading open enterprise data science management platform trusted by over 20% of the Fortune 100.

Conducted by DataIQ, the leading membership-based forum for connecting, educating and supporting the data and analytics community, the survey curated a research panel of influential data and analytics professionals across a wide range of industry sectors and company sizes in the UK. Seniority ranged from senior managers and heads of department to global directors and chief officers.

The survey found that one in four businesses¹ expect data science to impact topline revenue by more than 11 percent. However, the survey indicates a challenge with company culture, suggesting a positive, model-driven culture is difficult to build and still needs to be developed. 39 percent want a clearer definition of needs from stakeholders, 38 percent recognize the need to train business users in data science concepts, and 32 percent identify the need for a more positive relationship with stakeholders.

“Many companies begin their data science journey by hiring a few data scientists, but overlook the importance of building a model-driven culture that aligns with business users and their needs,” said Nick Elprin, CEO of Domino Data Lab. “This survey highlights the impact that the lack of positive culture can have on identifying proper use cases, setting appropriate expectations, and ultimately delivering a measurable impact to the business. Understanding these challenges is important for companies at all stages of maturity so they can course correct and successfully scale data science operations across their organizations.”

Additionally, 40 percent of respondents indicate that weak understanding or support for data science in business is one of their biggest challenges. One out of three organizations (34%) indicate that conflict between data science and IT is one of their biggest challenges. Even companies that describe themselves at the “advanced” and “reaching maturity” levels in terms of their adoption of data science and analytics are not free of culture conflict. For both of these groups, half (52 percent and 50 percent of both groups respectively) indicate that conflict between data science and IT is their biggest challenge.

Some other findings from the survey include:

• More than half of all organizations (57 percent) expect a revenue uplift of under five percent, showing that the failure to embrace data science contributes to low expectations.
• One out of five businesses (21 percent) are gaining a major competitive advantage through the use of data and analytics tools across their enterprise.
• Sixty-seven percent have grouped their data scientists together as a central function or department (e.g., a Center of Excellence), rather than federating them across the business.
• One out of three organizations (32 percent) need months to get models into production. This latency must be addressed, because market conditions can change quickly and models trained using outdated data will make suboptimal recommendations.
• One in 10 organizations (10 percent) have adopted a superior automated form of model monitoring that provides proactive alerts when models are starting to decay. Data scientists can then address potential model issues before they impact business results.

“For data science to deliver real value to the organization, a positive culture needs to be created in which business stakeholders and data science practitioners have a close bond and common goals,” said David Reed, Knowledge and Strategy Director at DataIQ. “As the survey results show, that’s easier said than done. Four in ten organizations identify a weak understanding or support for data science by the business as their biggest challenge, which creates a vicious circle that leads to one in eight failing to create compelling use cases.”

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