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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Machine learning and artificial intelligence are now moving from the realm of research into adoption. Machine learning adoption offers immense benefits which can provide any organization with a competitive edge — if executed well. Technological adoption requires a pragmatic and collaborative approach across the organization driven by agile practices.This also comes the need for trusted data sources, organizational change management, iterative revalidation practices and measuring the business value of the technology insertion.

In part one of this series on machine learning (ML), we defined machine learning, delved further into the various types of machine learning models, and described their common applications. This article focusses on the tactical execution steps and organizational modifications required to make the ML dream a reality.

5 Steps to Machine Learning Implementation

Establishing machine learning within any organization requires planning and collaboration. As with any technology insertion and/or transition, it starts with a vision and moves on to execution followed by continuous monitoring and improvement. The basic steps to building an ML implementation plan are described in five simple steps below: VDOCR — Vision, Data, Organizational alignment, Change management and Revalidation.

1: Establish a Vision

Establishing a vision is perhaps the most important step in implementing a new technology. It is not any different for machine learning. Business and IT must work together to establish a vision and define clear objectives for an ML implementation. The objectives could be as simple as improving the accuracy of the fraud detection system all the way to improving overall operational efficiency — but it needs business and IT alignment and the agreement to work towards a common goal.

Without a clear understanding of what you want to achieve, it’s hard to measure success. You’ll find the most common use cases by looking for places that are labor intensive and repetitive such as image classification, tuning/optimizing your data center operations, configuration management, and systems patching/updating. This step also includes establishing key performance indicators to measure the business value of the program.

2: Define Data Requirements

Data is perhaps the single most important element required for the success of a machine learning implementation. Collecting, storing and feeding the system vast amounts of reliable data is the key to improving the accuracy of machine learning algorithms. Data management processes need to be established for:

• Providing an initial set of historical data to train the ML processing system.
• For continuous data insertion to train and improve the accuracy of the model.

Beyond the initial model-training phase, infrastructure will be needed to collect new data from which to learn over time. Data requirements need to be established not only for collecting and storing data but also to ensure that the available data is reliable and secure and is available in a steady stream for continuous improvement.

3: Establish Roles and Responsibilities

Any successful technology implementation requires integration across the organizational landscape that is strategically led by a robust management function, clear establishment of roles and responsibilities and cultural integration. Begin with the creation of integrated solution teams with representatives from IT, marketing, sales, and other required stakeholders that meet regularly during the project to review progress and ensure adequate coordination with their respective groups.

4: Set Up a Change Management Process

Technology insertions often fail due to the lack of adequate change management processes. Change management and training are two of the key aspects of delivery and acceptance of any large-scale modernization effort, and ML implementation is no different in that respect. Change management includes looking at current business processes and re-engineering them based on the updated business model. In addition, training programs that cover mission objectives, product features as well as the newly created business processes are imperative to create collective support and awareness for the mission and its objectives as well as to increase efficiency and use.

5: Establish Monitoring and Revalidation

Gauging the success of an application and whether it needs changes can be established by measuring its business value. To ensure that ML models remain relevant and ultimately result in business value, they need to be continuously updated, retrained and validated. To achieve this, organizations need to ensure that any ML implementation plan includes the ability to update its criteria based upon evaluated outcomes and to incorporate improved and increasing amounts of data. Also important is to measure how the ML algorithm affects broader business goals.

For example, Amazon is continuously refining its prediction algorithms based on the past purchases of its customers. Similarly, Netflix improves its ability to provide customized content to its consumers based on the content they watch. Moreover, New York Times has even developed an ML system to ascertain the emotions evoked by news articles with the goal of helping advertisers’ places ads more effectively.

Imagine an article that changes its content based on what its consumers wants to read or a movie that changes its story based on the likes and dislikes of its viewers. Sounds eerie! Get ready for it — because it is coming ….

Get Started With Your Machine Learning Strategy

Businesses need to carefully plan and manage technology disruptions and ML is no different in that respect. If you want to get the most out of your business data and automate processes, the time is ripe for creating an ML strategy in your organization. Following the simple VDOCR (Vision, Data, Organization, Change and Revalidation) model will help your organization take its first step towards an ML implementation that considers cultural implications and delivers business value.

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