Improving Diagnostics with AI-powered Predictive Analytics in Healthcare

Sanam Malhotra | 20th December 2019

The collaboration of artificial intelligence (AI) with massive healthcare data is transforming the way care is delivered. At Oodles, we constantly innovate new therapeutics and strive to improve the existing ones by channelizing medical data using machine learning technologies. Recent years have witnessed a significant rise in the applications of predictive analytics in the healthcare sector for strengthening care decisions and facilities.

The underlying machine learning techniques of predictive analytics enable organizations to combat medical emergencies and take timely preventive measures. This blog post cumulates effective applications of AI development services for predictive analytics to improve health and healthcare.

Predicting Widespread of Chronic Diseases

In the face of rising population growth, it is cumbersome for medical authorities to track population health and take timely preventive measures. Inefficient risk prediction leads to the development of long-term chronic conditions that are difficult to treat and affect patient care drastically. 

With advancements in machine learning capabilities, healthcare organizations are now beginning to infuse AI-powered predictive analytics in population health management. Here’s how big data analytics derive valuable insights for patient care-

A) Risk Score Prediction based on electronic health records (EHR), biometric data, lab test reports and social determinants of health provides in-depth health insights. Machines trained using this data can identify population sections with high-risk patients and signal doctors to plan relative interventions.

B) Prediction of extreme epidemic conditions is now possible with big data analytics, machine learning, and high computational power. Prediction of infectious diseases is done with various data sources such as weather reports, reported cases, population density, economic profile, etc.

Predictive analytics for epidemic conditions

Source: Medium

 

With big data analytics, machine learning models become a key source to improve healthcare services in highly prone regions. Accurate and efficient prediction of chronic diseases such as heart attacks and cancer can improve healthcare quality and cost significantly.

 

How does Oodles practice predictive analytics in healthcare to prevent chronic disease?

At Oodles, our AI team is skilled at deploying machine learning algorithms for data-driven predictive analytics. We have hands-on experience in training ML models with EHR, 2D, and 3D medical imagery to generate accurate health insights. 

Our most recent AI solution for healthcare institutes combine machine learning libraries like Scikit-learn to develop a Diabetic Prediction System. The model works on structured data inputs such as Plasma Glucose, Tricep Thickness, and blood pressure to predict diabetes in patients.

 

 

The model’s USP is that it does not require any intervention of a physician to measure diabetes. Supported by a simplified interface, the system enables common individuals to test diabetes in an easy, quick, and accurate manner.

 

Ensuring Optimal Staff and Resource Allocation

Improper resource allocation and unbalanced distribution of healthcare facilities has been a major concern for hospitals in villages and suburban areas. Medical authorities often fail to judge unprecedented critical conditions and excessive demand for medical resources leading to overflowing emergency wards and mismanagement. 

With the advent of AI-driven predictive analytics, healthcare institutes can streamline medical resource allocation by-

A) Predicting the fluctuations in patient flow to ensure proper bed allocation.

B) Rescheduling staff according to patient flow to enhance patient care effectively.

C) Detecting patterns of utilization from patient data to manage appointment rate and service.

 

Deploying AI-powered Predictive Analytics in Healthcare with Oodles AI

We, at Oodles, build industry-specific predictive engines for eCommerce, marketing, healthcare, and financial businesses. Our AI team enables healthcare organizations to channelize big data analytics to extract meaningful insights from complex medical records. 

In addition, we have expertise in improving healthcare services with AI-powered conversational chatbot development. We enable doctors to automate remote assessment with natural language-based chatbots and other AI solutions.

Talk to our AI team to know more about our artificial intelligence services.

About Author

Sanam Malhotra

Sanam is a technical writer at Oodles who is currently covering Artificial Intelligence and its underlying disruptive technologies. Fascinated by the transformative potential of AI, Sanam explores how global businesses can harness AI-powered growth. Her writings aim at contributing the multidimensional values of AI, IoT, and machine learning to the digital landscape.

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