Skip to main content

Samyak Shrimali

Samyak Shrimali

2023 Davidson Fellow
$10,000 Scholarship

Age: 18
Hometown: Portland, OR

Engineering: “CareHAI: A Novel Automated Artificial Intelligence and Sensor-Based Multi-Modular System for Early Diagnosis and Prevention of Hospital-Acquired Infections”

About Samyak

I’m Samyak Shrimali from Portland, Oregon. I’m an incoming first-year student at the University of Illinois Urbana Champaign (UIUC) majoring in Computer Science. My main research interests are in interdisciplinary computer science, especially applying machine learning to solve prominent medical and environmental problems.

My dream is to start my own company that develops cutting-edge AI innovations to solve prominent problems facing the world. I run a nonprofit initiative called Sanjeevani that aims to bridge the education gap between developing and developed worlds through free and engaging STEM workshops.

Skip testimonial carousel

"I am truly honored to be named a 2023 Davidson Fellow. This recognition validates the hard work, passion, and dedication I've put into my research journey. I’m extremely excited to meet and connect with other outstanding fellows who share the same drive for exploration and innovation in STEM. Together, I believe we can make a real impact on global challenges, and I'm eager to collaborate with brilliant minds to create positive change in the world."

Project Description

Hospital-acquired infections (HAIs) cause a significant number of deaths and cases in the US due to poor hand hygiene compliance among staff. The World Health Organization (WHO) has strict guidelines to reduce HAIs, but hospitals lack effective tools to monitor and enforce compliance. Furthermore, HAIs mainly fall into three categories: CLABSI, CAUTI, and VAP, and early diagnosis is crucial for successful treatment but current methods are inefficient. To address these issues, I designed CareHAI, an automated system comprising two tools. The HAI Prevention Tool is an AI-powered IoT tool that tracks and enforces hand hygiene compliance, while the HAI Early Diagnosis Tool uses machine learning to efficiently and accurately early diagnose HAIs based on dynamic patient data and medical history.

Deeper Dive

According to the World Health Organization (WHO), hospital-acquired infections (HAIs) account for an estimated 99,000 deaths and 1.7 million cases in the United States. The main reason for the spread of these infections is poor staff hand hygiene compliance. WHO has stipulated strict hand hygiene guidelines to be followed in hospitals to reduce the rates of HAIs. However, currently, hospitals have no effective tools to track and enforce staff hand hygiene compliance which is a major problem. Furthermore, HAIs fall into three main categories: central line-associated bloodstream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and ventilator-associated pneumonia (VAP). These infections have extremely high morbidity worldwide, with an estimated overall crude mortality rate of 30%. Early diagnosis and initiation of care are key to successful treatment, but currently, the pathogens for these infections are only identifiable in blood culture which is a time-consuming process. This results in delays in dispensing antibiotics to patients and increases the risk of their death. CareHAI is a novel, automated, and scalable system for hand hygiene compliance monitoring in healthcare facilities and early diagnosis of HAIs. It addresses these problems through an HAI Prevention Tool and an HAI Early Diagnosis Tool. The HAI Prevention Tool is a smart AI-powered IoT tool that tracks and enforces staff hand hygiene compliance throughout hospitals to prevent the spread of HAIs. The HAI Early Diagnosis Tool is a novel machine learning-based software that utilizes an input of dynamic patient clinical test data and previous patient medical history for efficient and accurate early diagnosis of CLABSI, CAUTI, and VAP onset. By addressing both prevention and early detection, CareHAI aims to revolutionize infection control in healthcare settings, reduce mortality rates, and improve patient care.

I was motivated to develop CareHAI after personal experience with HAIs during a trip to India. My mother fell seriously ill with bloodline infection, witnessing her suffering and the subsequent challenges in her treatment sparked my determination to find a solution to this widespread problem.

One of the biggest hurdles I encountered during this project was in creating the modules for the HAI Prevention Tool. Each module needed to perform a specific set of hand hygiene checks, and this required careful consideration in designing each module with right hardware components, calibrating each sensor for desired behavior, and developing optimal software algorithms. It took hours of testing and debugging to ensure that each module was performing as expected. Another major challenge I faced was figuring out how to ensure that staff were not just cleaning hands but also cleaning their hands properly by following all the recommended WHO hand rub/wash process hand motions. To tackle this issue, I designed a hybrid CNN architecture that analyzed images captured by the Sink and Rub module cameras to accurately identify the completion of different WHO hand-cleaning motions. This required a great deal of hyperparameter optimization and model tuning. Lastly, developing the HAI Early Diagnosis Tool was also a significant challenge. With 12,000+ variables in the initial patient dataset, I had to narrow down to the 26 most important variables with high correlation scores with the diagnosis output to ensure maximum accuracy in the machine learning models. This involved using a combination of feature selection algorithms, such as Principal Component Analysis (PCA) and ExtraTreesClassifier, to reduce the dimensions of the dataset and identify the most important variables for early HAI diagnosis. Despite the challenges, developing CareHAI has been an incredibly fulfilling experience. As hospital-acquired infections continue to be a significant problem worldwide, I hope that CareHAI can make a meaningful impact in reducing their prevalence and saving lives.

CareHAI represents a significant contribution to the current and future world of computer science, specifically in the field of healthcare. The integration of AI in this system has the potential to revolutionize the way we approach HAI diagnosis. The use of AI in this system allows for real-time monitoring of patients' clinical variables, enabling early detection of infections and rapid initiation of treatment. This is a major departure from traditional methods, which rely on time consuming and often ineffective patient blood culture analyses. This research is also particularly important in the current world suffering from infectious diseases like COVID-19, where hand hygiene has become a critical measure to prevent the spread of disease. The project allows for real-time monitoring and enforcement of hand hygiene compliance, which can help to keep patients and staff safe. It not only addresses the critical issue of hospital-acquired infections but also highlights how we can leverage AI to solve some of the toughest problems in healthcare. By reducing the incidence of HAIs in hospitals, CareHAI can save countless lives and reduce the overall cost of healthcare. Its implementation in hospitals around the world has the potential to revolutionize the way we prevent and diagnose HAIs.


What is your favorite hobby?

Playing backyard badminton with my family. It's a chance for us to disconnect from the world’s distractions and simply enjoy each other's company.

What is your favorite tradition or holiday?

Diwali Puja with my parents and our family friends. We gather in the prayer room, offer prayers to deities, light oil lamps, and share a joyous feast together. The warmth, love, and togetherness during this festival of lights make it an unforgettable experience.

What is your favorite Olympic sport?

Basketball. There’s something about the fast-paced action, the teamwork, and the skill that makes it so exciting to watch. I especially loved watching the final match between USA and France at the Tokyo 2020 Summer Olympics - a nail-biter, with both teams playing their hearts out.

Click image to download high resolution files

In The News

Portland, Ore. – The Davidson Fellows Scholarship Program has announced the 2023 scholarship winners. Among the honorees is 18-year-old Samyak Shrimali of Portland. Shrimali won a $10,000 scholarship for his project, CareHAI: A Novel Automated Artificial Intelligence and Sensor-Based Multi-Modular System for Early Diagnosis and Prevention of Hospital-Acquired Infections. He is one of only 21 students across the country to be recognized as a 2023 scholarship winner.

Download the full press release here