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How Machine Learning Can Help Improve Blood Pressure Treatment

New research shows how a machine-learning technique could provide insight into how to find the patients that would benefit the most from treatment for hypertension.

The study, which came out of UCLA, describes how a machine-learning technique known as “casual forest” could determine the hypertension patients that would benefit the most from treatment rather than assuming that the highest-risk patients require the most clinical attention.

According to the Centers for Disease Control and Prevention (CDC), over 670,000 deaths in the US can be attributed annually to hypertension. In addition, while about 47 percent of US adults have hypertension, only 24 percent of this population has the condition under control.

Traditionally, clinicians treating patients with high blood pressure focus on those with the highest risk of poor outcomes, as the assumption is that they will require the highest level of treatment. The researchers set out to see if they could leverage AI to treat patients based on benefit rather than risk for improved outcomes. They found their solution in a new ML technique, coined “casual forest.”

The study included data from 10,672 participants, all of whom were randomized to systolic blood pressure (SBP) targets of either less than 120 mmHg or less than 140 mmHg from two randomized controlled trials.

The researchers used the casual forest technique to create a prediction model of individualized treatment effects related to the control of SBP and its correlation with reductions in adverse cardiovascular outcomes after three years.

They found that 78.9 percent of individuals with an SBP greater than 130 mmHg achieved benefits from intensive SBP control.

“We found that a substantial number of individuals without hypertension benefited from lowering their blood pressure,” said lead author Kosuke Inoue, MD, Ph.D., who undertook the study while an epidemiology graduate student at the UCLA Fielding School of Public Health and is now an associate professor of social epidemiology at Kyoto University, in a press release. “By applying the causal forest method, we found that treating individuals with high estimated benefits provided better population health outcomes than the traditional high-risk approach.”

Further, the researchers noted that high-benefit approaches could increase the efficacy associated with treatment, potentially being more reliable compared to high-risk approaches.

How BigRio Helps Bring Advanced AI Solutions to Healthcare

As the UCLA researchers have discovered, improving disease detection and making better decisions on the allocation of medical resources is an area where AI and machine learning are making a huge impact in healthcare.

BigRio prides itself on being a facilitator and incubator for such advances in leveraging AI to improve treatment and medical outcomes. In fact, it was my father’s own battle with and eventual death from lung disease that set me on my path to finding ways to use AI to provide earlier detection of serious medical conditions.

In fact, we have launched an AI Studio specifically for US-based Healthcare startups with AI centricity. Our mission is to help AI startups scale and gear up to stay one step ahead of the pack and emerge as winners in their respective domains.

AI Startups face numerous challenges when it comes to demonstrating their value proposition, particularly when it comes to advanced AI solutions for pharma and healthcare. We have taken an award-winning and unique approach to incubating and facilitating startups that allow the R&D team and stakeholders to efficiently collaborate and craft the process to best suit actual ongoing needs, which leads to a faster, more accurate output.

We provide:

  • Access to a top-level talent pool, including business executives, developers, data scientists, and data engineers.
  • Assistance in the development and testing of the MVP, Prototypes, and POCs.
  • Professional services for implementation and support of Pilot projects
  • Sales and Marketing support and potential client introductions.
  • Access to private capital sources.

BigRio excels in overcoming such initial hurdles, which present nearly insurmountable obstacles to a startup operation.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint.

Rohit Mahajan is a Managing Partner with BigRio. He has particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

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Can AI Be Used to Predict Suicide Risk?

There has been no lack of news about how AI is redefining healthcare and improving patient outcomes for physical ailments. There has not been that much news, however, on how AI can help in the treatment of emotional or mental disorders.

Until now.

Researchers have just released the results of a study in which they used AI to better predict suicide risk.

The scientists from Worcester Polytechnic Institute (WPI) and Harvard Medical School-affiliated McLean Hospital in Belmont conducted a study assessing the use of AI to predict and gain a better understanding of suicide and the mechanisms and emotional states that drive self-injury.

This particular study targeted women because, according to the CDC, death by suicide is increasing at an alarming rate among women. The group of researchers developed an algorithm that was designed to predict suicide attempts among participants and identify subgroups of patients who were at the highest risk of entering a suicidal mindset.

They then used AI approaches to cluster data to expose any existing patterns. The patterns revealed a broad set of dissociative symptoms, such as the lack of connection between one’s sense of self and the environment. This was often due to trauma, the researchers said.

Following this step, researchers trained an algorithm to distinguish between patients with various dissociation levels and the 30 healthy controls. They found that this tool could zero in on specific dissociative symptoms, predicting previous suicide attempts with an accuracy of nearly 90% percent.

Aside from AI being able to predict thoughts of self-harm and previous suicide attempts accurately, researchers also noted that the study emphasized the need for clinicians to assess patients for dissociative disorder symptoms.

“We’re trying to say that among these hundreds of symptoms and indicators, our results suggest these two or three symptoms may be helpful to focus in on,” said Dmitry Korkin, Ph.D., the Harold L. Jurist ’61 and Heather E. Jurist Dean’s Professor of Computer Science at WPI, one of the lead researchers on the project.

The key takeaway from this study is that it is one of the first to show that AI-driven programs can give clinicians predictive and theoretically preventive tools for mental illnesses, just as they are already doing in practice for many physical disorders.

How BigRio Helps Bring Advanced AI Solutions to Healthcare

The WPI research is one of the few studies that are now showing that AI has predictive value in mental health, just as it has already been put into practice in improving physical conditions. In fact, improving disease detection and diagnostics – whether that be physical, and now we see mental health — is the area where AI is making one of the technology’s biggest impacts.

BigRio prides itself on being a facilitator and incubator for such advances in leveraging AI to improve diagnostics.

In fact, we have launched an AI Studio specifically for US-based Healthcare startups with AI centricity. Our mission is to help AI startups scale and gear up to stay one step ahead of the pack and emerge as winners in their respective domains.

AI Startups face numerous challenges when it comes to demonstrating their value proposition, particularly when it comes to advanced AI solutions for pharma and healthcare. We have taken an award-winning and unique approach to incubating and facilitating startups that allow the R&D team and stakeholders to efficiently collaborate and craft the process to best suit actual ongoing needs, which leads to a faster, more accurate output.

We provide:

• Access to a top-level talent pool, including business executives, developers, data scientists, and data engineers.

• Assistance in the development and testing of the MVP, Prototypes, and POCs.

• Professional services for implementation and support of Pilot projects

• Sales and Marketing support and potential client introductions.

• Access to private capital sources.

BigRio excels in overcoming such initial hurdles, which present nearly insurmountable obstacles to a startup operation.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

 

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AI Leverages Nursing Expertise to Improve Patient Outcomes

The know-how and experience of nurses are a critical body of knowledge and expertise about patients and patient care. Wouldn’t it be wonderful if there was a way to pool all of that knowledge into one place for hospitals and health organizations to make better decisions about patients?

There is, and as you might imagine, it involves AI.

Several health systems, led by the Columbia University Irving Medical Center (CUIMC), are testing an AI-driven predictive tool that is attempting to emulate nurses’ seemingly innate ability to pick up cues about patients’ health from subtle changes in behavior and appearance.

According to its developers, CONCERN (COmmunicating Narrative Concerns Entered by RNs) is a predictive tool that extracts nurses’ expert and knowledge-driven behaviors within patient health records and transforms them into observable data that support early prediction of organ failure or other critical conditions in hospitalized patients.

CUIMC is partnering with three hospital systems — Mass General Brigham (MA), Vanderbilt University Medical Center (TN), and Washington University School of Medicine/Barnes-Jewish Hospital (MO) — to test the effectiveness of the CONCERN implementation toolkit, developed to support large-scale adoption of the tool.

This initiative recently received funding from the American Nurses Foundation through the Reimagining Nursing Initiative.

“CONCERN shows what nurses already know: Our risk identification is not simply a subjective clinical hunch,” said Sarah Rossetti, assistant professor of biomedical informatics and nursing at Columbia, in a statement. “We’re demonstrating that nurses have objective, expert-based knowledge that drives their practice, and we’re positioning nurses as knowledge workers with tremendous value to the entire care team.”

Annually, more than 200,000 patients die in US hospitals from cardiac arrest, and over 130,000 patients’ deaths are attributed to sepsis. Many of these deaths could be preventable if patients who are at risk are detected earlier. Prior work from the CONCERN team found that nursing documentation within EHRs contains information that could contribute to early detection and treatment, but these data are not being analyzed and exposed by EHRs to clinicians to initiate interventions quickly enough to save patients.

How BigRio Helps Bring Advanced AI Solutions to Healthcare

Like the CONCERN project, leveraging human expertise and adapting to the predictive power of AI algorithms is an area where AI and machine learning are making one of the technology’s biggest impacts in the healthcare field.

BigRio prides itself on being a facilitator and incubator for such advances in leveraging AI to improve patient outcomes. In fact, it was my father’s own battle with and eventual death from lung disease that set me on my path to finding ways to use AI to provide earlier detection of serious medical conditions for improved patient care.

Eventually, among our other success stories, we did collaborate with a researcher who is in the process of developing a cognitive digital twin of the human lung. Right now, that technology is being used specifically in the realm of testing inhalers for asthma patients, but like the CONCERN project, it has broader implications for better diagnostics and early interventions to save lives.

We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people in the biomedical community.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

 

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Artificial Intelligence and the Early Detection of Osteoarthritis

Most patients and every medical practitioner know that when it comes to chronic debilitating diseases, the earlier they can be detected and treated, the better. This is one of the major promises of AI in healthcare, improved diagnostics for earlier detection and better patient outcomes.

The latest such application comes as researchers are developing an AI solution that can find the early sign of osteoarthritis of the knee. Like many AI-driven diagnostic enhancements, this one can see subtle signs on X-rays better than the human eye. This is critical because x-rays are the primary diagnostic method for early knee osteoarthritis. An early diagnosis can save the patient from unnecessary examinations, treatments, and even knee replacement surgery.

Osteoarthritis is the most common joint-related ailment globally. In Finland alone – where this research took place — it causes as many as 600,000 medical visits every year. It has been estimated to cost the national economy up to EUR1 billion every year.

The new AI-based method was trained to detect a radiological feature predictive of osteoarthritis from x-rays. The method was developed in cooperation with the Digital Health Intelligence Lab at the University of Jyvaskyla as a part of the AI Hub Central Finland project. It utilizes neural network technologies that are widely used globally.

“The aim of the project was to train the AI to recognize an early feature of osteoarthritis from an x-ray. Something that many experienced doctors can visually distinguish from the image, but cannot be done automatically, and is often missed by the untrained eye,” explains Anri Patron, the researcher responsible for the development of the method.

The anomaly the AI has been trained to automatically detect is to see if there is “spiking” on the tibial tubercles in the knee joint or not. Tibial spiking is known to be an early sign of osteoarthritis.

The researchers say that the AI matched human doctors’ assessment of the presence of spiking in nearly 90% of cases, instantly, without the need to scrutinize and deeply examine the x-rays as the human orthopedic surgeons did.

The research offers definitive proof that AI can support early diagnosis of osteoarthritis at the point of primary healthcare before a patient is referred to an orthopedic specialist, which can make a major difference in catching and treating knee arthritis early.

“If we can make the diagnosis in the early stages, we can avoid uncertainty and expensive examinations such as MRI scanning. In addition, the patient can be motivated to take measures to slow down or even stop the progression of symptomatic osteoarthritis. In the best possible scenario, the patient might even avoid joint replacement surgery,” sums up professor of surgery Juha Paloneva, one of the Finnish researchers on the project.

How BigRio Helps Healthcare AI Startups

Like the technology developed by the researchers with the Central Finland Health Care District, BigRio is also a facilitator and incubator for AI startups, particularly in healthcare. In fact, it was my father’s own battle with and eventual death from lung disease that set me on my path to finding ways to use AI to improve healthcare delivery.
Eventually, among our other success stories, we did collaborate with a researcher who is in the process of developing a cognitive digital twin of the human lung. Right now, that technology is being used specifically in the realm of testing inhalers for asthma patients, but like the UWS tool, it has broader implications for better diagnostics and treatments for COPD and other lung diseases.
We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people in the biomedical community.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

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AI Is Being Used for Earlier Detection of Urinary Tract Infections

Urinary tract infections, commonly known as UTIs, usually do not pose serious health risks – when they are detected and treated early. However, when allowed to advance undetected past a certain point, a number of serious adverse outcomes can result from late or misdiagnosis of UTI.

A group of researchers from the University of Edinburgh and Heriot-Watt University are developing artificial intelligence and “socially assistive robots” to detect urinary tract UTIs earlier and ensure better patient outcomes.

UTIs affect 150 million people worldwide annually, making it one of the most common types of infection. When diagnosed early, it can be treated with antibiotics. If left untreated, UTIs can lead to sepsis, kidney damage, and even loss of life.

Diagnosis, however, can be difficult with lab analysis, a process taking up to 48 hours, providing the only definitive result. Early signs of a UTI can also be challenging to recognize because symptoms vary according to age and existing health conditions. There is no single sign of infection but a collection of symptoms which may include pain, fever, increased need to urinate, changes in sleep patterns, and tremors.

To address these concerns, the researchers are working with two industry partners from the care sector who are helping the scientists to develop machine learning methods and interactions with socially assistive robots to support earlier detection of potential infections and raise an alert for investigation by a clinician.

The project will gather continual data about the daily activities of individuals in their homes via sensors that could help spot changes in behavior or activity levels and trigger an interaction with a socially assistive robot. Known as “FEATHER,” the AI platform will combine and analyze these data points to flag potential infection signs before an individual or caretaker is even aware that there is a problem. Behavioral changes that could indicate UTI include changes in walking pace, increased frequency of urination, changes in cognitive function, or a change in sleep patterns, all of which could be noticed and documented by interaction with the assistive robot.

The AI and implementation aspects of the project will be led by Professor Kia Nazarpour, Dr. Nigel Goddard, and Dr. Lynda Webb from the University of Edinburgh. The Human-Robot Interaction aspects will be led by Professor Lynne Baillie, assisted by Dr. Mauro Dragone, from Heriot-Watt University.

Professor Kia Nazarpour, project lead and Professor of Digital Health at the School of Informatics, University of Edinburgh, said, “This unique data platform will help individuals, caretakers, and clinicians to recognize the signs of potential urinary tract infections far earlier, helping to prompt the investigations and medical tests needed. Earlier detection makes timely treatment possible, improving outcomes for patients, lowering the number of people presenting at hospital, and reducing costs to the NHS.”

How BigRio Helps Bring Advanced AI Solutions to Healthcare

Like the FEATHER project, improving disease detection, medical imaging, and diagnostics is an area where AI and machine learning are making one of the technology’s biggest impacts.

BigRio prides itself on being a facilitator and incubator for such advances in leveraging AI to improve diagnostics. In fact, it was my father’s own battle with and eventual death from lung disease that set me on my path to finding ways to use AI to provide earlier detection of serious medical conditions for improved patient outcomes.

Eventually, among our other success stories, we did collaborate with a researcher who is in the process of developing a cognitive digital twin of the human lung. Right now, that technology is being used specifically in the realm of testing inhalers for asthma patients, but like the FEATHER UTI detection tool, it has broader implications for better diagnostics and treatments for COPD and other lung diseases.

We like to think of ourselves as a “Shark Tank for AI.”

If you are familiar with the TV series, then you know that, basically, what they do is hyper-accelerate the most important part of the incubation process – visibility. You can’t get better visibility than getting in front of celebrity investors and a TV audience of millions of viewers. Many entrepreneurs who have appeared on that program – even those who did not get picked up by the sharks – succeeded because others who were interested in their concepts saw them on the show.

At BigRio, we may not have a TV audience, but we can do the same. We have the contacts and the expertise to not only weed out the companies that are not ready, as the sharks on the TV show do but also mentor and get those that we feel are readily noticed by the right people in the biomedical community.

Rohit Mahajan is a Managing Partner with BigRio. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation, and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.

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