Technology that helps make records and databases more efficient

In amyotrophic lateral sclerosis (YEAH), as in many diseases, patient registries, biorepositories, and natural history studies are useful both in clinical trial planning and as tools for scientists to learn more about how a person’s lifestyle, genetics, and environment can potentially lead to the ALS. These databases could also help find new ways to treat the disease.

ALS is a rare condition for which not much data is available. But ALS-related institutions are making it easier and more efficient to collect data hosted on platforms like records, thanks to technological advances in artificial intelligence (AI), computing, and smartphones.

Erin Dittoe, 55, was diagnosed with ALS two years ago and is sharing your health data in several different ways.

She has registered in the ALS National Registry from the Centers for Disease Control and Prevention (CDC), participated in genetic tests, and wears a watch that tracks his movements for an observation clinical trial (NCT05276349). That judgment is looking to determine if home measurements could replace repeat visits to the clinic in person.

diagnosed with sporadic ALS — the most common type, where there is no family history — as of November 2020, the progression of Dittoe’s disease has been slow. You can walk with help, talk (albeit slowly), and work from home.

The Ohioan hopes the information it provides will help scientists better understand what causes ALS and how to treat it.

“I feel like [it’s] for people who can’t answer questions for one reason or another, it’s for them,” Dittoe said. “He’s doing my part.”

The ALS Therapy Development Institute (ALS TDI), a nonprofit organization focused on disease research, is seeking Google’s help to optimize data analysis and use of patient data. Google’s application programming interface forms the analytical backbone of its Precision Medicine Program (PMP), which currently has data covering 813 fully enrolled patients, those who contributed information for three months or more. The PMP project, started in 2014, stores information about a patient’s movement capabilities, medical history, genetics, biomarkers, and clinical measurement scores, as well as voice recordings.

According to Fernando Vieira, MD, CEO and Chief Scientific Officer of ALS TDI, Google’s Looker platform, as the interface is called, is a big data analytics platform used primarily for business intelligence. The powerful tool allows researchers to search for people’s information, as detailed as their genome, for some, and compare it to the progression of their disability, as measured by the Revised ALS Functional Rating Score (ALSFRS-R).

Identifiable information, including participant names, Social Security numbers, or email addresses, is not available to researchers. Data that could identify anyone is protected by the Health Insurance Portability and Accountability Act (HIPAA) of 1996 in the US Each participant is only referred to as a number.

To date, ALS TDI has captured 9,873 voice recordings, 19,404 ALSFRS-R scores, and 7,422 weeks of patient data using accelerometers, which calculate how much each user moves.

ALS TDI considers it important to share the data collected with patients, Vieira said, so they can keep track of their own health.

“A key tenet of our program from the beginning has been to treat our participants as partners,” Vieira said. “That, I think, has been key to maintaining compliance and commitment to the efforts, and I would encourage that.”

Recognition of the ‘voices’ of patients

In 2018, AS TDI also partnered with Google on Project Euphonia, which aims to use artificial intelligence to help people with speech problems (dysarthria), a common problem ALS symptom — Speech-enabled interface on your platforms, such as the Google Pixel smartphone.

Google is using the available PMP voice recording data to train its AI to interpret dysarthria speech. The first publicly available tool to come from the Euphonia Project, Related projectis an app that is now in beta testing.

Voice recognition software, such as the one used with Siri or Google Home, recognizes wavelengths that correspond to spoken words. gather those wordsry, through a series of steps, comes to an understanding of someone’s speech and commands, for example, “Turn off the lights.” But it can only interpret phrases based on the quality of the voices used to train it. Speech-impaired people, whose voices are not normally part of a software’s training parameters, often have difficulty using this technology.

waste of vocal clarity and strength are common with ALS. in a YouTube Original Documentary on AI, the father of the former NFL linebacker Tim Shaw he recounts his son’s frustrations with his increasing lack of clarity in speaking. As his ALS progressed, the former football player had to change his phone contact information from “dad” to “yo-yo” because he couldn’t understand Shaw’s command to “call dad.”

ALS TDI’s work with Google also created a algorithm, derived from machine learning, that could better tracking of changes in disease severity based on a patient’s movement and speech data that ALSFRS-R tests taken at a given time. The code behind the tool has been done. available to scientists use and continue to improve.

While such technology is still being perfected, its use continues to grow. As part of his trial, Dittoe is recording your voice into an app that aims to track the progression of the disease through changes in speech.

What blood samples and past HIV work could teach

With its PMP project, ALS TDI is also collecting data from patient blood samples, looking for biological measures of disease progression. This work is supported by a $281,000 grant awarded to the institute in March by the Congressionally Directed Medical Research Programs, part of the US Department of Defense.

ALS TDI plans to send blood samplees, collected quarterly for a year, to SomaLogic, a Colorado company, “to look at the concentrations of thousands of cytokines, growth factors, … kinases, structural proteins, hormones, and other proteins in each sample” that could serve as biomarkers.

Vieira compares the race to cure ALS with the search for HIV treatments.

In the mid-1990s, scientists conducting a natural history study of HIV found a certain genetic variation that conferred resistance to the virus. Specifically, they found that people with gene mutations leading to a “non-functional” or inactive CCR5 receptor protein were “highly resistant” to the infectious disease. This pointed to CCR5 as a promising target to prevent HIV infection.

Through a study “designed to teach you,” Vieira said, scientists were able to more quickly develop “drugs that target that receptor” and better treat HIV.

“They were able to reveal that you can walk into a natural history study knowing nothing and allow it to show you something,“ he said, adding, “And that’s how, I think, we’ve come into this [ALS data] … assuming we know very little, collecting as much data as we can, and then allowing them to show us what’s important over time.”

Like that HIV natural history study, whose scientists “didn’t know how many people they needed, or how long they’d have to search…we don’t know how many people we need or how long we’re going to have to search.” Look,” Vieira said. “But it will be the source of many of our answers.”

This article is one of three published as part of the Rare Disease Grant through the National Press Foundation.