{"id":4727,"date":"2022-07-14T08:00:01","date_gmt":"2022-07-14T15:00:01","guid":{"rendered":"https:\/\/keck.usc.edu\/news\/algorithm-nation\/"},"modified":"2023-12-06T21:51:08","modified_gmt":"2023-12-07T05:51:08","slug":"algorithm-nation","status":"publish","type":"post","link":"https:\/\/keck.usc.edu\/news\/algorithm-nation\/","title":{"rendered":"Algorithm nation"},"content":{"rendered":"\n \n \n\n\n\n\n\n\n\n
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\n\n \n Press Release<\/span>\n\n\n\n<\/div>\n \n \n
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Algorithm nation<\/h1>\n\n\n<\/div>\n \n \n
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Artificial intelligence amplifies core research strengths at the Keck School of Medicine of USC<\/p>\n\n\n\n<\/div>\n \n

\n Wayne Lewis<\/span>\n \n July 14, 2022<\/span>\n <\/div>\n \n \n
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Illustration of a machine learning network used in neuroimaging. (Courtesy of Jim Stanis and Arthur W. Toga – USC Mark and Mary Stevens Neuroimaging and Informatics Institute)<\/figcaption>\n <\/figure>\n \n \n \n\n<\/div>\n \n\n <\/div><\/div>\n\r\n\r\n\n\n \n \n\n\n\n\n\n\n\n
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Since the idea of thinking machines first appeared in literature, artificial intelligence has often been portrayed as a future villain or hero. But the fact is, AI is here now and already suffuses our day-to-day lives. AI keeps people safer while driving. It helps around homes through myriad smart technologies. It answers questions from trivial to profound through search engines.<\/p>\n

At the Keck School of Medicine of USC, AI has potential for broader impact still \u2014 as an enabling tool for investigations to improve human health. Algorithmic brawn fortifies longtime areas of strength at USC, such as research into cancer, neurodegenerative disease and population health, and aids in snooping out the biomedical needle in the hay.<\/p>\n

\u201cOur innovative doctors and scientists are using AI to merge clinical medicine and research,\u201d said Steve Shapiro, MD, USC\u2019s senior vice president for health affairs. \u201cBy making sense of real-world data, we may learn things no one else knows. The results could help to individualize health care, to make care more affordable, accessible and equitable, and to create new cures.\u201d<\/p>\n

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\u201cOur academic enterprise focuses on addressing the most complex challenges to health, and to make progress, we embrace the opportunities presented by advances in computation,\u201d
\n\u2013Dean Carolyn Meltzer, MD<\/p>\n<\/blockquote>\n

Trojan scientists are penning early chapters of a barrier-breaking tale fusing human ingenuity and computational muscle. The lifesaving and life-alerting implications range from new preventive strategies to more-effective drugs, from earlier diagnosis to enhanced critical care.<\/p>\n

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The right environment for future-forward AI health exploration<\/h3>\n

What sets the Keck School of Medicine of USC apart in the quest to harness the power of health-related\u00a0 data with machine learning? The investigators setting the stage for tomorrow\u2019s AI-enabled breakthroughs point to several elements that constitute a distinctive edge.<\/p>\n

David Conti, PhD, Kenneth T. Norris, Jr. Chair in Cancer Prevention, says that multiethnic research cohorts \u2014\u00a0 including the diverse populations served by USC\u2019s medical teams \u2014 and success in community outreach are major advantages.<\/p>\n

\u201cI can\u2019t emphasize how important diversity is,\u201d he said. \u201cTo get the most out of these AI techniques, you need the right data. And you need community engagement, where USC is making strides forward.\u201d<\/p>\n

He also joins others in extolling the collegial, collaborative environment at the medical school, and USC in general. For Arthur Toga, PhD, director of the USC Stevens Neuroimaging and Informatics Institute, the easy flow of information between clinicians, scientists and engineers is imperative.<\/p>\n

\u201cFor our studies, we need communication about the needs of patients and those who care for them,\u201d he said. \u201cAdd to that our close relationship with the USC Viterbi School of Engineering \u2014 where I have an appointment \u2014 and we have a unique opportunity here.\u201d<\/p>\n

Likewise, Paul Thompson, PhD, associate director of the USC Stevens Neuroimaging and Informatics Institute, values the chance to tap the expertise of colleagues at the USC Information Sciences Institute about using machine learning. He sees the community spirit of the Trojan Family as a factor that encourages such connection.<\/p>\n

\u201cWhat\u2019s really special about USC is the positive, friendly communication,\u201d he said. \u201cCompared to other institutions, there\u2019s an ethos where people are willing to help each other out and teach each other a bit.\u201d<\/p>\n

Sebina Bulic, MD, director of the stroke neurology service, echoed that value of crossdisciplinary teamwork.<\/p>\n

\u201cYou need the clinician to identify problems,\u201d she said. \u201cOur engineers are eager to face this challenge. The school\u2019s informatics team is ready. We just need enough clinicians who are curious \u2014 or frustrated \u2014 about something that can be solved with machine learning.\u201d<\/p>\n<\/div>\n<\/div>\n

\u201cOur academic enterprise focuses on addressing the most complex challenges to health, and to make progress, we embrace the opportunities presented by advances in computation,\u201d said Dean Carolyn Meltzer, MD, holder of the May S. and John H. Hooval Dean\u2019s Chair in Medicine and professor of radiology. \u201cMore and more, our researchers are finding creative ways to channel artificial intelligence to uncover new knowledge and cultivate solutions to the problems that threaten life and erode quality of life. I\u2019m excited to see where these avenues of innovation will lead.\u201d<\/p>\n

AI\u2019s unique advantages for biomedical research<\/strong><\/p>\n

This story is not about devices or algorithms replacing humans. Rather, it\u2019s about experts deploying powerful tools to do things that they couldn\u2019t otherwise. This \u201cintelligence\u201d doesn\u2019t rival humans, but instead complements them.<\/p>\n

Machine learning is a primary enabling AI technique at the Keck School of Medicine. This method\u2019s potency as a force multiplier for research resides in today\u2019s abundance of data. On one hand, electronic health records and devices such as sensors provide mountains of metrics. On the other, insight into the umpteen elements of the invisible world controlling biology only grows.<\/p>\n

\u201cTwenty years ago, we were able to measure maybe a handful of genetic variants along the genome, so we had to use prior knowledge to pick which ones to study,\u201d said David Conti, PhD, holder of the Kenneth T. Norris, Jr. Chair in Cancer Prevention, professor of population and public health sciences, and associate director for data science integration at the Keck School. \u201cAs technology ramps up, we can measure all across the genome and get to these random variants while still looking at the ones that we think are biologically interesting. We want to build a model that aggregates all of that information, and that\u2019s where machine learning can really come into play.\u201d<\/p>\n

Data is fuel for machine learning. The more thoughtfully selected data that is fed to a machine-learning algorithm as it \u201ctrains,\u201d the more impact that the results will have. The fundamental application, across contexts, is separating signal from noise, identifying patterns within seeming chaos that confounds both human intellect and most previous technological tools.<\/p>\n

\u201cMachines are specifically designed to handle lots of decisions simultaneously,\u201d said Arthur Toga, PhD, holder of the Ghada Irani Chair in Neuroscience and Provost Professor of Ophthalmology, Neurology, Psychiatry and the Behavioral Sciences, Radiology and Engineering at USC. \u201cWe can combine an incredibly diverse array of observations in individuals and groups of individuals, feed them into these algorithms, and allow them to derive patterns that may be difficult for us to observe as humans. That\u2019s where the marriage between big data and sophisticated artificial intelligence must occur, because one is dependent on the other.\u201d<\/p>\n

Ultimately, machine learning is enabling Keck School of Medicine researchers to approximate reality. Each person\u2019s health and well-being is influenced by layered, interreacting factors: genes and environmental exposures, immunity and metabolism, lifestyle and socioeconomic factors. AI offers researchers the capacity to explore these numerous dimensions and sort what\u2019s important from what isn\u2019t.<\/p>\n

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\u201cWe can combine an incredibly diverse array of observations in individuals and groups of individuals, feed them into these algorithms, and allow them to derive patterns that may be difficult for us to observe as humans. That\u2019s where the marriage between big data and sophisticated artificial intelligence must occur, because one is dependent on the other.”
\n\u2013 Arthur Toga, PhD<\/p>\n<\/blockquote>\n

\u201cThere are just too many variables to deal with,\u201d said Neil Bahroos, chief research informatics officer and associate professor of research in population and public health sciences. \u201cWith AI, we can plug in all these factors and see which trends we\u2019re looking at. It\u2019s a total gamechanger.\u201d<\/p>\n

Leveraging AI across the spectrum of health care<\/u><\/strong><\/p>\n

AI brings the big picture into focus for prevention and community health<\/strong><\/p>\n

For Conti, who also serves as associate director for data science at the USC Norris Comprehensive Cancer Center, machine learning is one important tool in a more comprehensive kit of statistical techniques he uses to tease out causes for cancer and other diseases.<\/p>\n

AI helps him mine insights from so-called \u201comics\u201d \u2014 diverse fields of analysis seeking to comprehensively profile details coded in molecules such as DNA, RNA, proteins and metabolic products \u2014 and the \u201cexposome,\u201d environmental inputs such as chemicals and air pollution. His discoveries may, in turn, inform policy and empower people to make protective choices.<\/p>\n

\u201cEpidemiology is all about identifying a risk factor,\u201d Conti said. \u201cAs more dimensions come into focus, it\u2019s less likely we can leverage prior knowledge to pick out that specific risk factor. We need to leverage machine learning to say, How do we select from a massive number of features?\u201d<\/p>\n

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\u201cIf we develop models with data that is limited to individuals of a certain ancestry or ethnic group, they won\u2019t be applicable to others and it could actually increase health disparities\u201d
\n\u2013 David Conti, PhD<\/p>\n<\/blockquote>\n

Just as his work requires careful choosing of statistical tools, it also calls for conscious curation of data. That means the inclusion of datasets about diverse populations that have traditionally lacked adequate representation in research, such as African Americans. The endeavor may push forward efforts in health justice.<\/p>\n

\u201cIf we develop models with data that is limited to individuals of a certain ancestry or ethnic group, they won\u2019t be applicable to others and it could actually increase health disparities,\u201d Conti said. \u201cWe\u2019ve always been really focused on looking at how risk factors differ, or are similar, across different groups, and then making conclusions that are relevant across them.\u201d<\/p>\n

Computation that advances early detection and diagnosis<\/strong><\/p>\n

As Conti demonstrates the predictive power of his work, the results may provide new ways to screen for cancers, one of numerous health threats the medical school\u2019s researchers seek to ferret out before the damage is done.<\/p>\n

Among the cruelties of neurodegenerative diseases such as Alzheimer\u2019s is the fact that the brain is not built to recover from them. Early detection must accompany any interventions developed to ameliorate the conditions. That\u2019s why Toga and his colleagues hope to pinpoint markers that precede symptoms.<\/p>\n

\u201cWe aren\u2019t seeing an index for disease progression until fairly late in the game,\u201d said Toga, who directs the Mark and Mary Stevens Neuroimaging and Informatics Institute and leads the Laboratory of Neuro Imaging. \u201cWe need to know earlier, because what you lose, you don\u2019t get back. Machine learning helps us make the literally millions and millions of comparisons that might indicate somebody is in the earliest stages of change to brain tissue.\u201d<\/p>\n

He heads up programs that present tremendous assets. The USC Stevens Neuroimaging and Informatics Institute has the world\u2019s largest collection of training data for machine learning, comprising imaging, genetics, biosample, cognitive and electrophysiology data.<\/p>\n

With this resource, his team ties together machine vision reading out medical images with potential biochemical or genetic indications for Alzheimer\u2019s risk and signs of disease. Image analysis also drive progress in estimating \u201cbrain age,\u201d which sometimes varies significantly from calendar age.<\/p>\n

Importantly, USC shares the wealth and coordinates resources across institutions. Toga is a principal investigator of the Data Archive BRAIN Initiative (DABI), a shared repository for brain physiology data. Meanwhile, he also leads the Global Alzheimer\u2019s Association Interactive Network (GAAIN), with data about 560,000 participants with Alzheimer\u2019s disease. If a researcher identifies a pertinent dataset, they can then seek permission from those who gathered it.<\/p>\n

\u201cIt\u2019s a matchmaking thing that seems to have found an incredible sweet spot to facilitate global cooperation,\u201d he said. \u201cThis is important when you have a difficult set of problems such as Alzheimer\u2019s disease.\u201d<\/p>\n

Another major international effort with ample feedstock for AI is led by Toga\u2019s colleague and associate director of the USC Stevens Neuroimaging and Informatics Institute, Paul Thompson, PhD, professor of ophthalmology, neurology, psychiatry and the behavioral sciences, radiology and engineering. \u00a0<\/p>\n

The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines data and expertise from more than 2,000 scientists in over 40 countries to tackle a portfolio of 30 brain diseases. The consortium has mapped the effects in the brain of conditions including schizophrenia, bipolar disorder, depression, posttraumatic stress disorder and autism.<\/p>\n

Some of Thompson\u2019s own investigations employ machine learning to link images of the brain to genetic changes that could be cues for screening in Alzheimer\u2019s. Other pieces aim to forecast health outcomes. The future may hold algorithms that derive important information from comparing an individual to millions of others.<\/p>\n

\u201cWe envision that doctors will determine chances of recovery with alternative treatment choices, with help from machine learning,\u201d Thompson said. \u201cAI can deliver a better diagnosis, prognosis and treatment choice. Those are the big three we\u2019d like to happen.\u201d<\/p>\n

Breaking a new path for improved critical care<\/strong><\/p>\n

Neurologist Sebina Bulic started with a clinical issue that concerned her deeply. She ended up pioneering the use of AI to extract meaning from the abundance of data in electronic health records at USC.<\/p>\n

In the Neurocritical Care Program, fever, high counts of immune cells and accelerated heart rate\u00a0are common symptoms of brain injury. Elsewhere, these are signs of infection, and automated alerts that would be crucial in any other intensive care unit are often false positives. As a result, it is difficult to differentiate between real infection and an injured brain.<\/p>\n

\u201cThe stakes are so high in any ICU,\u201d said Bulic, MD, assistant professor of clinical neurology and director of the stroke neurology service. \u201cIn infection, the earlier you start treatment, the better. You simply cannot miss the signs.\u201d<\/p>\n

She decided to do something about it. She assembled a team to develop an algorithm customized to neurocritical care for identifying actual signs of infections that happen in the hospital.<\/p>\n

The pilot program was a classic example of USC collaboration. It pulled in the Keck enterprise data warehouse team, a research team from the Department of Surgery, the SC Clinical and Translational Science Institute and engineers from Amazon Web Services. Bulic is also grateful for the counsel of Daniel Pelletier, MD, holder of the Eric and Peggy Lieber Chair in Neurology, whose own investigations into multiple sclerosis use imaging, genetic data and machine learning.<\/p>\n

After two years of hard work, the collaboration\u2019s findings await peer-reviewed publication. Being the first to attempt a clinical application from USC patient data required persistence, especially amid the disruptions of the COVID-19 pandemic. Adjustments were also necessary along the way to gain value from essential data in results locked up in PDF form, using an AI subfield called natural language processing.<\/p>\n

\u201cThe algorithm took into consideration which data points made the most impact,\u201d Bulic said \u201cI want to continue to cut out the noise and identify data points that are available in any electronic health record, so this can be scaled up to all neuro ICUs in the country.\u201d<\/p>\n

Bahroos, the Keck School of Medicine\u2019s chief research informatics officer, identifies her project as a prototype for the future. Applying machine learning to health records for patient benefit is one main objective of the Health Data Innovation Program, his team\u2019s nascent venture to advance USC\u2019s biomedical research.<\/p>\n

\u201cWe want to make this a part of production for Keck Medicine,\u201d he said. \u201cThis can then serve as a template to tackle other priorities such as preventing pressure ulcers and urinary tract infections in the hospital. Predictive analytics could really increase quality and improve health outcomes.\u201d<\/p>\n

The vision of a digital guide to the most effective treatments<\/strong><\/p>\n

Studies led by Conti, of the Department of Population and Public Health Sciences and USC Norris cancer center, also set the table for precision medicine approaches to cancer. This is a vision for care custom tailored to each patient\u2019s case, such as the use of drugs that target a specific mutation found in a tumor.<\/p>\n

At the same time, neuroscientist Paul Thompson is turning AI toward drug discovery for Alzheimer\u2019s. He is the leader of the NIH-funded project Ultrascale Machine Learning to Empower Discovery in Alzheimer\u2019s Disease Biobanks, known as AI4AD, which involves 40\u00a0co-investigators at 11 research centers.<\/p>\n

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\u201cWe excel because we give independent minds all this autonomy to flourish,\u201d
\n\u2013 Neil Bahroos<\/p>\n<\/blockquote>\n

Alongside efforts to predict an Alzheimer\u2019s diagnosis and advance algorithms themselves, AI4AD nurtures investigations that may identify new drug targets \u2014 an urgent need.<\/p>\n

\u201cThere\u2019s a new FDA-approved drug for Alzheimer\u2019s, and it doesn\u2019t work for everyone,\u201d Thompson said. \u201cIt\u2019s very important to find new ones. We\u2019re developing AI methods to identify the hotspots in the genome that promote Alzheimer\u2019s, so we can find a way to switch them off.\u201d<\/p>\n

Just the start: lowering the barrier of entry for AI-enabled health research \u00a0<\/u><\/strong><\/p>\n

AI-enabled research is an area of growth at the Keck School of Medicine thanks to the Health Data Innovation Program. Bahroos is staking out a big tent.<\/p>\n

\u201cMy goal is to make sure that researchers in all domains can benefit,\u201d he said. \u201cIt can be a high burden for a research group to figure out all of this technology. We plan to make it easy.\u201d<\/p>\n

One early focus has been building a cloud-based system where data can be consolidated across USC research groups. A catalog comprehensive in scope and searchable detail would be a substantial boon to Trojan investigators. Basing it remotely offers the chance \u2014 as with Toga and his GAAIN collaborators \u2014 to provide access while protecting researchers\u2019 datasets.<\/p>\n

Bahroos sees the approach as well-suited to a fundamental USC advantage in research.<\/p>\n

\u201cWe excel because we give independent minds all this autonomy to flourish,\u201d he said. \u201cFor discovery-driven studies, people want to explore. I ask them, \u2018What is your question?\u2019 They say, \u2018Let me see what\u2019s there, and I\u2019ll come up with questions.\u2019 Our program is going to help them do that.\u201d<\/p>\n\n\n\n<\/div>\n <\/div>\n\n\n <\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":163,"featured_media":4728,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"advgb_blocks_editor_width":"","advgb_blocks_columns_visual_guide":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[6],"tags":[11,204,234,169,20],"class_list":["post-4727","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-press-release","tag-artificial-intelligence","tag-deans-page","tag-department-of-neurology","tag-latest","tag-research"],"acf":[],"yoast_head":"\nAlgorithm nation<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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