Philip Resnik, standing in the background, and a colleague in the Computational Linguistics and Information Processing Laboratory on the Maryland campus are seen collaborating together while looking at display screens
Philip Resnik, standing, is a computational linguist at the University of Maryland. He is working to apply machine learning techniques to social media data in an attempt to make predictions about important aspects of mental health. He is shown here with a colleague in the Computational Linguistics and Information Processing Laboratory on the Maryland campus.
Credit: John T. Consoli / University of Maryland

How a university researcher is using machine learning to help identify suicide risk

Using social media data, the University of Maryland's Philip Resnik aims to help clinicians prioritize individuals who may need immediate attention.

Philip Resnik was a computer science undergrad at Harvard when he accompanied a friend to her linguistics class. Through that course, he discovered a fascination with language. Given his background, he naturally approached the topic from a computational perspective.

Now a professor at the University of Maryland in the Department of Linguistics and the Institute for Advanced Computer Studies, Resnik has been doing research in computational linguistics for more than 30 years. One of his goals is to use technology to make progress on social problems. Influenced by his wife, clinical psychologist Rebecca Resnik, he became especially interested in applying computational models to identify linguistic signals related to mental health.

“Language is a crucial window into people's mental state,” Resnik said.

With the support of Amazon’s Machine Learning Research Award (MLRA), he and his colleagues are currently applying machine learning techniques to social media data in an attempt to make predictions about important aspects of mental health, including the risk of suicide.

Developing more sophisticated tools to prevent suicide is a pressing issue in the United States. Suicide was the second leading cause of death among people between the ages of 10 and 34 in 2018, according to the Centers for Disease Control and Prevention (CDC). Among all ages that year, more than 48,000 Americans died by suicide. Resnik noted the COVID-19 pandemic has further increased the urgency of this problem via an “echo pandemic.” That term has been used by some in the mental health community to characterize the long-term mental health effects of sustained isolation, anxiety, and disruption of normal life.

The value of social media data

Machine learning research projects on mental health historically have relied on various types of data, such as health records and clinical interviews. But Resnik and other researchers have found that social media provides an additional layer of information, giving a glimpse into the everyday experiences of patients when they are not being evaluated by a mental healthcare provider. Mindful of privacy and ethical concerns, Resnik envisions a system where patients who are already seeing a mental health professional are given the option to consent for access to their social media data for this monitoring purpose.

Philip Resnik, a professor at the University of Maryland in the Department of Linguistics and the Institute for Advanced Computer Studies, is seen standing in a hallway
Philip Resnik says one of his goals is to use technology to make progress on social problems. “Language is a crucial window into people's mental state,” he says.
Credit: John T. Consoli / University of Maryland

“Healthcare visits, where problems can be identified, are relatively few and far between compared to what so many people are doing every day, posting about their lived experience on social media,” Resnik said. The idea is to use social media data to discover patterns that are predictive, for example, of someone with schizophrenia having a psychotic episode, or someone with depression having a suicidal crisis.

The project is still in the technology research stage, said Resnik, but the ultimate goal is to have a practical impact by allowing mental healthcare professionals to access previously unavailable information about the people that they're helping treat.

These predictions are made possible via supervised machine learning. In this scenario, the model utilizes datasets comprised of social media posts to learn how to identify patterns or properties to make a prediction after being given a large number of correct examples.

In order to do this work, Resnik and colleagues are using social media data donated by volunteers using two sites, “OurDataHelps” and “OurDataHelps: UMD”, as well as data from Reddit. All their work receives careful ethical review and they take extra steps to anonymize the users, such as automatically masking anything that resembles a name or a location.

Prioritizing at-risk individuals

Previous work that used machine learning to make mental health predictions has generally aimed to make binary distinctions. For example: Should this person be flagged as at risk or not? However, Resnik and his team believe that simply flagging people who might require attention is not enough.

In the United States, more than 120 million people live in areas with mental healthcare provider shortages, according to the Bureau of Health Workforce. “This means that even if they know they need help with a mental health problem, they are likely to have a hard time seeing the mental healthcare provider, because there aren’t enough providers,” Resnik noted.

What happens when software identifies even more people that might need help in an already overburdened system? The answer, he said, is to find ways to help prioritize the cases that need the most attention the soonest.

You have a pipeline where, at every stage that you assess the patient, there might be an appropriate intervention. The idea is to find the right level of care across the population, as opposed to simply making a binary distinction.
Philip Resnik

This is why Resnik’s team shifted their emphasis from simple classification to prioritization. In one approach, a healthcare provider would be informed which patients are more at risk and require the most immediate attention. The system would not only rank the most at-risk individuals, but also rank, for each of them, which social media posts were most indicative of that person’s mental state. This way, when the provider got an alert, they wouldn’t have to go through possibly hundreds of social media updates to better evaluate that person’s condition. Instead, they would be shown the most concerning posts up front.

Resnik and colleagues described this in a recent paper. Although the idea hasn’t yet been put into practice by clinicians, it was developed in consultation with experts from organizations such as the American Association of Suicidology who provided valuable input and feedback into how these technologies should be designed to be both effective and ethical.

Resnik’s team is also working on another approach to patient prioritization, a system that would rely on multiple stages of patient assessment. For example, patients’ social media data could be evaluated unintrusively in the first stage. A subset of individuals then might be invited to go through to a second, interactive, stage, such as responding to questions through an automatic system where their answers and properties of their speech, for example their speaking rate and the quality of their voice, would be evaluated through machine learning techniques. Among those, the individuals at most immediate or serious risk could be directed to a third stage of evaluation that would involve a human being.

“You have a pipeline where, at every stage that you assess the patient, there might be an appropriate intervention,” Resnik said. “The idea is to find the right level of care across the population, as opposed to simply making a binary distinction.”

Both of these approaches have been supported by the MLRA. “It has been helpful not only in terms of the AWS credits to build infrastructure and the funding for graduate students, but also the engagement with people at Amazon,” said Resnik. “We’ve had active conversations with people inside AWS, who are themselves responsible for building important tools. The relationship that I have, as a researcher, with Amazon has been enormously helpful.”

Building a secure environment for sensitive data

Previous funding from the MLRA also helped sponsor the development of a secure computational environment to house mental health data. This is an important step to advance research in machine learning for mental health, as one of the main obstacles in this field is obtaining access to this very sensitive data.

The goal of this joint project between the University of Maryland and the independent research institution NORC at the University of Chicago: give qualified researchers ethical and secure access to mental health datasets. The resulting Mental Health Data Enclave, hosted on AWS, is designed to let researchers access datasets remotely from their own computers and work with them inside a secure environment, without ever being able to copy or send the data elsewhere.

The enclave will be used this spring for an exercise at the Computational Linguistics and Clinical Psychology Workshop (held in conjunction with NAACL), an event that brings together clinicians and technologists. A sensitive mental health dataset will be shared among different teams, who will work on it within the enclave to solve a problem. The solutions will then be discussed at the workshop.

Resnik said that the AWS award will make it possible for all the teams to ethically access and work on this sensitive data. “I view this as a proof of concept for what I hope will become a lasting paradigm going forward, where we use secure environments to get the community working in a shared way on sensitive data,” he added. “This is the way that real progress has been made for decades in other research areas.”  Crucially, though, Resnik observes, research progress is not an end in itself: ultimately it needs to feed into practical and ethical deployment within the mental healthcare ecosystem. As he and collaborating suicide prevention experts noted in a recent article, “The key to progress is closer and more consistent engagement of the suicidology and technology communities.”

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We’re looking for a passionate, talented, and inventive Senior Applied Scientist to help build industry-leading technologies in speech translation. Our team's mission is to enable Alexa to break down language barriers for our customers.Job responsibilitiesAs a Senior Applied Scientist with the Alexa Artificial Intelligence (AI) team, you will be responsible for developing novel algorithms that advance the state-of-the-art in language processing and entity resolution, driving model and algorithmic improvements, formulating evaluation methodologies and for influencing design and architecture choices. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to build novel products and services that make use of speech and language technology. You will work in a hybrid, fast-paced organization where scientists and engineers work together and drive improvements to production. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Redmond
Are you interested in building and driving the technical vision, strategy, and implementation for Kuiper’s LEO Capacity Management Services? Kuiper is hiring a Principal Data Scientist to help lead the analysis, definition and implementation of our global, highly reliable, predictive data driven services that manage the end-to-end resources of Kuiper’s Internet Service for ground and constellation networks.A day in the lifeYou will partner with Product Management, customers, RF, Networking and Beam Planning engineers to understand all capabilities and designs of the Kuiper ISP. You will drive the data driven models of bandwidth, latency and customer segment consumption to create highly reliable, time sensitive, predictive capacity management systems that drive overall monetization and customer experience.An ideal candidate will have analytical, data science, and system engineering skills to model the interdependent business and technical processes needed to operate and expand a world-wide fleet of space communication and ground assets. The candidate will use these models to enhance customer delight by meeting performance agreements, faster decisions, reduced costs, and simplified interactions.About the hiring groupThe Team is responsible for Architecture, design and delivering and end to end Networking systems for both constellation and ground, as well as the services that utilize the network to delivery last mile and and back-haul internet services. This includes extreme scale of global Software Defined Network and Capacity ManagementJob responsibilitiesWe are looking for a Principal Data Scientist on this team. You will be responsible for identifying, scoping, and delivering capacity planning solutions with a focus on Europe; based on a deep understand of your customers' needs, you will work closely with senior leaders, scientists, engineers, and business teams worldwide to develop and implement advanced mathematical and economic models and algorithms. You will identify data and science-related bottlenecks, anticipate and make trade-offs, balance business needs versus scientific and technical complexity and constraints, and guide and manage escalations, collaborating closely with multiple teams to ensure the relevance and impact of your work to business stakeholders.You will need an ability to take large, scientifically complex projects and break them down into manageable hypotheses, design meaningful research questions and analyze the resulting data to inform functional specifications, and then deliver features in a successful and timely manner. You excel at being a thought leader as we chart new courses with our capacity planning technologies, and at defining a vision for products in early stages. Maturity, high judgment, negotiation skills, and the ability to influence and earn the trust of senior leaders are essential to success in this role.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, CA, Sunnyvale
Are you a passionate scientist in the area of computer vision and machine learning who is aspired to develop new and innovative technologies to new product categories? Are you interested in applying your deep knowledge to new and challenging areas? Are you looking to scale capabilities computer vision and machine learning capabilities to new workload sizes? Are you up to the task of delivering innovative and scalable technology that manages automated recognition of millions of items?You will be part of a passionate team whose missions is to push the frontier of computer vision and machine learning technology into the smart home application area. This is a great opportunity for you to innovate in this space by developing algorithms at the edge and in the cloud, and integrating them into consumer services to enable a premium customer experience. In this role, you will be an owner of the full algorithm development cycle, from sensor evaluation and data engineering to algorithm design, implementation, optimization and deployment. This position also requires experience with developing efficient software components on resource-constrained computing platforms on the edge. You will collaborate with different Amazon teams to make informed decisions on the best practices in machine learning to build highly-optimized integrated hardware and software platforms.Main Responsibilities· Apply best practices to investigate, acquire, process and analyze data sources for algorithm development.· Research and implement the state-of-the-art methods in computer vision and machine learning to deliver algorithms that meets product specifications.· Design, build algorithm evaluation frameworks, schedule and report algorithm performance on a regular basis.· Optimize and deploy algorithms on target hardware platforms.· Establish, develop and maintain frameworks and procedures for image sensor selection and evaluation and image quality monitoring.· Influence system design by making informed decisions on the selection of data sources, algorithms and sensors.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
RO, Timisoara
The Amazon Devices team designs and engineers consumer electronics,including the best-selling Ring cameras, Kindle family of products, Firetablets, Fire TV, Amazon Dash, and Amazon Echo.As an Applied Scientist, you will participate in the design,development, and evaluation of models and machine learning (ML)technology to delight our customers. More specifically, as a member of the team, you will be involved in researching state ofthe art Computer Vision (CV) & ML solutions for Amazon devices and cloud services.You will be part of a team delivering features that are well received byour customers.
RO, Timisoara
The Amazon Devices team designs and engineers consumer electronics,including the best-selling Ring cameras, Kindle family of products, Firetablets, Fire TV, Amazon Dash, and Amazon Echo.As an Applied Scientist, you will participate in the design,development, and evaluation of models and machine learning (ML)technology to delight our customers. More specifically, as a member of the team, you will be involved in researching state ofthe art Computer Vision (CV) & ML solutions for Amazon devices and cloud services.You will be part of a team delivering features that are well received byour customers.
US, CA, San Diego
Are you excited to help customers discover the hottest and best reviewed products?The Marketing Tech and Science team helps customers discover and engage with new, popular and relevant products across Amazon worldwide. We do this by combining technology, science, and innovation to build new customer-facing features and experiences alongside cutting edge tools for marketers. You will be responsible for creating and building critical services that automatically generate, target, and optimize Amazon’s cross-category marketing and merchandising. Through the enablement of intelligent marketing campaigns that leverage machine-learning models, you will help to deliver the best possible shopping experience for Amazon’s customers all over the globe.We are looking for analytical problem solvers who enjoy diving into data, excited about data science and statistics, can multi-task, and can credibly interface between engineering teams and business stakeholders. Your analytical abilities, business understanding, and technical savvy will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your domain spans the design, development, testing, and deployment of data-driven and highly scalable machine learning solutions in product recommendation.As an Applied Scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.To know more about Amazon science, please visit https://www.amazon.science
US, WA, Seattle
Are you excited about powering Amazon’s physical stores’ expansion through the application of Machine Learning and Big Data technologies? Do you thrive in a fast-moving, innovative environment that values data-driven decision making, scalable solutions, and sound scientific practices? We are looking for experienced scientists to build the next level of intelligence that will help Amazon physical stores grow and succeed.Our team is responsible for building the core intelligence, insights, and algorithms that support the real estate acquisition strategies for Amazon physical stores. We are tackling cutting-edge, complex problems — such as predicting the optimal location for new Amazon stores — by bringing together numerous data assets from disparate sources inside and outside of Amazon, and using best-in-class modeling solutions to extract the most information out of them.You will have a proven track-record of delivering solutions using advanced science approaches. You will be comfortable using a variety of tools and data sources to answer high-impact business questions. You will transform one-off models into automated systems. You will be able to break down complex information and insights into clear and concise language and be comfortable presenting your findings to audiences with a broad range of backgrounds.Responsibilities:· Develop production software systems utilizing advanced algorithms to solve business problems.· Analyze and validate data to ensure high data quality and reliable insights.· Partner with data engineering teams across multiple business lines to improve data assets, quality, metrics and insights.· Proactively identify interesting areas for deep dive investigations and future product development.· Design and execute experiments, and analyze experimental results in collaboration with Product Managers, Business Analysts, Economists, and other specialists.· Leverage industry best practices to establish repeatable applied science practices, principles & processes.
US, IL, Chicago
Are you excited about powering Amazon’s physical stores’ expansion through the application of Machine Learning and Big Data technologies? Do you thrive in a fast-moving, innovative environment that values data-driven decision making, scalable solutions, and sound scientific practices? We are looking for experienced scientists to build the next level of intelligence that will help Amazon physical stores grow and succeed.Our team is responsible for building the core intelligence, insights, and algorithms that support the real estate acquisition strategies for Amazon physical stores. We are tackling cutting-edge, complex problems — such as predicting the optimal location for new Amazon stores — by bringing together numerous data assets from disparate sources inside and outside of Amazon, and using best-in-class modeling solutions to extract the most information out of them.You will have a proven track-record of delivering solutions using advanced science approaches. You will be comfortable using a variety of tools and data sources to answer high-impact business questions. You will transform one-off models into automated systems. You will be able to break down complex information and insights into clear and concise language and be comfortable presenting your findings to audiences with a broad range of backgrounds.Responsibilities:· Develop production software systems utilizing advanced algorithms to solve business problems.· Analyze and validate data to ensure high data quality and reliable insights.· Partner with data engineering teams across multiple business lines to improve data assets, quality, metrics and insights.· Proactively identify interesting areas for deep dive investigations and future product development.· Design and execute experiments, and analyze experimental results in collaboration with Product Managers, Business Analysts, Economists, and other specialists.· Leverage industry best practices to establish repeatable applied science practices, principles & processes.