New DATAMIND Glossary: Simplifying Mental Health Language

A Collaborative Effort to Improve Understanding

In a significant leap towards promoting accessibility and transparency in the world of mental health data science, we are proud to announce the launch of our eagerly awaited DATAMIND Glossary. Developed in collaboration with our Patient and Public Group, this user-friendly resource aims to make the complex language of mental health data science understandable to everyone, regardless of their level of knowledge or experience.

Empowering Understanding: The DATAMIND Glossary

The DATAMIND Glossary, now available at, represents a collective effort to bridge the gap between the intricate terminology of mental health data science and our broader audience. It offers clear and concise explanations of key terms, ensuring that not only researchers and healthcare professionals but also individuals with little or no prior knowledge can navigate this specialised field with confidence. 

What sets this glossary apart is its roots in collaboration. Our Patient and Public Group played a pivotal role in its creation, ensuring that it addresses the real-world needs and concerns of those seeking to understand mental health data science better. 

Examples from the DATAMIND Glossary include:

  1. Natural Language Processing (NLP):

Computer software exists to “read”free textwritten in a natural (human) language, and attempt to extract it as structured information. However, there are limitations because words can have different meanings, and the software cannot understand emotions or the intentions behind why certain words were chosen. 
Examples of NLP include, programs to find medications, drug treatment side effects, diagnoses, blood tests, recorded thoughts of suicide, “negative” symptoms of schizophrenia, and so on. 
NLP is difficult because grammar is complex. An NLP program to find hopelessness as a symptom of depression might need to distinguish “X is feeling hopeless” from “X used to feel hopeless but is now better”, “X’s spouse is feeling hopeless”, and “X said he is hopeless at football”. 
NLP programs are imperfect, and need checking when they are designed in one context and then used in another, but may still be very useful. NLP is mostly used for research, but as NLP improves, it could become common in clinics and hospitals because it helps doctors understand and use patient information better. For instance, it might assist doctors in quickly finding important details in medical records, making diagnoses faster and more accurate.

  1. Artificial Intelligence (AI):

Artificial Intelligence (AI) is a branch of science that aims to create technology that may perform tasks and make decisions in a way that resembles human intelligence.

Progress and Gap:
AI has advanced from rule-based systems to complex algorithms like deep learning. However, it lacks common sense, true understanding, and emotions, unlike human intelligence. Exaggerated expectations have led to misconceptions about AI’s capabilities.

Potential pros of AI:

  • Efficiency: AI may automate tasks, boosting productivity.
  • Insights: AI may analyse data for better decision-making.
  • Personalisation: AI may tailor recommendations to individual preferences.
  • Healthcare: AI may aid in diagnosing medical conditions.
  • Language: AI could translate languages in real-time.
  • Automation: AI-driven robots may streamline industries.

Potential concerns about AI:

  • Bias: AI may perpetuate biases in its decisions.
  • Job Impact: Automation might lead to job displacement.
  • Privacy: AI’s data use may raise privacy questions.
  • Ethics: AI decisions may raise ethical dilemmas.
  • Security: AI systems may be vulnerable to attacks.
  • Data Dependence: AI’s accuracy might rely on quality data.

Example: Large-Language Models:
Large-language models like GPT-3 exemplify AI. They understand and generate human-like text. These models fall under Natural Language Processing (NLP), part of machine learning, where computers learn from data patterns.

In essence, AI has made strides, but gaps remain in achieving human-like intelligence. Balancing benefits and concerns is crucial for responsible AI use.

  1. Structured Query Language (SQL):

A language that helps organise and work with information stored in databases. It allows people to easily find and use data from databases, like looking up specific information or making changes to the data. 

A Message from Professor Ann John, Co-Director of DATAMIND

Commenting on the launch of the glossary, Professor Ann John, Co-Director of DATAMIND, expresses enthusiasm about its potential impact. “We want to empower people and communities to engage more effectively with the world of mental health data science. That requires a common understanding between people, researchers and others to enable those conversations to happen. This glossary takes the mystique out of terms commonly used in our field. It’s not just a resource; it’s a testament to the power of collaboration and the commitment of the Patient and Public Group to make meaningful change.”

Join Us in Spreading the Word

We invite everyone, including those with little or no prior knowledge but a keen interest, to explore the glossary, learn more about mental health data science, and join in the efforts to make this valuable resource known to a wider audience. We will be sharing news and updates about the glossary on our Twitter as the glossary continues to evolve, with new terms and insights added to keep pace with the ever-evolving field of mental health data science.

DATAMIND, The Hub for Mental Health Informatics Research Development, is funded by The Medical Research Council and is delivered in partnership with Health Data Research UK. For more information about the glossary and DATAMIND’s initiatives, please visit

You can also follow us on Twitter @DatamindUK.

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