Deep Knowledge Search+ Gen AI = Engaging LinkedIn Articles

Unleash the power of Deep Knowledge Search and Gen AI to create comprehensive and engaging LinkedIn articles for professional audience.

May 17, 2024 | Article

In our previous article, we discussed how using simple prompts with Generative AI (Gen AI) can lead to responses that lower the quality of content intended for a professional audience like LinkedIn. We highlighted various issues that can arise in these AI-generated responses. This situation reveals a significant opportunity: by equipping Gen AI with a richer vocabulary and providing more detailed prompts that align with the article’s theme, we can significantly enhance its capabilities.

In this article, we will be introducing about the patented innovative tool known as Deep Knowledge Search (DKS) and how this tool can be helpful in writi ng interesting LinkedIn articles. Imagine this: you enter a topic related to your article, and DKS delivers a list of highly relevant and specific phrases related to that topic that can enrich your content and ensure it covers all the important angles.

By combining DKS with Gen AI, we unlock an even more powerful approach. Gen AI can use these DKS-provided phrases to craft detailed and informative content that directly addresses the specific aspects of your chosen topic. This empowers you, the author, to create well-rounded and in-depth articles that are likely to resonate with the professional audience on LinkedIn.

How to Craft Compelling LinkedIn Articles with Deep Knowledge Search and Gen AI?



Crafting compelling LinkedIn articles requires strategic planning and execution. In this guide, we’ll walk you through a three-step process that leverages the power of Deep Knowledge Search (DKS) and Generative AI (Gen AI) to turn your ideas into engaging content that resonates with your audience. 

  1. Spark Your Creativity: Unearth Compelling Topics with DKS 
  2. Craft Your Outline – Leverage Knowledge Phrases from DKS with Gen AI to Structure Your Ideas  
  3. Turn those ideas and knowledge phrases into engaging LinkedIn articles with the power of Gen AI. 
  4. Use the ideas and knowledge phrases to inspire creative visuals for your LinkedIn article

STEP-1 : Spark Your Creativity: Unearth Compelling Topics with DKS



In this first step, we’ll leverage the power of Deep Knowledge Search (DKS) to ignite your creativity and discover engaging themes for your LinkedIn article. DKS doesn’t require a fully formed topic; instead, you can start with broad industry keywords or even a general idea. DKS analyses these starting points and generates a rich list of “knowledge phrases” related to your chosen area. Think of these knowledge phrases as springboards for ideas and building blocks for a well-rounded and informative article.

For example, imagine you’re writing an article about “Anomaly Detection in Healthcare”. Here’s how DKS can help: Simply enter “Anomaly Detection in Healthcare” as your query. DKS will then analyse this topic and provide you with a comprehensive list of highly relevant knowledge phrases specifically related to anomaly detection within the healthcare field. Think of these knowledge phrases as building blocks for your article. They might include specific techniques used for anomaly detection, common applications in healthcare settings, or potential challenges and benefits associated with this technology.



STEP-1 : Spark Your Creativity: Unearth Compelling Topics with DKS



After DKS provides you with a rich list of knowledge phrases in Step 1, Gen AI comes into play in Step 2 to help you structure your article. Here’s how it works: 

  1. Refine Your Topic: Based on the DKS knowledge phrases, solidify your core topic for the article. 
  2. Group Knowledge Phrases: Organize the DKS knowledge phrases into thematic clusters. These clusters will become the building blocks for your article’s sections. 
  3. Outline Creation with Gen AI: Use Gen AI to create a draft outline for your article. Here’s how you can leverage it: 
    • Provide Input: Feed Gen AI your chosen topic and the thematic clusters of knowledge phrases you identified. 
    • Prompt Gen AI: Instruct Gen AI to generate a potential outline with section headings based on the knowledge phrases. 
    • Refine the Outline: Review and adapt the Gen AI-suggested outline to your specific needs and ensure a logical flow between sections.

    Benefits of using Gen AI for Outlining:

    • Saves Time: Gen AI helps you jumpstart the outlining process, saving you time brainstorming section ideas. 
    • Identifies Interesting Sections: By analysing the knowledge phrases, Gen AI can suggest sections that might be particularly interesting or informative for your audience. 
    • Ensures Structure: A well-structured outline ensures a clear and focused article. 

    This approach leverages the strengths of both DKS and Gen AI. DKS provides the knowledge base, and Gen AI helps you shape it into a structured and potentially engaging article outline. 

    For example: 

    Prompt –

    “ Based on the provided knowledge phrases related to anomaly detection in healthcare, identify 3-5 distinct themes that encompass these phrases. For each theme, list the associated knowledge phrases that contribute to it.”

    The above prompt provides the list of sections along with each section’s associated knowledge phrases.

    Prompt – “Considering the theme: [Insert Theme Here] and the associated knowledge phrases: [List Knowledge Phrases], suggest 2-3 compelling section headings for an article on anomaly detection in healthcare that reflects this theme” 

    The above prompt will provide interesting headings for each section. We can then select the best heading for each section from the responses.   

    You can also provide the following additional points in the prompts that aligns to your style of the article. 

    Prompt – “Considering the theme: [Insert Theme Here] and the associated knowledge phrases: [List Knowledge Phrases], suggest 2-3 compelling section headings for an article on anomaly detection in healthcare that reflects this theme” 

    The above prompt will provide interesting headings for each section. We can then select the best heading for each section from the responses.   

    You can also provide the following additional points in the prompts that aligns to your style of the article. 

    • Target Audience: If you have a specific target audience in mind (e.g., doctors, patients, healthcare IT professionals), you could add it to the prompt: “Considering the theme: [Insert Theme Here] and the associated knowledge phrases: [List Knowledge Phrases], suggest 2-3 compelling headings for an article on anomaly detection in healthcare that reflects this theme, targeting [Target Audience].”
    • Desired Tone: If you have a preferred tone for the headings (e.g., informative, persuasive, attention-grabbing), you could mention it: “Considering the theme: [Insert Theme Here] and the associated knowledge phrases: [List Knowledge Phrases], suggest 2-3 compelling headings for an article on anomaly detection in healthcare that reflects this theme, using an [Informative / Persuasive / Attention-grabbing] tone.”

    We derived the following final list of themes of the “Anomalies Detection in Healthcare” example. 

    1. Building a Secure Healthcare Ecosystem: Integrating Expert Rules and Anomaly Detection for Fraud Prevention and Event Detection
    2. Empowering Healthcare AI: Self-Supervised Anomaly Detection for Improved Disease Detection and Clinical Text Analysis
    3. Transforming Healthcare with AI: Anomaly Detection for Proactive Health Management in Wearables and IoT
    4. Data-Driven Healthcare Policy: Leveraging Anomaly Detection to Optimize Costs and Individualize Care
    5. AI-powered Assistant: Anomaly Detection in Healthcare Applications for Proactive Intervention
    6. From Crisis to Innovation: Anomaly Detection in Healthcare Queries and Actions to Empower Technology for Pandemic Response
    7. Unlocking Hidden Patterns: Predictive Healthcare through Anomaly Detection in Observational Data

    Observations and Opportunities:

    The generated themes are not only highly relevant to the topic of anomaly detection in healthcare, but they also offer exciting opportunities for content creation. Here’s how:

    • Comprehensive Coverage: These themes address a broad spectrum of how anomaly detection is transforming healthcare, encompassing fraud prevention, disease detection, proactive health management, personalized care, and pandemic response.
    • Content Expansion: Each theme can be developed into a standalone article, allowing you to delve deeper into specific aspects of anomaly detection. This approach can help you establish yourself as a thought leader on this topic within the LinkedIn community and expand your content portfolio.
    • Strategic Series: Alternatively, you could consider these themes as a framework for a series of interlinked articles. This would allow you to provide a comprehensive overview of anomaly detection in healthcare while still offering readers focused, in-depth discussions on each theme.

    Number of articles –

    With the multitude of themes generated, we can get different ideas about generating the high-quality articles. There can be several possibilities for article creation:

    • Individual Articles: Each theme can be expanded into a separate, detailed article, resulting in six distinct articles. It allows for thorough exploration and detailed analysis, enhancing the depth of content. Through associated knowledge phrases in the theme, we can get different ideas for writing high-quality articles.
    • Combined Article: Alternatively, all themes can be integrated into a comprehensive single article that covers various aspects of Anomaly Detection in healthcare. This article can summarize and link the different aspects of the Anomaly Detection together. It provides a wide-ranging view that captures the complexity of the topic, offering valuable insights.

    Thus, DKS enhances our capability to produce multiple high-quality articles based on distinct but interconnected themes.



    This step focuses on enriching each section with informative and engaging content using DKS for research and Gen AI for creative writing assistance. Building on the foundation of your outline and thematic clusters from DKS, Step 3 leverages Gen AI’s power through prompt engineering to create compelling content for each section.

    DKS for Targeted Research: Use DKS to conduct focused research for each section. Based on the section’s theme and the knowledge phrases associated with it, use DKS to find relevant articles, research papers, or other credible sources.

    Prompt Engineering for Authorial Voice:

    Here’s how you can utilize Gen AI prompt engineering along with inputs related to your author’s style and expertise:

    1. Tailor Prompts to Each Section: For each section in your outline, identify the key theme and relevant knowledge phrases from DKS.
    2. Craft Your Prompts: Write clear and concise prompts that guide Gen AI’s content generation while infusing your authorial voice. Here are some elements to consider incorporating:
    • Section Theme: Clearly state the main topic of the section.
    • Knowledge Phrases: Include relevant knowledge phrases from DKS to ensure factual accuracy and focus.
    • Author’s Style: Indicate your preferred writing style (e.g., formal, informal, humorous) to guide Gen AI’s tone.
    • Expertise: Highlight your area of expertise to steer the content towards your unique perspective and knowledge.

    Here’s an example prompt incorporating these elements:

    Section Theme: Benefits of Anomaly Detection in Healthcare

    Knowledge Phrases: Machine Learning, Early Disease Detection, Improved Patient Outcomes

    Author’s Style: Informative and Engaging Expertise: Healthcare Data Analyst

    Prompt: “Write an informative and engaging paragraph explaining the benefits of anomaly detection in healthcare. Focus on how machine learning algorithms can be used to detect diseases earlier, potentially leading to improved patient outcomes. Incorporate my expertise as a healthcare data analyst for a credible and insightful perspective.”

    3. Generate and Refine Content: Based on your prompts, Gen AI will generate content variations for each section. Review and refine the generated content, ensuring:

    • Accuracy: Verify the information aligns with your knowledge and credible sources.
    • Clarity: Refine the language for easy readability and flow.
    • Voice: Ensure the content aligns with your preferred writing style.

    This approach empowers you to leverage Gen AI’s capabilities while maintaining your authorial voice and expertise. By crafting specific prompts, you guide Gen AI to generate content relevant to each section, saving you time and effort in content creation. 

    Benefits of this approach:

    • Efficient Research: DKS helps you find targeted information for each section, saving you time searching for relevant sources.
    • Content Creativity: Gen AI helps you generate different variations of content, overcoming writer’s block and sparking new ideas.
    • Maintains Your Voice: While Gen AI provides suggestions, you remain in control, refining the content to match your style and expertise.

    STEP-4: Unleashing Creativity: Crafting Visuals with Gen AI



    Now that you have a well-structured outline and engaging content thanks to DKS and Gen AI, let’s explore how to create visuals that will captivate your LinkedIn audience.

    Images can enhance the visual appeal of an article, capturing the reader’s attention and enticing them to delve deeper into the content. By incorporating relevant visuals such as photographs, infographics, or charts, the article becomes more dynamic and immersive, effectively complementing the text. Visuals can also aid in clarifying complex information, providing context, and evoking emotions, thereby strengthening the overall impact of the article on the audience. Ultimately, well-chosen images can contribute significantly to the readability, comprehension, and engagement of the article, thereby, increasing the reachability.

    In this section, we’ll explore how to leverage Gen AI alongside the knowledge phrases generated by DKS to craft compelling visuals for your article on anomaly detection in healthcare.

    1. Brainstorm with Knowledge Phrases:

    • Review Your Knowledge Phrases: Revisit the knowledge phrases DKS generated for your chosen theme (e.g., “machine learning,” “early disease detection,” “improved patient outcomes”). These phrases act as building blocks for your visuals.
    • Spark Ideas with Gen AI: Use Gen AI to brainstorm visual concepts based on these knowledge phrases. Here’s an example prompt: “Considering the knowledge phrases: machine learning, early disease detection, improved patient outcomes, suggest 3-5 creative visual concepts for an article on anomaly detection in healthcare.”

    2. Generate Visuals with Gen AI:

    • Choose a Visual Concept: Select the visual concept that best resonates with your content and target audience.
    • Refine with Gen AI: Provide Gen AI with the chosen concept and additional details (e.g., desired style, colors) to generate different visual variations. Here’s an example prompt: “Create 2-3 variations of an infographic that depicts how machine learning algorithms can be used for early disease detection in healthcare, leading to improved patient outcomes.”

    3. Select and Refine Your Visuals:

    • Review and Choose: Carefully review the generated visuals and select the one that best complements your article and brand identity.
    • Customize for Perfection: Refine the chosen visual using editing software to ensure it aligns perfectly with your overall content strategy.

    Benefits of using Gen AI for Visuals:

    • Overcome Creative Block: Gen AI can help you spark new ideas and overcome creative roadblocks when brainstorming visuals.
    • Time-Saving Efficiency: Gen AI can generate various visual concepts and variations, saving you time compared to creating them from scratch.
    • Personalized Touch: While Gen AI provides a starting point, you maintain control, allowing you to refine the visuals to reflect your unique style and branding.

    By following these steps and leveraging Gen AI, you can transform DKS-generated knowledge phrases into eye-catching visuals that will elevate your LinkedIn article and maximize engagement with your audience. 

    CONCLUSION :

    This article explores the power of Deep Knowledge Search and Gen AI for crafting engaging LinkedIn content. For a practical example, see the appendix for an article generated using this approach. The final article created using the Deep Knowledge Search (DKS) tool and Generative AI is of high quality, offering valuable insights and engaging content for LinkedIn readers. By using DKS, we gathered specific and relevant knowledge phrases, ensuring the article is accurate and detailed. This approach transformed the content from generic to highly specific, making it more interesting and useful. Generative AI, guided by these detailed prompts, provided thorough and well-informed responses, covering various aspects of the topic effectively. Including relevant visuals also made the article more attractive and easier to understand.

    In summary, the combination of DKS and Generative AI produced a comprehensive and engaging article, demonstrating the benefits of using advanced tools for content creation.

    APPENDIX

    Unlocking Hidden Patterns: Predictive Healthcare through Anomaly Detection in Observational Data 

    Introduction

    In the rapidly evolving landscape of healthcare, the ability to detect anomalies within vast datasets has become increasingly vital. Leveraging various types of healthcare data, from observational records to complex multivariate time series, healthcare professionals can uncover hidden patterns that signal potential issues. This article explores the critical role of healthcare data analysis in anomaly detection, highlighting how advanced techniques and technologies can transform raw data into actionable insights. By understanding and utilizing unstructured data, managing the growing volume of electronic health records, and applying unsupervised outlier detection, healthcare providers can enhance patient outcomes and operational efficiency. Join us as we delve into the intricacies of predictive healthcare and the tools that make it possible to foresee and mitigate health crises before they escalate. 

    Observational Health Care :

    Observational healthcare data is like a detailed diary of everyday patient care, capturing real-world information about patient outcomes, disease progression, and how effective treatments are. This data comes from routine clinical practices, including sources like electronic health records (EHRs), patient registries, and administrative databases. By examining this data, healthcare professionals can spot unusual patterns or anomalies, such as unexpected side effects of drugs or possible misdiagnoses. This rich data helps in thoroughly monitoring how healthcare is delivered and what patients experience, leading to better decision-making. Additionally, it aids in creating predictive models that can foresee potential health issues before they become serious. Using observational healthcare data effectively can greatly enhance patient safety and the quality of healthcare services.

    Observational data from EHRs and database



    Multivariate Time Series Healthcare Data :

    Multivariate Time Series Healthcare Data



    Multivariate time series healthcare data involves tracking multiple health indicators over time, much like a multi-dimensional graph showing various aspects of a patient’s health journey. This data is crucial for spotting anomalies, such as sudden changes in vital signs or unusual patterns in hospital resource usage. By monitoring several health metrics simultaneously—like heart rate, blood pressure, and medication levels—healthcare providers can identify early signs of serious conditions. The multivariate approach allows for a deeper understanding of how different health indicators interact with each other. This helps in distinguishing between normal variations and actual anomalies, reducing false alarms. As a result, healthcare providers can respond more accurately and swiftly to patient needs.

    Unstructured Healthcare Data :

    Unstructured healthcare data includes all the messy, free-form information like doctor’s notes, medical images, and patient-reported outcomes. Unlike structured data that fits neatly into spreadsheets, unstructured data requires advanced techniques like natural language processing (NLP) to make sense of it. For example, by analyzing clinical notes, healthcare providers can find undocumented symptoms or adverse reactions that aren’t captured in structured data. This comprehensive analysis can reveal hidden anomalies, such as patterns of misdiagnosis or overlooked side effects. Effectively using unstructured healthcare data provides a more accurate and complete picture of patient health. This, in turn, improves the overall quality of care and patient safety.

    Unstructured data using NLP and ML



    Electronic Healthcare Record Volume :

    Lab reports available in EHR in large volume



    The volume of electronic healthcare records (EHRs) is ever-growing, bringing both challenges and opportunities for anomaly detection. With vast amounts of patient data available—including demographics, treatment histories, and lab results—healthcare providers can conduct detailed analyses to spot unusual patterns and trends. However, managing this enormous data volume requires efficient processing and storage solutions. Advanced data analytics and machine learning techniques can help sift through the massive amounts of EHRs to identify anomalies like irregular medication patterns or unexpected lab results. Effectively handling and analyzing the volume of EHRs can significantly enhance the ability to detect and address potential health issues early. This proactive approach improves patient outcomes and the efficiency of healthcare services.

    Large-Scale Unsupervised Heterogeneous Outlier Detection:

    Large-scale unsupervised heterogeneous outlier detection is a method for identifying anomalies in diverse healthcare datasets without needing labelled examples. This technique uses advanced machine learning algorithms to detect outliers across various data types, such as numbers, categories, and time-series information. By analysing these mixed datasets, healthcare providers can find unusual patterns that might indicate errors, fraud, or emerging health threats. For example, unexpected clusters of symptoms in patient records could suggest a new disease outbreak. The unsupervised nature of this approach is particularly valuable because it doesn’t rely on prelabelled data, which is often scarce in healthcare. Implementing large-scale outlier detection can enhance the ability to quickly respond to anomalies, ensuring patient safety and efficient resource use.

    Heterogeneous Outlier detection methods



    Predictive Healthcare :

    Predictive models for healthcare



    Predictive healthcare uses data analytics and machine learning to anticipate future health events and outcomes. By leveraging both historical and real-time healthcare data, predictive models can identify patients at risk for chronic conditions, predict hospital readmission rates, and forecast disease outbreaks. Anomaly detection is crucial in this process, as spotting deviations from normal patterns can provide early warnings of health issues. For instance, a predictive model might notice an unusual increase in emergency room visits, prompting further investigation and preventive actions. Predictive healthcare enables more proactive and personalized care, allowing healthcare providers to intervene early and tailor treatments to individual patient needs. This forward-looking approach can significantly improve patient outcomes and reduce overall healthcare costs. 

    Conclusion: A Future Transformed by Data-Driven Healthcare 

    In conclusion, the integration of advanced healthcare data analysis techniques into daily practice is revolutionizing anomaly detection and overall patient care. By harnessing observational healthcare data, multivariate time series data, and unstructured data, healthcare providers can gain deeper insights and uncover hidden patterns that signal potential health issues. The growing volume of electronic healthcare records and the application of large-scale unsupervised outlier detection are pushing the boundaries of what is possible, enabling more precise and timely interventions. Predictive healthcare, powered by sophisticated models, allows for early detection and proactive management of health crises, ultimately improving patient outcomes and operational efficiency. As we continue to innovate and refine these data-driven approaches, the healthcare industry stands to benefit immensely, paving the way for a safer, more efficient, and more responsive healthcare system. The future of healthcare lies in the intelligent use of data, and those who embrace this transformation will lead the way in delivering superior patient care.