Anjali Goyal
Software Engineer @ Azure Data, Microsoft
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Anjali Goyal is a passionate software engineer at Microsoft, specializing in Azure Data solutions. Beyond her role at Microsoft, Anjali is an ardent hackathon enthusiast and complex problem solver. With her experience in numerous science fairs and technical competitions, she has showcased her ability to identify market gaps and propose effective solutions. She's also a proud member of Women at Microsoft, an initiative aimed at fostering women in technology.
Healthcare Data Solutions in Microsoft Fabric
The healthcare industry grapples with challenges in utilizing vast amounts of unstructured data, hindering advancements in treatments and compliance with regulations. Organizations struggle with accessing patient insights efficiently, wasting significant analysis time. To combat these issues, Microsoft introduced Healthcare Data Solutions in Microsoft Fabric, a comprehensive analytics solution ensuring data privacy and security. It integrates various types of crucial healthcare data, like clinical and engagement data, Clinical data, Engagement data, Imaging data, Genomics, Conversational, Claims, SDOH enabling seamless exploratory analysis and large-scale analytics. The solution breaks down data silos, harmonizing disparate data into a unified store for easy accessibility and usability. With features like data pipelines and notebooks, users can navigate complex datasets effortlessly.
The feature includes capabilities such as FHIR data ingestion, healthcare data foundations setup, unstructured clinical notes enrichment, OMOP analytics, and Dynamics 365 Customer Insights integration. This solution aims to accelerate the transformation of healthcare data into actionable insights, driving innovation and improving patient outcomes.
As part of our tutorial/session, we will
• Deploy Sample Healthcare Data.
We will use this data for trying below functionality.
• Deploy and configure Healthcare data foundations
Healthcare data foundations offer ready-to-run data pipelines that are designed to efficiently structure data for analytics and AI/machine learning modelling.
• Deploy and configure FHIR data ingestion
FHIR data ingestion enables you to bring your Fast Healthcare Interoperability Resources (FHIR) data to One Lake from a FHIR service such as Azure Health Data Services. Will briefly touch upon what is one lake.
• Deploy and configure Unstructured clinical notes enrichment
Unstructured clinical notes enrichment utilizes Azure AI Language's Text Analytics for health service to extract and add structure to unstructured clinical notes for analytics
• Deploy and configure OMOP analytics
OMOP analytics enables data preparation for standardized analytics through Observational Medical Outcomes Partnership (OMOP) open community standards.
Deploy and configure Dynamics 365 Customer Insights - Data preparation
Dynamics 365 Customer Insights - Data preparation enables you to connect Customer Insight Data to your One Lake on Fabric for creating patient or member lists for outreach.
This tutorial/session will touch pace upon multiple Microsoft Fabric and Azure AI capabilities and will give end to end hands on understanding of how to deploy a Microsoft Fabric for Healthcare Industry solution.
Portfolio Management Business Solution - Microsoft Fabric
Problem statement - Currently Investors face challenges in effectively managing their portfolios, particularly in bucketizing stocks for short, medium, and long-term investments, and maintaining optimal balance through timely portfolio rebalancing.
Collecting data from diverse sources like Stock exchange platforms, Textual data from news websites and user reactions from social media platforms like X and cleanse, processing them to build a model that can identify the top performing stocks based on actual trading numbers and user sentiment is currently a big technical challenge in Portfolio Management Industry.
We will demonstrate how easy it is to build an AI Data solution with our service that can identify the top performing stocks and solve these business/technical problems using Microsoft Fabric, Azure Open AI and LangChain.
The idea is to create a Portfolio Management Service in Microsoft Fabric that can be leveraged by financial advisors to use AI for Asset Management without having the deep knowledge of AI/ML.
What the solution offers -
1) Data -
a) Fundamental data of the stocks of various exchanges by time and volume for last two decades.
2) Data Cleaning and Engineering -
a) Capability to clean and normalize data for various recommendation models.
b) Adding more mathematical features like integral derivatives over the fundamental features.
3) Range of trained ML and AI Models -
a) They can train/reuse a combination of models like decision tree to get the top n stocks and their weightage to invest in for a period of time.
b) The software can use the capabilities of ChatGPT to eliminate few of the stocks from the selected stocks using sentiment analysis.
c) The CEO/LT of the company can get real time crisp positive, negative and neutral news of their company as well as competitors. This uses LangChain along with Azure OpenAI to perform real time sentiment analysis from above captured data.
4) Strong Back testing Framework -
a) The product will offer a strong unbiased back testing framework to verify that the model that the user came up with is working as per expected.
5) Create Automated PPT of Recommended Stocks
a) Create the ppt explaining why the given n stocks were selected and their financial analysis.
6) Tracking the portfolio -
a) The tracking mechanism will actively do news and sentiment analysis and recommend re-balancing of the portfolio. The tracking mechanism can be customized as per user needs.
We are proposing this service as a key component of the industry solution feature of Microsoft Fabric. Each of the aforementioned functionalities will be represented by its own dedicated tile in the Fabric project. This design empowers Asset Managers to seamlessly load all relevant data into the project, clean and normalize it, add custom mathematical fields, run pre-trained ML/AI models, conduct sentiment analysis on text data, and finalize the stock bucket. Dedicated tiles will display the criteria for stock selection, based on the previous steps, and provide alerts when portfolio rebalancing is needed. This comprehensive solution offers a robust, out-of-the-box toolkit, positioning itself as a one-stop solution for all asset management needs.
Anjali Goyal
Software Engineer @ Azure Data, Microsoft
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