Bell Eapen MD, PhD.

In the evolving landscape of health information systems, interoperability is no longer a luxury—it’s a necessity. The Fast Healthcare Interoperability Resources (FHIR) standard, developed by HL7, has emerged as a cornerstone for structuring and exchanging electronic health data. But while FHIR excels at standardization and data sharing, it stumbles when faced with the demands of modern analytics and AI workflows. Enter the fhiry package—a Python toolkit that bridges this gap with elegance and efficiency.

🏥 The Promise of FHIR in Health Information Systems

FHIR was designed to solve a fundamental problem: how to enable seamless, standardized communication between disparate healthcare systems. It provides:

• Modular Resources: Patient, Observation, Condition, Medication, and more—each defined with a consistent schema.
• RESTful APIs: Making it easy to query and retrieve data using standard HTTP methods.
• Extensibility: Supporting custom extensions while maintaining core interoperability.
• Global Adoption: Used by major EHR vendors, government agencies, and research institutions.

In short, FHIR is the lingua franca of health data exchange. But when it comes to analytics and AI, its strengths become limitations.

⚠️ Why FHIR Is Not Conducive to AI and Analytics

Despite its utility, FHIR data presents several challenges for data scientists and machine learning practitioners:

1. Nested and Complex Structure

FHIR resources are deeply nested JSON objects. For example, a Patient resource might contain arrays of telecom entries, addresses, and extensions. This structure is great for flexibility but terrible for tabular analysis.

2. Inconsistent Representations

Even within the same resource type, fields may vary based on context or implementation. This inconsistency complicates feature engineering and model training.

3. Lack of Native Support for ML Pipelines

FHIR was not designed with TensorFlow, PyTorch, or scikit-learn in mind. Converting FHIR data into a format suitable for these tools requires significant preprocessing.

4. Limited Query Capabilities

FHIR servers support basic search parameters, but lack the expressive power of SQL or natural language queries. This limits exploratory data analysis and hypothesis generation.

5. Scalability Issues

Bulk data exports in NDJSON format are helpful, but parsing and flattening them into usable datasets is non-trivial—especially at scale.

🚀 Enter fhiry: FHIR to Pandas for AI and ML

The fhiry package, is a game-changer for anyone working at the intersection of healthcare and data science. It transforms FHIR’s complexity into analytical clarity.

🔧 What Is fhiry?

fhiry is a Python package that converts FHIR bundles and NDJSON files into flat, analysis-ready pandas DataFrames. It supports:

• FHIR Server Search: Pull data directly from FHIR servers using the Search API.
• Bulk NDJSON Import: Parse and flatten NDJSON files from SMART Bulk Data exports.
• Google BigQuery Integration: Query FHIR tables hosted on BigQuery.
• Natural Language Queries: Use LLMs to query FHIR data conversationally.
• Custom Column Filtering and Renaming: Tailor the DataFrame to your needs.

📦 Key Features

This tool offers a range of features designed to efficiently manage and analyze FHIR data. It includes a flattening capability that converts nested FHIR JSON into flat DataFrames, simplifying data manipulation. The tool supports NDJSON, allowing for the efficient parsing of bulk exports. With the FHIR Search API, users can fetch resources using parameterized queries, enhancing data retrieval flexibility. Additionally, BigQuery access is enabled, providing SQL-like querying capabilities for FHIR datasets. LLM integration is supported through llama-index, which facilitates natural language queries. Finally, the tool offers configurable columns, allowing users to remove or rename fields through a JSON configuration.

The fhiry package includes a FlattenFhir class that transforms complex FHIR bundles or resources into flattened textual representations, making them suitable for LLM ingestion and reasoning.

🧰 Customization

You can pass a config JSON to remove or rename columns:

🌐 Why fhiry Matters

By flattening FHIR data and integrating with ML tools, fhiry unlocks new possibilities:

• Accelerated Research: Quickly prototype models using real-world health data.
• Improved Accessibility: Lower the barrier for data scientists unfamiliar with FHIR.
• Enhanced Interoperability: Combine FHIR with other datasets in unified pipelines.
• Scalable Analytics: Leverage BigQuery and LLMs for large-scale insights.

⭐ Final Thoughts

FHIR is indispensable for health data exchange, but its analytical limitations have long frustrated researchers and developers. fhiry elegantly solves this problem, transforming FHIR from a data silo into a launchpad for AI innovation.

Whether you’re building predictive models, exploring patient cohorts, or experimenting with LLMs in healthcare, fhiry is the missing link between interoperability and intelligence.

Explore the project on GitHub and give it a ⭐️ if it helps you unlock the full potential of FHIR.

FHIR to pandas dataframe for data analytics, AI and ML!
https://github.com/dermatologist/fhiry
8 forks.
38 stars.
7 open issues.

Recent commits:

• Merge pull request #214 from dermatologist/feature/cron-1ci: update pytest workflow schedule and add test for CLI, GitHub
• fix cli test, Bell Eapen
• Update tests/test_cli.pyCo-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>, GitHub
• Update tests/test_cli.pyCo-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>, GitHub
• ci: update pytest workflow schedule and add test for CLI, dermatologist

Neural networks (NN) are everywhere, from image analytics to NLP to clinical decision support systems. Embeddings are a popular class of techniques that emerged out of NN with diverse applications. embedding is a low-dimensional translation of high dimensional space. For clinicians, embedding is nothing but a simple representation of complex data.

Embeddings are typically used in text analytics and NLP. However, the same concept can be extended to other domains. Take our typing pattern on a keyboard, for example. It is potentially a complex pattern that involves speed, the sequence of keys and the lag between keys. Typingdna finds an innovative way of converting this complex pattern into an embedding. The obvious use case is to use that embedding to identify the user and use it as a supplementary authentication mechanism. Typingdna provides a Javascript library that you can embed on any webpage and capture the typing embedding from any textbox! typingdna also provides a backend API for comparing the embedding for similarity.

Typing is a complex activity that requires interaction between the nervous system and the musculoskeletal system. I presume that any change in the neurocognitive state or muscle weakness could impact the typing pattern. Monitoring the changes in the typing embeddings may have applications in the monitoring or diagnosis of these disorders. To cut a long story short, I recently participated in a Devpost hackathon organized by typingdna. I submitted a simple solution that records the typing pattern and graphs the changes and proposed it as a monitoring application for Dermatomyositis. Details of the submission are available here and the simple prototype is here.

On a different note, I recently discovered that TensorflowJS has an inbuilt facemesh model that can detect facial landmarks from webcam streams, all within the browser! I have created a React component called skinmesh, that can harvest facial landmark coordinates (facial embedding) from a webcam or any facial image. I feel this has potential applications in cosmetic dermatology. I have blogged about this here. Give me a shout, if anybody wants to collaborate on creating a suitable backend.

Standardized data collection forms are vital for health information systems. This is particularly true in public health, where there is a host of data collection forms shared by various organizations. InterRAI is a typical example. Standardization is important for collaborative data analytics at various levels, a need that became painfully apparent during the recent COVID-19 pandemic.

Though standardization of content is widely addressed, the standardization of the form appearance and rendering is less so. Many healthcare teams find their information systems incapable of dealing with this requirement. They have to often resort to expensive resources to create these forms using various technologies such as javascript. It is not easy to share such electronic forms (E-Forms) with other organizations because of the different requirements of the host systems.

The HL7 FHIR Questionnaire is emerging as a standard that is capable of dealing with both the content and presentation of E-forms. We proposed the FHIRForm framework to use this standard for form management and introduced an open-source stack for form management. One of the components of the framework is an npm module (javascript) to convert the FHIR Questionnaire resource to a JSON schema that can be used by popular form rendering libraries can use. The component called FHIRFormJS can also convert the form submission into a QuestionnaireResponse resource that can be submitted to any FHIR compliant servers. Below is a sample ReactJS application that uses FHIRFormJS to render FHIR Questionnaire.

Render FHIR Questionnaire as a web form using FHIRFormJS
https://github.com/dermatologist/fhir-questionnaire-render-react
5 forks.
20 stars.
11 open issues.

Recent commits:

• Merge pull request #286 from dermatologist/dependabot/npm_and_yarn/jsoneditor-9.10.4Bump jsoneditor from 9.10.0 to 9.10.4, GitHub
• Bump jsoneditor from 9.10.0 to 9.10.4Bumps [jsoneditor](https://github.com/josdejong/jsoneditor) from 9.10.0 to 9.10.4.- [Changelog](https://github.com/josdejong/jsoneditor/blob/develop/HISTORY.md)- [Commits](https://github.com/josdejong/jsoneditor/compare/v9.10.0…v9.10.4)—updated-dependencies:- dependency-name: jsoneditor dependency-type: direct:production update-type: version-update:semver-patch…Signed-off-by: dependabot[bot] <support@github.com>, GitHub
• Merge pull request #261 from dermatologist/dependabot/npm_and_yarn/json5-1.0.2Bump json5 from 1.0.1 to 1.0.2, GitHub
• Merge pull request #281 from dermatologist/dependabot/npm_and_yarn/word-wrap-1.2.4Bump word-wrap from 1.2.3 to 1.2.4, GitHub
• Bump word-wrap from 1.2.3 to 1.2.4Bumps [word-wrap](https://github.com/jonschlinkert/word-wrap) from 1.2.3 to 1.2.4.- [Release notes](https://github.com/jonschlinkert/word-wrap/releases)- [Commits](https://github.com/jonschlinkert/word-wrap/compare/1.2.3…1.2.4)—updated-dependencies:- dependency-name: word-wrap dependency-type: indirect…Signed-off-by: dependabot[bot] <support@github.com>, GitHub

FHIRFormJS was a work in progress for a long time and was not really stable enough to use. I am excited to introduce the new version of FHIRFormJS that is better and easier made possible by the @ahryman40k/ts-fhir-types library. If you have been using older systems such as LHC-Forms, give FHIRFormJS a try and let me know how we can improve it. Pull requests are most welcome and I have added this repository as a hacktoberfest participant. There is also a sample react application that uses FHIRFormJS (repository link above). If you are using Vue, I have a separate library that targets one of the popular form rendering engines for Vue.

Let me know if you find this interesting and use the GitHub issues for feature requests.

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Counting conditional occurrences using Prolog.

I wanted to implement the following rule in prolog for my IISA project.

If the percentage of coil region is greater than 20% the homologue detection algorithms may become unreliable. The coil module of IISA returns prolog database in the following format.

coil(105,a,0.000).
coil(109,g,0.001).
coil(110,l,0.004).
coil(111,l,0.014).
coil(112,v,0.055).
coil(113,g,0.055).
coil(114,s,0.371).
coil(115,e,0.416).
coil(116,k,0.860).
coil(117,v,0.955).
coil(118,t,0.998).
coil(119,m,0.999).
coil(120,q,0.999).
coil(121,n,0.999).

ie coil(position, aminoacid, probability of being part of coil).

I wanted to count the number of facts with the probability exceeding a cutoff value, say 0.7.

Though it is simple problem, I could not find any solution for this even after googling for few hours. Finally I found this code on the net.

%Code to count the number of proofs for a goal. Found on the net.
count_proof(Goal, N) :-
            flag(counter, Outer, 0),
            (   call(Goal),
                flag(counter, Inner, Inner+1),
                fail;
                flag(counter, Count, Outer),
                N = Count
            ).
Though it worked well (I have still not figured out how!) the code is SWI-Prolog specific and did not work in JLog prolog applet I use for IISA web interface.

Finally I learned about findall and bagof
But still it took some time to figure out how to use findall for my purpose because the description read findall (Var, Database, Array) and to use length(Array). Using coil (_,_,A) as database will count only the number of aminoacids and I didn’t know how to implement the condition A>0.7.
Finally somewhere I found the actual definition for findall (Var, Goal, Array) which solved my problem, though I wasted a whole day on this simple problem.

Here is the final code.

%The region is coil if the value is greater than threshold
coil_region(Cutoff, Val):-
                     coil(_,_,Val),
                     Val>Cutoff.
                    
coil_percent(Region, Total, Percent):-
                     setting(coil,X),
                     findall(Val,coil_region(X,Val),R),
                     length(R,Region),
                     findall(C,coil(_,_,C),A),
                     length(A,Total),
                     Total>0,        %Avoid division by zero
                     Percent is (Region/Total) * 100,!.

coil_percent(0,0,0).

IISA Home Page.

Merck recently gained approval for a vaccine called Zostavax for preventing herpes zoster reactivation in elderly people. This live virus vaccine is unusual in that it is to prevent reemergence, not to prevent initial infection. Can the same concept be used for recurrent herpes simplex infection which is much more disabling than herpes zoster in younger people?

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