TY  - JOUR
AU  - Sharma, Rahul
PY  - 2026
DA  - 2026/09/03
TI  - Advanced Architectures in Preventive Health Informatics: A Hybrid Clinical Decision Support System for Early Endocrine and Metabolic Risk Screening
JO  - Biomedical Informatics and Smart Healthcare
T2  - Biomedical Informatics and Smart Healthcare
JF  - Biomedical Informatics and Smart Healthcare
VL  - 2
IS  - 3
SP  - 129
EP  - 135
DO  - 10.62762/BISH.2026.397062
UR  - https://www.icck.org/article/abs/BISH.2026.397062
KW  - EndocrineAI
KW  - clinical decision support system
KW  - metabolic disorders
KW  - machine learning
KW  - preventive health informatics
KW  - actionable intelligence
AB  - Contemporary medicine is shifting from reactive, symptom-based treatment toward proactive, preventive intervention, yet endocrine and metabolic disorders frequently progress through subtle, subclinical phases that are overlooked during routine primary-care encounters, delaying diagnosis and increasing the longitudinal cost of care. This paper presents EndocrineAI, a full-stack, high-integrity clinical decision support system (CDSS) for early-stage endocrine and metabolic risk screening. The platform adopts a hybrid architecture that synthesizes deterministic, rule-based clinical logic with probabilistic Machine Learning (ML) inference and Generative AI summarization. An eight-stage processing pipeline validates patient profiles, extracts eight clinical markers from unstructured laboratory text via regular-expression parsing, computes rule-based risk levels against established reference ranges, optionally refines borderline cases through a scikit-learn fallback chain, and generates natural-language assessments. The system stratifies risk across five physiological categories---thyroid dysfunction, insulin resistance / type 2 diabetes risk, PCOS risk, adrenal stress, and metabolic syndrome---and emits a standardized JSON risk schema for Electronic Health Record (EHR) interoperability, while a ``safe fallback'' design guarantees a baseline rule-based assessment whenever ML or generative components fail, preventing black-box failure modes. The paper further describes the clinical-governance framework that separates decision support from formal diagnosis, and the deployment parameters---managed PostgreSQL persistence, role-based access control, and secret management---required for longitudinal tracking in cloud environments. EndocrineAI demonstrates that a decoupled deterministic/probabilistic pipeline can convert unstructured clinical data into structured, actionable intelligence, offering a scalable blueprint for preventive CDSS that mitigate clinician burnout while empowering proactive metabolic care.
SN  - 3068-5524
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Sharma2026Advanced,
  author = {Rahul Sharma},
  title = {Advanced Architectures in Preventive Health Informatics: A Hybrid Clinical Decision Support System for Early Endocrine and Metabolic Risk Screening},
  journal = {Biomedical Informatics and Smart Healthcare},
  year = {2026},
  volume = {2},
  number = {3},
  pages = {129-135},
  doi = {10.62762/BISH.2026.397062},
  url = {https://www.icck.org/article/abs/BISH.2026.397062},
  abstract = {Contemporary medicine is shifting from reactive, symptom-based treatment toward proactive, preventive intervention, yet endocrine and metabolic disorders frequently progress through subtle, subclinical phases that are overlooked during routine primary-care encounters, delaying diagnosis and increasing the longitudinal cost of care. This paper presents EndocrineAI, a full-stack, high-integrity clinical decision support system (CDSS) for early-stage endocrine and metabolic risk screening. The platform adopts a hybrid architecture that synthesizes deterministic, rule-based clinical logic with probabilistic Machine Learning (ML) inference and Generative AI summarization. An eight-stage processing pipeline validates patient profiles, extracts eight clinical markers from unstructured laboratory text via regular-expression parsing, computes rule-based risk levels against established reference ranges, optionally refines borderline cases through a scikit-learn fallback chain, and generates natural-language assessments. The system stratifies risk across five physiological categories---thyroid dysfunction, insulin resistance / type 2 diabetes risk, PCOS risk, adrenal stress, and metabolic syndrome---and emits a standardized JSON risk schema for Electronic Health Record (EHR) interoperability, while a ``safe fallback'' design guarantees a baseline rule-based assessment whenever ML or generative components fail, preventing black-box failure modes. The paper further describes the clinical-governance framework that separates decision support from formal diagnosis, and the deployment parameters---managed PostgreSQL persistence, role-based access control, and secret management---required for longitudinal tracking in cloud environments. EndocrineAI demonstrates that a decoupled deterministic/probabilistic pipeline can convert unstructured clinical data into structured, actionable intelligence, offering a scalable blueprint for preventive CDSS that mitigate clinician burnout while empowering proactive metabolic care.},
  keywords = {EndocrineAI, clinical decision support system, metabolic disorders, machine learning, preventive health informatics, actionable intelligence},
  issn = {3068-5524},
  publisher = {Institute of Central Computation and Knowledge}
}