Kaustabh Ganguly stabgan

Generating a Comprehensive, Multi-Modal, Ontology-Driven Value Set for Renal Replacement Therapy (RRT) Identification in Critical Care Databases

Introduction and Strategic Overview

This document outlines a comprehensive, multi-modal strategy for the creation of a definitive value set to identify all instances of Renal Replacement Therapy (RRT) within the MIMIC-IV and eICU-CRD databases. The accurate and exhaustive identification of RRT is paramount for the integrity of the Sepsis-Associated Acute Kidney Injury (SA-AKI) cohort, where the initiation of RRT within the first 24 hours of ICU admission is a critical exclusion criterion that fundamentally alters the natural history of the disease.

The primary challenge in this task is the fragmented nature of RRT evidence in electronic health records (EHRs). RRT is not documented in a single, standardized field but is scattered across disparate data sources, including procedure records, charted machine parameters, specific fluid and medication inputs

A Comprehensive, Ontology-Driven Strategy for Vasopressor and Inotrope Value Set Generation

Introduction: The Imperative for an Ontology-Driven Value Set

The accurate calculation of the Sequential Organ Failure Assessment (SOFA) score is a cornerstone of modern critical care research, particularly in defining cohorts for conditions like sepsis-associated acute kidney injury (SA-AKI). A critical and often challenging component of this score is the cardiovascular assessment, which quantifies the level of pharmacological support required to maintain hemodynamic stability. The reliance on a manually curated, hardcoded list of vasopressor and inotrope medications to identify these interventions is scientifically untenable. Such static lists are inherently fragile; they are prone to omissions, fail to account for the continuous introduction of new drug formulations and brand names, and lack the fundamental scientific principles of transparency and reproducibility.1 An incomplete value set directly le

A Multi-Ontology Strategy for the Generation of a Systemic Antibiotic Value Set for Sepsis Cohort Identification

Executive Summary

The accurate identification of a sepsis cohort from electronic health record (EHR) data is critically dependent on the ability to comprehensively detect all instances of systemic antibiotic administration. Relying on small, hardcoded lists of common antibiotics is a methodologically flawed approach that leads to under-ascertainment and significant selection bias, thereby compromising the scientific validity of the research. This report outlines a robust, reproducible, and exhaustive strategy for programmatically generating a definitive value set for systemic antibiotic medications. By leveraging the rich semantic structures and relational data within multiple standard ontologies—including the Anatomical Therapeutic Chemical (ATC) classification, RxNorm, SNOMED CT, and the Unified Medical Language System (UMLS)—this multi-modal approach transcends simple lexical sear

A Comprehensive Framework for Sepsis-Associated Acute Kidney Injury (SA-AKI) Cohort Extraction and Staging from MIMIC-IV and eICU-CRD Databases

I. Executive Summary

Sepsis-Associated Acute Kidney Injury (SA-AKI) represents a formidable clinical challenge within intensive care, profoundly increasing patient mortality and long-term morbidity.1 The accurate and reproducible identification of this specific patient cohort is a foundational step for developing effective predictive models and advancing clinical research in critical care. This report delineates a rigorous, ontology-driven methodology for defining SA-AKI patient cohorts within two prominent critical care databases: MIMIC-IV v3.1 and eICU-CRD. The framework integrates internationally recognized clinical criteria for sepsis and acute kidney injury with advanced data mapping techniques, ensuring high data fidelity and reproducibility across these heterogeneous electronic health record (EHR) systems. The ultimate output includes the SQL D

Mastering Backend Engineering: A First-Principles Deep Dive (2025)

Table of Contents

Introduction
System Design: Scalability and Microservices
- Monolithic vs. Microservices Architecture
- Scaling Principles and Design Patterns
- [Designing for Fauldesigning-for-fault-tolerance)
APIs: REST, GraphQL, and gRPC
RESTful APIs

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<meta http-equiv="X-UA-Compatible" content="ie=edge">
	<title>JSON JavaScript Course</title>
	</head>
	<body>
	<div id="addButton">Get User</div>

	const DODGER = document.getElementById('dodger')
	const GAME = document.getElementById('game')
	const GAME_HEIGHT = 400
	const GAME_WIDTH = 400
	const LEFT_ARROW = 37 // use e.which!
	const RIGHT_ARROW = 39 // use e.which!
	const START = document.getElementById('start')

	const ROCKS = []
	var SCORE = 0

	a = [[1,2,[3,4,5]],6,[7,[8,9,10,[11,12,13]]]]


	def qq(a) :
	current = a
	next = []

	while (current or current == 0) :
	if(type(current) is int and current > 7) :
	return current