wsheffel

hospital-chargemaster

hospital chargemaster lists for open source healthcare

nexmon_csi

Channel State Information Extraction on Various Broadcom Wi-Fi Chips

predicting-Paid-amount-for-Claims-Data

Introduction The context is the 2016 public use NH medical claims files obtained from NH CHIS (Comprehensive Health Care Information System). The dataset contains Commercial Insurance claims, and a small fraction of Medicaid and Medicare payments for dually eligible people. The primary purpose of this assignment is to test machine learning (ML) skills in a real case analysis setting. You are expected to clean and process data and then apply various ML techniques like Linear and no linear models like regularized regression, MARS, and Partitioning methods. You are expected to use at least two of R, Python and JMP software. Data details: Medical claims file for 2016 contains ~17 millions rows and ~60 columns of data, containing ~6.5 million individual medical claims. These claims are all commercial claims that were filed by healthcare providers in 2016 in the state of NH. These claims were ~88% for residents of NH and the remaining for out of state visitors who sought care in NH. Each claim consists of one or more line items, each indicating a procedure done during the doctor’s visit. Two columns indicating Billed amount and the Paid amount for the care provided, are of primary interest. The main objective is to predict “Paid amount per procedure” by mapping a plethora of features available in the dataset. It is also an expectation that you would create new features using the existing ones or external data sources. Objectives: Step 1: Take a random sample of 1 million unique claims, such that all line items related to each claim are included in the sample. This will result in a little less than 3 million rows of data. Step 2: Clean up the data, understand the distributions, and create new features if necessary. Step 3: Run predictive models using validation method of your choice. Step 4: Write a descriptive report (less than 10 pages) describing the process and your findings.

PyTorchNLPBook

Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://amzn.to/3JUgR2L

wsheffel/Adversarial-Tradecraft-in-Cybersecurity

A repo to support the book

wsheffel/AMLSim

The AMLSim project is intended to provide a multi-agent based simulator that generates synthetic banking transaction data together with a set of known money laundering patterns - mainly for the purpose of testing machine learning models and graph algorithms. We welcome you to enhance this effort since the data set related to money laundering is critical to advance detection capabilities of money laundering activities.

wsheffel/api

services to access govinfo content and metadata

wsheffel/Architecting-Enterprise-React-Applications-with-Hooks

Architecting Enterprise React Applications with Hooks, published by Packt

wsheffel/bokeh

Interactive Data Visualization in the browser, from Python

wsheffel/cleanlab

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

wsheffel/CogStack-SemEHR

Surfacing Semantic Data from Clinical Notes in Electronic Health Records for Tailored Care, Trial Recruitment and Clinical Research

wsheffel/dash-sample-apps

Open-source demos hosted on Dash Gallery

wsheffel/Data-science

Collection of useful data science topics along with code and articles

wsheffel/Data-Science-for-Marketing-Analytics-Second-Edition

wsheffel/datasets

Datasets used in Plotly examples and documentation

wsheffel/ExData_Plotting1

Plotting Assignment 1 for Exploratory Data Analysis

wsheffel/fastai

The fastai deep learning library

wsheffel/fastbook

The fastai book, published as Jupyter Notebooks

wsheffel/fastcore

Python supercharged for the fastai library

wsheffel/flask

The Python micro framework for building web applications.

wsheffel/Hands-On-Data-Analysis-with-Pandas-2nd-edition

Materials for following along with Hands-On Data Analysis with Pandas – Second Edition

wsheffel/Healthcare-Fraud-Detection

An end to end web application developed using Streamlit to predict if a healthcare provider is fraud or not based on inpatient claims, outpatient claims and beneficiary details.

wsheffel/Jason-Brownlee-books

wsheffel/jekyll

Jekyll-based static site for The Programming Historian

wsheffel/jinja

A very fast and expressive template engine.

wsheffel/jupyter-dash

Develop Dash apps in the Jupyter Notebook and JupyterLab

wsheffel/MedCAT

Medical Concept Annotation Tool

wsheffel/Microsoft-Azure-Security-Technologies-Certification-and-Beyond

Microsoft Azure Security Technologies Certification and Beyond, published by Packt

wsheffel/my-first-binderhub-

How to install BinderHub in Repos to see and run code in github

wsheffel/ovc

the Open Vision Computer

wsheffel/plotly.js

Open-source JavaScript charting library behind Plotly and Dash

wsheffel/s3cogstack

wsheffel/spark-nlp-workshop

Public runnable examples of using John Snow Labs' NLP for Apache Spark.

wsheffel/Synthea-Medgraph