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metapub

Python toolkit for NCBI metadata (via eutils) and pubmed article text mining -- official primary repo.

minicbor

nanoserde

Serialisation library with zero dependencies

nebari

A pure Rust database implementation using an append-only B-Tree file format.

no-std-net

Rust's std::net except without the std.

nonin-3230-ble

Nonin 3230 BLE library for Node.js

pallet-nft

A Substrate FRAME pallet that defines and implements a non-fungible token (NFT) interface.

predicting-icu-patient-clinical-deterioration---report

For this project, I used publicly available Electronic Health Records (EHRs) datasets. The MIT Media Lab for Computational Physiology has developed MIMIC-IIIv1.4 dataset based on 46,520 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center of Boston between 2001 and 2012. MIMIC-IIIv1.4 dataset is freely available to researchers across the world. A formal request should be made directly to www.mimic.physionet.org, to gain access to the data. There is a required course on human research ‘Data or Specimens Only Research’ prior to data access request. I have secured one here -www.citiprogram.org/verify/?kb6607b78-5821-4de5-8cad-daf929f7fbbf-33486907. We built flexible and better performing model using the same 17 variables used in the SAPS II severity prediction model. The question ‘Can we improve the prediction performance of widely used severity scores using a more flexible model?’ is the central question of our project. I used the exact 17 variables used to develop the SAPS II severity prediction algorithm. These are 13 physiological variables, three underlying (chronic) disease variables and one admission variable. The physiological variables includes demographic (age), vital (Glasgow Comma Scale, systolic blood pressure, Oxygenation, Renal, White blood cells count, serum bicarbonate level, blood sodium level, blood potassium level, and blood bilirubin level). The three underlying disease variables includes Acquired Immunodeficiency Syndrome (AIDS), metastatic cancer, and hematologic malignancy. Finally, whether admission was scheduled surgical or unscheduled surgical was included in the model. The dataset has 26 relational tables including patient’s hospital admission, callout information when patient was ready for discharge, caregiver information, electronic charted events including vital signs and any additional information relevant to patient care, patient demographic data, list of services the patient was admitted or transferred under, ICU stay types, diagnoses types, laboratory measurments, microbiology tests and sensitivity, prescription data and billing information. Although I have full access to the MIMIC-IIIv1.4 datasets, I can not share any part of the data publicly. If you are interested to learn more about the data, there is a MIMIC III Demo dataset based on 100 patients https://mimic.physionet.org/gettingstarted/demo/. If you are interested to requesting access to the data - https://mimic.physionet.org/gettingstarted/access/. Linked repositories: Exploratory-Data-Analysis-Clinical-Deterioration, Data-Wrangling-MIMICIII-Database, Clinical-Deterioration-Prediction-Model--Inferential-Statistics, Clinical-Deterioration-Prediction-Model--Ensemble-Algorithms-, Clinical-Deterioration-Prediction-Model--Logistic-Regression, Clinical-Deterioration-Prediction-Model---KNN © 2020 GitHub, Inc.

prediction_of_patients_deterioration_in_cardiac_wards

A super learner was built by stacking logistic regression, random forest, and gradient boosting models (XGBoost) to predict whether a patient in the cardiac wards needs to be transferred to ICU.

pubmed_parser

:clipboard: A Python Parser for PubMed Open-Access XML Subset and MEDLINE XML Dataset

pubreader

A new way to view journal articles

pubsub-ws

pubsub-ws is a simple Node JS library for communicating over websockets using the publish-subscribe pattern.

puck

An HTTP library for the Lunatic VM.

pyentropy

Entropy for Python

pygetpapers

a Python version of getpapers

pysad

Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

remote-monitoring-services-dotnet

Azure IoT .NET solution for Remote Monitoring

remotebatteryalarm

Android app. Monitors battery level and sends notification when it's lower than threshold level

rouille

Web framework in Rust

roxmltree

Represent an XML 1.0 document as a read-only tree.

rquery

HTML/XML DOM tree representation

rs-merkle

The most advanced Merkle tree library for Rust

rulesengine

A Json based Rules Engine with extensive Dynamic expression support

rumdb

Blazing fast log-structured key-value storage based on Bitcask[1] whitepaper.

rust-forest

A tree of reference-counted nodes, with `RefCell` for mutability

rust-punkt

Implementation of the Punkt sentence tokenizing algorithm in Rust.

rust-zip-structs

Rust library for handling ZIP structures / ZIPファイルの構造体を扱うRustライブラリ

rxbattery

monitors battery state of the Android device

scalachain

Blockchain implemented using the Scala programming language

serde-json-core

`serde-json` for `no_std` programs