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Endoclinic SPMS is a Secure Patient Management System for Endocrinologic Clinics to record patients with Thyreoid/Myeloid cancer. A patients' EMR handles demographics, metastasis, followups, therapies(8 types), operations and histologic tests.
A feature rich Open Source Core Electronic Medical Record for small medical providers developed with Plone/ Python/ Zope. Core EMR functionalities: Patient History, Past Visits, Rx, Health Maint., Allergies, Labs, Vitals, Notes, and Procedures.
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Lists and indexes files, even inside archives, packages, and images
...There has been a tremendous amount of work done on this project (years, including lots of new filetypes) that has yet to be well-tested, cleaned up and uploaded, so please be patient and occasionally check back.
NOTE: command line syntax and parameters have changed in newer versions significantly, to incorporate easy searches and easier database (flat file) creation.
Saukhya-Lite is a user-friendly, mini HIS that automates the entire spectrum of general hospital activities such as the patient history, physical examination, progress notes, Operations, medicine dispensing, lab tests, results, billing, accounts etc.
The Jasper Patient Tracker is a small and secure Java Swing network
application that handles patient profile, scheduling, lobby queue,
vitality recording, itemized insurance claim handling, billing
accounts payable and receivable.
Pain Management Medical Records system. This software provides a combination of file and database records to track patient data and produce required documentation. In addition to records-keeping, it also has a drop-in template area for new forms
ClinicalBERT model trained on MIMIC notes for clinical NLP tasks
Bio_ClinicalBERT is a domain-specific language model tailored for clinical natural language processing (NLP), extending BioBERT with additional training on clinical notes. It was initialized from BioBERT-Base v1.0 and further pre-trained on all clinical notes from the MIMIC-III database (~880M words), which includes ICU patient records. The training focused on improving performance in tasks like named entity recognition and natural language inference within the healthcare domain. Notes were processed using rule-based sectioning and tokenized with SciSpacy. Training was done for 150,000 steps using a batch size of 32, max sequence length of 128, and a masked language modeling objective with a 0.15 mask probability. ...