Our API enables real-time transformation of unstructured healthcare data into structured data that can be used for search and analysis. Identify and extract over 9 million healthcare concepts with a single line of code.

More than 80% of healthcare data is unstructured; it is either physical or in a digital format that requires manual labor to analyze, such as images or typed/handwritten notes. Healthcare runs on notes, scans, and PDFs. Because healthcare data is designed for manual labor, it cannot be easily searched, analyzed, or used to improve patients' quality of care or reduce cost of care. Manual labor does not scale, and the data is growing: 2 zettabytes of healthcare data are generated each year. Historically, the healthcare industry has invested in protocols for the structured data it has, digitizing analog data but leaving it unstructured, and brute-forcing the unstructured data problem through manual labor. These solutions have plateaued. We have to solve the unstructured data problem in healthcare. ScienceIO envisions a future where we enable all healthcare companies — across R&D, clinical trials, real world evidence, digital health, reimbursement, to care delivery — to learn from their data in real-time, so we can develop the next generation of healthcare solutions.

CLAMP (Clinical Language, Annotation, Modeling and Processing) toolkit is an NLP system specifically designed for clinical text. Built on award winning algorithms and provider user friendly interfaces for developing customized NLP solutions to meet diverse user needs.

Melax Technologies is an emerging company, specialized in machine learning and natural language processing (NLP) in the biomedical domain. The company is based on NLP expertise in multidisciplinary research projects and national NLP challenges over a decade. Several NLP-based solutions such as computer-assisted medical coding, clinical decision support, surveillance, care quality and patient safety reporting were implemented and tested in leading large-scale Hospitals in Texas Medical Center.