Healthcare Analytics Services

The healthcare industry is evolving rapidly with large volumes of data through various sources such as genomics, electronic health records, medical monitoring devices and health-related mobile apps. Besides, the industry is also facing increasing challenges in cost and patient outcomes. Early adopters of AI in the healthcare space are reaping the benefits in terms of patient care and adding to their bottom-line results, and everyone is taking notice.

At Pepgra Healthcare, we help enterprises to organize, annotate, unify, analyse and visualize data for better business decision and to drive business transformation. We apply analytics or advanced algorithms to generate clinical and operational insights. The companies are using AI for a number of scenarios, including managing claims, detecting fraud, improving clinical workflow and increase performance, improve image quality and consistency, and predicting hospital-acquired infections. Pepgra, automation leader in AI, empowering leading healthcare companies to deliver AI solutions that are changing the industry. Pepgra healthcare applies artificial intelligence to unravel the complexity of human disease to meet the right treatment to the right patient at the right time.

Our Comprehensive Healthcare Analytics Solutions

• Ontologies in Healthcare: Medical information systems need to be able to communicate complex and detailed medical data (e.g. medications, health conditions, prior treatments, etc.) securely and efficiency. However, such a task is challenging due to the structure and the concept of medical terminologies. At Pepgra, we construct medical domain ontologies for representing medical terminology system where our ontology development process starts with the requirement analyses phase where concepts, attributes, relationships, and axioms are identified (e.g. e-healthcare, the process of patient’s admission, clinical examination, treatment). Our experts are familiar with various standard ontology frameworks, including SNOMED CT, RxNorm, MeSH, ICD-10, Gene Ontology. Our experts closely work with healthcare application developers to develop ontologies that can assist in communication between humans, to achieve interoperability and to facilitate communication between software.
• Semantic Annotation: Our S3 Healthcare Annotation service tags your medical documents which can further enhance your semantic analysis. The tagger is more focused on recognizing named entities related to drug component (including brand, and generic names, qty, measurements units, etc.), medical device or active substances in the food linking to a specific disease.
• Medical Imaging Analyses: At pepgra, MIA2 have developed a tool for semantic medical image annotation and retrieval. Our semantic annotation tool is automated and context-sensitive workflow based on an image parsing system complemented by an ontology-based context annotation tool.
• Text Mining and Natural Language Processing of Medical documents: We apply Machine Learning (ML) techniques such as natural language processing (NLP) method to extract information from clinical notes. Our proprietary product provides text analytics services for news, life science and healthcare articles as well as social media messages. The product can able to analyse disease, interactions, genes and sequences and link them to entities. Our expert team analyse the technical difficulty of your task by creating annotation guidelines and gold standard corpora. We help to create text analytic model by adapting rules, managing vocabularies, training ML models. Besides, we also make continuous improvement by enhancing the performance of the model by training them through human feedback.
• Deep Learning Application: Our deep learning algorithms have been trained in real-world medical images / HER data, thereby boost radiologist / physician confidence in AI. Our algorithms /software ensure to adhere the US FDA guidelines. We also offer solutions for better asset utilization, caseload management, automatic triage, decision support, quantitative reporting, biomarkers for predicting prognosis and therapeutic responses.
• Predictive Modelling for Personalized Treatment: Data from Electronic Health Records (EHR), remote monitors and wearable devices allow doctors to predict illness and prescribe personalized treatment.
• Algorithms flow chart or rulemaking Medical Recommender Systems: We use ontologies to support context-sensitive searching of information, as well as creating context-based rules for appointments, procedures, and test thereby we enhance the quality of healthcare.
• Visual Analytics for healthcare