Application of data science in healthcare

Transcript

1. APPLICATIONS OF DATA SCIENCE IN HEALTHCARE
2. Data science in healthcare refers to the application of data-driven techniques to improve healthcare outcomes, optimize operations, and support clinical decision-making. It leverages a combination of statistical analysis, machine learning, AI, and domain-specific knowledge to extract meaningful insights from vast amounts of healthcare data, including medical records, clinical trials, patient demographics, and more. INTRODUCTION
3. Patient Health Record Maintenance Medical Imaging Pharmaceutical Development Virtual Assistance Predictive Modeling and Analytics
4. Electronic Health Record (EHR) Analysis: Data science enhances EHR management, helping to structure unstructured data, integrate it across different healthcare systems, and provide a unified view of patient histories. Interoperability and Data Integration: Advanced data engineering techniques allow integration of data from various sources (e.g., lab results, patient history, imaging) to create comprehensive patient records that can be easily accessed and analyzed. Health Record Personalization: Machine learning can analyze EHRs to provide personalized recommendations and care pathways, based on a patient’s medical history and current condition. Patient Health Record Maintenance
5. Disease Outbreak Prediction: Data science models use historical data and real-time patient data to predict the likelihood of disease outbreaks, enabling early intervention. Patient Readmission Prediction: Predictive models can forecast which patients are most likely to be readmitted based on their clinical histories, allowing hospitals to provide preventative care and reduce readmission rates. Chronic Disease Management: Predictive analytics can be used to manage chronic diseases like diabetes and heart disease by identifying patients at risk of complications, ensuring they receive timely care. Predictive Modeling and Analytics
6. AI-Powered Chatbots: Virtual assistants can provide basic patient care by answering questions, scheduling appointments, and offering reminders for medication adherence, freeing up healthcare workers for more critical tasks. Telemedicine Support: Virtual assistants powered by data science can assist in triage, collecting patient symptoms, and recommending preliminary care via telemedicine platforms. Symptom Checkers: These AI-driven tools ask patients questions about their symptoms and offer preliminary diagnoses or advice, guiding them on whether to seek in- person care or self-manage. Virtual Assistance
7. Drug Discovery: Data science helps accelerate drug discovery by analyzing molecular data, identifying potential drug candidates, and predicting their interactions with biological targets. AI models can reduce the time and cost involved in discovering new drugs. Clinical Trials Optimization: Machine learning models predict patient outcomes, helping in better trial design and patient selection. This leads to more efficient and targeted trials, reducing failure rates. Predictive Toxicology: Data-driven models can predict how different compounds will behave in the body, reducing the need for extensive animal or human testing in the early stages of drug development. Pharmaceutical Development
8. AI and Machine Learning for Diagnostics: Data science models are applied to analyze medical images such as X-rays, MRIs, and CT scans. AI tools can identify patterns in images, detect abnormalities (like tumors or fractures), and assist radiologists in making faster, more accurate diagnoses. Image Segmentation and Enhancement: Algorithms can segment images, highlighting areas of interest (e.g., organs, tissues), and enhance image quality, improving diagnosis precision. Automated Image Analysis: Automated tools powered by deep learning can scan large volumes of images, reducing the manual workload for radiologists while improving detection accuracy. Medical Imaging
9. Thank You KRUTHIKA BS 2437032 REFERENCES https://www.orientsoftware.com/blog/application-of-data-science-in-healthcare/ https://www.knowledgehut.com/blog/data-science/data-science-in-healthcare https://www.indeed.com/career-advice/career-development/data-science-healthcare