Cancer Prediction - AI in facilitating the diagnosis and predicting prognosis of malignancies
Use of machine learning and data mining techniques has revolutionized the whole process of breast cancer Diagnosis and Prognosis. Breast Cancer Diagnosis distinguishes benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is likely to recur in patients that have had their cancers excised. Thus, these two problems are mainly in the scope of the classification problems. This project summarizes various use of data on breast cancer diagnosis and prognosis and research being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.
Data driven Patient recruitment - designed to boost clinical trial recruitment
Clinical Trial Recruitment: Understanding Participation and Strategies for Boosting Enrolment. Meet your patients where they are. Build trust with your patients by choosing our patient-centric approach to clinical trials. Understand your target patients from a clinical and behavioural perspective, and meet them where they are digitally. extracting pertinent electronic medical record (EMR) information, sifting through physicians’ notes, reading binary data from images and medical scans and comparing them to a study’s inclusion and exclusion criteria, AI can more efficiently and effectively identify appropriate patients for clinical trial enrolment. And, during trials, AI can help by predicting which patients are at risk of dropping out.
Mental Health - Program that diagnoses patients and matches them with an appropriate therapist
Improving access to mental health treatments, AI can play a big role within personalised treatments. We covered cover both, an online application that uses AI and machine learning and clinical network, tailors its suggestions to the needs of the user and provides access to a variety of treatments. Algorithm suggest that the most suitable course of action is cognitive behavioural therapy (CBT). CBT is a popular talking therapy that aids to reframe the way you think and behave, to change the way in which you address problems.
Patient Monitoring – Advanced patient monitoring is the Program
Wireless and sensor technologies to patient monitoring in order to generate the high quality clinical data sets required for real-time predictive analytics, meaningful clinical AI and machine assisted learning. Artificial Intelligence and Machine Learning with Advanced Analytics are Driving New Levels of Patient Safety and Study Quality. Selected patients to be sent home with the wearable patient-monitoring devices, which provided the hospital staff with real-time alerts on a patient’s condition. The technology would act as an early warning system, help avoid hospitalizations and prioritize patient’s needs. Hospital staff home visits declined 22 percent, freeing up more time and resources for nurses and 92 percent adherence to the remote monitoring system.
Hybrid Trials - Patient-centric trials makes them easier to recruit, monitor and retain patients and accelerate timelines
Providing patient data to researchers in real time. Clinical trial sponsors face increasing challenges based on the need for more complex study protocols and larger digitised data sets to support the next medical breakthrough. Couple this with the geographic growth of clinical trials – many spread across multiple countries to target just the right patient populations – and it’s no wonder that we’ve reached a point where humans are struggling to keep up. And it’s not just increasing data volume that keeps trial sponsors awake at night. Data velocity, data variety and data veracity are problems as well. Against this backdrop, the clinical trials industry needs disruption more than ever before. This is where the dynamic trio of artificial intelligence (AI), the cloud and a data lake comes in. We leverage voice assistant (VA) technology, along with data from a data lake, enable patients to have real-time conversational experiences (CX) that can simplify their routine interactions as they participate in clinical trials.
IoT Integration - Real-time monitoring via connected devices
Wearable sensors record data such as body temperature, heart rate, and blood glucose levels in near real time, which are sent automatically to the study’s electronic data capture (EDC) system, and then routed to the data lake for immediate ingestion, curation and integration. The study personnel visit patients at home for drug administration and follow up. When a visit is approaching, the patient’s mobile device provides automated reminders, allowing the patient to reschedule the appointment within a timeframe permitted by the study protocol.