How can machine learning help improve healthcare?

For example, ML algorithm can be used to prevent stroke by identifying several attributes that correlate to the root cause of the data from cardiovascular disease. We can consider Hypertension, Average Glucose, BMI, Age, Smoking habit and etc. as attributes and apply various algorithms to find out the pattern from the population data. That could help create policies to manage heart disease and prevent death from stroke. ML algorithm can be used to estimate underreporting of adverse events in any healthcare industry hence serving as a check on existing AE capture methodologies by detecting a few symptoms from the data source such as instances of Blood Loss, Allergic Reaction, Cardiac Event, Medication Error, Patient Fall. It could help to predict the hospital readmission rate, and insist patients continue the treatment without failure. ML algorithm can be used to predict triggering the peritonitis protocol in the dialysis industry, hence augmenting infection surveillance by detecting a few symptoms from RN’s notes such as instances of abdominal pain.

Patient/member engagement is one of the most challenging issues. ML can be used in understanding SDOH factors to improve engagement. This in turn will also help reduce costs associated to factors beyond the physicians control like hiding co morbidities and situations where members do not adhere to the care plan or do not refill prescriptions as needed. ML can help identify such members early in the journey and can help payers and providers devise innovative strategies to keep members engaged and thereby help arrest the increasing curve of healthcare costs.

We explored ML in wound imaging to predict healing and infection (Wang C, et al., 2015) and under typical clinical settings using clinical and biomarker data as well as imaging using smartphones/tablets (Kim RB, et.al., 2020).??We used ML models of random forests and support vector machines with radial basic function kernel to predict healing using hand crafted features of texture and color, clinical and biomarker data, and deep learning.??We found hand crafted models described more of the area under the curve (AUROC) than deep learning or clinical features alone although combining all three improved the AUROC.??While artificial intelligence in this area relies less on natural linguistic programming using provider documentation, certain image issues, such as standardized image acquisition protocols and image artifacts of sloughed blisters, tape abrasions, etc. can affect accuracy in non-supervised settings.??Nonetheless, being able to better predict infection and time-to-healing can potentially improve wound care outcomes through personalized patient education and sooner use of advanced therapies.??Wound image analysis can be scalable to telehealth platforms improving access for addressing racial disparities in wound outcomes.?

Very relevant topic. Machine learning is transforming the healthcare industry by changing the way care is delivered, and there is potential for greater impact in the future benefiting patients. Real world data is increasingly used to inform research, patient care and population health in oncology; however using #rwd at scale requires accurate methods to identify clinically relevant attributes. This rich data combined with the application of increasingly advanced ML produce better models that continuously improve their predictive capabilities. Example use cases include: enable early cancer detection through machine learning, detect early onset of adverse events, predict occurrence. ML today in a life science setting is leveraged to garner new biological insights; identify and prioritize new targets; move forward the most promising compounds into clinical development; execute more efficient, targeted and diverse trials by going where the patients are; identify rare and difficult-to-diagnose diseases earlier, and so much more. Other potential use of Ml across healthcare in my view are in the area of at-risk patient prediction.