Machine Learning and NLP to Track Disease Progression and Predict Health Outcomes
Li Zhou, MD, PhD, is a lead investigator at the Division of General Internal Medicine and Primary Care. Zhou’s research focuses on the application of various subfields of AI including natural language processing and machine learning to various clinical domains, such as phenotyping, medication reconciliation, and adverse drug reaction detection. Currently, Zhou is developing innovative methods for error detection in clinical notes generated by speech software, and using machine learning to extract knowledge from malpractice claims to enhance coding and analytics for risk management.
“Society is aging; nowadays, patients are living longer with chronic diseases. There is an urgent need for a significant increase in the depth and breadth of palliative care delivery for seriously ill patients. Timely referral to palliative care can raise the quality of care for patients and their families.”
- One of the biggest challenges in palliative care is identifying patients who can benefit from care and the right time to initiate interventions. There is an urgent need to leverage information technology and electronic health record (EHR) data towards guiding palliative interventions.
- In a 2018 study, Zhou et al. studied dementia patients over the last years of life. Natural language processing was used to capture themes mentioned in EHR notes over this time.
- They then developed a deep learning neural network system to predict mortality with longitudinal clinical notes. Preliminary work shows promising results in predicting two-year mortality for dementia patients – performing better than clinician predictions.
- Next, Zhou’s team hopes to enhance the model by inputting other data sources and to put the tool into clinical practice.