In the US, healthcare expenditure accounts for approximately 18% GDP, almost twice as much that in Australia. And the percentage keeps growing. One possible explanation is that after having cheap, accessible everything, people want to spend more and more on is their own health.
Given this big fat cake, it is no surprise that healthcare is the next target for the current AI wave. At present, startups pop up every week, all hoping to claim a big share.
Central to modern healthcare systems is Electronic Medical Records (EMRs), the personal database of any encounter with the healthcare systems, usually consists of information regarding diseases, treatments, billing, measurements, social care and more. EMRs are the promise of the modern healthcare to improve efficiency, accessibility and personalized medicine.
We will focus our attention to predictive medicine, a new approach that is not just about diagnosis (what happens now), but also about prognosis (what will happen if we do X). Not surprisingly, to predict the future, we need to study the past. Ultimately, we end up modeling the entire health trajectory since birth (if the data is available).
Two things that make EMRs a modeling challenge are:
- Data are episodic. Data is only recorded when patient turns up at clinic or hospital. There are time gap in between, and the gap is irregular.
- There is "care" in healthcare, that is, interventions done by clinician. Treatments disrupt the natural course of health trajectory. Treatments are supposed to lessen or eliminate the illness. But medical errors do also occur, making the illness worse.
Our recent model, DeepCare, is a deep architecture that directly models the effect of irregular time gap and treatment. It modifies the gates of the popular Long Short-Term Memory (LSTM). "Memory" plays a great role here because there is weak and irrelevant information in the records, and we do not know which one! LSTM is great because it can decide to ignore or keep certain new information as well as forget or keep the old illness memory.
What can DeepCare do? You can think of treatment recommendation, disease progression prediction, readmission prediction, attributing the past illness to future event and more. Check out the paper here.
Update references
- DeepCare: A Deep Dynamic Memory Model for Predictive Medicine, Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh, PAKDD'16, Auckland, NZ, April 2016.
- Deepr: A Convolutional Net for Medical Records, Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh, IEEE Journal of Biomedical and Health Informatics, 2017.
As used in this section, the term "adverse action" means the imposition of any tax or tax penalty; local chiropractic offices
ReplyDeleteIntervened Long Short-Term Memory (LSTM) models can be effectively applied to the analysis of medical records. Here’s how they can be used:
ReplyDeleteOverview of LSTM in Medical Records
LSTM is a type of recurrent neural network (RNN) particularly well-suited for sequence prediction tasks. Its architecture allows it to remember information for long periods, making it ideal for analyzing time-series data or sequential records, such as medical histories.
Applications
Patient Health Prediction
Usage: Analyze past medical records to predict future health events (e.g., hospital readmissions, disease progression).
Benefit: Early identification of patients at risk enables timely interventions.
Natural Language Processing (NLP)
Usage: Process unstructured text data in electronic health records (EHRs) for extracting meaningful insights (e.g., symptoms, treatments).
Benefit: Improves data usability for clinical decision-making and research.
Time-Series Analysis
Usage: Analyze time-stamped medical data (e.g., vital signs, lab results) to identify trends and anomalies.
Benefit: Supports real-time monitoring of patient health.
Drug Interaction and Side Effect Prediction
Usage: Predict potential drug interactions or adverse effects based on patient history and medication regimens.
Benefit: Enhances patient safety and treatment efficacy.
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