Caring deeper: motif detection from medical records using convolutional nets

In the previous post, I have introduced DeepCare, a LSTM-based model for (potentially very long) medical records with irregular timing and treatments.

Here I introduce another deep net called Deepr, a CNN-based model for relatively short medical records. The main purpose is learning to discover medical motifs that lead to some future events (e.g., death).

Unlike DeepCare which assumes a clear temporal dynamics in the medical records, Deepr requires only repeated short patterns (motifs) over the data sequence. Time gaps are discretized into symbols which are treated in the same way as diagnoses, procedures and medications. All symbols are then sequenced. Those co-occurring will be randomly ordered.

Once Deepr has been learnt, motif segments in a record that respond well to an outcome can be detected.

Note that Deepr can be used in other situations where irregular time gaps and discrete data are present.

Update references

• Deepr: A Convolutional Net for Medical Records, Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh, IEEE Journal of Biomedical and Health Informatics, 2017.
• DeepCare: A Deep Dynamic Memory Model for Predictive Medicine, Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh, PAKDD'16, Auckland, NZ, April 2016.

Caring deeply: Intervened long short-term memory for medical records

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.