AI FOR MEDICINE
A SleepFM évekkel a tünetek megjelenése előtt jelzi előre a neurológiai betegségeket
Difficulty sleeping often precedes heart disease, psychiatric disorders, and many other illnesses. Researchers used data gathered during sleep studies to detect such conditions. SleepFM is a system that classifies Alzheimer’s, Parkinson’s, prostate cancer, stroke, congestive heart failure, and many other conditions based on a person’s vital signs while asleep — as much as 6 years before they show symptoms. Rahul Thapa and Magnus Ruud Kjaer worked with colleagues at Stanford University, Danish Center for Sleep Medicine, Technical University of Denmark, BioSerenity, Harvard Medical School, and University of Copenhagen. SleepFM comprises a convolutional neural network (CNN), transformer, and LSTM. The authors trained the system in two stages: (i) to encode patterns in sleep data and (ii) to classify diseases. The training data comprised roughly 585,000 hours of sleep-study recordings that included, in addition to each patient’s age and sex, signals of activity in the brain, heart, respiratory system (airflow, snoring, and blood oxygen level), and leg muscles.
- Input/output: Recordings of one night of sleep in, disease classifications out
- Architecture: Convolutional neural network encoder, transformer, LSTM
- Performance: Can accurately classify over 130 conditions, including experiencing congestive heart failure or stroke within six years.
- Availability: Weights, training code, and inference code are available for download for commercial and noncommercial uses.
- The CNN learned to produce embeddings of each signal type, while the transformer modified them to capture relationships within a signal type across time.
- The authors added an LSTM and separately trained it on 9 hours of sleep data to classify more than 1,000 diseases.
- In testing, SleepFM achieved a 0.75 AUC for PTSD classification, significantly outperforming systems without pretraining.
Miért fontos?
AI’s ability to recognize subtle patterns has amazing potential in medicine and beyond. In this application, it could provide early warning of serious diseases, enabling people to take steps to prevent illness before it develops.