Alzheimer's Disease (AD) is the most common form of dementia in the United States. Sleep is one of the lifestyle-related factors that has been shown critical for optimal cognitive function in old age. However, there is a lack of research studying the association between sleep and AD incidence. A major bottleneck for conducting such research is that the traditional way to acquire sleep information is time-consuming, inefficient, non-scalable, and limited to patients' subjective experience. A gold standard dataset is created from manual annotation of 570 randomly sampled clinical note documents from the adSLEEP, a corpus of 192,000 de-identified clinical notes of 7,266 AD patients retrieved from the University of Pittsburgh Medical Center (UPMC). We developed a rule-based Natural Language Processing (NLP) algorithm, machine learning models, and Large Language Model(LLM)-based NLP algorithms to automate the extraction of sleep-related concepts, including snoring, napping, sleep problem, bad sleep quality, daytime sleepiness, night wakings, and sleep duration, from the gold standard dataset. Rule-based NLP algorithm achieved the best performance of F1 across all sleep-related concepts. In terms of Positive Predictive Value (PPV), rule-based NLP algorithm achieved 1.00 for daytime sleepiness and sleep duration, machine learning models: 0.95 and for napping, 0.86 for bad sleep quality and 0.90 for snoring; and LLAMA2 with finetuning achieved PPV of 0.93 for Night Wakings, 0.89 for sleep problem, and 1.00 for sleep duration. The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts. This study focused on the clinical notes of patients with AD, but could be extended to general sleep information extraction for other diseases.

Parents cleverly pull a ruse on their daughter who pretends to be asleep by suggesting actions that can only be done while awake -- check this out! For the 30% of children who have problems falling or staying asleep -- their genes may be to blame. That's according to a 15-year study recently published in the Journal of Child Psychology and Psychiatry, which found that certain genetic variants can have an impact on children's sleep quality and quantity. Researchers from the Department of Sleep and Cognition at the Netherlands Institute for Neuroscience in Amsterdam analyzed the sleep patterns of 2,458 children, as reported by their mothers. Those who were "genetically predisposed" to insomnia -- based on polygenic risk scores that had previously been used for adults -- were more likely to have sleep problems between 1½ and 15 years of age.

ChatGPT will see you now. The artificial intelligence tool may be better than a doctor at following recognised treatment standards for depression, and without the gender or social class biases sometimes seen in the physician-patient relationship, a study suggests. The findings were published in Family Medicine and Community Health, the open access journal owned by British Medical Journal. The researchers said further work was needed to examine the risks and ethical issues arising from AI's use. Globally, an estimated 5% of adults have depression, according to the World Health Organization.

Artificial intelligence (AI) seems to know no boundaries. Innovation in this area has been exciting, and developments over the next five years will be nothing short of astonishing. Even while you're sleeping, it's possible to have new AI technologies hard at work to ensure you get a good night of quality, uninterrupted sleep. As Americans, we're getting a failing grade when it comes to sleep. This is just a small sample of data.