A Nature Machine Intelligence study reveals that many machine learning models used in healthcare exhibit low responsiveness to critical or rapidly deteriorating patient conditions. Researchers evaluated publicly available datasets and models predicting mortality, length of stay, and readmission risk, finding that model predictions often remained static even when faced with significant changes in patient physiology, like acute hypotensive episodes. This lack of sensitivity stems from models prioritizing readily available static features, like demographics or pre-existing conditions, over dynamic physiological data that better reflect real-time health changes. Consequently, these models may fail to provide timely alerts for critical deteriorations, hindering effective clinical intervention and potentially jeopardizing patient safety. The study emphasizes the need for developing models that incorporate and prioritize high-resolution, time-varying physiological data to improve responsiveness and clinical utility.
Microsoft has introduced Dragon Ambient eXperience (DAX) Copilot, an AI-powered assistant designed to reduce administrative burdens on healthcare professionals. It automates note-taking during patient visits, generating clinical documentation that can be reviewed and edited by the physician. DAX Copilot leverages ambient AI and large language models to create summaries, suggest diagnoses and treatments based on doctor-patient conversations, and integrate information with electronic health records. This aims to free up doctors to focus more on patient care, potentially improving both physician and patient experience.
HN commenters express skepticism and concern about Microsoft's Dragon Copilot for healthcare. Several doubt its practical utility, citing the complexity and nuance of medical interactions as difficult for AI to handle effectively. Privacy is a major concern, with commenters questioning data security and the potential for misuse. Some highlight the existing challenges of EHR integration and suggest Copilot may exacerbate these issues rather than solve them. A few express cautious optimism, hoping it could handle administrative tasks and free up doctors' time, but overall the sentiment leans toward pragmatic doubt about the touted benefits. There's also discussion of the hype cycle surrounding AI and whether this is another example of overpromising.
A new study published in the journal Dreaming found that using the Awoken lucid dreaming app significantly increased dream lucidity. Participants who used the app experienced a threefold increase in lucid dream frequency compared to a control group. The app employs techniques like reality testing reminders and dream journaling to promote lucid dreaming. This research suggests that smartphone apps can be effective tools for enhancing metacognition during sleep and inducing lucid dreams.
Hacker News commenters discuss the efficacy and methodology of the lucid dreaming study. Some express skepticism about the small sample size and the potential for bias, particularly given the app's creators conducted the study. Others share anecdotal experiences with lucid dreaming, some corroborating the app's potential benefits, while others suggesting alternative induction methods like reality testing and MILD (Mnemonic Induction of Lucid Dreams). Several commenters express interest in the app, inquiring about its name (Awoken) and discussing the ethics of dream manipulation and the potential for negative dream experiences. A few highlight the subjective and difficult-to-measure nature of consciousness and dream recall, making rigorous study challenging. The overall sentiment leans towards cautious optimism, tempered by a desire for further, more robust research.
Summary of Comments ( 25 )
https://news.ycombinator.com/item?id=43482792
HN users discuss the study's limitations, questioning the choice of AUROC as the primary metric, which might obscure significant changes in individual patient risk. They suggest alternative metrics like calibration and absolute risk change would be more clinically relevant. Several commenters highlight the inherent challenges of using static models with dynamically changing patient conditions, emphasizing the need for continuous monitoring and model updates. The discussion also touches upon the importance of domain expertise in interpreting model outputs and the potential for human-in-the-loop systems to improve clinical decision-making. Some express skepticism towards the generalizability of the findings, given the specific datasets and models used in the study. Finally, a few comments point out the ethical considerations of deploying such models, especially concerning potential biases and the need for careful validation.
The Hacker News post "Low responsiveness of ML models to critical or deteriorating health conditions" (linking to a Nature Machine Intelligence article) sparked a discussion with several insightful comments. Many commenters focused on the core issue highlighted in the article: the difficulty of training machine learning models to accurately predict and react to sudden, critical health declines.
Several users pointed out the inherent challenge of capturing rare events in training data. Because datasets are often skewed towards stable patient conditions, models may not be adequately exposed to the subtle indicators that precede a rapid deterioration. This lack of representation makes it difficult for the models to learn the relevant patterns. One commenter specifically emphasized the importance of high-quality, diverse datasets that include these crucial, albeit rare, events.
Another prominent theme was the difference between correlation and causation. Commenters cautioned against relying solely on correlations within the data, as these might not reflect the actual causal mechanisms driving health changes. They highlighted the risk of models learning spurious correlations that lead to inaccurate predictions or, worse, inappropriate interventions. One commenter suggested incorporating domain expertise and causal inference techniques into model development to address this limitation.
The discussion also touched upon the complexities of physiological data. Commenters noted that vital signs, while valuable, can be noisy and influenced by various factors unrelated to underlying health conditions. This inherent variability makes it difficult for models to discern true signals from noise. One commenter proposed exploring more sophisticated signal processing techniques to extract meaningful features from physiological data.
Furthermore, the limitations of current evaluation metrics were discussed. Commenters argued that standard metrics like AUROC might not be sufficient for assessing model performance in critical care settings. They emphasized the need for metrics that specifically capture the model's ability to detect and predict rare, high-stakes events like sudden deteriorations. One commenter mentioned the potential of using metrics like precision and recall at specific operating points relevant to clinical decision-making.
Finally, several commenters raised the importance of human oversight and clinical judgment. They emphasized that ML models should be viewed as tools to assist clinicians, not replace them. They argued that human expertise is crucial for interpreting model predictions, considering contextual factors, and making informed decisions, especially in complex and dynamic situations like critical care.