Stroke risk is not linear

Orfanoudaki A, Chesley E, Cadisch C, Stein B, Nouh A, Alberts MJ, et al. (2020) Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score. PLoS ONE 15(5): e0232414. https://doi.org/10.1371/journal.pone.0232414

Current stroke risk assessment tools presume the impact of risk factors is linear and cumulative. However, both novel risk factors and their interplay influencing stroke incidence are difficult to reveal using traditional additive models. The goal of this study was to improve upon the established Revised Framingham Stroke Risk Score and design an interactive Non-Linear Stroke Risk Score. Leveraging machine learning algorithms, our work aimed at increasing the accuracy of event prediction and uncovering new relationships in an interpretable fashion. A two-phase approach was used to create our stroke risk prediction score. First, clinical examinations of the Framingham offspring cohort were utilized as the training dataset for the predictive model. Optimal Classification Trees were used to develop a tree-based model to predict 10-year risk of stroke. Unlike classical methods, this algorithm adaptively changes the splits on the independent variables, introducing non-linear interactions among them. Second, the model was validated with a multi-ethnicity cohort from the Boston Medical Center. Our stroke risk score suggests a key dichotomy between patients with history of cardiovascular disease and the rest of the population. While it agrees with known findings, it also identified 23 unique stroke risk profiles and highlighted new non-linear relationships; such as the role of T-wave abnormality on electrocardiography and hematocrit levels in a patient’s risk profile. Our results suggested that the non-linear approach significantly improves upon the baseline in the c-statistic (training 87.43% (CI 0.85–0.90) vs. 73.74% (CI 0.70–0.76); validation 75.29% (CI 0.74–0.76) vs 65.93% (CI 0.64–0.67), even in multi-ethnicity populations. The clinical implications of the new risk score include prioritization of risk factor modification and personalized care at the patient level with improved targeting of interventions for stroke prevention.

學習程式設計的心法與方法

心法

Discovering faster matrix multiplication algorithms with reinforcement learning

Fawzi, A., Balog, M., Huang, A. et al. Discovering faster matrix multiplication algorithms with reinforcement learning. Nature 610, 47–53 (2022). https://doi.org/10.1038/s41586-022-05172-4. (data and code)

Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall speed of a large amount of computations. Matrix multiplication is one such primitive task, occurring in many systems—from neural networks to scientific computing routines. The automatic discovery of algorithms using machine learning offers the prospect of reaching beyond human intuition and outperforming the current best human-designed algorithms. However, automating the algorithm discovery procedure is intricate, as the space of possible algorithms is enormous. Here we report a deep reinforcement learning approach based on AlphaZero for discovering efficient and provably correct algorithms for the multiplication of arbitrary matrices. Our agent, AlphaTensor, is trained to play a single-player game where the objective is finding tensor decompositions within a finite factor space. AlphaTensor discovered algorithms that outperform the state-of-the-art complexity for many matrix sizes. Particularly relevant is the case of 4 × 4 matrices in a finite field, where AlphaTensor’s algorithm improves on Strassen’s two-level algorithm for the first time, to our knowledge, since its discovery 50 years ago. We further showcase the flexibility of AlphaTensor through different use-cases: algorithms with state-of-the-art complexity for structured matrix multiplication and improved practical efficiency by optimizing matrix multiplication for runtime on specific hardware. Our results highlight AlphaTensor’s ability to accelerate the process of algorithmic discovery on a range of problems, and to optimize for different criteria.

Deep reinforcement learning for inventory control

R.N. Boute, J. Gijsbrechts, W. van Jaarsveld, and N. Vanvuchelen, Deep reinforcement learning for inventory control: A roadmap, European Journal of Operational Research, Volume 298, Issue 2, 16 April 2022, Pages 401-412.

Deep reinforcement learning (DRL) has shown great potential for sequential decision-making, including early developments in inventory control. Yet, the abundance of choices that come with designing a DRL algorithm, combined with the intense computational effort to tune and evaluate each choice, may hamper their application in practice. This paper describes the key design choices of DRL algorithms to facilitate their implementation in inventory control. We also shed light on possible future research avenues that may elevate the current state-of-the-art of DRL applications for inventory control and broaden their scope by leveraging and improving on the structural policy insights within inventory research. Our discussion and roadmap may also spur future research in other domains within operations management.

技術陷阱

許恬寧譯，從工業革命到AI時代，技術創新下的資本、勞動力與權力，八旗文化，2020

Carl Benedikt Frey, The Technology Trap: Capital, Labor, and Power in the Age of Automation, Princeton University Press, 2020.

《技術陷阱》縱橫技術發展史，從工業革命談到人工智慧時代，探討技術如何大幅移轉社會成員間的經濟與政治力量分布。本書作者、牛津大學經濟資深研究員弗雷指出，從長期的角度來看，工業革命創造出前所未有的財富與繁榮，然而機械化在當下所帶來的影響，卻使大量人口深受其害。中等收入的工作機會萎縮、薪資停滯不前、勞動所占的收入份額下降，即便利潤大增，卻造成貧富差距飆升。弗雷指出工業革命的潮流，與今日由電腦革命開啟的AI時代，大有相互呼應之處。