Machine Learning and Precision Medicine in Emergency Medicine: The Basics

Lee, Sangil; Lam, Samuel H.F.; Rocha, Thiago Augusto Hernandes; Fleischman, Ross J.; Staton, Catherine A.; Taylor, Richard A.

doi:10.7759/cureus.17636

Cited by 3 publications

(3 citation statements)

References 57 publications

(71 reference statements)

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“…Machine learning have two categories: frst is supervised learning and second is unsupervised learning. Tese two categories will wrap all the combination of classifcation, and techniques of clustering [26]. Supervised learning strategies enclosed combination of various base classifers; whereas, unsupervised learning strategies enclosed anticipation maximization algorithms as well clustering techniques.…”

Section: Machine Learning Classifiers/algorithmsmentioning

confidence: 99%

Cognitive Lightweight Logistic Regression-Based IDS for IoT-Enabled FANET to Detect Cyberattacks

Rahman

Aziz

Usman

et al. 2023

Mobile Information Systems

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In recent few years, flying ad hoc networks are utilized more for interconnectivity. In the topological scenario of FANETs, IoT nodes are available on ground where UAVs collect information. Due to high mobility patterns of UAVs cause disruption where intruders easily deploy cyberattacks like DoS/DDoS. Flying ad hoc networks use to have UAVs, satellite, and base station in the physical structure. IoT-based UAV networks are having many applications which include agriculture, rescue operations, tracking, and surveillance. However, DoS/DDoS attacks disturb the behaviour of entire FANET which lead to unbalance energy, end-to-end delay, and packet loss. This research study is focused about the detail study of machine learning-based IDS. Also, cognitive lightweight-LR approach is modeled using UNSW-NB 15 dataset. IoT-based UAV network is introduced using machine learning to detect possible security attacks. The queuing and data traffic model is utilized to implement DT, RF, XGBoost, AdaBoost, Bagging and logistic regression in the environment of IoT-based UAV network. Logistic regression is the proposed approach which is used to estimate statistical possibility. Overall, experimentation is based on binomial distribution. There exists linear association approach in logistic regression. In comparison with other techniques, logistic regression behaviour is lightweight and low cost. The simulation results presents logistic regression better results in contrast with other techniques. Also, high accuracy is balanced well in optimal way.

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Section: Machine Learning Classifiers/algorithmsmentioning

confidence: 99%

Cognitive Lightweight Logistic Regression-Based IDS for IoT-Enabled FANET to Detect Cyberattacks

Rahman

Aziz

Usman

et al. 2023

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Good results have been also published in its use for vessel identification and fluid prediction responsiveness via pattern recognition and quantitative measurement (e.g. inferior vena cava ultrasound) [8,9]. Transfer of data capabilities and remote consulting augment the safety and the efficacy of healthcare intervention.…”

Section: Medical Imagingmentioning

confidence: 99%

“…In a recent meta-analysis about AI use in predicting cardiac arrest, AI-Early Warning Systems outperformed traditional warning systems (Early Warning Score, Modified Early Warning Score, National Early Warning Score and Paediatric Early Warning Score) in 9 out 10 studies included in the review [14]. Natural language processing have been also used for improving diagnosis of diseases and conditions that are difficult to identify by clinical gestalt only, such as anorexia nervosa, aneurysms, coronary artery disease or Kawasaki disease [8,15]. Other machine learning models have been tested for Digital Medicine and Health Technology 3/10 prediction of hospital's practice to perform coronary angiography in adult patients after out-of-hospital-cardiac-arrest and subsequent neurologic outcomes, with promising results [16].…”

Section: Triagementioning

confidence: 99%

Precision Medicine in Emergency Medicine

Aslanidis

2022

DMHT

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Personalized medicine was always a part of medicine. However, with technological advances in data mining, machine learning, artificial intelligence and computing, the term “personalized” has been surpassed by precision medicine, a multidisciplinary bridge that aims to provide unique approaches for each patient. Acute care is an area where current precision medicine methods is starting to transform. In this mini review, we describe in brief some of the applications used in emergencies that promote precision medicine.

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Danger, Danger, Gaston Labat! Does zero-shot artificial intelligence correlate with anticoagulation guidelines recommendations for neuraxial anesthesia?

Hurley,

Gupta,

Schroeder

et al. 2024

Reg Anesth Pain Med

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IntroductionArtificial intelligence and large language models (LLMs) have emerged as potentially disruptive technologies in healthcare. In this study GPT-3.5, an accessible LLM, was assessed for its accuracy and reliability in performing guideline-based evaluation of neuraxial bleeding risk in hypothetical patients on anticoagulation medication. The study also explored the impact of structured prompt guidance on the LLM’s performance.MethodsA dataset of 10 hypothetical patient stems and 26 anticoagulation profiles (260 unique combinations) was developed based on American Society of Regional Anesthesia and Pain Medicine guidelines. Five prompts were created for the LLM, ranging from minimal guidance to explicit instructions. The model’s responses were compared with a “truth table” based on the guidelines. Performance metrics, including accuracy and area under the receiver operating curve (AUC), were used.ResultsBaseline performance of GPT-3.5 was slightly above chance. With detailed prompts and explicit guidelines, performance improved significantly (AUC 0.70, 95% CI (0.64 to 0.77)). Performance varied among medication classes.DiscussionLLMs show potential for assisting in clinical decision making but rely on accurate and relevant prompts. Integration of LLMs should consider safety and privacy concerns. Further research is needed to optimize LLM performance and address complex scenarios. The tested LLM demonstrates potential in assessing neuraxial bleeding risk but relies on precise prompts. LLM integration should be approached cautiously, considering limitations. Future research should focus on optimization and understanding LLM capabilities and limitations in healthcare.

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Machine Learning and Precision Medicine in Emergency Medicine: The Basics

Cited by 3 publications

References 57 publications

Cognitive Lightweight Logistic Regression-Based IDS for IoT-Enabled FANET to Detect Cyberattacks

Cognitive Lightweight Logistic Regression-Based IDS for IoT-Enabled FANET to Detect Cyberattacks

Precision Medicine in Emergency Medicine

Danger, Danger, Gaston Labat! Does zero-shot artificial intelligence correlate with anticoagulation guidelines recommendations for neuraxial anesthesia?

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