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AI-based clinical decision-making systems in palliative medicine: ethical challenges

#artificialintelligence

Background Improving palliative care (PC) is demanding due to the increase in people with PC needs over the next few years. An early identification of PC needs is fundamental in the care approach: it provides effective patient-centred care and could improve outcomes such as patient quality of life, reduction of the overall length of hospitalisation, survival rate prolongation, the satisfaction of both the patients and caregivers and cost-effectiveness. Methods We reviewed literature with the objective of identifying and discussing the most important ethical challenges related to the implementation of AI-based data processing services in PC and advance care planning. Results AI-based mortality predictions can signal the need for patients to obtain access to personalised communication or palliative care consultation, but they should not be used as a unique parameter to activate early PC and initiate an ACP. A number of factors must be included in the ethical decision-making process related to initiation of ACP conversations, among which are autonomy and quality of life, the risk of worsening healthcare status, the commitment by caregivers, the patients’ psychosocial and spiritual distress and their wishes to initiate EOL discussions Conclusions Despite the integration of artificial intelligence (AI)-based services into routine healthcare practice could have a positive effect of promoting early activation of ACP by means of a timely identification of PC needs, from an ethical point of view, the provision of these automated techniques raises a number of critical issues that deserve further exploration.


Researchers use machine learning to modify the current PTSD diagnostic criteria - Mental Daily

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A group of researchers from the Boston University School of Public Health and the VA Boston Healthcare System utilized machine learning to streamline the diagnosis tool for post-traumatic stress disorder (PTSD). According to their new study, released in the journal Assessment, some of the questions imposed in the Structural Clinical Interview for the Diagnostic Statistical Manual of Mental Disorders, Fifth Edition (SCID-5) could be eliminated, leading to more relevancy of the veteran population. "Our study is only a first step--but an important one, because it shows that machine learning methods can be used to help inform efforts to make care more efficient, without sacrificing or degrading the quality of care provided," said co-author Jaimie Graudus, in a news release. The new research included data from the SCID-5 assessments related to more than 1,200 military soldiers, half of which were male and the rest female, who served during the Afghanistan and Iraq conflicts. The use of random forests, a form of machine-learning system, was also incorporated into the study.


Decision Tree Algorithms-Machine Learning

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Decision Tree Algorithm one of the easiest and popular Algorithms to predict the output. The Decision Tree Algorithm is a part of the supervised machine learning algorithm. Here the problem is represented in a form of a tree to predict the outcome. This algorithm aims to create a model that should predict the value of a variable that is targeted, and for this purpose, it is represented in a form of a decision tree. It is used for classification problems and also for regression problems.


Chest X-ray AI solution which detects 124 clinical findings launched

#artificialintelligence

The company was originally formed as a joint venture between Australian healthcare technology company Harrison.ai The launch of Annalise CXR coincides with its publication of a peer-reviewed diagnostic accuracy study published by The Lancet Digital Health, which is the largest of its kind ever undertaken in terms of the number of findings concurrently evaluated. The study found that when used as an assist device, Annalise CXR significantly improved the ability for radiologists to perceive 102 chest X-ray (CXR) findings in a non-clinical environment, was statistically non-inferior for 19 findings and no findings showed a decrease in accuracy. The study also assessed the standalone performance of the model in a non-clinical environment against radiologists in identifying chest x-ray pathology, as well as investigating the effect of model output on radiologist performance when used as an assist device. Annalise CXR's AI model classification alone was significantly more accurate than unassisted radiologists for 117 (94%) of 124 clinical findings predicted by the model and was non-inferior to unassisted radiologists for all other clinical findings.


Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis

#artificialintelligence

We introduce a new dataset called Synthetic COVID-19 Chest X-ray Dataset for training machine learning models. The dataset consists of 21,295 synthetic COVID-19 chest X-ray images to be used for computer-aided diagnosis. These images, generated via an unsupervised domain adaptation approach, are of high quality. We find that the synthetic images not only improve performance of various deep learning architectures when used as additional training data under heavy imbalance conditions, but also detect the target class with high confidence. We also find that comparable performance can also be achieved when trained only on synthetic images.


Labelling Drifts in a Fault Detection System for Wind Turbine Maintenance

arXiv.org Artificial Intelligence

A failure detection system is the first step towards predictive maintenance strategies. A popular data-driven method to detect incipient failures and anomalies is the training of normal behaviour models by applying a machine learning technique like feed-forward neural networks (FFNN) or extreme learning machines (ELM). However, the performance of any of these modelling techniques can be deteriorated by the unexpected rise of non-stationarities in the dynamic environment in which industrial assets operate. This unpredictable statistical change in the measured variable is known as concept drift. In this article a wind turbine maintenance case is presented, where non-stationarities of various kinds can happen unexpectedly. Such concept drift events are desired to be detected by means of statistical detectors and window-based approaches. However, in real complex systems, concept drifts are not as clear and evident as in artificially generated datasets. In order to evaluate the effectiveness of current drift detectors and also to design an appropriate novel technique for this specific industrial application, it is essential to dispose beforehand of a characterization of the existent drifts. Under the lack of information in this regard, a methodology for labelling concept drift events in the lifetime of wind turbines is proposed. This methodology will facilitate the creation of a drift database that will serve both as a training ground for concept drift detectors and as a valuable information to enhance the knowledge about maintenance of complex systems.


Next-Gen Machine Learning Supported Diagnostic Systems for Spacecraft

arXiv.org Artificial Intelligence

Future short or long-term space missions require a new generation of monitoring and diagnostic systems due to communication impasses as well as limitations in specialized crew and equipment. Machine learning supported diagnostic systems present a viable solution for medical and technical applications. We discuss challenges and applicability of such systems in light of upcoming missions and outline an example use case for a next-generation medical diagnostic system for future space operations. Additionally, we present approach recommendations and constraints for the successful generation and use of machine learning models aboard a spacecraft.


Handling Continuous Attributes in Decision Trees

#artificialintelligence

It can be seen that the computation of splitting measures assumes finite (read: discrete) attribute values. This begs the question, How are continuous-valued attributes handled in decision trees? The test condition for continuous-valued attributes can either be expressed using a comparison operator (,). Alternatively, the continuous-valued attribute can be split into a finite set of range buckets. It is important to note that a comparison-based test condition gives us a binary split whereas range buckets give us a multiway split.


ModelArts 3.0: a Arue AI Accelerator

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HUAWEI CLOUD's Enterprise Intelligence (EI) has achieved strong results in numerous industry competitions and evaluations. HUAWEI CLOUD has invested heavily in basic research AI in three domains: computer vision, speech and semantics, and decision optimization. To help AI empower all industries, the ModelArts enabling platform supports plug-and-play deployment of HUAWEI CLOUD's research results in areas such as automatic machine learning, small sample learning, federated learning, and pre-training models. In the area of perception, HUAWEI CLOUD continues to be an industry-leader in ImageNet large-scale image classification, WebVision large-scale network image classification, MS-COCO two-dimensional object detection, nuScenes three-dimensional object detection, and visual pre-training model verification, including downstream classification, detection, and segmentation. Perception models driven by ModelArts have been widely used in sectors such as medical image analysis, oil and gas exploration, and fault detection in manufacturing. In cognition, HUAWEI CLOUD integrates industry data based on its expertise in semantic analysis and knowledge graphs.


Sample Selection Bias in Evaluation of Prediction Performance of Causal Models

arXiv.org Machine Learning

Causal models are notoriously difficult to validate because they make untestable assumptions regarding confounding. New scientific experiments offer the possibility of evaluating causal models using prediction performance. Prediction performance measures are typically robust to violations in causal assumptions. However prediction performance does depend on the selection of training and test sets. In particular biased training sets can lead to optimistic assessments of model performance. In this work, we revisit the prediction performance of several recently proposed causal models tested on a genetic perturbation data set of Kemmeren [Kemmeren et al., 2014]. We find that sample selection bias is likely a key driver of model performance. We propose using a less-biased evaluation set for assessing prediction performance on Kemmeren and compare models on this new set. In this setting, the causal model tested have similar performance to standard association based estimators such as Lasso. Finally we compare the performance of causal estimators in simulation studies which reproduce the Kemmeren structure of genetic knockout experiments but without any sample selection bias. These results provide an improved understanding of the performance of several causal models and offer guidance on how future studies should use Kemmeren.