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The age of diagnosis : how our obsession with medical labels is making us sicker
\"From a neurologist and award-winning author of The Sleeping Beauties, a meticulous and compassionate exploration of how our culture of medical diagnosis can harm, rather than help, patients I'm a neurologist. Diagnosis is my bread and butter. So why then would I, an experienced medical doctor, be very careful about which diagnosis I would pursue for myself or would be willing to accept if foisted upon me? We live in an age of diagnosis. The advance of sophisticated genetic sequencing techniques means that we may all soon be screened for potential abnormalities. The internet provides a vast array of information that helps us speculate about our symptoms. Conditions like ADHD and Autism are on the rapid rise, while other new categories like Long Covid are driven by patients themselves. When we are suffering, it feels natural to seek a diagnosis. We want a clear label, understanding, and, of course, treatment. But is diagnosis an unqualified good thing? Could it sometimes even make us worse instead of better? Through the moving stories of real people, neurologist Suzanne O'Sullivan explores the complex world of modern diagnosis, comparing the impact of a medical label to the pain of not knowing. With scientific authority and compassionate storytelling, she opens up new possibilities for how we might approach our health and our suffering\"-- Provided by publisher.
BOOK
Machine learning in medicine: a practical introduction
Background
Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data.
Methods
We demonstrate the use of machine learning techniques by developing three predictive models for cancer diagnosis using descriptions of nuclei sampled from breast masses. These algorithms include regularized General Linear Model regression (GLMs), Support Vector Machines (SVMs) with a radial basis function kernel, and single-layer Artificial Neural Networks. The publicly-available dataset describing the breast mass samples (
N
=683) was randomly split into evaluation (
n
=456) and validation (
n
=227) samples.
We trained algorithms on data from the evaluation sample before they were used to predict the diagnostic outcome in the validation dataset. We compared the predictions made on the validation datasets with the real-world diagnostic decisions to calculate the accuracy, sensitivity, and specificity of the three models. We explored the use of averaging and voting ensembles to improve predictive performance. We provide a step-by-step guide to developing algorithms using the open-source
R
statistical programming environment.
Results
The trained algorithms were able to classify cell nuclei with high accuracy (.94 -.96), sensitivity (.97 -.99), and specificity (.85 -.94). Maximum accuracy (.96) and area under the curve (.97) was achieved using the SVM algorithm. Prediction performance increased marginally (accuracy =.97, sensitivity =.99, specificity =.95) when algorithms were arranged into a voting ensemble.
Conclusions
We use a straightforward example to demonstrate the theory and practice of machine learning for clinicians and medical researchers. The principals which we demonstrate here can be readily applied to other complex tasks including natural language processing and image recognition.
Journal Article
MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction
Background
Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer’s disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence magnetic resonance imaging (MRI) scans.
Results
We propose unsupervised medical anomaly detection generative adversarial network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (
Reconstruction
) Wasserstein loss with Gradient Penalty + 100
ℓ
1
loss—trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones—reconstructs unseen healthy/abnormal scans; (
Diagnosis
) Average
ℓ
2
loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our self-attention MADGAN can detect AD on T1 scans at a very early stage, mild cognitive impairment (MCI), with area under the curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921.
Conclusions
Similar to physicians’ way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen healthy images. As the first unsupervised various disease diagnosis, MADGAN can reliably detect the accumulation of subtle anatomical anomalies and hyper-intense enhancing lesions, such as (especially late-stage) AD and brain metastases on multi-sequence MRI scans.
Journal Article
Saint Anthony's Fire from Antiquity to the Eighteenth Century
After the discovery of the ergotism epidemics and its etiology, 18th-century physicians interpreted medieval chronicles in their medical texts in order to recognize the occurrences of ergotic diseases through retrospective diagnosis. This book examines this historical prejudice through textual analysis, comparing medieval and early modern sources.
eBook
Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS)
Compared to the booming industry of AIMDSS, the usage of AIMDSS among healthcare professionals is relatively low in the hospital. Thus, a research on the acceptance and adoption intention of AIMDSS by health professionals is imperative. In this study, an integration of Unified theory of user acceptance of technology and trust theory is proposed for exploring the adoption of AIMDSS. Besides, two groups of additional factors, related to AIMDSS (task complexity, technology characteristics, and perceived substitution crisis) and health professionals’ characteristics (propensity to trust and personal innovativeness in IT) are considered in the integrated model. The data set of proposed research model is collected through paper survey and Internet survey in China. The empirical examination demonstrates a high predictive power of this proposed model in explaining AIMDSS adoption. Finally, the theoretical contribution and practical implications of this research are discussed.
Journal Article
A deep learning approach for Parkinson’s disease diagnosis from EEG signals
An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by the gradual degradation of motor function in the brain. Since it is related to the brain abnormality, electroencephalogram (EEG) signals are usually considered for the early diagnosis. In this work, we have used the EEG signals of twenty PD and
twenty
normal subjects in this study. A
thirteen
-layer CNN architecture which can overcome the need for the conventional feature representation stages is implemented. The developed model has achieved a promising performance of 88.25% accuracy, 84.71% sensitivity, and 91.77% specificity. The developed classification model is ready to be used on large population before installation of clinical usage.
Journal Article