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Book: Der mündige Trinker

Sadowski, Peter

Selbstmanagement-Therapie für Alkoholkranke

2007

Author's details	Peter Sadowski
Keywords	Alkoholismus ; Selbstmanagement-Therapie
Subject	Alkoholabhängigkeit ; Trunksucht ; Alkoholbedingte Krankheit ; Alkoholkrankheit ; Alkoholsucht ; Alkohol
Subject code	616.8610651
Language	German
Size	196 S., graph. Darst., 24 cm
Publisher	Dgvt-Verl
Publishing place	Tübingen
Publishing country	Germany
Document type	Book
HBZ-ID	HT015447497
ISBN	978-3-87159-066-5 ; 3-87159-066-5
Database	Catalogue ZB MED Medicine, Health

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Article: Performance of Progressive Generations of GPT on an Exam Designed for Certifying Physicians as Certified Clinical Densitometrists.

Valdez, Dustin / Bunnell, Arianna / Lim, Sian Y / Sadowski, Peter / Shepherd, John A

Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry

2024 Volume 27, Issue 2, Page(s) 101480

Abstract: Background: Artificial intelligence (AI) large language models (LLMs) such as ChatGPT have demonstrated the ability to pass standardized exams. These models are not trained for a specific task, but instead trained to predict sequences of text from large ...

Abstract	Background: Artificial intelligence (AI) large language models (LLMs) such as ChatGPT have demonstrated the ability to pass standardized exams. These models are not trained for a specific task, but instead trained to predict sequences of text from large corpora of documents sourced from the internet. It has been shown that even models trained on this general task can pass exams in a variety of domain-specific fields, including the United States Medical Licensing Examination. We asked if large language models would perform as well on a much narrower subdomain tests designed for medical specialists. Furthermore, we wanted to better understand how progressive generations of GPT (generative pre-trained transformer) models may be evolving in the completeness and sophistication of their responses even while generational training remains general. In this study, we evaluated the performance of two versions of GPT (GPT 3 and 4) on their ability to pass the certification exam given to physicians to work as osteoporosis specialists and become a certified clinical densitometrists. The CCD exam has a possible score range of 150 to 400. To pass, you need a score of 300. Methods: A 100-question multiple-choice practice exam was obtained from a 3rd party exam preparation website that mimics the accredited certification tests given by the ISCD (International Society for Clinical Densitometry). The exam was administered to two versions of GPT, the free version (GPT Playground) and ChatGPT+, which are based on GPT-3 and GPT-4, respectively (OpenAI, San Francisco, CA). The systems were prompted with the exam questions verbatim. If the response was purely textual and did not specify which of the multiple-choice answers to select, the authors matched the text to the closest answer. Each exam was graded and an estimated ISCD score was provided from the exam website. In addition, each response was evaluated by a rheumatologist CCD and ranked for accuracy using a 5-level scale. The two GPT versions were compared in terms of response accuracy and length. Results: The average response length was 11.6 ±19 words for GPT-3 and 50.0±43.6 words for GPT-4. GPT-3 answered 62 questions correctly resulting in a failing ISCD score of 289. However, GPT-4 answered 82 questions correctly with a passing score of 342. GPT-3 scored highest on the "Overview of Low Bone Mass and Osteoporosis" category (72 % correct) while GPT-4 scored well above 80 % accuracy on all categories except "Imaging Technology in Bone Health" (65 % correct). Regarding subjective accuracy, GPT-3 answered 23 questions with nonsensical or totally wrong responses while GPT-4 had no responses in that category. Conclusion: If this had been an actual certification exam, GPT-4 would now have a CCD suffix to its name even after being trained using general internet knowledge. Clearly, more goes into physician training than can be captured in this exam. However, GPT algorithms may prove to be valuable physician aids in the diagnoses and monitoring of osteoporosis and other diseases.
MeSH term(s)	Humans ; Certification ; Artificial Intelligence ; Osteoporosis/diagnosis ; Clinical Competence ; Educational Measurement/methods ; United States
Language	English
Publishing date	2024-02-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	2040951-5
ISSN	1094-6950
ISSN	1094-6950
DOI	10.1016/j.jocd.2024.101480
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Book ; Online: Diffusion Models for High-Resolution Solar Forecasts

Hatanaka, Yusuke / Glaser, Yannik / Galgon, Geoff / Torri, Giuseppe / Sadowski, Peter

2023

Abstract: Forecasting future weather and climate is inherently difficult. Machine learning offers new approaches to increase the accuracy and computational efficiency of forecasts, but current methods are unable to accurately model uncertainty in high-dimensional ... ...

Abstract	Forecasting future weather and climate is inherently difficult. Machine learning offers new approaches to increase the accuracy and computational efficiency of forecasts, but current methods are unable to accurately model uncertainty in high-dimensional predictions. Score-based diffusion models offer a new approach to modeling probability distributions over many dependent variables, and in this work, we demonstrate how they provide probabilistic forecasts of weather and climate variables at unprecedented resolution, speed, and accuracy. We apply the technique to day-ahead solar irradiance forecasts by generating many samples from a diffusion model trained to super-resolve coarse-resolution numerical weather predictions to high-resolution weather satellite observations.
Keywords	Computer Science - Machine Learning ; Physics - Atmospheric and Oceanic Physics
Publishing date	2023-01-31
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Oral to subcutaneous methotrexate dose-conversion strategy in the treatment of rheumatoid arthritis.

Schiff, Michael H / Sadowski, Peter

Rheumatology international

2017 Volume 37, Issue 2, Page(s) 213–218

Abstract: Both the American College of Rheumatology (ACR) and European League Against Rheumatism (EULAR) guidelines recommend the use of methotrexate (MTX) for the treatment of rheumatoid arthritis (RA) when there is no contraindication. While MTX is the ... ...

Abstract	Both the American College of Rheumatology (ACR) and European League Against Rheumatism (EULAR) guidelines recommend the use of methotrexate (MTX) for the treatment of rheumatoid arthritis (RA) when there is no contraindication. While MTX is the foundation of RA therapy (Singh et al. in Arthritis Care Res 64:625-639,2012), absorption saturation compromises its oral bioavailability (BA). Differences in the relative BA of oral versus subcutaneous (SC) MTX demonstrate the need for guidance on successful dose-conversion strategies. This study was designed to compare MTX PK profiles as a result of MTX administration via three different treatment administrations: oral, SC MTX administered via an auto-injector (MTXAI) into the abdomen (MTXAI
MeSH term(s)	Administration, Oral ; Aged ; Antirheumatic Agents/administration & dosage ; Antirheumatic Agents/therapeutic use ; Arthritis, Rheumatoid/drug therapy ; Cross-Over Studies ; Dose-Response Relationship, Drug ; Female ; Humans ; Injections, Subcutaneous ; Male ; Methotrexate/administration & dosage ; Methotrexate/therapeutic use ; Middle Aged ; Treatment Outcome
Chemical Substances	Antirheumatic Agents ; Methotrexate (YL5FZ2Y5U1)
Language	English
Publishing date	2017-02
Publishing country	Germany
Document type	Journal Article ; Multicenter Study ; Randomized Controlled Trial
ZDB-ID	8286-7
ISSN	1437-160X ; 0172-8172
ISSN (online)	1437-160X
ISSN	0172-8172
DOI	10.1007/s00296-016-3621-1
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Book ; Online: Tourbillon

Tavakoli, Mohammadamin / Baldi, Pierre / Sadowski, Peter

a Physically Plausible Neural Architecture

2021

Abstract: In a physical neural system, backpropagation is faced with a number of obstacles including: the need for labeled data, the violation of the locality learning principle, the need for symmetric connections, and the lack of modularity. Tourbillon is a new ... ...

Abstract	In a physical neural system, backpropagation is faced with a number of obstacles including: the need for labeled data, the violation of the locality learning principle, the need for symmetric connections, and the lack of modularity. Tourbillon is a new architecture that addresses all these limitations. At its core, it consists of a stack of circular autoencoders followed by an output layer. The circular autoencoders are trained in self-supervised mode by recirculation algorithms and the top layer in supervised mode by stochastic gradient descent, with the option of propagating error information through the entire stack using non-symmetric connections. While the Tourbillon architecture is meant primarily to address physical constraints, and not to improve current engineering applications of deep learning, we demonstrate its viability on standard benchmark datasets including MNIST, Fashion MNIST, and CIFAR10. We show that Tourbillon can achieve comparable performance to models trained with backpropagation and outperform models that are trained with other physically plausible algorithms, such as feedback alignment.
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2021-07-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans.

Leong, Lambert T / Wong, Michael C / Liu, Yong E / Glaser, Yannik / Quon, Brandon K / Kelly, Nisa N / Cataldi, Devon / Sadowski, Peter / Heymsfield, Steven B / Shepherd, John A

Communications medicine

2024 Volume 4, Issue 1, Page(s) 13

Abstract: Background: Body shape, an intuitive health indicator, is deterministically driven by body composition. We developed and validated a deep learning model that generates accurate dual-energy X-ray absorptiometry (DXA) scans from three-dimensional optical ... ...

Abstract	Background: Body shape, an intuitive health indicator, is deterministically driven by body composition. We developed and validated a deep learning model that generates accurate dual-energy X-ray absorptiometry (DXA) scans from three-dimensional optical body scans (3DO), enabling compositional analysis of the whole body and specified subregions. Previous works on generative medical imaging models lack quantitative validation and only report quality metrics. Methods: Our model was self-supervised pretrained on two large clinical DXA datasets and fine-tuned using the Shape Up! Adults study dataset. Model-predicted scans from a holdout test set were evaluated using clinical commercial DXA software for compositional accuracy. Results: Predicted DXA scans achieve R Conclusions: This work highlights the potential of generative models in medical imaging and reinforces the importance of quantitative validation for assessing their clinical utility.
Language	English
Publishing date	2024-01-30
Publishing country	England
Document type	Journal Article
ISSN	2730-664X
ISSN (online)	2730-664X
DOI	10.1038/s43856-024-00434-w
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Article ; Online: A theory of local learning, the learning channel, and the optimality of backpropagation.

Baldi, Pierre / Sadowski, Peter

Neural networks : the official journal of the International Neural Network Society

2016 Volume 83, Page(s) 51–74

Abstract: In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, ... ...

Abstract	In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying the space of local learning rules is obtained by first specifying the nature of the local variables, and then the functional form that ties them together into each learning rule. Such a framework enables also the systematic discovery of new learning rules and exploration of relationships between learning rules and group symmetries. We study polynomial local learning rules stratified by their degree and analyze their behavior and capabilities in both linear and non-linear units and networks. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex input-output functions requires local deep learning where target information is communicated to the deep layers through a backward learning channel. The nature of the communicated information about the targets and the structure of the learning channel partition the space of learning algorithms. For any learning algorithm, the capacity of the learning channel can be defined as the number of bits provided about the error gradient per weight, divided by the number of required operations per weight. We estimate the capacity associated with several learning algorithms and show that backpropagation outperforms them by simultaneously maximizing the information rate and minimizing the computational cost. This result is also shown to be true for recurrent networks, by unfolding them in time. The theory clarifies the concept of Hebbian learning, establishes the power and limitations of local learning rules, introduces the learning channel which enables a formal analysis of the optimality of backpropagation, and explains the sparsity of the space of learning rules discovered so far.
MeSH term(s)	Algorithms ; Feedback ; Machine Learning ; Neural Networks (Computer)
Language	English
Publishing date	2016-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2016.07.006
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Article: Learning in the Machine: Random Backpropagation and the Deep Learning Channel.

Baldi, Pierre / Sadowski, Peter / Lu, Zhiqin

Artificial intelligence

2018 Volume 260, Page(s) 1–35

Abstract: Random backpropagation (RBP) is a variant of the backpropagation algorithm for training neural networks, where the transpose of the forward matrices are replaced by fixed random matrices in the calculation of the weight updates. It is remarkable both ... ...

Abstract	Random backpropagation (RBP) is a variant of the backpropagation algorithm for training neural networks, where the transpose of the forward matrices are replaced by fixed random matrices in the calculation of the weight updates. It is remarkable both because of its effectiveness, in spite of using random matrices to communicate error information, and because it completely removes the taxing requirement of maintaining symmetric weights in a physical neural system. To better understand random backpropagation, we first connect it to the notions of local learning and learning channels. Through this connection, we derive several alternatives to RBP, including skipped RBP (SRPB), adaptive RBP (ARBP), sparse RBP, and their combinations (e.g. ASRBP) and analyze their computational complexity. We then study their behavior through simulations using the MNIST and CIFAR-10 bechnmark datasets. These simulations show that most of these variants work robustly, almost as well as backpropagation, and that multiplication by the derivatives of the activation functions is important. As a follow-up, we study also the low-end of the number of bits required to communicate error information over the learning channel. We then provide partial intuitive explanations for some of the remarkable properties of RBP and its variations. Finally, we prove several mathematical results, including the convergence to fixed points of linear chains of arbitrary length, the convergence to fixed points of linear autoencoders with decorrelated data, the long-term existence of solutions for linear systems with a single hidden layer and convergence in special cases, and the convergence to fixed points of non-linear chains, when the derivative of the activation functions is included.
Language	English
Publishing date	2018-04-03
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1468341-6
ISSN	0004-3702
ISSN	0004-3702
DOI	10.1016/j.artint.2018.03.003
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Article ; Online: Deep learning predicts all-cause mortality from longitudinal total-body DXA imaging.

Glaser, Yannik / Shepherd, John / Leong, Lambert / Wolfgruber, Thomas / Lui, Li-Yung / Sadowski, Peter / Cummings, Steven R

Communications medicine

2022 Volume 2, Page(s) 102

Abstract: Background: Mortality research has identified biomarkers predictive of all-cause mortality risk. Most of these markers, such as body mass index, are predictive cross-sectionally, while for others the longitudinal change has been shown to be predictive, ... ...

Abstract	Background: Mortality research has identified biomarkers predictive of all-cause mortality risk. Most of these markers, such as body mass index, are predictive cross-sectionally, while for others the longitudinal change has been shown to be predictive, for instance greater-than-average muscle and weight loss in older adults. And while sometimes markers are derived from imaging modalities such as DXA, full scans are rarely used. This study builds on that knowledge and tests two hypotheses to improve all-cause mortality prediction. The first hypothesis is that features derived from raw total-body DXA imaging using deep learning are predictive of all-cause mortality with and without clinical risk factors, meanwhile, the second hypothesis states that sequential total-body DXA scans and recurrent neural network models outperform comparable models using only one observation with and without clinical risk factors. Methods: Multiple deep neural network architectures were designed to test theses hypotheses. The models were trained and evaluated on data from the 16-year-long Health, Aging, and Body Composition Study including over 15,000 scans from over 3000 older, multi-race male and female adults. This study further used explainable AI techniques to interpret the predictions and evaluate the contribution of different inputs. Results: The results demonstrate that longitudinal total-body DXA scans are predictive of all-cause mortality and improve performance of traditional mortality prediction models. On a held-out test set, the strongest model achieves an area under the receiver operator characteristic curve of 0.79. Conclusion: This study demonstrates the efficacy of deep learning for the analysis of DXA medical imaging in a cross-sectional and longitudinal setting. By analyzing the trained deep learning models, this work also sheds light on what constitutes healthy aging in a diverse cohort.
Language	English
Publishing date	2022-08-16
Publishing country	England
Document type	Journal Article
ISSN	2730-664X
ISSN (online)	2730-664X
DOI	10.1038/s43856-022-00166-9
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Article: The Dropout Learning Algorithm.

Baldi, Pierre / Sadowski, Peter

Artificial intelligence

2014 Volume 210, Page(s) 78–122

Abstract: Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli ... ...

Abstract	Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable rates. The framework allows a complete analysis of the ensemble averaging properties of dropout in linear networks, which is useful to understand the non-linear case. The ensemble averaging properties of dropout in non-linear logistic networks result from three fundamental equations: (1) the approximation of the expectations of logistic functions by normalized geometric means, for which bounds and estimates are derived; (2) the algebraic equality between normalized geometric means of logistic functions with the logistic of the means, which mathematically characterizes logistic functions; and (3) the linearity of the means with respect to sums, as well as products of independent variables. The results are also extended to other classes of transfer functions, including rectified linear functions. Approximation errors tend to cancel each other and do not accumulate. Dropout can also be connected to stochastic neurons and used to predict firing rates, and to backpropagation by viewing the backward propagation as ensemble averaging in a dropout linear network. Moreover, the convergence properties of dropout can be understood in terms of stochastic gradient descent. Finally, for the regularization properties of dropout, the expectation of the dropout gradient is the gradient of the corresponding approximation ensemble, regularized by an adaptive weight decay term with a propensity for self-consistent variance minimization and sparse representations.
Language	English
Publishing date	2014-04-26
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1468341-6
ISSN	0004-3702
ISSN	0004-3702
DOI	10.1016/j.artint.2014.02.004
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