A Machine Learning alternative to placebo-controlled clinical trials upon new diseases: A primer

• URL: http://arxiv.org/abs/2003.12454v1
• Date: Thu, 26 Mar 2020 17:53:10 GMT
• Title: A Machine Learning alternative to placebo-controlled clinical trials upon new diseases: A primer
• Authors: Ezequiel Alvarez (ICAS, Argentina), Federico Lamagna (CAB, Argentina) and Manuel Szewc (ICAS, Argentina)
• Abstract summary: A new dangerous and contagious disease requires the development of a drug therapy faster than what is foreseen by usual mechanisms. We compare a new technique in which all patients receive a different and reasonable combination of drugs and use this outcome to feed a Neural Network. By averaging out fluctuations and recognizing different patient features, the Neural Network learns the pattern that connects the patients initial state to the outcome of the treatments.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The appearance of a new dangerous and contagious disease requires the development of a drug therapy faster than what is foreseen by usual mechanisms. Many drug therapy developments consist in investigating through different clinical trials the effects of different specific drug combinations by delivering it into a test group of ill patients, meanwhile a placebo treatment is delivered to the remaining ill patients, known as the control group. We compare the above technique to a new technique in which all patients receive a different and reasonable combination of drugs and use this outcome to feed a Neural Network. By averaging out fluctuations and recognizing different patient features, the Neural Network learns the pattern that connects the patients initial state to the outcome of the treatments and therefore can predict the best drug therapy better than the above method. In contrast to many available works, we do not study any detail of drugs composition nor interaction, but instead pose and solve the problem from a phenomenological point of view, which allows us to compare both methods. Although the conclusion is reached through mathematical modeling and is stable upon any reasonable model, this is a proof-of-concept that should be studied within other expertises before confronting a real scenario. All calculations, tools and scripts have been made open source for the community to test, modify or expand it. Finally it should be mentioned that, although the results presented here are in the context of a new disease in medical sciences, these are useful for any field that requires a experimental technique with a control group.

Related papers

• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals [0.0]
  Depression, while being one of the most common mental illnesses, is still poorly understood in both research and clinical practice. We propose a method for major depressive disorder (MDD) diagnosis as well as a method for predicting the drug response in patient with MDD using EEG signals.
  arXiv  Detail & Related papers  (2023-03-09T09:15:19Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trials [12.266020657495618]
  We describe causal inference for application in a novel clinical design called basket trial. We propose a multi-task adversarial learning (MTAL) method to estimate potential outcomes across different tumor types.
  arXiv  Detail & Related papers  (2022-03-10T02:41:26Z)
• Conditional Generation Net for Medication Recommendation [73.09366442098339]
  Medication recommendation targets to provide a proper set of medicines according to patients' diagnoses, which is a critical task in clinics. We propose Conditional Generation Net (COGNet) which introduces a novel copy-or-predict mechanism to generate the set of medicines. We validate the proposed model on the public MIMIC data set, and the experimental results show that the proposed model can outperform state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-02-14T10:16:41Z)
• HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data [56.53715632642495]
  Clinical trials face uncertain outcomes due to issues with efficacy, safety, or problems with patient recruitment. In this paper, we propose Hierarchical INteraction Network (HINT) for more general, clinical trial outcome predictions.
  arXiv  Detail & Related papers  (2021-02-08T15:09:07Z)
• Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments [6.505217121471555]
  For mental disorders, patients' underlying mental states are non-observed latent constructs which have to be inferred from observed measurements. We propose an integrated learning framework that can simultaneously learn patients' underlying mental states and recommend optimal treatments for each individual.
  arXiv  Detail & Related papers  (2020-10-30T20:30:31Z)
• Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective [61.70045118068213]
  We build a dataset by collecting symptoms of PD patients, and their prescription drug provided by neurologists. Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug. For the new coming patients, we could recommend (predict) suitable prescription drug on their observed symptoms by our prescription model.
  arXiv  Detail & Related papers  (2020-07-31T14:34:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.