An automated approach to extracting positive and negative clinical research results

Failure is common in clinical trials since the successful failures presented in negative results always indicate the ways that should not be taken. In this paper, we proposed an automated approach to extracting positive and negative clinical research results by introducing a PICOE (Population, Intervention, Comparation, Outcome, and Effect) framework to represent randomized controlled trials (RCT) reports, where E indicates the effect between a specific I and O. We developed a pipeline to extract and assign the corresponding statistical effect to a specific I-O pair from natural language RCT reports. The extraction models achieved a high degree of accuracy for ICO and E descriptive words extraction through two rounds of training. By defining a threshold of p-value, we find in all Covid-19 related intervention-outcomes pairs with statistical tests, negative results account for nearly 40%. We believe that this observation is noteworthy since they are extracted from the published literature, in which there is an inherent risk of reporting bias, preferring to report positive results rather than negative results. We provided a tool to systematically understand the current level of clinical evidence by distinguishing negative results from the positive results.

DP-FP: Differentially Private Forward Propagation for Large Models

When applied to large-scale learning problems, the conventional wisdom on privacy-preserving deep learning, known as Differential Private Stochastic Gradient Descent (DP-SGD), has met with limited success due to significant performance degradation and high memory overhead when compared to the non-privacy counterpart. We show how to mitigate the performance drop by replacing the DP-SGD with a novel DP Forward-Propagation (DP-FP) followed by an off-the-shelf non-DP optimizer. Our DP-FP employs novel (1) representation clipping followed by noise addition in the forward propagation stage, as well as (2) micro-batch construction via subsampling to achieve DP amplification and reduce noise power to $1/M$, where $M$ is the number of micro-batch in a step. When training a classification model, our DP-FP with all of the privacy-preserving operations on the representation is innately free of gradient bias, total noise proportionally to model size, and memory issues in DP-SGD. As a result, our DP-FP outperforms cutting-edge DP-SGD while retaining the same level of privacy, and it approaches non-private baselines and significantly outperforms state-of-the-art DP-SGD variants. When applied to RoBERTa-large on four downstream tasks, for example, DP-FP achieves an average accuracy of 91.34\% with privacy budgets less than 3, representing a 3.81\% performance improvement over the state-of-the-art DP-SGD and only a 0.9\% loss compared to the non-private baseline but with a significantly lower privacy leakage risk.

Distinguishing Transformative from Incremental Clinical Evidence: A Classifier of Clinical Research using Textual features from Abstracts and Citing Sentences

In clinical research and clinical decision-making, it is important to know if a study changes or only supports the current standards of care for specific disease management. We define such a change as transformative and a support as incremental research. It usually requires a huge amount of domain expertise and time for humans to finish such tasks. Faculty Opinions provides us with a well-annotated corpus on whether a research challenges or only confirms established research. In this study, a machine learning approach is proposed to distinguishing transformative from incremental clinical evidence. The texts from both abstract and a 2-year window of citing sentences are collected for a training set of clinical studies recommended and labeled by Faculty Opinions experts. We achieve the best performance with an average AUC of 0.755 (0.705-0.875) using Random Forest as the classifier and citing sentences as the feature. The results showed that transformative research has typical language patterns in citing sentences unlike abstract sentences. We provide an efficient tool for identifying those clinical evidence challenging or only confirming established claims for clinicians and researchers.

A comment-driven evidence appraisal approach for decision-making when only uncertain evidence available

Purpose: To explore whether comments could be used as an assistant tool for heuristic decision-making, especially in cases where missing, incomplete, uncertain, or even incorrect evidence is acquired. Methods: Six COVID-19 drug candidates were selected from WHO clinical guidelines. Evidence-comment networks (ECNs) were completed of these six drug candidates based on evidence-comment pairs from all PubMed indexed COVID-19 publications with formal published comments. WHO guidelines were utilized to validate the feasibility of comment-derived evidence assertions as a fast decision supporting tool. Results: Out of 6 drug candidates, comment-derived evidence assertions of leading subgraphs of 5 drugs were consistent with WHO guidelines, and the overall comment sentiment of 6 drugs was aligned with WHO clinical guidelines. Additionally, comment topics were in accordance with the concerns of guidelines and evidence appraisal criteria. Furthermore, half of the critical comments emerged 4.5 months earlier than the date guidelines were published. Conclusions: Comment-derived evidence assertions have the potential as an evidence appraisal tool for heuristic decisions based on the accuracy, sensitivity, and efficiency of evidence-comment networks. In essence, comments reflect that academic communities do have a self-screening evaluation and self-purification (argumentation) mechanism, thus providing a tool for decision makers to filter evidence.

Extracting and Measuring Uncertain Biomedical Knowledge from Scientific Statements

Purpose: This study aims to develop a novel approach to extracting and measuring uncertain biomedical knowledge from scientific statements. Design/methodology/approach: Taking cardiovascular research publications in China as a sample, we extracted the SPO triples as knowledge unit and the hedging/conflicting uncertainties as the knowledge context. We introduced Information Entropy and Uncertainty Rate as potential metrics to quantity the uncertainty of biomedical knowledge claims represented at different levels, such as the SPO triples (micro level), as well as the semantic type pairs (micro-level). Findings: The results indicated that while the number of scientific publications and total SPO triples showed a liner growth, the novel SPO triples occurring per year remained stable. After examining the frequency of uncertain cue words in different part of scientific statements, we found hedging words tend to appear in conclusive and purposeful sentences, whereas conflicting terms often appear in background and act as the premise (e.g., unsettled scientific issues) of the work to be investigated. Practical implications: Our approach identified major uncertain knowledge areas, such as diagnostic biomarkers, genetic characteristics, and pharmacologic therapies surrounding cardiovascular diseases in China. These areas are suggested to be prioritized in which new hypotheses need to be verified, and disputes, conflicts, as well as contradictions to be settled further.

A comment-derived evidence appraisal approach for decision-making using uncertain evidence

Purpose: To explore whether comments could be used as an assistant tool for heuristic decision-making, especially in cases where missing, incomplete, uncertain, or even incorrect evidence is acquired. Methods: Six COVID-19 drug candidates were selected from WHO clinical guidelines. Evidence-comment networks (ECNs) were completed of these six drug candidates based on evidence-comment pairs from all PubMed indexed COVID-19 publications with formal published comments. WHO guidelines were utilized to validate the feasibility of comment-derived evidence assertions as a fast decision supporting tool. Results: Out of 6 drug candidates, comment-derived evidence assertions of leading subgraphs of 5 drugs were consistent with WHO guidelines, and the overall comment sentiment of 6 drugs was aligned with WHO clinical guidelines. Additionally, comment topics were in accordance with the concerns of guidelines and evidence appraisal criteria. Furthermore, half of the critical comments emerged 4.5 months earlier than the date guidelines were published. Conclusions: Comment-derived evidence assertions have the potential as an evidence appraisal tool for heuristic decisions based on the accuracy, sensitivity, and efficiency of evidence-comment networks. In essence, comments reflect that academic communities do have a self-screening evaluation and self-purification (argumentation) mechanism, thus providing a tool for decision makers to filter evidence.

Dynamic Differential-Privacy Preserving SGD

Differentially-Private Stochastic Gradient Descent (DP-SGD) prevents training-data privacy breaches by adding noise to the clipped gradient during SGD training to satisfy the differential privacy (DP) definition. On the other hand, the same clipping operation and additive noise across training steps results in unstable updates and even a ramp-up period, which significantly reduces the model's accuracy. In this paper, we extend the Gaussian DP central limit theorem to calibrate the clipping value and the noise power for each individual step separately. We, therefore, are able to propose the dynamic DP-SGD, which has a lower privacy cost than the DP-SGD during updates until they achieve the same target privacy budget at a target number of updates. Dynamic DP-SGD, in particular, improves model accuracy without sacrificing privacy by gradually lowering both clipping value and noise power while adhering to a total privacy budget constraint. Extensive experiments on a variety of deep learning tasks, including image classification, natural language processing, and federated learning, show that the proposed dynamic DP-SGD algorithm stabilizes updates and, as a result, significantly improves model accuracy in the strong privacy protection region when compared to DP-SGD.

FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation

Federated adversary domain adaptation is a unique distributed minimax training task due to the prevalence of label imbalance among clients, with each client only seeing a subset of the classes of labels required to train a global model. To tackle this problem, we propose a distributed minimax optimizer referred to as FedMM, designed specifically for the federated adversary domain adaptation problem. It works well even in the extreme case where each client has different label classes and some clients only have unsupervised tasks. We prove that FedMM ensures convergence to a stationary point with domain-shifted unsupervised data. On a variety of benchmark datasets, extensive experiments show that FedMM consistently achieves either significant communication savings or significant accuracy improvements over federated optimizers based on the gradient descent ascent (GDA) algorithm. When training from scratch, for example, it outperforms other GDA based federated average methods by around $20\%$ in accuracy over the same communication rounds; and it consistently outperforms when training from pre-trained models with an accuracy improvement from $5.4\%$ to $9\%$ for different networks.