Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Medical recommender systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks.

ABSTRACT:

SUBMITTER: Ochoa JGD

PROVIDER: S-EPMC8194023 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Medical recommender systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks.

Ochoa Juan G Diaz JGD Csiszár Orsolya O Schimper Thomas T

BMC medical informatics and decision making 20210611 1

<h4>Background</h4>Out of the pressure of Digital Transformation, the major industrial domains are using advanced and efficient digital technologies to implement processes that are applied on a daily basis. Unfortunately, this still does not happen in the same way in the medical domain. For this reason, doctors usually do not have the time or knowledge to evaluate all alternative treatment options for each patient accurately and individually. However, physicians can reduce their workload by usin ...[more]

PMID: 34112161

Similar Datasets

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems

Project description: Not available

| S-EPMC10495971 | biostudies-literature

A machine learning-based hybrid recommender framework for smart medical systems

Project description: Not available

| S-EPMC10909219 | biostudies-literature

A novel approach to decision making in rice quality management using interval-valued Pythagorean fuzzy Schweizer and Sklar power aggregation operators.

Project description:The Pythagorean fuzzy set and interval-valued intuitionistic fuzzy set are the basis of the interval-valued Pythagorean fuzzy set (IVPFS) which offers an effective approach to addressing the complex uncertainty in decision-analysis processes, making it applicable across a broad spectrum of applications. This paper introduces several aggregation operators within the IVPF framework, such as the interval-valued Pythagorean fuzzy SS power weighted average operator, and the interval-valued Pythagorean fuzzy SS power geometric operator using the notion of power aggregation operators through Schweizer and Sklar (SS) operations. The existence of SS t-norms and t-conorms in the IVPF framework for addressing multi-attribute decision-making problems gives the generated operator's ability to make the information aggregation approach more adaptable compared to other current ones. The application of the proposed approach holds the potential to enhance crop yield, optimize resource utilization, and contribute to the overall sustainability of agriculture. Additionally, sensitivity and comparative analyses are provided to clarify the stability and dependability of the results acquired through this approach.

| S-EPMC11500917 | biostudies-literature

Health Recommender Systems: Systematic Review.

Project description:BackgroundHealth recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior.ObjectiveWe aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations.MethodsWe conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review.ResultsRecommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used.ConclusionsThere is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.

| S-EPMC8278303 | biostudies-literature

Optics-Enabled Highly Scalable Inverter for Multi-Valued Logic.

Project description:The rapid advancements in machine learning have exacerbated the interconnect bottleneck inherent in binary logic-based computing architectures. An interesting approach to tackle this problem involves increasing the information density per interconnect, i.e., by switching from a two-valued to a multi-valued logic (MVL) architecture. However, current MVL implementations offer limited overall performance and face challenges in scaling to process data signals with radix (number of logic levels) even just above 3. In this work, a novel concept is introduced for implementation of a highly scalable and fully passive inverter based on the frequency-domain phase-only linear manipulation of the input MVL data signal, which is encoded in the amplitude variations of an electromagnetic wave along the time axis. As a key advantage, this solution is entirely independent of the input radix. The proposed design is implemented using an optical fibre Bragg grating device. Inversion of quaternary signals is experimentally demonstrated, as well as binary and ternary signals, at a remarkable operation speed of 32 GBaud, with an estimated energy consumption of ≈ 24 fJ/bit. The proposed method is universal and can be applied to any system that supports transmission and detection of coherent waves, such as microwave, plasmonic, mechanical, or quantum.

| S-EPMC11626543 | biostudies-literature

Crowdsourced audit of Twitter's recommender systems.

Project description:This research conducts an audit of Twitter's recommender system, aiming to examine the disparities between users' curated timelines and their subscription choices. Through the combined use of a browser extension and data collection via the Twitter API, our investigation reveals a high amplification of friends from the same community, a preference for amplifying emotionally charged and toxic tweets and an uneven algorithmic amplification across friends' political leaning. This audit emphasizes the importance of transparency, and increased awareness regarding the impact of algorithmic curation.

| S-EPMC10556069 | biostudies-literature

Recommender Systems in Antiviral Drug Discovery.

Project description:Recommender systems (RSs), which underwent rapid development and had an enormous impact on e-commerce, have the potential to become useful tools for drug discovery. In this paper, we applied RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL. Two main RS approaches were applied: collaborative filtering (Surprise implementation) and content-based filtering (sparse-group inductive matrix completion (SGIMC) method). The effectiveness of RS approaches was investigated for prediction of antiviral activity classes ("interactions") for compounds and viruses, for which some of their interactions with other viruses or compounds are known, and for prediction of interaction profiles for new compounds. Both approaches achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score >0.9. Thus, even simple recommender systems may serve as an effective tool in antiviral drug discovery.

| S-EPMC7315437 | biostudies-literature

Multi-valued and Fuzzy Logic Realization using TaOx Memristive Devices.

Project description:Among emerging non-volatile storage technologies, redox-based resistive switching Random Access Memory (ReRAM) is a prominent one. The realization of Boolean logic functionalities using ReRAM adds an extra edge to this technology. Recently, 7-state ReRAM devices were used to realize ternary arithmetic circuits, which opens up the computing space beyond traditional binary values. In this manuscript, we report realization of multi-valued and fuzzy logic operators with a representative application using ReRAM devices. Multi-valued logic (MVL), such as Łukasiewicz logic generalizes Boolean logic by allowing more than two truth values. MVL also permits operations on fuzzy sets, where, in contrast to standard crisp logic, an element is permitted to have a degree of membership to a given set. Fuzzy operations generally model human reasoning better than Boolean logic operations, which is predominant in current computing technologies. When the available information for the modelling of a system is imprecise and incomplete, fuzzy logic provides an excellent framework for the system design. Practical applications of fuzzy logic include, industrial control systems, robotics, and in general, design of expert systems through knowledge-based reasoning. Our experimental results show, for the first time, that it is possible to model fuzzy logic natively using multi-state memristive devices.

| S-EPMC5758575 | biostudies-literature

Multi-criteria group decision making based on Archimedean power partitioned Muirhead mean operators of q-rung orthopair fuzzy numbers.

Project description:Two critical tasks in multi-criteria group decision making (MCGDM) are to describe criterion values and to aggregate the described information to generate a ranking of alternatives. A flexible and superior tool for the first task is q-rung orthopair fuzzy number (qROFN) and an effective tool for the second task is aggregation operator. So far, nearly thirty different aggregation operators of qROFNs have been presented. Each operator has its distinctive characteristics and can work well for specific purpose. However, there is not yet an operator which can provide desirable generality and flexibility in aggregating criterion values, dealing with the heterogeneous interrelationships among criteria, and reducing the influence of extreme criterion values. To provide such an aggregation operator, Muirhead mean operator, power average operator, partitioned average operator, and Archimedean T-norm and T-conorm operations are concurrently introduced into q-rung orthopair fuzzy sets, and an Archimedean power partitioned Muirhead mean operator of qROFNs and its weighted form are presented and a MCGDM method based on the weighted operator is proposed in this paper. The generalised expressions of the two operators are firstly defined. Their properties are explored and proved and their specific expressions are constructed. On the basis of the specific expressions, a method for solving the MCGDM problems based on qROFNs is then designed. Finally, the feasibility and effectiveness of the method is demonstrated via a numerical example, a set of experiments, and qualitative and quantitative comparisons.

| S-EPMC6728046 | biostudies-literature

Distilling experience into a physically interpretable recommender system for computational model selection

Project description:Model selection is a chronic issue in computational science. The conventional approach relies heavily on human experience. However, gaining experience takes years and is severely inefficient. To address this issue, we distill human experience into a recommender system. A trained recommender system tells whether a computational model does well or poorly in handling a physical process. It also tells if a physical process is important for a quantity of interest. By accumulating this knowledge, the system is able to make recommendations about computational models. We showcase the power of the system by considering Reynolds-averaged-Navier–Stokes (RANS) model selection in the field of computational fluid dynamics (CFD). Since turbulence is stochastic, there is no universal RANS model, and RANS model selection has always been an issue. A working model recommending system saves fluid engineers years and allows junior CFD practitioners to make sensible model choices like senior ones.

| S-EPMC9908871 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data