Dataset Information

Hardware implementation of Bayesian network based on two-dimensional memtransistors.

ABSTRACT: Bayesian networks (BNs) find widespread application in many real-world probabilistic problems including diagnostics, forecasting, computer vision, etc. The basic computing primitive for BNs is a stochastic bit (s-bit) generator that can control the probability of obtaining '1' in a binary bit-stream. While silicon-based complementary metal-oxide-semiconductor (CMOS) technology can be used for hardware implementation of BNs, the lack of inherent stochasticity makes it area and energy inefficient. On the other hand, memristors and spintronic devices offer inherent stochasticity but lack computing ability beyond simple vector matrix multiplication due to their two-terminal nature and rely on extensive CMOS peripherals for BN implementation, which limits area and energy efficiency. Here, we circumvent these challenges by introducing a hardware platform based on 2D memtransistors. First, we experimentally demonstrate a low-power and compact s-bit generator circuit that exploits cycle-to-cycle fluctuation in the post-programmed conductance state of 2D memtransistors. Next, the s-bit generators are monolithically integrated with 2D memtransistor-based logic gates to implement BNs. Our findings highlight the potential for 2D memtransistor-based integrated circuits for non-von Neumann computing applications.

SUBMITTER: Zheng Y

PROVIDER: S-EPMC9508127 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Hardware implementation of Bayesian network based on two-dimensional memtransistors.

Zheng Yikai Y Ravichandran Harikrishnan H Schranghamer Thomas F TF Trainor Nicholas N Redwing Joan M JM Das Saptarshi S

Nature communications 20220923 1

Bayesian networks (BNs) find widespread application in many real-world probabilistic problems including diagnostics, forecasting, computer vision, etc. The basic computing primitive for BNs is a stochastic bit (s-bit) generator that can control the probability of obtaining '1' in a binary bit-stream. While silicon-based complementary metal-oxide-semiconductor (CMOS) technology can be used for hardware implementation of BNs, the lack of inherent stochasticity makes it area and energy inefficient. ...[more]

PMID: 36151079

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Project description:The current DSM-oriented diagnostic paradigm has introduced the issue of heterogeneity, as it fails to account for the identification of the neurological processes underlying mental illnesses, which affects the precision of treatment. The Research Domain Criteria (RDoC) framework serves as a recognized approach to addressing this heterogeneity, and several assessment and translation techniques have been proposed. Among these methods, transforming RDoC scores from electronic medical records (EMR) using Natural Language Processing (NLP) has emerged as a suitable technique, demonstrating clinical effectiveness. Numerous studies have sought to use RDoC to understand the Diagnostic and Statistical Manual of Mental Disorders (DSM) categories from a qualified perspective, but few studies have examined the distribution variations and interaction characteristics of RDoC within various DSM categories through retrospective analyses. Therefore, we employed unsupervised learning to translate five domains of eRDoC scores derived from electronic medical records (EMR) of patients diagnosed with Major Depressive Disorder (MDD), Schizophrenia (SCZ), and Bipolar Disorder (BD) at West China Hospital between 2008 and 2021. The distribution characteristics, interaction networks, and potential clinical effectiveness of RDoC domains were analyzed. Using non-parametric statistical tests, we found that MDD had the highest score in Negative Valence System (NVS) (4.1, p < 0.001), while BD exhibited the highest score in Positive Valence System (PVS) score (4.9, p < 0.001) and Arousal System (AS) (4.4, p < 0.001). SCZ demonstrated the highest scores in Cognitive Systems (CS) (5.8, p < 0.001) and Social Processes Systems (SPS) (4.6, p < 0.001). Through Bayesian network (BN) analysis, we identified relatively consistent interaction relationships among various RDoC domains (NVS → AS, NVS → CS, NVS → PVS, as well as CS → SPS; parameter range = 0.156 to 0.635, p < 0.001). Lastly, using logistic regression and Cox proportional hazards models, we demonstrated that AS was significantly associated with the length of hospital stay (-0.21, p < 0.05) and 30-day readmission risk (adjusted odds ratio [aOR] = 0.91, 95% confidence interval [CI] 0.91-0.99) to some extent. In conclusion, we suggest that the eRDoC characteristics varied in different DSM. By Bayesian Network, we found NVS and CS might be potential source in interacting with other system. Furthermore, CS, SPS and AS were associated with the length of stay and 30-days readmission, making them effective for predicting prognosis of psychiatric disorders.

Project description:IntroductionSpatial neglect, a neurocognitive disorder of lateralized spatial attention, is prevalent among stroke survivors especially in inpatient rehabilitation facilities (IRFs). The ultimate goal of the project was to improve spatial neglect care in inpatient rehabilitation and trained as many OTs as possible using both tools in their regular practices as the means to achieve our overall objective. Therefore, we conducted a project aimed at implementing two evidence-based protocols, one for assessment (KF-NAP®) and the other for treatment (KF-PAT®), and share the implementation process, which included barriers and facilitators identified during and after the process, and implementation outcomes.MethodsSixteen IRFs were involved. The Knowledge-To-Action Cycle was used to describe the process of knowledge inquiry (training), translating knowledge (implementation) and evaluating the use of knowledge in clinical practice (outcomes). Barriers and strategies were reported using the Consolidated Framework for Implementation Research and identified through a survey, after the study concluded.ResultsThirty-two therapists at the participating sites were trained to some level of the KF-NAP and KF-PAT. Throughout the project and also once after it finished, different barriers were identified by researchers and clinicians, who then determined together actions to eliminate or minimize the barriers. For example, multiple sites reported: "not having time to train other staff at their hospital due to high patient volume and other responsibilities."DiscussionThe project shared our implementation process which demonstrated the importance of using implementation methods and incorporating a researcher-clinician partnership, not only for knowledge generation but also knowledge translation. Frequent communications and exchanging information with stakeholders at different levels, may be determinant to the success of each implementation phase. Further research is needed.

Dataset Information

Hardware implementation of Bayesian network based on two-dimensional memtransistors.

Publications

Hardware implementation of Bayesian network based on two-dimensional memtransistors.

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