MetaLAB-HOI: Template standardization of health outcomes enable massive and accurate detection of adverse drug reactions from electronic health records.

PURPOSE: This study aimed to advance the MetaLAB algorithm and verify its performance with multicenter data to effectively detect major adverse drug reactions (ADRs), including drug-induced liver injury. METHODS: Based on MetaLAB, we created an optimal scenario for detecting ADRs by considering demographic and clinical records. MetaLAB-HOI was developed to identify ADR signals using common model-based multicenter electronic health record (EHR) data from the clinical health outcomes of interest (HOI) template and design for drug-exposed and nonexposed groups. In this study, we calculated the odds ratio of 101 drugs for HOI in Konyang University Hospital, Seoul National University Hospital, Chungbuk National University Hospital, and Seoul National University Bundang Hospital. RESULTS: The overlapping drugs in four medical centers are amlodipine, aspirin, bisoprolol, carvedilol, clopidogrel, clozapine, digoxin, diltiazem, methotrexate, and rosuvastatin. We developed MetaLAB-HOI, an algorithm that can detect ADRs more efficiently using EHR. We compared the detection results of four medical centers, with drug-induced liver injuries as representative ADRs. CONCLUSIONS: MetaLAB-HOI's strength lies in fully utilizing the patient's clinical information, such as prescription, procedure, and laboratory results, to detect ADR signals. Considering changes in the patient's condition over time, we created an algorithm based on a scenario that accounted for each drug exposure and onset period supervised by specialists for HOI. We determined that when a template capable of detecting ADR based on clinical evidence is developed and manualized, it can be applied in medical centers for new drugs with insufficient data.

Polypharmacy and Elevated Risk of Severe Adverse Events in Older Adults Based on the Korea Institute of Drug Safety and Risk Management-Korea Adverse Event Reporting System Database.

BACKGROUND: Older adults are at a higher risk of severe adverse drug events (ADEs) because of multimorbidity, polypharmacy, and lower physiological function. This study aimed to determine whether polypharmacy, defined as the use of ≥ 5 active drug ingredients, was associated with severe ADEs in this population. METHODS: We used ADE reports from the Korea Institute of Drug Safety and Risk Management-Korea Adverse Event Reporting System Database, a national spontaneous ADE report system, from 2012 to 2021 to examine and compare the strength of association between polypharmacy and severe ADEs in older adults (≥ 65 years) and younger adults (20-64 years) using disproportionality analysis. RESULTS: We found a significant association between severe ADEs of cardiac and renal/urinary Medical Dictionary for Regulatory Activities System Organ Classes (MedDRA SOC) with polypharmacy in older adults. Regarding individual-level ADEs included in these MedDRA SOCs, acute cardiac arrest and renal failure were more significantly associated with polypharmacy in older adults compared with younger adults. CONCLUSION: The addition of new drugs to the regimens of older adults warrants close monitoring of renal and cardiac symptoms.

Hippocampal integration and separation processes with different temporal and spatial dynamics during learning for associative memory.

The hippocampus is known to be critically involved in associative memory formation. However, the role of the hippocampus during the learning of associative memory is still controversial; while the hippocampus is considered to play a critical role in the integration of related stimuli, numerous studies also suggest a role of the hippocampus in the separation of different memory traces for rapid learning. Here, we employed an associative learning paradigm consisting of repeated learning cycles. By tracking the changes in the hippocampal representations of associated stimuli on a cycle-by-cycle basis as learning progressed, we show that both integration and separation processes occur in the hippocampus with different temporal dynamics. We found that the degree of shared representations for associated stimuli decreased significantly during the early phase of learning, whereas it increased during the later phase of learning. Remarkably, these dynamic temporal changes were observed only for stimulus pairs remembered 1 day or 4 weeks after learning, but not for forgotten pairs. Further, the integration process during learning was prominent in the anterior hippocampus, while the separation process was obvious in the posterior hippocampus. These results demonstrate temporally and spatially dynamic hippocampal processing during learning that can lead to the maintenance of associative memory.

Comparison of cardiocerebrovascular disease incidence between angiotensin converting enzyme inhibitor and angiotensin receptor blocker users in a real-world cohort.

BACKGROUND: Both angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) are known to be effective in managing cardiovascular diseases, but more evidence supports the use of an ACEI. This study investigated the difference in cardiovascular disease incidence between relatively low-compliance ACEIs and high-compliance ARBs in the clinical setting. METHODS: Patients who were first prescribed ACEIs or ARBs at two tertiary university hospitals in Korea were observed in this retrospective cohort study for the incidence of heart failure, angina, acute myocardial infarction, cerebrovascular disease, ischemic heart disease, and major adverse cardiovascular events for 5 years after the first prescription. Additionally, 5-year Kaplan-Meier survival curves were used based on the presence or absence of statins. RESULTS: Overall, 2,945 and 9,189 patients were prescribed ACEIs and ARBs, respectively. When compared to ACEIs, the incidence of heart failure decreased by 52% in those taking ARBs (HR [95% CI] = 0.48 [0.39-0.60], P < 0.001), and the incidence of cerebrovascular disease increased by 62% (HR [95% CI] = 1.62 [1.26-2.07], P < 0.001). The incidence of ischemic heart disease (P = 0.223) and major adverse cardiovascular events (P = 0.374) did not differ significantly between the two groups. CONCLUSIONS: ARBs were not inferior to ACEIs in relation to reducing the incidence of cardiocerebrovascular disease in the clinical setting; however, there were slight differences for each disease. The greatest strength of real-world evidence is that it allows the follow-up of specific drug use, including drug compliance. Large-scale studies on the effects of relatively low-compliance ACEIs and high-compliance ARBs on cardiocerebrovascular disease are warranted in the future.

False memory confidence depends on the prefrontal reinstatement of true memory.

For confidence of memory, a neural basis such as traces of stored memories should be required. However, because false memories have never been stored, the neural basis for false memory confidence remains unclear. Here we monitored the brain activity in participants while they viewed learned or novel objects, subsequently decided whether each presented object was learned and assessed their confidence levels. We found that when novel objects are presented, false memory confidence significantly depends on the shared representations with learned objects in the prefrontal cortex. However, such a tendency was not found in posterior regions including the visual cortex, which may be involved in the processing of perceptual gist. Furthermore, the confidence-dependent shared representations were not observed when participants correctly answered novel objects as non-learned objects. These results demonstrate that false memory confidence is critically based on the reinstatement of high-level semantic gist of stored memories in the prefrontal cortex.

Stronger memory representation after memory reinstatement during retrieval in the human hippocampus.

Memory retrieval allows us to reinstate previously encoded information but is also considered to contribute to memory enhancement. Retrieval-induced enhancement may involve processing to strengthen memory traces, but neural processing beyond reinstatement during retrieval remains elusive. Here, we show that hippocampal processing, different from memory reinstatement, exists during retrieval in the human brain. By tracking changes in the response patterns in the selected hippocampal and cortical regions over time during retrieval based on functional MRI, we found that the representation of associative memory in CA3/DG became stronger even after cortical memory reinstatement, while CA1 showed significant memory representation at retrieval onset with the cortical reinstatement, but not afterwards. This tendency was not observed in the condition without active retrieval. Moreover, subsequent long-term memory performance depended on the delayed CA3/DG representation during retrieval. These findings suggest that CA3/DG contributes to neural processing beyond memory reinstatement during retrieval, which may lead to memory enhancement.

Angiotensin-converting enzyme inhibitors versus angiotensin receptor blockers: New-onset diabetes mellitus stratified by statin use.

WHAT IS KNOWN AND OBJECTIVES: Regardless of statin use, which is known to induce hyperglycaemia, comparative studies on the risk of new-onset diabetes mellitus (NODM) with angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) are needed. This study evaluated the effects of ACEIs and ARBs on NODM in the clinical setting. METHODS: This retrospective cohort study utilized electronic medical record data from Seoul St. Mary's Hospital and Seoul National University Hospital from 2009 to 2012. Patients who were prescribed ACEIs or ARBs for the first time (irrespective of concomitant statin use) were followed up for 5 years. RESULTS AND DISCUSSIONS: A total of 11,703 patients were included, 24.9% (n = 2916) were taking ACEIs and 75.1% (n = 9189) were taking ARBs. Patients on ACEIs had a significantly lower incidence of NODM both with statin use (HR = 0.13, p < 0.001) and without (HR = 0.15, p = 0.009) than patients on ARBs. Age ≥60 years (HR = 1.49, p = 0.010), BMI ≥25 (HR = 1.96, p < 0.010), use of calcium channel blockers (HR = 1.47, p = 0.010), and diuretics (HR = 1.48, p = 0.010) were risk factors for NODM with statin use. WHAT IS NEW AND CONCLUSION: Patients taking ACEIs are less likely to develop NODM than patients taking ARBs, irrespective of statin use. Patients' conditions, including the risk of NODM, should be considered before prescribing ACEIs or ARBs. Future randomized clinical trials are needed to clarify further the relationship between ACEIs and ARBs and their effect on NODM.

A Data-Driven Reference Standard for Adverse Drug Reaction (RS-ADR) Signal Assessment: Development and Validation.

BACKGROUND: Pharmacovigilance using real-world data (RWD), such as multicenter electronic health records (EHRs), yields massively parallel adverse drug reaction (ADR) signals. However, proper validation of computationally detected ADR signals is not possible due to the lack of a reference standard for positive and negative associations. OBJECTIVE: This study aimed to develop a reference standard for ADR (RS-ADR) to streamline the systematic detection, assessment, and understanding of almost all drug-ADR associations suggested by RWD analyses. METHODS: We integrated well-known reference sets for drug-ADR pairs, including Side Effect Resource, Observational Medical Outcomes Partnership, and EU-ADR. We created a pharmacovigilance dictionary using controlled vocabularies and systematically annotated EHR data. Drug-ADR associations computed from MetaLAB and MetaNurse analyses of multicenter EHRs and extracted from the Food and Drug Administration Adverse Event Reporting System were integrated as "empirically determined" positive and negative reference sets by means of cross-validation between institutions. RESULTS: The RS-ADR consisted of 1344 drugs, 4485 ADRs, and 6,027,840 drug-ADR pairs with positive and negative consensus votes as pharmacovigilance reference sets. After the curation of the initial version of RS-ADR, novel ADR signals such as "famotidine-hepatic function abnormal" were detected and reasonably validated by RS-ADR. Although the validation of the entire reference standard is challenging, especially with this initial version, the reference standard will improve as more RWD participate in the consensus voting with advanced pharmacovigilance dictionaries and analytic algorithms. One can check if a drug-ADR pair has been reported by our web-based search interface for RS-ADRs. CONCLUSIONS: RS-ADRs enriched with the pharmacovigilance dictionary, ADR knowledge, and real-world evidence from EHRs may streamline the systematic detection, evaluation, and causality assessment of computationally detected ADR signals.

Time to surgery and survival in breast cancer.

BACKGROUND: This study aimed to investigate the effect of the time from diagnosis to breast cancer surgery on breast cancer patients' prognosis. METHODS: Of the 1900 patients diagnosed with invasive (stage 1-3) breast cancer who underwent surgery in KUH between 2012 and 2019, 279 patients were enrolled in this study. All patients, including those who received neoadjuvant chemotherapy, were classified as Model 1 subjects, and those who received immediate surgical treatment were classified as Model 2 subjects. We conducted a Cox regression analysis to identify prognostic factors of breast cancer associated with the time from diagnosis to surgery. RESULTS: The univariate results indicated a sharp drop in both groups' survival rates when the time to surgery was delayed for more than 8 weeks (Model 1 p = 0.000; Model 2 p = 0.001). In the multivariate analysis, the hazard ratio (HR) of Model 1increased (HR = 6.84, 95% CI 1.06-44.25) in response to a delay in surgery of more than 8 weeks. Smoking and the American Joint Committee on Cancer (AJCC) staging system had a negative effect on breast cancer prognosis, while hormone therapy had a positive effect. CONCLUSION: For all patients, a delay in breast cancer surgery of more than 8 weeks was inversely associated with survival.

Strong correspondence between prefrontal and visual representations during emotional perception.

Emotion is thought to cause focal enhancement or distortion of certain components of memory, indicating a complex property of emotional modulation on memory rather than simple enhancement. However, the neural basis for detailed modulation of emotional memory contents has remained unclear. Here has been shown that the information processing of the prefrontal cortex differentially affects sensory representations during experience of emotional information compared with neutral information, using functional magnetic resonance imaging (fMRI). It was found that during perception of emotional pictures, information representation in primary visual cortex (V1) significantly corresponded with the representations in dorsolateral prefrontal cortex (dlPFC). This correspondence was not observed for neutral pictures. Furthermore, participants with greater correspondence between visual and prefrontal representations showed better memory for high-level semantic components but not for low-level visual components of emotional stimuli. These results suggest that sensory representation during experience of emotional stimuli, compared with neutral stimuli, is more directly influenced by internally generated higher-order information from the prefrontal cortex.

An OMOP-CDM based pharmacovigilance data-processing pipeline (PDP) providing active surveillance for ADR signal detection from real-world data sources.

BACKGROUND: Adverse drug reactions (ADRs) are regarded as a major cause of death and a major contributor to public health costs. For the active surveillance of drug safety, the use of real-world data and real-world evidence as part of the overall pharmacovigilance process is important. In this regard, many studies apply the data-driven approaches to support pharmacovigilance. We developed a pharmacovigilance data-processing pipeline (PDP) that utilized electronic health records (EHR) and spontaneous reporting system (SRS) data to explore pharmacovigilance signals. METHODS: To this end, we integrated two medical data sources: Konyang University Hospital (KYUH) EHR and the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). As part of the presented PDP, we converted EHR data on the Observation Medical Outcomes Partnership (OMOP) data model. To evaluate the ability of using the proposed PDP for pharmacovigilance purposes, we performed a statistical validation using drugs that induce ear disorders. RESULTS: To validate the presented PDP, we extracted six drugs from the EHR that were significantly involved in ADRs causing ear disorders: nortriptyline, (hazard ratio [HR] 8.06, 95% CI 2.41-26.91); metoclopramide (HR 3.35, 95% CI 3.01-3.74); doxycycline (HR 1.73, 95% CI 1.14-2.62); digoxin (HR 1.60, 95% CI 1.08-2.38); acetaminophen (HR 1.59, 95% CI 1.47-1.72); and sucralfate (HR 1.21, 95% CI 1.06-1.38). In FAERS, the strongest associations were found for nortriptyline (reporting odds ratio [ROR] 1.94, 95% CI 1.73-2.16), sucralfate (ROR 1.22, 95% CI 1.01-1.45), doxycycline (ROR 1.30, 95% CI 1.20-1.40), and hydroxyzine (ROR 1.17, 95% CI 1.06-1.29). We confirmed the results in a meta-analysis using random and fixed models for doxycycline, hydroxyzine, metoclopramide, nortriptyline, and sucralfate. CONCLUSIONS: The proposed PDP could support active surveillance and the strengthening of potential ADR signals via real-world data sources. In addition, the PDP was able to generate real-world evidence for drug safety.

Differential Representations of Perceived and Retrieved Visual Information in Hippocampus and Cortex.

Memory retrieval is thought to depend on interactions between hippocampus and cortex, but the nature of representation in these regions and their relationship remains unclear. Here, we performed an ultra-high field fMRI (7T) experiment, comprising perception, learning and retrieval sessions. We observed a fundamental difference between representations in hippocampus and high-level visual cortex during perception and retrieval. First, while object-selective posterior fusiform cortex showed consistent responses that allowed us to decode object identity across both perception and retrieval one day after learning, object decoding in hippocampus was much stronger during retrieval than perception. Second, in visual cortex but not hippocampus, there was consistency in response patterns between perception and retrieval, suggesting that substantial neural populations are shared for both perception and retrieval. Finally, the decoding in hippocampus during retrieval was not observed when retrieval was tested on the same day as learning suggesting that the retrieval process itself is not sufficient to elicit decodable object representations. Collectively, these findings suggest that while cortical representations are stable between perception and retrieval, hippocampal representations are much stronger during retrieval, implying some form of reorganization of the representations between perception and retrieval.

Composite CDE: modeling composite relationships between common data elements for representing complex clinical data.

BACKGROUND: Semantic interoperability is essential for improving data quality and sharing. The ISO/IEC 11179 Metadata Registry (MDR) standard has been highlighted as a solution for standardizing and registering clinical data elements (DEs). However, the standard model has both structural and semantic limitations, and the number of DEs continues to increase due to poor term reusability. Semantic types and constraints are lacking for comprehensively describing and evaluating DEs on real-world clinical documents. METHODS: We addressed these limitations by defining three new types of semantic relationship (dependency, composite, and variable) in our previous studies. The present study created new and further extended existing semantic types (hybrid atomic and repeated and dictionary composite common data elements [CDEs]) with four constraints: ordered, operated, required, and dependent. For evaluation, we extracted all atomic and composite CDEs from five major clinical documents from five teaching hospitals in Korea, 14 Fast Healthcare Interoperability Resources (FHIR) resources from FHIR bulk sample data, and MIMIC-III (Medical Information Mart for Intensive Care) demo dataset. Metadata reusability and semantic interoperability in real clinical settings were comprehensively evaluated by applying the CDEs with our extended semantic types and constraints. RESULTS: All of the CDEs (n = 1142) extracted from the 25 clinical documents were successfully integrated with a very high CDE reuse ratio (46.9%) into 586 CDEs (259 atomic and 20 unique composite CDEs), and all of CDEs (n = 238) extracted from the 14 FHIR resources of FHIR bulk sample data were successfully integrated with high CDE reuse ration (59.7%) into 96 CDEs (21 atomic and 28 unique composite CDEs), which improved the semantic integrity and interoperability without any semantic loss. Moreover, the most complex data structures from two CDE projects were successfully encoded with rich semantics and semantic integrity. CONCLUSION: MDR-based extended semantic types and constraints can facilitate comprehensive representation of clinical documents with rich semantics, and improved semantic interoperability without semantic loss.

Simple Postoperative AKI Risk (SPARK) Classification before Noncardiac Surgery: A Prediction Index Development Study with External Validation.

BACKGROUND: Researchers have suggested models to predict the risk of postoperative AKI (PO-AKI), but an externally validated risk index that can be practically implemented before patients undergo noncardiac surgery is needed. METHODS: We performed a retrospective observational study of patients without preexisting renal failure who underwent a noncardiac operation (≥1 hour) at two tertiary hospitals in Korea. We fitted a proportional odds model for an ordinal outcome consisting of three categories: critical AKI (defined as Kidney Disease Improving Global Outcomes AKI stage ≥2, post-AKI death, or dialysis within 90 days after surgery), low-stage AKI (defined as PO-AKI events not fulfilling the definition of critical AKI), and no PO-AKI. RESULTS: The study included 51,041 patients in a discovery cohort and 39,764 patients in a validation cohort. The Simple Postoperative AKI Risk (SPARK) index included a summation of the integer scores of the following variables: age, sex, expected surgery duration, emergency operation, diabetes mellitus, use of renin-angiotensin-aldosterone inhibitors, baseline eGFR, dipstick albuminuria hypoalbuminemia, anemia, and hyponatremia. The model calibration plot showed tolerable distribution of observed and predicted probabilities in both cohorts. The discrimination power of the SPARK index was acceptable in both the discovery (c-statistic 0.80) and validation (c-statistic 0.72) cohorts. When four SPARK classes were defined on the basis of the sum of the risk scores, the SPARK index and classes fairly reflected the risks of PO-AKI and critical AKI. CONCLUSIONS: Clinicians may consider implementing the SPARK index and classifications to stratify patients' PO-AKI risks before performing noncardiac surgery.

Real-world Evidence versus Randomized Controlled Trial: Clinical Research Based on Electronic Medical Records.

Real-world evidence (RWE) and randomized control trial (RCT) data are considered mutually complementary. However, compared with RCT, the outcomes of RWE continue to be assigned lower credibility. It must be emphasized that RWE research is a real-world practice that does not need to be executed as RCT research for it to be reliable. The advantages and disadvantages of RWE must be discerned clearly, and then the proper protocol can be planned from the beginning of the research to secure as many samples as possible. Attention must be paid to privacy protection. Moreover, bias can be reduced meaningfully by reducing the number of dropouts through detailed and meticulous data quality management. RCT research, characterized as having the highest reliability, and RWE research, which reflects the actual clinical aspects, can have a mutually supplementary relationship. Indeed, once this is proven, the two could comprise the most powerful evidence-based research method in medicine.

A cellular model of memory reconsolidation involves reactivation-induced destabilization and restabilization at the sensorimotor synapse in Aplysia.

The memory reconsolidation hypothesis suggests that a memory trace becomes labile after retrieval and needs to be reconsolidated before it can be stabilized. However, it is unclear from earlier studies whether the same synapses involved in encoding the memory trace are those that are destabilized and restabilized after the synaptic reactivation that accompanies memory retrieval, or whether new and different synapses are recruited. To address this issue, we studied a simple nonassociative form of memory, long-term sensitization of the gill- and siphon-withdrawal reflex in Aplysia, and its cellular analog, long-term facilitation at the sensory-to-motor neuron synapse. We found that after memory retrieval, behavioral long-term sensitization in Aplysia becomes labile via ubiquitin/proteasome-dependent protein degradation and is reconsolidated by means of de novo protein synthesis. In parallel, we found that on the cellular level, long-term facilitation at the sensory-to-motor neuron synapse that mediates long-term sensitization is also destabilized by protein degradation and is restabilized by protein synthesis after synaptic reactivation, a procedure that parallels memory retrieval or retraining evident on the behavioral level. These results provide direct evidence that the same synapses that store the long-term memory trace encoded by changes in the strength of synaptic connections critical for sensitization are disrupted and reconstructed after signal retrieval.

Convergent representation of values from tactile and visual inputs for efficient goal-directed behavior in the primate putamen.

Animals can discriminate diverse sensory values with a limited number of neurons, raising questions about how the brain utilizes neural resources to efficiently process multi-dimensional inputs for decision-making. Here, we demonstrate that this efficiency is achieved by reducing sensory dimensions and converging towards the value dimension essential for goal-directed behavior in the putamen. Humans and monkeys performed tactile and visual value discrimination tasks while their neural responses were examined. Value information, whether originating from tactile or visual stimuli, was found to be processed within the human putamen using fMRI. Notably, at the single-neuron level in the macaque putamen, half of the individual neurons encode values independently of sensory inputs, while the other half selectively encode tactile or visual value. The responses of bimodal value neurons correlate with value-guided finger insertion behavior in both tasks, whereas modality-selective value neurons show task-specific correlations. Simulation using these neurons reveals that the presence of bimodal value neurons enables value discrimination with a significantly reduced number of neurons compared to simulations without them. Our data indicate that individual neurons in the primate putamen process different values in a convergent manner, thereby facilitating the efficient use of constrained neural resources for value-guided behavior.

Time Series AI Model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network: Development and Verification Study.

Background: Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)-based time series data are rare. Objective: In this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)-based model to hospital electronic health record (EHR)-based time series data in patients who took nephrotoxic drugs using a DRN. Methods: We conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMV-LSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model's contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA. Results: This study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin tended to decrease over time, and urine pH and prothrombin time tended to increase. Conclusions: Early surveillance of AKI outbreaks can be achieved by applying an IMV-LSTM based on time series data through an EHR-based DRN. This approach can help identify risk factors and enable early detection of adverse drug reactions when prescribing drugs that cause renal toxicity before AKI occurs.

Pneumatic tactile stimulus delivery system for studying brain responses evoked by active finger touch with fMRI.

BACKGROUND: Primates use their hands to actively touch objects and collect information. To study tactile information processing, it is important for participants to experience tactile stimuli through active touch while monitoring brain activities. NEW METHOD: Here, we developed a pneumatic tactile stimulus delivery system (pTDS) that delivers various tactile stimuli on a programmed schedule and allows voluntary finger touches during MRI scanning. The pTDS uses a pneumatic actuator to move tactile stimuli and place them in a finger hole. A photosensor detects the time when an index finger touches a tactile stimulus, enabling the analysis of the touch-elicited brain responses. RESULTS: We examined brain responses while the participants actively touched braille objects presented by the pTDS. BOLD responses during tactile perception were significantly stronger in a finger touch area of the contralateral somatosensory cortex compared with that of visual perception. CONCLUSION: The pTDS enables MR studies of brain mechanisms for tactile processes through natural finger touch.

Effective data quality management for electronic medical record data using SMART DATA.

OBJECTIVES: In the medical field, we face many challenges, including the high cost of data collection and processing, difficult standards issues, and complex preprocessing techniques. It is necessary to establish an objective and systematic data quality management system that ensures data reliability, mitigates risks caused by incorrect data, reduces data management costs, and increases data utilization. We introduce the concept of SMART data in a data quality management system and conducted a case study using real-world data on colorectal cancer. METHODS: We defined the data quality management system from three aspects (Construction - Operation - Utilization) based on the life cycle of medical data. Based on this, we proposed the "SMART DATA" concept and tested it on colorectal cancer data, which is actual real-world data. RESULTS: We define "SMART DATA" as systematized, high-quality data collected based on the life cycle of data construction, operation, and utilization through quality control activities for medical data. In this study, we selected a scenario using data on colorectal cancer patients from a single medical institution provided by the Clinical Oncology Network (CONNECT). As SMART DATA, we curated 1,724 learning data and 27 Clinically Critical Set (CCS) data for colorectal cancer prediction. These datasets contributed to the development and fine-tuning of the colorectal cancer prediction model, and it was determined that CCS cases had unique characteristics and patterns that warranted additional clinical review and consideration in the context of colorectal cancer prediction. CONCLUSIONS: In this study, we conducted primary research to develop a medical data quality management system. This will standardize medical data extraction and quality control methods and increase the utilization of medical data. Ultimately, we aim to provide an opportunity to develop a medical data quality management methodology and contribute to the establishment of a medical data quality management system.