Comparative Time Series Analysis Research Articles

Comparative time-series analyses of gut microbiome profiles in genetically and chemically induced lupus-prone mice and the impacts of fecal transplantation

Although the association between gut dysbiosis (imbalance of the microbiota) in systemic lupus erythematosus (SLE) is well-known, the simultaneous exploration in gut dysbiosis in fecal and different intestinal sections before and after lupus onset (at 2, 4, 6, 8, and 10 months old) resulting from the loss of inhibitory Fc gamma receptor IIb (FcGIIb) and pristane induction have never been conducted. Anti-dsDNA (an important lupus autoantibody) and proteinuria developed as early as 6 months old in both models, with higher levels in FcGRIIb deficient (FcGRIIb-/-) mice. Compared to the healthy control at 2 and 4 months, the lupus mice (both FcGRRIIb-/- and pristane) and healthy mice at 6 months old demonstrated an alteration as indicated by the Shannon alpha diversity index, highlighting influences of lupus- and age-induced dysbiosis, respectively. Non-metric multidimensional scaling (NMDS) revealed that the fecal microbiota of FcGRIIb-/- mice were distinct from the age-matched healthy control at all timepoints (at 6 month, p < 0.05), while pristane mice showed divergence at only some timepoints. Analyses of different intestinal sections revealed similarity among microbiota in the cecum, colon, and feces, contrasting with those in the small intestines (duodenum, jejunum, and ileum). Subtle differences were found between FcGRIIb-/- and pristane mice in feces and the intestinal sections as assessed by several analyses, for examples, the similar or dissimilar distances (NMDS), the neighbor-joining clustering, and the potential metabolisms (KEGG pathway analysis). Due to the differences between the gut microbiota (feces and intestinal sections) in the lupus mice and the healthy control, rebalancing of the microbiota using rectal administration of feces from the healthy control (fecal transplantation; FMT) to 7-month-old FcGIIb-/- mice (the established lupus; positive anti-dsDNA and proteinuria) was performed. In comparison to FcGRIIb-/- mice without FMT, FMT mice (more effect on the female than the male mice) showed the lower anti-dsDNA levels with similar fecal microbiome diversity (16s DNA gene copy number) and microbiota patterns to the healthy control. In conclusion, gut microbiota (feces and intestinal sections) of lupus mice (FcGRIIb-/- and pristane) diverged from the control as early as 4–6 months old, correlating with lupus characteristics (anti-dsDNA and proteinuria). The different gut microbiota in FcGRIIb-/- and pristane suggested a possible different gut microbiota in lupus with various molecular causes. Furthermore, FMT appeared to mitigate gut dysbiosis and reduce anti-dsDNA, supporting the benefit of the rebalancing gut microbiota in lupus, with more studies are warranted.

High-resolution sedimentary cyclostratigraphy and astronomical signals in the Upper Ordovician of Southwest China

During the Late Ordovician, a series of significant biotic and geological events occurred globally and more specifically in South China. The development of a high-resolution orbital cyclostratigraphic framework and a precise radioisotope dating has greatly enhanced our understanding of the factors controlling these events. In this study, quantitative proxies for palaeo-water depths, derived from sedimentological analyses, serve as indicators for orbital cyclostratigraphic assessment.Relatively shallow-marine carbonates with well-developed shallowing-upward cycles were deposited in the western Yangtze Platform during the Late Ordovician. High-resolution geochronological data were obtained from high-precision isotope dilution thermal ionization mass spectrometry (ID-TIMS) zircon dating of K-bentonite beds at the studied section. This facilitated sedimentary facies and environmental analyses, as well as the recognition of sedimentary cycles. Based on mud content, bioclastic content, and laminations, four sedimentary facies, twelve sedimentary microfacies, and forty-eight shallowing-upward sedimentary cycles were identified in the Daduhe Formation.Through comparative time series analysis of depth rank, lime mudstone-calcareous mudstone binary data, non-carbonate content, and anhysteretic remanent magnetization (ARM), astronomical cycles and trends in sedimentation rates have been identified. This study confirms the reliability of radioisotope dating, establishing a 0.46 Myr duration for the Dicellograptus complexus graptolite biozone and a 0.96 Myr duration for the Paraorthograptus pacificus graptolite biozone, both with an empirical uncertainty of 0.1 Myr. This research proposes a reliable method for identifying astronomical cycles through high-precision sedimentary microfacies and cyclostratigraphy, in comparison with rock magnetic cyclostratigraphy studies.

COMPARATIVE ANALYSIS OF TIME SERIES FORECASTING MODELS USING ARIMA AND NEURAL NETWORK AUTOREGRESSION METHODS

Gold price fluctuations have a significant impact because gold is a haven asset. When financial markets are volatile, investors tend to turn to safer instruments such as gold, so gold price forecasting becomes important in economic uncertainty. The novelty of this research is the comparative analysis of time series forecasting models using ARIMA and the NNAR methods to predict gold price movements specifically applied to gold price data with non-stationary and non-linear characteristics. The aim is to identify the strengths and limitations of ARIMA and NNAR on such data. ARIMA can only be applied to time series data that are already stationary or have been converted to stationary form through differentiation. However, ARIMA may struggle to capture complex non-linear patterns in non-stationary data. Instead, NNAR can handle non-stationary data more effectively by modeling the complex non-linear relationships between input and output variables. In the NNAR model, the lag values of the time series are used as input variables for the neural network. The dataset used is the closing price of gold with 1449 periods from January 2, 2018, to October 5, 2023. The augmented Dickey-Fuller test dataset obtained a p-value = 0.6746, meaning the data is not stationary. The ARIMA(1, 1, 1) model was selected as the gold price forecasting model and outperformed other candidate ARIMA models based on parameter identification and model diagnosis tests. Model performance is evaluated based on the RMSE and MAE values. In this study, the ARIMA(1, 1, 1) model obtained RMSE = 16.20431 and MAE = 11.13958. The NNAR(1, 10) model produces RMSE = 16.10002 and MAE = 11.09360. Based on the RMSE and MAE values, the NNAR(1, 10) model produces better accuracy than the ARIMA(1, 1, 1) model.

Comparative time series analysis of Anatolian water buffalo stock in Türkiye

Water buffalo holds an important place in the livestock heritage in Türkiye. In the current study, buffalo numbers between 2023 and 2032 were estimated by using buffalo number data from 1991 to 2022. In the study, FAOSTAT data between 1961 and 2021 and TÜİK data for 2022 were used to obtain the time series. ARIMA modeling method was used in the study conducted in R programming language. ARIMA is a time series model that can make predictions from time series based on explaining univariate data. According to the results of the analysis made in the study, it is seen that the number of buffaloes in Türkiye will continue to decrease. According to the results obtained, it is estimated that the number of buffaloes will be 157,755 heads in 2023, 96,851 heads in 2027 and 19,136 heads in 2032. The disease resistance of buffaloes, their high adaptability, the high demand for water buffalo products, the preference for high-yielding cows for milk production despite government support payments, increasing mechanization, the reduction of buffalo habitats and the increase in input costs have led to a decline in Anatolian water buffalo populations. To prevent the Anatolian water buffalo from being threatened with extinction, the population decline must be halted and improved. To ensure this, the decline of the pastures that form the buffalo’s habitat must be halted, pasture areas must be increased, and existing pastures rehabilitated, government subsidies for buffalo and calf rearing must be increased and market opportunities for buffalo products must be expanded.

Comparative analysis of decision tree, time series, and semi-distributed hydrological models for runoff prediction in snowmelt-influenced and human-activity-intensive basins: a case study of the Shiribeshi-Toshibetsu basin, Japan

ABSTRACT This study evaluates the performance of decision tree model (XGBoost), time series model (LSTM), and semi-distributed hydrological model (HBV) in simulating daily runoff in a watershed with significant snowmelt contributions and intensive human activity. Daily runoff data from the Shiribeshi-Toshibetsu basin in Japan, spanning the period from 1998 to 2015, were used for model training, with data from 2016 to 2019 for validation. Comparative analysis reveals that the XGBoost model outperforms the LSTM and HBV models,achieving a Nash–Sutcliffe Efficiency (NSE) of 0.94, compared to 0.85 and 0.84 for the LSTM and HBV models, respectively. Additionally, Partial Dependence Plots (PDPs) were utilized to investigate the nonlinear impacts of climatic factors on runoff, identifying precipitation, average temperature, and wind speed as the most significant drivers. Specifically, the relationship between temperature and runoff follows a unique pattern: runoff initially increases with rising temperatures, then decreases, and subsequently increases again. These findings underscore the robustness of decision tree models in handling complex hydrological data and highlight the importance of understanding the intricate interactions between climatic variables and runoff. This study provides scientific support for water resource management in similar watersheds under changing climatic conditions.

Comparative Analysis of Time Series Forecasting using ARIMA, and GRNNs Models: A Case Study of Death Rate of Diabetic Mellitus in Canada

This research aims to compare ARIMA and GRNN models alone. For this comparison the death rate for diabetes mellitus time series data of Canada is used. Autoregressive Integrated Moving Average (ARIMA), and Generalized Regression Neural Networks (GRNN) models were applied for time series prediction of the death rate for diabetes mellitus—trained data for two models from 2000 to 2015. Test data was used to compare the precision through data from 2016 to 2021. The ARIMA model was applied using the auto-command through R package which provided the least BIC and AIC values. The mean absolute deviation (MAD), root mean squared error (RMSE), and mean absolute percentage error (MAPE) were employed to measure the forecasting efficiency of the models. The ARIMA model had the highest prediction accuracy as compared to the GRNN model. ARIMA predicts the death rate for diabetes mellitus more accurately and robustly compared to the GRNNs model.

Quantum Temporal Winds: Turbulence in Financial Markets

This paper leverages turbulence theory from physics to examine the similarities and differences between financial market volatility and turbulent phenomena on a statistical physics level. By drawing analogies between the dynamics of financial markets and fluid turbulence, an innovative analytical framework has been developed to enhance our understanding of the complexity inherent in financial markets. The research methodology involves a comparative analysis of several national stock market indices and simulated turbulent velocity time series, with a particular focus on key statistical properties such as probability distributions, correlation structures, and power spectral densities. Furthermore, a financial market capital flow model has been established, and corresponding solutions have been proposed. Through computational simulations and data analysis, it was discovered that financial market volatility shares some statistical characteristics with turbulence, yet there are significant differences in the shape of probability distributions and the timescales of correlations. This indicates that although financial markets exhibit patterns similar to turbulence, as a multivariate-driven complex system, their behavioral patterns do not completely correspond to natural turbulence phenomena, highlighting the limitations of directly applying turbulence theory to financial market analysis. Additionally, the study explores the use of Bézier curves to simulate market volatility and, based on these analyses, formulates trading strategies that demonstrate practical applications in risk management. This research provides fresh perspectives for the fields of financial market theory and econophysics, offering new insights into the complexity of financial markets and the prevention and management of financial risks.

Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants

Objective. Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from >700 extremely preterm infants to identify physiologic features that predict respiratory outcomes. Approach. We calculated a subset of 33 HCTSA features on >7 M 10 min windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on >3500 HCTSA algorithms. We hypothesized that the best HCTSA algorithms would compare favorably to optimal PreVent physiologic predictor IH90_DPE (duration per event of intermittent hypoxemia events below 90%). Main Results. The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90_DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90_DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850). Significance. These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90_DPE as an optimal predictor of respiratory outcomes.

Exploring Predictive Models for Airfare Forecasting: A Comparative Analysis of Time Series and Machine Learning Approaches

Exploring Predictive Models for Airfare Forecasting: A Comparative Analysis of Time Series and Machine Learning Approaches

Impacto da pandemia do covid-19 no diagnóstico e tratamento do câncer de colo de útero: um estudo retrospectivo brasileiro

The Covid-19 pandemic has represented a crisis in healthcare systems. The impact of its effects on the diagnosis and treatment of incident malignancies, such as cervical cancer, can be evidenced in metrics. Materials and methods: In this cohort, we carried out a retrospective and quantitative comparative analysis of time series, between 2014 and 2022, with the total data of diagnoses and hysterectomies performed for the treatment of malignant cervical neoplasms, through the Department of Informatics of the Unified Health System (DATASUS). Results: From 2014 to 2022, Brazil recorded 173,300 cases of malignant cervical neoplasms. Increasing trends in the number of annual diagnoses were examined, with a decrease in the pattern between 2019 and 2020, coinciding with the pandemic period. Similarly, the total number of hysterectomies, the main treatment for cervical cancer, was affected. Between 2014 and 2019, an average of 53,652 surgeries were performed annually. However, in 2020 it fell to 30,698 (40.2%) interventions compared to 2019. Conclusion: In the period from 2019 to 2020, there were substantial reductions in the number of procedures coinciding with the COVID-19 pandemic. Consequently, the data suggest a delay in the diagnosis and surgical treatment of cervical neoplasms, impacting relevant clinical outcomes and overall survival.

Impacto da pandemia do covid-19 no diagnóstico e tratamento do câncer de colo de útero: um estudo retrospectivo brasileiro

The Covid-19 pandemic has represented a crisis in healthcare systems. The impact of its effects on the diagnosis and treatment of incident malignancies, such as cervical cancer, can be evidenced in metrics. Materials and methods: In this cohort, we carried out a retrospective and quantitative comparative analysis of time series, between 2014 and 2022, with the total data of diagnoses and hysterectomies performed for the treatment of malignant cervical neoplasms, through the Department of Informatics of the Unified Health System (DATASUS). Results: From 2014 to 2022, Brazil recorded 173,300 cases of malignant cervical neoplasms. Increasing trends in the number of annual diagnoses were examined, with a decrease in the pattern between 2019 and 2020, coinciding with the pandemic period. Similarly, the total number of hysterectomies, the main treatment for cervical cancer, was affected. Between 2014 and 2019, an average of 53,652 surgeries were performed annually. However, in 2020 it fell to 30,698 (40.2%) interventions compared to 2019. Conclusion: In the period from 2019 to 2020, there were substantial reductions in the number of procedures coinciding with the COVID-19 pandemic. Consequently, the data suggest a delay in the diagnosis and surgical treatment of cervical neoplasms, impacting relevant clinical outcomes and overall survival.

Comparative analysis of power consumption time series in deprived and developed regions of Iran

This paper presents a comparative analysis of power consumption time series at 12 o'clock every day between 2020 and 2022 for one distribution network in Sistan and one in Tehran. The aim of this study is to compare the development and climate differences between these regions, as well as the impact of social, industrial, and environmental factors. By comparing a deprived area with an area in the capital, we aim to identify potential disparities in power consumption and identify potential areas for improvement. We employed the CRP tool software and toolkit for time series analysis and used various methods to compare and predict the predictability of each time series. Our findings suggest significant differences in power consumption between the two regions, which could be attributed to socio-economic and environmental factors. Overall, this study sheds light on the potential impact of regional differences on power consumption and highlights the need for further research in this area.

Deep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series

BackgroundIndividuals in the early stages of Alzheimer’s Disease (AD) are typically diagnosed with Mild Cognitive Impairment (MCI). MCI represents a transitional phase between normal cognitive function and AD. Electroencephalography (EEG) records carry valuable insights into cerebral cortex brain activities to analyze neuronal degeneration. To enhance the precision of dementia diagnosis, automatic and intelligent methods are required for the analysis and processing of EEG signals. New methodsThis paper aims to address the challenges associated with MCI diagnosis by leveraging EEG signals and deep learning techniques. The analysis in this study focuses on processing the information embedded within the sequence of raw EEG time series data. EEG recordings are collected from 10 Healthy Controls (HC) and 10 MCI participants using 19 electrodes during a 30 min eyes-closed session. EEG time series are transformed into 2 separate formats of input tensors and applied to deep neural network architectures. Convolutional Neural Network (CNN) and ResNet from scratch are performed with 2D time series with different segment lengths. Furthermore, EEGNet and DeepConvNet architectures are utilized for 1D time series. ResultsResNet demonstrates superior effectiveness in detecting MCI when compared to CNN architecture. Complete discrimination is achieved using EEGNet and DeepConvNet for noisy segments. Comparison with existing methodsResNet has yielded a 3 % higher accuracy rate compared to CNN. None of the architectures in the literature have achieved 100 % accuracy except proposed EEGNet and DeepConvnet. ConclusionDeep learning architectures hold great promise in enhancing the accuracy of early MCI detection.

Explanatory comparative analysis of time series forecasting algorithms for air quality prediction

Explanatory comparative analysis of time series forecasting algorithms for air quality prediction

A comparative time-series analysis and deep learning projection of innate radon gas risk in Canadian and Swedish residential buildings

A comparative time-series analysis and deep learning projection of innate radon gas risk in Canadian and Swedish residential buildings

Heart rate patterns predicting cerebral palsy in preterm infants.

Heart rate (HR) patterns can inform on central nervous system dysfunction. We previously used highly comparative time series analysis (HCTSA) to identify HR patterns predicting mortality among patients in the neonatal intensive care unit (NICU) and now use this methodology to discover patterns predicting cerebral palsy (CP) in preterm infants. We studied NICU patients <37 weeks' gestation with archived every-2-s HR data throughout the NICU stay and with or without later diagnosis of CP (n = 57 CP and 1119 no CP). We performed HCTSA of >2000 HR metrics and identified 24 metrics analyzed on HR data from two 7-day periods: week 1 and 37 weeks' postmenstrual age (week 1, week 37). Multivariate modeling was used to optimize a parsimonious prediction model. Week 1 HR metrics with maximum AUC for CP prediction reflected low variability, including "RobustSD" (AUC 0.826; 0.772-0.870). At week 37, high values of a novel HR metric, "LongSD3," the cubed value of the difference in HR values 100 s apart, were added to week 1 HR metrics for CP prediction. A combined birthweight + early and late HR model had AUC 0.853 (0.805-0.892). Using HCTSA, we discovered novel HR metrics and created a parsimonious model for CP prediction in preterm NICU patients. We discovered new heart rate characteristics predicting CP in preterm infants. Using every-2-s HR from two 7-day periods and highly comparative time series analysis, we found a measure of low variability HR week 1 after birth and a pattern of recurrent acceleration in HR at term corrected age that predicted CP. Combined clinical and early and late HR features had AUC 0.853 for CP prediction.

A comparative time-series analysis and deep learning projection of innate radon risk in Canadian and Swedish residential buildings

A comparative time-series analysis and deep learning projection of innate radon risk in Canadian and Swedish residential buildings

Localized keyhole pore prediction during laser powder bed fusion via multimodal process monitoring and X-ray radiography

Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors.

Determining recovery of marine water quality of the Rio Doce using statistical and temporal comparisons with nearby river systems.

The Fundão dam breach affected the Rio Doce's estuarine and marine environments with water, tailings, scoured soil and/or sediments, and other debris. Time series and standard exceedance analyses are typically used to assess water quality recovery to baseline conditions after deteriorating water quality events. In the absence of historical measurements, impacts to water quality from the Fundão Event were compared with measurements of nearby rivers. Similar river systems with available water quality measurements were grouped into affected and unaffected estuarine and marine waters. Statistical and temporal comparisons of marine waters unaffected by the Rio Doce with those affected by the Rio Doce were evaluated for systematic differences. Multivariate statistical techniques were also used to assess water quality differences. Our results demonstrate that the Fundão dam breach had a short-term water quality impact on marine waters near the Rio Doce mouth. Principal component and comparative time series analyses clearly demonstrated this impact during the 2015/2016 and 2016/2017 wet seasons. The ephemeral effects of the breach, however, did not compromise marine water quality. Exceedances of CONAMA standards for metals remained either at zero or at very low levels during the affected period (<5.7%). Before the start of the next wet season in October 2017, water quality impacts from the Event were statistically indistinguishable from unaffected marine waters, indicating recovery to baseline conditions. Integr Environ Assess Manag 2024;20:99-116. © 2023 NewFields Companies, LLC. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Changes in the Use of Long-Term Medications Following Incident Dementia Diagnosis

Dementia is a life-altering diagnosis that may affect medication safety and goals for chronic disease management. To examine changes in medication use following an incident dementia diagnosis among community-dwelling older adults. In this cohort study of adults aged 67 years or older enrolled in traditional Medicare and Medicare Part D, patients with incident dementia diagnosed between January 2012 and December 2018 were matched to control patients based on demographics, geographic location, and baseline medication count. The index date was defined as the date of first dementia diagnosis or, for controls, the date of the closest office visit. Data were analyzed from August 2021 to June 2023. Incident dementia diagnosis. The main outcomes were overall medication counts and use of cardiometabolic, central nervous system (CNS)-active, and anticholinergic medications. A comparative time-series analysis was conducted to examine quarterly changes in medication use in the year before through the year following the index date. The study included 266 675 adults with incident dementia and 266 675 control adults; in both groups, 65.1% were aged 80 years or older (mean [SD] age, 82.2 [7.1] years) and 67.8% were female. At baseline, patients with incident dementia were more likely than controls to use CNS-active medications (54.32% vs 48.39%) and anticholinergic medications (17.79% vs 15.96%) and less likely to use most cardiometabolic medications (eg, diabetes medications, 31.19% vs 36.45%). Immediately following the index date, the cohort with dementia had a greater increase in mean number of medications used (0.41 vs -0.06; difference, 0.46 [95% CI, 0.27-0.66]) and in the proportion of patients using CNS-active medications (absolute change, 3.44% vs 0.79%; difference, 2.65% [95% CI, 0.85%-4.45%]) owing to an increased use of antipsychotics, antidepressants, and antiepileptics. The cohort with dementia also had a modestly greater decline in use of anticholinergic medications (quarterly change in use, -0.53% vs -0.21%; difference, -0.32% [95% CI, -0.55% to -0.08%]) and most cardiometabolic medications (eg, quarterly change in antihypertensive use: -0.84% vs -0.40%; difference, -0.44% [95% CI, -0.64% to -0.25%]). One year after diagnosis, 75.2% of the cohort with dementia were using 5 or more medications (2.8% increase). In this cohort study of Medicare Part D beneficiaries, following an incident dementia diagnosis, patients were more likely to initiate CNS-active medications and modestly more likely to discontinue cardiometabolic and anticholinergic medications compared with the control group. These findings suggest missed opportunities to reduce burdensome polypharmacy by deprescribing long-term medications with high safety risks or limited likelihood of benefit or that may be associated with impaired cognition.