Investigating Rare Events by Transition Interface Sampling

We briefly review simulation schemes for the investigation of rare transitions and we resume the recently introduced Transition Interface Sampling, a method in which the computation of rate constants is recast into the computation of fluxes through interfaces dividing the reactant and product state.Comment: 12 pages, 1 figure, contributed paper to the proceedings of NEXT 2003, Second Sardinian International Conference on News and Expectations in Thermostatistics, 21-28 Sep 2003, Cagliari (Italy

Efficient Dynamic Importance Sampling of Rare Events in One Dimension

Exploiting stochastic path integral theory, we obtain \emph{by simulation} substantial gains in efficiency for the computation of reaction rates in one-dimensional, bistable, overdamped stochastic systems. Using a well-defined measure of efficiency, we compare implementations of ``Dynamic Importance Sampling'' (DIMS) methods to unbiased simulation. The best DIMS algorithms are shown to increase efficiency by factors of approximately 20 for a $5 k_B T$ barrier height and 300 for $9 k_B T$, compared to unbiased simulation. The gains result from close emulation of natural (unbiased), instanton-like crossing events with artificially decreased waiting times between events that are corrected for in rate calculations. The artificial crossing events are generated using the closed-form solution to the most probable crossing event described by the Onsager-Machlup action. While the best biasing methods require the second derivative of the potential (resulting from the ``Jacobian'' term in the action, which is discussed at length), algorithms employing solely the first derivative do nearly as well. We discuss the importance of one-dimensional models to larger systems, and suggest extensions to higher-dimensional systems.Comment: version to be published in Phys. Rev.

Potentially inappropriate polypharmacy is an important predictor of 30-day emergency hospitalisation in older adults:a machine learning feature validation study

BACKGROUND: Machine learning (ML) models in healthcare are crucial for predicting clinical outcomes, and their effectiveness can be significantly enhanced through improvements in accuracy, generalisability, and interpretability. To achieve widespread adoption in clinical practice, risk factors identified by these models must be validated in diverse populations.METHODS: In this cohort study, 86 870 community-dwelling older adults ≥65 years from the UK Biobank database were used to train and test three ML models to predict 30-day emergency hospitalisation. The three ML models, Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR), utilised all extracted variables, consisting of demographic and geriatric syndromes, comorbidities, and the Drug Burden Index (DBI), a measure of potentially inappropriate polypharmacy, which quantifies exposure to medications with anticholinergic and sedative properties. 30-day emergency hospitalisation was defined as any hospitalisation related to any clinical event within 30 days of the index date. The model performance metrics included the area under the receiver operating characteristics curve (AUC-ROC) and the F1 score.RESULTS: The AUC-ROC for the RF, XGB and LR models was 0.78, 0.86 and 0.61, respectively, signifying good discriminatory power. The DBI, mobility, fractures, falls, hazardous alcohol drinking and smoking were validated as important variables in predicting 30-day emergency hospitalisation.CONCLUSIONS: This study validated important risk factors for predicting 30-day emergency hospitalisation. The validation of important risk factors will inform the development of future ML studies in geriatrics. Future research should prioritise the development of targeted interventions to address the risk factors validated in this study, ultimately improving patient outcomes and alleviating healthcare burdens.</p

Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis

BACKGROUND: Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care.DESIGN: Systematic review and meta-analyses.PARTICIPANTS: Older adults (≥ 65 years) in any setting.INTERVENTION: Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months.OUTCOME MEASURES: Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric.RESULTS: Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power.CONCLUSION: The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.</p

Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis

BACKGROUND: Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care.DESIGN: Systematic review and meta-analyses.PARTICIPANTS: Older adults (≥ 65 years) in any setting.INTERVENTION: Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months.OUTCOME MEASURES: Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric.RESULTS: Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power.CONCLUSION: The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.</p

Improving rates of advanced care planning discussion and documentation in the primary care setting : a DNP quality improvement study

Background: Advance Care Planning (ACP) documents enable patients to receive medical care that aligns with treatment preferences and goals. Discussions regarding ACP in primary care are often inadequate due to patient and provider barriers. The lack of completed ACP documents leads to patient care goals not being addressed and ACP metrics not being met. Major themes in the literature demonstrated that multidisciplinary teams, workflow redesign, staff education, and the use of the Electronic Health Record (EHR) can aid in improving ACP discussion and documentation rates. Purpose: The purpose of this QI project was to improve the existing ACP processes for patients 50 years and older within our designated family medicine clinic by increasing discussion and documentation rates. Methods: The project was implemented in a family medicine clinic in an urban area over a 3 month time period. An ACP focused workflow was implemented defining specific roles and responsibilities for the team members. Pre and post intervention data was collected through the EHR dashboard for patients 50 years and older with and without existing ACP documents. Implementation: During staff meetings, clinic staff and providers were educated about new roles and responsibilities. The Making Choices Michigan Advance Directive form was made available to patients and providers. Staff addressed if a patient had an existing ACP and providers then discussed ACP with the patient. Both were documented in the EHR. The ACP form was given to interested patients to be filled out at home or in the office at a separate appointment with staff. Results: Project outcome goal of 30% increase in ACP discussion and documentation was not met. There was a slight increase in the percentage of 5.62%. No statistical change (p=0.16) was noticed when comparing pre-intervention rates of ACP CPT II code use, 1123F (80) and 1124F (41), with post-intervention CPT II code use, 1123F (66) and 1124F (26) after chi square test.Thesis (D.N.P.)--Michigan State University. Family Nurse Practitioner, 2023Includes bibliographical references (pages 31-35

Drug Burden Index is a Modifiable Predictor of 30-Day-Hospitalization in Community-Dwelling Older Adults with Complex Care Needs:Machine Learning Analysis of InterRAI Data

BACKGROUND: Older adults (≥ 65 years) account for a disproportionately high proportion of hospitalization and in-hospital mortality, some of which may be avoidable. Although machine learning (ML) models have already been built and validated for predicting hospitalization and mortality, there remains a significant need to optimise ML models further. Accurately predicting hospitalization may tremendously impact the clinical care of older adults as preventative measures can be implemented to improve clinical outcomes for the patient.METHODS: In this retrospective cohort study, a dataset of 14,198 community-dwelling older adults (≥ 65 years) with complex care needs from the Inter-Resident Assessment Instrument database was used to develop and optimise three ML models to predict 30-day-hospitalization. The models developed and optimized were Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR). Variable importance plots were generated for all three models to identify key predictors of 30-day-hospitalization.RESULTS: The area under the receiver operating characteristics curve for the RF, XGB and LR models were 0.97, 0.90 and 0.72, respectively. Variable importance plots identified the Drug Burden Index and alcohol consumption as important, immediately potentially modifiable variables in predicting 30-day-hospitalization.CONCLUSIONS: Identifying immediately potentially modifiable risk factors such as the Drug Burden Index and alcohol consumption is of high clinical relevance. If clinicians can influence these variables, they could proactively lower the risk of 30-day-hospitalization. ML holds promise to improve the clinical care of older adults. It is crucial that these models undergo extensive validation through large-scale clinical studies before being utilized in the clinical setting.</p

Noncoding deletions reveal a gene that is critical for intestinal function.

Large-scale genome sequencing is poised to provide a substantial increase in the rate of discovery of disease-associated mutations, but the functional interpretation of such mutations remains challenging. Here we show that deletions of a sequence on human chromosome 16 that we term the intestine-critical region (ICR) cause intractable congenital diarrhoea in infants1,2. Reporter assays in transgenic mice show that the ICR contains a regulatory sequence that activates transcription during the development of the gastrointestinal system. Targeted deletion of the ICR in mice caused symptoms that recapitulated the human condition. Transcriptome analysis revealed that an unannotated open reading frame (Percc1) flanks the regulatory sequence, and the expression of this gene was lost in the developing gut of mice that lacked the ICR. Percc1-knockout mice displayed phenotypes similar to those observed upon ICR deletion in mice and patients, whereas an ICR-driven Percc1 transgene was sufficient to rescue the phenotypes found in mice that lacked the ICR. Together, our results identify a gene that is critical for intestinal function and underscore the need for targeted in vivo studies to interpret the growing number of clinical genetic findings that do not affect known protein-coding genes