An inpatient fall risk assessment tool: Application of machine learning models on intrinsic and extrinsic risk factors

Jahangiri, Sonia; Abdollahi, Masoud; Patil, Rasika; Rashedi, Ehsan; Azadeh-Fard, Nasibeh

doi:10.1016/j.mlwa.2023.100519

Cited by 4 publications

(4 citation statements)

References 34 publications

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“…One of the reasons for high accuracies could be the infusion of dual tasking. Prior assessments of fall risk under divided attention conditions in the form of motor-cognitive dual-tasks have led to an increased fall-risk in patients of diverse neurological disorders, including stroke [8,17,18,[32][33][34]. The dual-task challenges mobility and balance, unmasking deficits not apparent in single tasks, agreeing with findings from previous studies that evaluated the impact of dual tasks on movement [32,33].…”

Section: Discussionsupporting

confidence: 83%

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Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking

Abdollahi,

Rashedi,

Jahangiri

et al. 2024

Sensors

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Background: Falls are common and dangerous for stroke survivors. Current fall risk assessment methods rely on subjective scales. Objective sensor-based methods could improve prediction accuracy. Objective: Develop machine learning models using inertial sensors to objectively classify fall risk in stroke survivors. Determine optimal sensor configurations and clinical test protocols. Methods: 21 stroke survivors performed balance, Timed Up and Go, 10 Meter Walk, and Sit-to-Stand tests with and without dual-tasking. A total of 8 motion sensors captured lower limb and trunk kinematics, and 92 spatiotemporal gait and clinical features were extracted. Supervised models—Support Vector Machine, Logistic Regression, and Random Forest—were implemented to classify high vs. low fall risk. Sensor setups and test combinations were evaluated. Results: The Random Forest model achieved 91% accuracy using dual-task balance sway and Timed Up and Go walk time features. Single thorax sensor models performed similarly to multi-sensor models. Balance and Timed Up and Go best-predicted fall risk. Conclusion: Machine learning models using minimal inertial sensors during clinical assessments can accurately quantify fall risk in stroke survivors. Single thorax sensor setups are effective. Findings demonstrate a feasible objective fall screening approach to assist rehabilitation.

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Section: Discussionsupporting

confidence: 83%

“…Several efforts have been made to develop scales to evaluate the risk of fall in SS [5][6][7][8]. Examples of such scales include the Fugl-Meyer assessment of motor recovery, Berg Balance Score (BBS), Fall Efficacy Scale (FES), Postural Assessment Scale for Stroke (PASS), and Activity-Specific Balance Confidence (ABC) Scale.…”

Section: Introductionmentioning

confidence: 99%

Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking

Abdollahi,

Rashedi,

Jahangiri

et al. 2024

Sensors

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“…The metrics showing pronounced stroke-related impairments like TUG turn time and 10MWT gait speed could also serve as sensitive outcomes to track subtle longitudinal improvements resulting from interventions. Collaborations spanning engineers, clinicians, and data scientists would allow the development of predictive models leveraging these metrics to forecast fall risk or functional prognosis [55,56]. Overall, the methodology presented contributes to future technologically enabled precision rehabilitation paradigms and could optimize the quality of care.…”

Section: Discussionmentioning

confidence: 99%

Post-Stroke Functional Changes: In-Depth Analysis of Clinical Tests and Motor-Cognitive Dual-Tasking Using Wearable Sensors

Abdollahi,

Rashedi,

Kuber

et al. 2024

Bioengineering

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Clinical tests like Timed Up and Go (TUG) facilitate the assessment of post-stroke mobility, but they lack detailed measures. In this study, 21 stroke survivors and 20 control participants underwent TUG, sit-to-stand (STS), and the 10 Meter Walk Test (10MWT). Tests incorporated single tasks (STs) and motor-cognitive dual-task (DTs) involving reverse counting from 200 in decrements of 10. Eight wearable motion sensors were placed on feet, shanks, thighs, sacrum, and sternum to record kinematic data. These data were analyzed to investigate the effects of stroke and DT conditions on the extracted features across segmented portions of the tests. The findings showed that stroke survivors (SS) took 23% longer for total TUG (p < 0.001), with 31% longer turn time (p = 0.035). TUG time increased by 20% (p < 0.001) from STs to DTs. In DTs, turning time increased by 31% (p = 0.005). Specifically, SS showed 20% lower trunk angular velocity in sit-to-stand (p = 0.003), 21% longer 10-Meter Walk time (p = 0.010), and 18% slower gait speed (p = 0.012). As expected, turning was especially challenging and worsened with divided attention. The outcomes of our study demonstrate the benefits of instrumented clinical tests and DTs in effectively identifying motor deficits post-stroke across sitting, standing, walking, and turning activities, thereby indicating that quantitative motion analysis can optimize rehabilitation procedures.

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“…Data plays a crucial role in healthcare extracting invaluable knowledge and insights (Auffray et al, 2016 ). The abundance of patient information collected from diverse sources has given rise to data analytics as a powerful tool for comprehending intricate medical conditions (Shameer et al, 2017 ; Jahangiri et al, 2024 ). Given the significance of reducing readmission rates, numerous studies have been conducted to explore the factors influencing readmission rates among HF patients.…”

Section: Introductionmentioning

confidence: 99%

A machine learning model to predict heart failure readmission: toward optimal feature set

Jahangiri,

Abdollahi,

Rashedi

et al. 2024

Front. Artif. Intell.

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BackgroundHospital readmissions for heart failure patients remain high despite efforts to reduce them. Predictive modeling using big data provides opportunities to identify high-risk patients and inform care management. However, large datasets can constrain performance.ObjectiveThis study aimed to develop a machine learning based prediction model leveraging a nationwide hospitalization database to predict 30-day heart failure readmissions. Another objective of this study is to find the optimal feature set that leads to the highest AUC value in the prediction model.Material and methodsHeart failure patient data was extracted from the 2020 Nationwide Readmissions Database. A heuristic feature selection process incrementally incorporated predictors into logistic regression and random forest models, which yields a maximum increase in the AUC metric. Discrimination was evaluated through accuracy, sensitivity, specificity and AUC.ResultsA total of 566,019 discharges with heart failure diagnosis were recognized. Readmission rate was 8.9% for same-cause and 20.6% for all-cause diagnoses. Random forest outperformed logistic regression, achieving AUCs of 0.607 and 0.576 for same-cause and all-cause readmissions respectively. Heuristic feature selection resulted in the identification of optimal feature sets including 20 and 22 variables from a pool of 30 and 31 features for the same-cause and all-cause datasets. Key predictors included age, payment method, chronic kidney disease, disposition status, number of ICD-10-CM diagnoses, and post-care encounters.ConclusionThe proposed model attained discrimination comparable to prior analyses that used smaller datasets. However, reducing the sample enhanced performance, indicating big data complexity. Improved techniques like heuristic feature selection enabled effective leveraging of the nationwide data. This study provides meaningful insights into predictive modeling methodologies and influential features for forecasting heart failure readmissions.

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An inpatient fall risk assessment tool: Application of machine learning models on intrinsic and extrinsic risk factors

Cited by 4 publications

References 34 publications

Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking

Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking

Post-Stroke Functional Changes: In-Depth Analysis of Clinical Tests and Motor-Cognitive Dual-Tasking Using Wearable Sensors

A machine learning model to predict heart failure readmission: toward optimal feature set

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