Predictive Modeling of 30-Day Emergency Hospital Transport of Patients Using a Personal Emergency Response System: Prognostic Retrospective Study

Buijs, Jorn op den; Nikolova-Simons, Mariana; Golas, Sara; Fischer, Nils; Felsted, Jennifer; Schertzer, Linda; Agboola, Stephen; Kvedar, Joseph C.; Jethwani, Kamal

doi:10.2196/medinform.9907

Cited by 10 publications

(14 citation statements)

References 32 publications

(37 reference statements)

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“…The service is partially privately paid for by participants and not covered by their health insurance. 20 To limit sampling bias, all subscribers to the service between January 2012 and June 2014 who subscribed for at least 3 months were included in the eligible cohort. We selected a convenience sample that included all self-reported PD participants (n = 2063).…”

Section: Methodology Study Design and Participantsmentioning

confidence: 99%

Home‐Based Monitoring of Falls Using Wearable Sensors in Parkinson's Disease

et al. 2019

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Introduction Falling is among the most serious clinical problems in Parkinson's disease (PD). We used body‐worn sensors (falls detector worn as a necklace) to quantify the hazard ratio of falls in PD patients in real life. Methods We matched all 2063 elderly individuals with self‐reported PD to 2063 elderly individuals without PD based on age, gender, comorbidity, and living conditions. We analyzed fall events collected at home via a wearable sensor. Fall events were collected either automatically using the wearable falls detector or were registered by a button push on the same device. We extracted fall events from a 2.5‐year window, with an average follow‐up of 1.1 years. All falls included were confirmed immediately by a subsequent telephone call. The outcomes evaluated were (1) incidence rate of any fall, (2) incidence rate of a new fall after enrollment (ie, hazard ratio), and (3) 1‐year cumulative incidence of falling. Results The incidence rate of any fall was higher among self‐reported PD patients than controls (2.1 vs. 0.7 falls/person, respectively; P < .0001). The incidence rate of a new fall after enrollment (ie, hazard ratio) was 1.8 times higher for self‐reported PD patients than controls (95% confidence interval, 1.6–2.0). Conclusion Having PD nearly doubles the incidence of falling in real life. These findings highlight PD as a prime “falling disease.” The results also point to the feasibility of using body‐worn sensors to monitor falls in daily life. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

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Section: Methodology Study Design and Participantsmentioning

confidence: 99%

Home‐Based Monitoring of Falls Using Wearable Sensors in Parkinson's Disease

et al. 2019

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“…During the study, the participants’ risk will be assessed by a predictive model of hospital transport in the next 30 days. This predictive risk model was developed on a retrospective data set of more than 8000 deceased German PERS subscribers using a methodology similar as described in [ 7 ], which is used by the Philips CareSage predictive analytics engine in the United States. The predictive model achieved an area under the receiver operator characteristic curve of 0.75.…”

Section: Methodsmentioning

confidence: 99%

“…The main benefit of a PERS service is the reassurance that help will always be available in case of an emergency, such as a fall or respiratory issues. Previous studies in the United States (US) [ 7 , 8 ] have shown that PERS data can be used to develop prediction models of decline in patient status. Such models thus provide early warning signs of impending emergencies and can be used by case managers to provide timely intervention [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Impact of a Risk Assessment System With Tailored Interventions in Germany: Protocol for a Prospective Study With Matched Controls

2020

Self Cite

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Background With a worldwide increase in the elderly population, and an associated increase in health care utilization and costs, preventing avoidable emergency department visits and hospitalizations is becoming a global priority. A personal emergency response system (PERS), consisting of an alarm button and a means to establish a live connection to a response center, can help the elderly live at home longer independently. Individual risk assessment through predictive modeling can help indicate what PERS subscribers are at elevated risk of hospital transport so that early intervention becomes possible. Objective The aim is to evaluate whether the combination of risk scores determined through predictive modeling and targeted interventions offered by a case manager can result in a reduction of hospital admissions and health care costs for a population of German PERS subscribers. The primary outcome of the study is the difference between the number of hospitalizations in the intervention and matched control groups. Methods As part of the Sicher Zuhause program, an intervention group of 500 PERS subscribers will be tracked for 8 months. During this period, risk scores will be determined daily by a predictive model of hospital transport, and at-risk participants may receive phone calls from a case manager who assesses the health status of the participant and recommends interventions. The health care utilization of the intervention group will be compared to a group of matched controls, retrospectively drawn from a population of PERS subscribers who receive no interventions. Results Differences in health care utilization and costs between the intervention group and the matched controls will be determined based on reimbursement records. In addition, qualitative data will be collected on the participants’ satisfaction with the Sicher Zuhause program and utilization of the interventions offered as part of the program. Conclusions The study evaluation will offer insight into whether a combination of predictive analytics and case manager-driven interventions can help in avoiding hospital admissions and health care costs for PERS subscribers in Germany living at home independently. In the future, this may lead to improved quality of life and reduced medical costs for the population of the study. Trial Registration Deutsches Register Klinischer Studien (DRKS), DRKS00017328; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00017328 International Registered Report Identifier (IRRID) DERR1-10.2196/17584

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“…In [21], a predictive model was developed to predict the risk of emergency hospital transport of elderly patients within the next 30 days. Based on a dataset of ≈ 290 000 patients and 128 features (of which approximately half were binary) and a binary output variable (transport/no transport), a predictive model was constructed using extreme gradient boosted trees.…”

Section: Applicationmentioning

confidence: 99%

“…Because the dataset is highly skewed towards no transport required (≈ 98% of cases), we present the accuracy of the method using precision and recall for three thresholds (90th, 95th and 99th percentile). We refer to [21] for the details.…”

Section: Applicationmentioning

confidence: 99%

Secure training of decision trees with continuous attributes

Abspoel

Escudero

Volgushev

2020

Proceedings on Privacy Enhancing Technologies

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We apply multiparty computation (MPC) techniques to show, given a database that is secret-shared among multiple mutually distrustful parties, how the parties may obliviously construct a decision tree based on the secret data. We consider data with continuous attributes (i.e., coming from a large domain), and develop a secure version of a learning algorithm similar to the C4.5 or CART algorithms. Previous MPC-based work only focused on decision tree learning with discrete attributes (De Hoogh et al. 2014). Our starting point is to apply an existing generic MPC protocol to a standard decision tree learning algorithm, which we then optimize in several ways. We exploit the fact that even if we allow the data to have continuous values, which a priori might require fixed or floating point representations, the output of the tree learning algorithm only depends on the relative ordering of the data. By obliviously sorting the data we reduce the number of comparisons needed per node to O(N log2N) from the naive O(N2), where N is the number of training records in the dataset, thus making the algorithm feasible for larger datasets. This does however introduce a problem when duplicate values occur in the dataset, but we manage to overcome this problem with a relatively cheap subprotocol. We show a procedure to convert a sorting network into a permutation network of smaller complexity, resulting in a round complexity of O(log N) per layer in the tree. We implement our algorithm in the MP-SPDZ framework and benchmark our implementation for both passive and active three-party computation using arithmetic modulo 264. We apply our implementation to a large scale medical dataset of ≈ 290 000 rows using random forests, and thus demonstrate practical feasibility of using MPC for privacy-preserving machine learning based on decision trees for large datasets.

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Predictive Modeling of 30-Day Emergency Hospital Transport of Patients Using a Personal Emergency Response System: Prognostic Retrospective Study

Cited by 10 publications

References 32 publications

Home‐Based Monitoring of Falls Using Wearable Sensors in Parkinson's Disease

Home‐Based Monitoring of Falls Using Wearable Sensors in Parkinson's Disease

Evaluating the Impact of a Risk Assessment System With Tailored Interventions in Germany: Protocol for a Prospective Study With Matched Controls

Secure training of decision trees with continuous attributes

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