Data silos undermine efforts to characterize, predict, and mitigate dementia-related missing person incidents

Miguel-Cruz, Antonio; Marshall, Samantha; Daum, Christine; Perez, Hector; Hirdes, John P.; Liu, Lili

doi:10.1177/08404704221106156

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…However, while the evidence may be low, given the nature of a scoping review to summarize all existing literature, this is not prohibitive to our results. Most selected studies included data from police reports and newspapers; their credibility is as good as the quality of the data (Güss et al, 2020;Miguel-Cruz et al, 2022;O'Connor et al, 2021). Interestingly, few studies included the perspectives of persons living with dementia and care partners.…”

Section: Accessibility To Supportmentioning

confidence: 99%

Risk Factors Associated with Missing Incidents among Persons Living with Dementia: A Scoping Review

Perez,

Miguel Cruz,

Neubauer

et al. 2024

Can. J. Aging

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Worldwide, over 55-million people have dementia, and the number will triple by 2050. Persons living with dementia are exposed to risks secondary to cognitive challenges including getting lost. The adverse outcomes of going missing include injuries, death, and premature institutionalization. In this scoping review, we investigate risk factors associated with going missing among persons living with dementia. We searched and screened studies from four electronic databases (Medline, CINAHL, Embase, and Scopus), and extracted relevant data. We identified 3,376 articles, of which 73 met the inclusion criteria. Most studies used quantitative research methods. We identified 27 variables grouped into three risk factor domains: (a) demographics and personal characteristics, (b) health conditions and symptoms, and (c) environmental and contextual antecedents. Identification of risk factors associated with getting lost helps to anticipate missing incidents. Risk factors can be paired with proactive strategies to prevent incidents and inform policies to create safer communities.

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Section: Accessibility To Supportmentioning

confidence: 99%

Risk Factors Associated with Missing Incidents among Persons Living with Dementia: A Scoping Review

Perez,

Miguel Cruz,

Neubauer

et al. 2024

Can. J. Aging

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“…This is further complicated by the fact, that many countries have not yet fully defined ownership of medical data in their legal frameworks ( 8 ). Consequently, health data of different sources is often kept in isolated data silos, and its value for further secondary analyses remains underutilized ( 9 , 10 ). Connecting silos can accomplish both vertical linkage (i.e., more data for one patient) as well as horizontal linkage (i.e., more patients for specific data) and thus provide more holistic views on patients and diseases increasing the data’s value for research even further.…”

Section: Introductionmentioning

confidence: 99%

Health data space nodes for privacy-preserving linkage of medical data to support collaborative secondary analyses

Baumgartner,

Kreiner,

Lauschensky

et al. 2024

Front. Med.

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IntroductionThe potential for secondary use of health data to improve healthcare is currently not fully exploited. Health data is largely kept in isolated data silos and key infrastructure to aggregate these silos into standardized bodies of knowledge is underdeveloped. We describe the development, implementation, and evaluation of a federated infrastructure to facilitate versatile secondary use of health data based on Health Data Space nodes.Materials and methodsOur proposed nodes are self-contained units that digest data through an extract-transform-load framework that pseudonymizes and links data with privacy-preserving record linkage and harmonizes into a common data model (OMOP CDM). To support collaborative analyses a multi-level feature store is also implemented. A feasibility experiment was conducted to test the infrastructures potential for machine learning operations and deployment of other apps (e.g., visualization). Nodes can be operated in a network at different levels of sharing according to the level of trust within the network.ResultsIn a proof-of-concept study, a privacy-preserving registry for heart failure patients has been implemented as a real-world showcase for Health Data Space nodes at the highest trust level, linking multiple data sources including (a) electronical medical records from hospitals, (b) patient data from a telemonitoring system, and (c) data from Austria’s national register of deaths. The registry is deployed at the tirol kliniken, a hospital carrier in the Austrian state of Tyrol, and currently includes 5,004 patients, with over 2.9 million measurements, over 574,000 observations, more than 63,000 clinical free text notes, and in total over 5.2 million data points. Data curation and harmonization processes are executed semi-automatically at each individual node according to data sharing policies to ensure data sovereignty, scalability, and privacy. As a feasibility test, a natural language processing model for classification of clinical notes was deployed and tested.DiscussionThe presented Health Data Space node infrastructure has proven to be practicable in a real-world implementation in a live and productive registry for heart failure. The present work was inspired by the European Health Data Space initiative and its spirit to interconnect health data silos for versatile secondary use of health data.

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“…This estimation is only going to get higher as AI/ML techniques and our expertise of extracting insights evolves at a phenomenal pace ( Dash et al, 2019 ; Hirschler, n.d. ). There may be several barriers associated with accessing and extracting meaningful insights from healthcare data, including patient privacy and data integrity, but these roadblocks are actively being addressed by fostering collaborations with the ML community while embracing Open Science approaches to tackle healthcare challenges ( Dash et al, 2019 ; Seh et al, 2020 ; Batko and Ślęzak, 2022 ; Miguel Cruz et al, 2022 ; Singhal and Carlton, n.d. ). At its core, Open Science encourages transparency and collaboration with all stakeholders throughout the scientific research cycle, from conception and design to data production, analysis, and dissemination ( OECD, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS

Geraci,

Bhargava,

Qorri

et al. 2024

Front. Comput. Neurosci.

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IntroductionAdvances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are necessary to tackle the plethora of known disease states.MotivationIn addition to providing much needed insights into potential therapeutic protein targets, we also aim to demonstrate that small patient datasets have the potential to provide insights that usually require many samples (>5,000). There are many such datasets available and novel advancements in ML can provide valuable insights from these patient datasets.Problem statementUsing a public dataset made available by patient advocacy group AnswerALS and a multidisciplinary Open Science approach with a systems biology augmented ML technology, we aim to validate previously reported drug targets in ALS and provide novel insights about ALS subpopulations and potential drug targets using a unique combination of ML methods and graph theory.MethodologyWe use NetraAI to generate hypotheses about specific patient subpopulations, which were then refined and validated through a combination of ML techniques, systems biology methods, and expert input.ResultsWe extracted 8 target classes, each comprising of several genes that shed light into ALS pathophysiology and represent new avenues for treatment. These target classes are broadly categorized as inflammation, epigenetic, heat shock, neuromuscular junction, autophagy, apoptosis, axonal transport, and excitotoxicity. These findings are not mutually exclusive, and instead represent a systematic view of ALS pathophysiology. Based on these findings, we suggest that simultaneous targeting of ALS has the potential to mitigate ALS progression, with the plausibility of maintaining and sustaining an improved quality of life (QoL) for ALS patients. Even further, we identified subpopulations based on disease onset.ConclusionIn the spirit of Open Science, this work aims to bridge the knowledge gap in ALS pathophysiology to aid in diagnostic, prognostic, and therapeutic strategies and pave the way for the development of personalized treatments tailored to the individual’s needs.

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Data silos undermine efforts to characterize, predict, and mitigate dementia-related missing person incidents

Cited by 5 publications

References 33 publications

Risk Factors Associated with Missing Incidents among Persons Living with Dementia: A Scoping Review

Risk Factors Associated with Missing Incidents among Persons Living with Dementia: A Scoping Review

Health data space nodes for privacy-preserving linkage of medical data to support collaborative secondary analyses

Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS

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