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1.
J Med Internet Res ; 23(8): e26843, 2021 Aug 27.
Artigo em Inglês | MEDLINE | ID: mdl-34448704

RESUMO

BACKGROUND: Kidney transplantation is the optimal treatment for patients with end-stage renal disease. Short- and long-term kidney graft survival is influenced by a number of donor and recipient factors. Predicting the success of kidney transplantation is important for optimizing kidney allocation. OBJECTIVE: The aim of this study was to predict the risk of kidney graft failure across three temporal cohorts (within 1 year, within 5 years, and after 5 years following a transplant) based on donor and recipient characteristics. We analyzed a large data set comprising over 50,000 kidney transplants covering an approximate 20-year period. METHODS: We applied machine learning-based classification algorithms to develop prediction models for the risk of graft failure for three different temporal cohorts. Deep learning-based autoencoders were applied for data dimensionality reduction, which improved the prediction performance. The influence of features on graft survival for each cohort was studied by investigating a new nonoverlapping patient stratification approach. RESULTS: Our models predicted graft survival with area under the curve scores of 82% within 1 year, 69% within 5 years, and 81% within 17 years. The feature importance analysis elucidated the varying influence of clinical features on graft survival across the three different temporal cohorts. CONCLUSIONS: In this study, we applied machine learning to develop risk prediction models for graft failure that demonstrated a high level of prediction performance. Acknowledging that these models performed better than those reported in the literature for existing risk prediction tools, future studies will focus on how best to incorporate these prediction models into clinical care algorithms to optimize the long-term health of kidney recipients.

2.
Artif Intell Med ; 118: 102127, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34412844

RESUMO

In case of comorbidity, i.e., multiple medical conditions, Clinical Decision Support Systems (CDSS) should issue recommendations based on all relevant disease-related Clinical Practice Guidelines (CPG). However, treatments from multiple comorbid CPG often interact adversely (e.g., drug-drug interactions) or introduce operational inefficiencies (e.g., redundant scans). A common solution is the a-priori integration of computerized CPG, which involves integration decisions such as discarding, replacing or delaying clinical tasks (e.g., treatments) to avoid adverse interactions or inefficiencies. We argue this insufficiently deals with execution-time events: as the patient's health profile evolves, acute conditions occur, and real-time delays take place, new CPG integration decisions will often be needed, and prior ones may need to be reverted or undone. Any realistic CPG integration effort needs to further consider temporal aspects of clinical tasks-these are not only restricted by temporal constraints from CPGs (e.g., sequential relations, task durations) but also by CPG integration efforts (e.g., avoid treatment overlap). This poses a complex execution-time challenge and makes it difficult to determine an up-to-date, optimal comorbid care plan. We present a solution for dynamic integration of CPG in response to evolving health profiles and execution-time events. CPG integration policies are formulated by clinical experts for coping with comorbidity at execution-time, with clearly defined integration semantics that build on Description and Transaction Logics. A dynamic planning approach reconciles temporal constraints of CPG tasks at execution-time based on their importance, and continuously updates an optimal task schedule.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Semântica , Comorbidade , Humanos , Tempo
3.
Clin Biochem ; 2021 Aug 23.
Artigo em Inglês | MEDLINE | ID: mdl-34437886

RESUMO

BACKGROUND: Sellar masses (SM) frequently present with insidious hormonal dysfunction. We previously showed that, by utilizing a combined reflex/reflecting approach involving a laboratory clinician (LC) on common endocrine test results requested by non-specialists, and subsequently adding further warranted tests, previously undiagnosed pituitary disorders can be identified. However, manually employing these strategies by an LC is not feasible for wider screening of pituitary disorders. OBJECTIVE: The aim of this study was to compare the accuracy and financial impact of an Artificial Intelligence (AI) based, fully computerized reflex protocol with manual reflex/reflective intervention protocol led by an LC. METHODS: We developed a proof-of-concept AI-based framework to fully computerize multi-stage reflex testing protocols for pituitary dysfunction using automated reasoning methods. We compared the efficacy of this AI-based protocol with a reflex/reflective protocol based on manually curated retrospective data in identifying pituitary dysfunction based on 12 months of laboratory testing. RESULTS: The AI-based reflex protocol, as compared with the manual protocol, would have identified laboratory tests for add-on that either directly matched or included all manual add-on tests in 92% of cases, and recommended a similar specialist referral in 90% of the cases. The AI-based protocol would have issued 2.8 times the total number of manual add-on laboratory tests at an 85% lower operation cost than the manual protocol when considering marginal test costs, technical staff and specialist salary. CONCLUSION/DISCUSSION: Our AI-based reflex protocol can successfully identify patients with pituitary dysfunction, with lower estimated laboratory cost. Future research will focus on enhancing the protocol's accuracy and incorporating the AI-based reflex protocol into institutional laboratory and hospital information systems for the detection of undiagnosed pituitary disorders.

4.
Stud Health Technol Inform ; 281: 724-728, 2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34042671

RESUMO

This paper explores the use of semantic- and evidence-based biomedical knowledge to build the RiskExplorer knowledge graph that outlines causal associations between risk factors and chronic disease or cancers. The intent of this work is to offer an interactive knowledge synthesis platform to empower health-information-seeking individuals to learn about and mitigate modifiable risk factors. Our approach analyzes biomedical text (from PubMed abstracts), Semantic Medline database, evidence-based semantic associations, literature-based discovery, and graph database to discover associations between risk factors and breast cancer. Our methodological framework involves (a) identifying relevant literature on specified chronic diseases or cancers, (b) extracting semantic associations via knowledge mining tool, (c) building rich semantic graph by transforming semantic associations to nodes and edges, (d) applying frequency-based methods and using semantic edge properties to traverse the graph and identify meaningful multi-node NCD risk paths. Generated multi-node risk paths consist of a source node (representing the source risk factor), one or more intermediate nodes (representing biomedical phenotypes), a target node (representing a chronic disease or cancer), and edges between nodes representing meaningful semantic associations. The results demonstrate that our methodology is capable of generating biomedically valid knowledge related to causal risk and protective factors related to breast cancer.


Assuntos
Neoplasias da Mama , Reconhecimento Automatizado de Padrão , Neoplasias da Mama/epidemiologia , Humanos , Incidência , Descoberta do Conhecimento , Fatores de Risco , Semântica
5.
Stud Health Technol Inform ; 281: 729-733, 2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34042672

RESUMO

Cognitive Behavioural Therapy (CBT) is an action-oriented psychotherapy that combines cognitive and behavioural techniques for psychosocial treatment for depression, and is considered by many to be the golden standard in psychotherapy. More recently, computerized CBT (CCBT) has been deployed to help increase availability and access to this evidence-based therapy. In this vein, a CBT ontology, as a shared common understanding of the domain, can facilitate the aggregation, verification, and operationalization of computerized CBT knowledge. Moreover, as opposed to black-box applications, ontology-enabled systems allow recommended, evidence-based treatment interventions to be traced back to the corresponding psychological concepts. We used a Knowledge Management approach to synthesize and computerize CBT knowledge from multiple sources into a CBT ontology, which allows generating personalized action plans for treating mild depression, using the Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL). We performed a formative evaluation of the CBT ontology in terms of its completeness, consistency, and conciseness.


Assuntos
Terapia Cognitivo-Comportamental , Transtorno Depressivo , Cognição , Depressão/terapia , Humanos
6.
Stud Health Technol Inform ; 281: 188-192, 2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34042731

RESUMO

This paper investigates the clinical attributes that contribute to kidney graft failure following live and deceased donor transplantation using an association rule mining approach. The generated rules are used to analyze the distinctive co-occurrence of attributes for those with or without all-cause graft failure. Analysis of a kidney transplantation dataset acquired from the Scientific Registry of Transplant Recipients that included over 95000 deceased and live donor recipients over 5-years was performed. Using an association rule mining approach, we were able to confirm established risk factors for graft loss after live and deceased donor transplantation and identify novel combinations of factors that may have implications for clinical care and risk prediction post kidney transplantation. Using lift as the metric to evaluate association rules, our results indicate that advanced recipient age (i.e. over 60 years), end stage kidney disease due to diabetes, the presence of recipient peripheral vascular disease and recipient coronary artery disease have a high likelihood of graft failure within 5 years after transplantation.


Assuntos
Transplante de Rim , Rejeição de Enxerto , Sobrevivência de Enxerto , Humanos , Rim , Doadores Vivos , Fatores de Risco , Doadores de Tecidos , Transplantados , Resultado do Tratamento
7.
Stud Health Technol Inform ; 281: 223-227, 2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34042738

RESUMO

Blood products and their derivatives are perishable commodities that require an efficient inventory management to ensure both a low wastage rate and a high product availability rate. To optimize blood product inventory, blood transfusion services need to reduce wastage by avoiding outdates and improve availability of different blood products. We used advance visualization techniques to design and develop a highly interactive real-time web-based dashboard to monitor the blood product inventory and the on-going blood unit transactions in near-real-time based on analysis of transactional data. Blood transfusion staff use the dashboard to locate units with specific characteristics, investigate the lifecycle of the units, and efficiently transfer units between facilities to minimize outdates.


Assuntos
Bancos de Sangue , Transfusão de Sangue , Humanos
8.
Stud Health Technol Inform ; 281: 392-396, 2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34042772

RESUMO

This paper proposes an automated knowledge synthesis and discovery framework to analyze published literature to identify and represent underlying mechanistic associations that aggravate chronic conditions due to COVID-19. We present a literature-based discovery approach that integrates text mining, knowledge graphs and ontologies to discover semantic associations between COVID-19 and chronic disease concepts that were represented as a complex disease knowledge network that can be queried to extract plausible mechanisms by which COVID-19 may be exacerbated by underlying chronic conditions.


Assuntos
COVID-19 , Diabetes Mellitus , Nefropatias , Mineração de Dados , Humanos , Reconhecimento Automatizado de Padrão , SARS-CoV-2
9.
Stud Health Technol Inform ; 281: 417-421, 2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34042777

RESUMO

Electronic Medical Records (EMRs) are increasingly being deployed at primary points of care and clinics for digital record keeping, increasing productivity and improving communication. In practice, however, there still exists an often incomplete picture of patient profiles, not only because of disconnected EMR systems but also due to incomplete EMR data entry - often caused by clinician time constraints and lack of data entry restrictions. To complete a patient's partial EMR data, we plausibly infer missing causal associations between medical EMR concepts, such as diagnoses and treatments, for situations that lack sufficient raw data to enable machine learning methods. We follow a knowledge-based approach, where we leverage open medical knowledge sources such as SNOMED-CT and ICD, combined with knowledge-based reasoning with explainable inferences, to infer clinical encounter information from incomplete medical records. To bootstrap this process, we apply a semantic Extract-Transform-Load process to convert an EMR database into an enriched domain-specific Knowledge Graph.


Assuntos
Registros Eletrônicos de Saúde , Reconhecimento Automatizado de Padrão , Humanos , Bases de Conhecimento , Semântica , Systematized Nomenclature of Medicine
10.
Artif Intell Med ; 108: 101931, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32972660

RESUMO

In a digitally enabled healthcare setting, we posit that an individual's current location is pivotal for supporting many virtual care services-such as tailoring educational content towards an individual's current location, and, hence, current stage in an acute care process; improving activity recognition for supporting self-management in a home-based setting; and guiding individuals with cognitive decline through daily activities in their home. However, unobtrusively estimating an individual's indoor location in real-world care settings is still a challenging problem. Moreover, the needs of location-specific care interventions go beyond absolute coordinates and require the individual's discrete semantic location; i.e., it is the concrete type of an individual's location (e.g., exam vs. waiting room; bathroom vs. kitchen) that will drive the tailoring of educational content or recognition of activities. We utilized Machine Learning methods to accurately identify an individual's discrete location, together with knowledge-based models and tools to supply the associated semantics of identified locations. We considered clustering solutions to improve localization accuracy at the expense of granularity; and investigate sensor fusion-based heuristics to rule out false location estimates. We present an AI-driven indoor localization approach that integrates both data-driven and knowledge-based processes and artifacts. We illustrate the application of our approach in two compelling healthcare use cases, and empirically validated our localization approach at the emergency unit of a large Canadian pediatric hospital.

11.
JMIR Med Inform ; 7(4): e14993, 2019 Oct 23.
Artigo em Inglês | MEDLINE | ID: mdl-31558433

RESUMO

BACKGROUND: Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited. OBJECTIVE: This study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance. METHODS: We investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), naïve Bayesian, random forest, and decision trees. RESULTS: Only 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm's performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a P=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; P=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; P=.03). CONCLUSIONS: Although delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model's performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients' outcomes.

12.
Stud Health Technol Inform ; 264: 571-575, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31437988

RESUMO

Developmental delay is a deviation from the regular development of normative milestones during childhood. Early stimulation is a standardized and straightforward technique to support children with developmental delays (aged 0-3 years) in reaching basic motor skills, which are essential for the execution of everyday activities, such as playing, feeding and locomotion. In doing so, early stimulation reduces the chances of permanent motor impairment, thus allowing the child to live a more functional life. However, outcomes of this treatment depend heavily on the involvement of the family, who are required to continue the early stimulation activities at home on a daily basis. To empower and educate families to administer standardized early stimulation programs at home, we developed an electronic early stimulation program, which provides personalized guidance to parents to administer early stimulation; together with evidence-based clinical decision support to therapists in tailoring ESP to observed needs.


Assuntos
Deficiências do Desenvolvimento , Pais , Pessoal Técnico de Saúde , Pré-Escolar , Humanos , Lactente , Recém-Nascido
13.
Stud Health Technol Inform ; 264: 858-862, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438046

RESUMO

Clinical Decision Support Systems (CDSS) utilize computerized Clinical Practice Guidelines (CPG) to deliver evidence-based care recommendations. However, when dealing with comorbidity (i.e., patients with multiple conditions), disease-specific CPG often interact in adverse ways (e.g., drug-drug, drug-disease interactions), and may involve redundant elements as well (e.g., repeated care tasks). To avoid adverse interactions and optimize care, current options involve the static, a priori integration of comorbid CPG by replacing or removing therapeutic tasks. Nevertheless, many aspects are relevant to a clinically safe and efficient integration, and these may change over time-task delays, test outcomes, and health profiles-which are not taken into account by static integrations. Moreover, in case of comorbidity, clinical practice often demands nuanced solutions, based on current health profiles. We propose an execution-time approach to safely and efficiently cope with comorbid conditions, leveraging knowledge from medical Linked Open Datasets to aid during CIG integration.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Comorbidade , Humanos
14.
Stud Health Technol Inform ; 264: 863-867, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438047

RESUMO

Assisted Ambient Living (AAL) focuses on self-sufficiency, assisting disabled people to perform activities of daily living (ADL) by automating assistive actions in smart environments. Importantly, AAL provides opportunities for dynamically guiding patients with a cognitive decline through an ADL. Activity recognition is a pivotal task since it allows detecting when an ADL is started by recognizing its constituent activities. When dealing cognitive decline, activity recognition should also be able to detect when activities are performed incorrectly-e.g., performed out-of-order, at the wrong location or time, or with the wrong objects (e.g., utensils) - which is nevertheless not a common goal in activity recognition. Moreover, it should be able to cope with non-uniform ways of performing the ADL that are nevertheless correct. We present a novel knowledge-driven activity recognition approach, which employs semantic reasoning to recognize both correct and incorrect actions, based on the ADL workflow as well as associated environment context.


Assuntos
Atividades Cotidianas , Pessoas com Deficiência , Humanos
15.
Stud Health Technol Inform ; 264: 935-939, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438061

RESUMO

Chronic diseases are the leading cause of morbidity, disability and mortality worldwide. It is well established that the majority of chronic diseases can be prevented by targeting modifiable lifestyle-related risk factors. Thus, early risk assessment and mitigation at the individual level can significantly reduce the health and economic burden of chronic diseases. Lifetime health has emerged as a potential paradigm to assist individuals to avoid harmful lifestyle-related habits to reduce the risk of chronic morbidity. In this paper, we leverage eHealth and Quantified Self technologies, novel health data visualizations, and artificial intelligence methods to develop a digital-based lifetime health platform (PRISM) to empower individuals to self-assess, self-monitor, and self-manage their risks for multiple chronic diseases and associated morbidities.


Assuntos
Multimorbidade , Medição de Risco , Telemedicina , Doença Crônica , Humanos , Fatores de Risco
16.
Stud Health Technol Inform ; 264: 1337-1341, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438143

RESUMO

Juvenile Idiopathic Arthritis (JIA) is the most common chronic rheumatic disease of childhood, with outcomes including pain, prolonged dependence on medications, and disability. Parents of children with JIA report being overwhelmed by the volume of information in the patient education materials that are available to them. This paper addresses this educational gap by applying an artificial intelligence method, based on an extended model of argument, to design and implement a dialogue system that allows users get the educational material they need, when they need it. In the developed system, the studied model of argument was leveraged as part of the system's dialogue manager. A qualitative evaluation of the system, using cognitive walkthroughs and semi-structured interviews with JIA domain experts, suggests that these methods show great promise for providing quality information to families of children with JIA when they need it.


Assuntos
Artrite Juvenil , Doenças Reumáticas , Inteligência Artificial , Criança , Humanos , Pais , Qualidade de Vida
17.
Healthc Manage Forum ; 32(4): 178-182, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31117831

RESUMO

Healthcare is a living system that generates a significant volume of heterogeneous data. As healthcare systems are pivoting to value-based systems, intelligent and interactive analysis of health data is gaining significance for health system management, especially for resource optimization whilst improving care quality and health outcomes. Health data analytics is being influenced by new concepts and intelligent methods emanating from artificial intelligence and big data. In this article, we contextualize health data and health data analytics in terms of the emerging trends of artificial intelligence and big data. We examine the nature of health data using the big data criterion to understand "how big" is health data. Next, we explain the working of artificial intelligence-based data analytics methods and discuss "what insights" can be derived from a broad spectrum of health data analytics methods to improve health system management, health outcomes, knowledge discovery, and healthcare innovation.


Assuntos
Inteligência Artificial , Big Data , Interpretação Estatística de Dados , Ciência de Dados , Tomada de Decisões Assistida por Computador
18.
JMIR Med Inform ; 6(2): e25, 2018 Apr 18.
Artigo em Inglês | MEDLINE | ID: mdl-29669705

RESUMO

BACKGROUND: Behavioral science is now being integrated into diabetes self-management interventions. However, the challenge that presents itself is how to translate these knowledge resources during care so that primary care practitioners can use them to offer evidence-informed behavior change support and diabetes management recommendations to patients with diabetes. OBJECTIVE: The aim of this study was to develop and evaluate a computerized decision support platform called "Diabetes Web-Centric Information and Support Environment" (DWISE) that assists primary care practitioners in applying standardized behavior change strategies and clinical practice guidelines-based recommendations to an individual patient and empower the patient with the skills and knowledge required to self-manage their diabetes through planned, personalized, and pervasive behavior change strategies. METHODS: A health care knowledge management approach is used to implement DWISE so that it features the following functionalities: (1) assessment of primary care practitioners' readiness to administer validated behavior change interventions to patients with diabetes; (2) educational support for primary care practitioners to help them offer behavior change interventions to patients; (3) access to evidence-based material, such as the Canadian Diabetes Association's (CDA) clinical practice guidelines, to primary care practitioners; (4) development of personalized patient self-management programs to help patients with diabetes achieve healthy behaviors to meet CDA targets for managing type 2 diabetes; (5) educational support for patients to help them achieve behavior change; and (6) monitoring of the patients' progress to assess their adherence to the behavior change program and motivating them to ensure compliance with their program. DWISE offers these functionalities through an interactive Web-based interface to primary care practitioners, whereas the patient's self-management program and associated behavior interventions are delivered through a mobile patient diary via mobile phones and tablets. DWISE has been tested for its usability, functionality, usefulness, and acceptance through a series of qualitative studies. RESULTS: For the primary care practitioner tool, most usability problems were associated with the navigation of the tool and the presentation, formatting, understandability, and suitability of the content. For the patient tool, most issues were related to the tool's screen layout, design features, understandability of the content, clarity of the labels used, and navigation across the tool. Facilitators and barriers to DWISE use in a shared decision-making environment have also been identified. CONCLUSIONS: This work has provided a unique electronic health solution to translate complex health care knowledge in terms of easy-to-use, evidence-informed, point-of-care decision aids for primary care practitioners. Patients' feedback is now being used to make necessary modification to DWISE.

19.
Stud Health Technol Inform ; 247: 546-550, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29678020

RESUMO

Families of children with Juvenile Idiopathic Arthritis need a way to interact with Patient Education Materials (PEM) so that learning occurs at their own pace, on topics that are relevant to them. This paper proposes a novel, dialogue-based approach to address these needs. Using an extended version of Toulmin's model of argument as a theory-based classification method, we digitized paper-based PEM to render an interactive dialogue. The dialogue allows the user to explore a topic with respect to their interests and apprehensions as opposed to providing a static, generic document.


Assuntos
Artrite Juvenil , Educação de Pacientes como Assunto , Treinamento por Simulação , Ansiedade , Humanos
20.
Stud Health Technol Inform ; 247: 785-789, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29678068

RESUMO

Developmental delay is a deviation development from the normative milestones during the childhood and it may be caused by neurological disorders. Early stimulation is a standardized and simple technique to treat developmental delays in children (aged 0-3 years), allowing them to reach the best development possible and to mitigate neuropsychomotor sequelae. However, the outcomes of the treatment depending on the involvement of the family, to continue the activities at home on a daily basis. To empower and educate parents of children with neurodevelopmental delays to administer standardized early stimulation programs at home, we developed a mobile early stimulation program that provides timely and evidence-based clinical decision support to health professionals and a personalized guidance to parents about how to administer early stimulation to their child at home.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Deficiências do Desenvolvimento , Brasil , Criança , Pessoal de Saúde , Humanos , Pais
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