Academic literature on the topic 'Patient's health trajectory modeling'
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Journal articles on the topic "Patient's health trajectory modeling"
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Nagin, Daniel S., Bobby L. Jones, Valéria Lima Passos, and Richard E. Tremblay. "Group-based multi-trajectory modeling." Statistical Methods in Medical Research 27, no. 7 (October 17, 2016): 2015–23. http://dx.doi.org/10.1177/0962280216673085.
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Identifying and monitoring multiple disease biomarkers and other clinically important factors affecting the course of a disease, behavior or health status is of great clinical relevance. Yet conventional statistical practice generally falls far short of taking full advantage of the information available in multivariate longitudinal data for tracking the course of the outcome of interest. We demonstrate a method called multi-trajectory modeling that is designed to overcome this limitation. The method is a generalization of group-based trajectory modeling. Group-based trajectory modeling is designed to identify clusters of individuals who are following similar trajectories of a single indicator of interest such as post-operative fever or body mass index. Multi-trajectory modeling identifies latent clusters of individuals following similar trajectories across multiple indicators of an outcome of interest (e.g., the health status of chronic kidney disease patients as measured by their eGFR, hemoglobin, blood CO2 levels). Multi-trajectory modeling is an application of finite mixture modeling. We lay out the underlying likelihood function of the multi-trajectory model and demonstrate its use with two examples.
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Brice, Sandrine, Aude Jabouley, Sonia Reyes, Carla Machado, Christina Rogan, Nathalie Dias-Gastellier, Hugues Chabriat, and Sophie Tezenas du Montcel. "Modeling the Cognitive Trajectory in CADASIL." Journal of Alzheimer's Disease 77, no. 1 (September 1, 2020): 291–300. http://dx.doi.org/10.3233/jad-200310.
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Background: For developing future clinical trials in Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), it seems crucial to study the long-term changes of cognition. Objective: We aimed to study the global trajectory of cognition, measured by the Mini-Mental State Examination (MMSE) and the Mattis Dementia Rating Scale (MDRS), along the course of CADASIL. Methods: Follow-up data of 185 CADASIL patients, investigated at the French National Referral center CERVCO from 2003, were considered for analysis based on strict inclusion criteria. Assuming that the MMSE and the MDRS provide imprecise measures of cognition, the trajectory of a common cognitive latent process during follow-up was delineated using a multivariate latent process mixed model. After adjustment of this model for sex and education, the sensitivities of the two scales to cognitive change were compared. Results: Analysis of the cognitive trajectory over a time frame of 60 years of age showed a decrease of performances with aging, especially after age of 50 years. This decline was not altered by sex or education but patients who graduated from high school had a higher mean cognitive level at baseline. The sensitivities of MMSE and MDRS scales were similar and the two scales suffered from a ceiling effect and curvilinearity. Conclusion: These data support that cognitive decline is not linear and mainly occurs after the age of 50 years during the course of CADASIL. They also showed that MMSE and MDRS scales are hampered by major limitations for longitudinal studies.
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Budhwani, Suman, Rahim Moineddin, Walter P. Wodchis, Camilla Zimmermann, and Doris Howell. "Longitudinal Symptom Burden Trajectories in a Population-Based Cohort of Women with Metastatic Breast Cancer: A Group-Based Trajectory Modeling Analysis." Current Oncology 28, no. 1 (February 14, 2021): 879–97. http://dx.doi.org/10.3390/curroncol28010087.
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Understanding the symptom burden trajectory for metastatic breast cancer patients can enable the provision of appropriate supportive care for symptom management. The aim of this study was to describe the longitudinal trajectories of symptom burden for metastatic breast cancer patients at the population-level. A cohort of 995 metastatic breast cancer patients with 16,146 Edmonton Symptom Assessment System (ESAS) assessments was constructed using linked population-level health administrative databases. The patient-reported ESAS total symptom distress score (TSDS) was studied over time using group-based trajectory modeling, and covariate influences on trajectory patterns were examined. Cohort patients experienced symptom burden that could be divided into six distinct trajectories. Patients experiencing a higher baseline TSDS were likely to be classified into trajectory groups with high, uncontrolled TSDS within the study follow-up period (χ2 (1, N = 995) = 136.25, p < 0.001). Compared to patients classified in the group trajectory with the highest relative TSDS (Group 6), patients classified in the lowest relative TSDS trajectory group (Group 1) were more likely to not have comorbidities (97.34% (for Groups 1–3) vs. 91.82% (for Group 6); p < 0.05), more likely to receive chemotherapy (86.52% vs. 80.50%; p < 0.05), and less likely to receive palliative care (52.81% vs. 79.25%; p < 0.0001). Receiving radiotherapy was a significant predictor of how symptom burden was experienced in all identified groups. Overall, metastatic breast cancer patients follow heterogeneous symptom burden trajectories over time, with some experiencing a higher, uncontrolled symptom burden. Understanding trajectories can assist in establishing risk-stratified care pathways for patients.
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Balasubramanian, Ishwarya, Eric Finkelstein, Rahul Malhotra, Semra Ozdemir, Chetna Malhotra, and _. _. "Healthcare Cost Trajectories in the Last 2 Years of Life Among Patients With a Solid Metastatic Cancer: A Prospective Cohort Study." Journal of the National Comprehensive Cancer Network 20, no. 9 (September 2022): 997–1004. http://dx.doi.org/10.6004/jnccn.2022.7038.
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Background: Most studies describe the “average healthcare cost trend” among patients with cancer. We aimed to delineate heterogeneous trajectories of healthcare cost during the last 2 years of life of patients with a metastatic cancer and to assess the associated sociodemographic and clinical characteristics and healthcare use. Patients and Methods: We analyzed a sample of 353 deceased patients from a cohort of 600 with a solid metastatic cancer in Singapore, and we used group-based trajectory modeling to identify trajectories of total healthcare cost during the last 2 years of life. Results: The average cost trend showed that mean monthly healthcare cost increased from SGD $3,997 during the last 2 years of life to SGD $7,516 during the last month of life (USD $1 = SGD $1.35). Group-based trajectory modeling identified 4 distinct trajectories: (1) low and steadily decreasing cost (13%); (2) steeply increasing cost in the last year of life (14%); (3) high and steadily increasing cost (57%); and (4) steeply increasing cost before the last year of life (16%). Compared with the low and steadily decreasing cost trajectory, patients with private health insurance (β [SE], 0.75 [0.37]; P=.04) and a greater preference for life extension (β [SE], −0.14 [0.07]; P=.06) were more likely to follow the high and steadily increasing cost trajectory. Patients in the low and steadily decreasing cost trajectory were most likely to have used palliative care (62%) and to die in a hospice (27%), whereas those in the steeply increasing cost before the last year of life trajectory were least likely to have used palliative care (14%) and most likely to die in a hospital (75%). Conclusions: The study quantifies healthcare cost and shows the variability in healthcare cost trajectories during the last 2 years of life. Policymakers, clinicians, patients, and families can use this information to better anticipate, budget, and manage healthcare costs.
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M’bailara, K., O. Cosnefroy, A. Desage, S. Gard, L. Zanouy, I. Minois, and C. Henry. "Group-based trajectory modeling a good model to explore sub-groups of recovery during acute bipolar mood episodes." European Psychiatry 26, S2 (March 2011): 233. http://dx.doi.org/10.1016/s0924-9338(11)71943-5.
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Group-based trajectory modeling (GBTM) is a statistical method created to explore the heterogeneity of clinical groups based on their longitudinal outcomes by identifying distinct trajectories of change. This model can be applied to assess heterogeneity in responses to treatment. This pilot study explored the relevance of the GBTM associated with the dimensional evaluation of mood (MATHYS) to define trajectory of recovery in acute bipolar mood episodes on a short period of time during a naturalistic study.MethodThe sample consisted in 118 bipolar patients and all patients were recruited during an acute phase: 56% had a major depressive episode, 26% a manic or hypomanic episode, and 18% a mixed state using the DSM-IV criteria. Patients were assessed four times with MATHYS during a three weeks follow-up period. It is an observational study and treatment was prescribed as usual. We applied the GBTM method and MATHYS total score to define trajectories of recovery.ResultsThis method allows identifying 4 trajectories of recovery. At Baseline, two of them started with a score of inhibition but with quite different evolutive profiles (stable inhibition versus improvement). The two others trajectories started with a score of activation (mild versus moderate) and showed a linear improvement of symptoms but with a more rapid recovery for the patients with the higher activation at baseline.ConclusionWhen considering the diagnosis of patients belonging in each trajectory, there model seems particular relevant to explore the high heterogeneity in response to treatment in bipolar patients during an acute depressive episode.
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Senay, Andréa, Julio C. Fernandes, Josée Delisle, Suzanne N. Morin, Daniel Nagin, and Sylvie Perreault. "Trajectories of Follow-up Compliance in a Fracture Liaison Service and Their Predictors: A Longitudinal Group-Based Trajectory Analysis." Health Services Research and Managerial Epidemiology 8 (January 2021): 233339282110470. http://dx.doi.org/10.1177/23333928211047024.
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Introduction/Objectives Identification of groups of patients following similar trajectories of time-varying patient characteristics are often of considerable clinical value. This study provides an example of how the identification of trajectory groups of patients can be useful. Methods Using clinical and administrative data of a prospective cohort study aiming to improve the secondary prevention of osteoporosis-related fractures with a Fracture Liaison Service (FLS), trajectory groups for visit compliance over time (2-year follow-up) were predicted using group-based trajectory modeling. Predictors of trajectory groups were identified using multinomial logistic regressions. Results Among 532 participants (86% women, mean age 63 years), three trajectories were identified and interpreted as high followers, intermediate followers, and low followers. The predicted probability for group-membership was: 48.4% high followers, 28.1% intermediate followers, 23.5% low followers. A lower femoral bone mineral density and polypharmacy were predictors of being in the high followers compared to the low followers group; predictors for being in the intermediate followers group were polypharmacy and referral to a bone specialist at baseline. Conclusions Results provided information on visit compliance patterns and predictors for the patients undergoing the intervention. This information has important implications when implementing such health services and determining their effectiveness.
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Chen, Kezhou, Xu Lu, Rongjun Chen, and Jun Liu. "Wireless wearable biosensor smart physiological monitoring system for risk avoidance and rescue." Mathematical Biosciences and Engineering 19, no. 2 (2021): 1496–514. http://dx.doi.org/10.3934/mbe.2022069.
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<abstract> <p>Most existing physiological testing systems broadly classify monitored physiological data into three categories: normal, abnormal, and highly abnormal, but do not consider differences in the importance of data within the same category, which may result in the loss of data of higher importance. In addition, the purpose of physiological monitoring is to detect health abnormalities in patients earlier and faster, thus enabling risk avoidance and real-time rescue. Therefore, we designed a system called the adaptive physiological monitoring and rescue system (APMRS) that innovatively incorporates emergency rescue functions into traditional physiological monitoring systems using the rescue of modified-MAC (RM-MAC) protocol. The relay selection (RS) algorithm of APMRS can select the appropriate relay to forward based on the importance of the physiological data, thus ensuring priority transmission of more important monitoring data. In addition, we apply deep learning target trajectory prediction technology to the indoor rescue module (IRM) of APMRS to provide high-performance scheduling of location tracking nodes in advance by trajectory prediction. It reduces network energy consumption and ensures perceptual tracking accuracy. When APMRS monitors abnormal physiological data that may endanger a patient's life, IRM can implement effective and fast location rescue to avoid risks.</p> </abstract>
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Müller, Ebba Gløersen, Trine Holt Edwin, Bjørn Heine Strand, Caroline Stokke, Mona Elisabeth Revheim, and Anne-Brita Knapskog. "Is Amyloid Burden Measured by 18F-Flutemetamol PET Associated with Progression in Clinical Alzheimer’s Disease?" Journal of Alzheimer's Disease 85, no. 1 (January 4, 2022): 197–205. http://dx.doi.org/10.3233/jad-215046.
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Background: Patients with Alzheimer’s disease (AD) show heterogeneity in clinical progression rate, and we have limited tools to predict prognosis. Amyloid burden from 18F-Flutemetamol positron emission tomography (PET), as measured by standardized uptake value ratios (SUVR), might provide prognostic information. Objective: We investigate whether 18F-Flutemetamol PET composite or regional SUVRs are associated with trajectories of clinical progression. Methods: This observational longitudinal study included 94 patients with clinical AD. PET images were semi-quantified with normalization to pons. Group-based trajectory modeling was applied to identify trajectory groups according to change in the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) over time. Multinomial logistic regression models assessed the association of SUVRs with trajectory group membership. Results: Three trajectory groups were identified. In the regression models, neither composite nor regional SUVRs were associated with trajectory group membership. Conclusion: There were no associations between CDR progression and 18F-Flutemetamol PET-derived composite SUVRs or regional SUVRs in clinical AD.
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Mara, Constance A., and Adam C. Carle. "Understanding Variation in Longitudinal Data Using Latent Growth Mixture Modeling." Journal of Pediatric Psychology 46, no. 2 (February 18, 2021): 179–88. http://dx.doi.org/10.1093/jpepsy/jsab010.
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Abstract Objective This article guides researchers through the process of specifying, troubleshooting, evaluating, and interpreting latent growth mixture models. Methods Latent growth mixture models are conducted with small example dataset of N = 117 pediatric patients using Mplus software. Results The example and data show how to select a solution, here a 3-class solution. We also present information on two methods for incorporating covariates into these models. Conclusions Many studies in pediatric psychology seek to understand how an outcome changes over time. Mixed models or latent growth models estimate a single average trajectory estimate and an overall estimate of the individual variability, but this may mask other patterns of change shared by some participants. Unexplored variation in longitudinal data means that researchers can miss critical information about the trajectories of subgroups of individuals that could have important clinical implications about how one assess, treats, and manages subsets of individuals. Latent growth mixture modeling is a method for uncovering subgroups (or “classes”) of individuals with shared trajectories that differ from the average trajectory.
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Fan, Yen-Chun, Sheng-Feng Lin, Chia-Chi Chou, and Chyi-Huey Bai. "Developmental Trajectories and Predictors of Incident Dementia among Elderly Taiwanese People: A 14-Year Longitudinal Study." International Journal of Environmental Research and Public Health 20, no. 4 (February 9, 2023): 3065. http://dx.doi.org/10.3390/ijerph20043065.
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The aim of this study was to identify dementia trajectories and their associated predictors among elderly Taiwanese people over a 14-year period using a nationwide representative longitudinal study. This retrospective cohort study was performed using the National Health Insurance Research Database. Group-based trajectory modeling (GBTM) was used to distinguish the specific trajectory groups of incident dementia during 2000–2013. All 42,407 patients were classified by GBTM to identify the trajectory of incident dementia, which included high- (n = 11,637, 29.0%), moderate- (n = 19,036, 44.9%), and low-incidence (n = 11,734, 26.1%) groups. Those diagnosed with hypertension (adjusted odds ratio [aOR] = 1.43; 95% confidence interval [CI] = 1.35–1.52), stroke (aOR = 1.45, 95% CI = 1.31–1.60), coronary heart disease (aOR = 1.29, 95% CI = 1.19–1.39), heart failure (aOR = 1.62, 95% CI = 1.36–1.93), and chronic obstructive pulmonary disease (aOR = 1.10, 95% CI = 1.02–1.18) at baseline revealed tendencies to be classified into high-incidence groups in dementia risk. The results from a 14-year longitudinal study identified three distinct trajectories of incident dementia among elderly Taiwanese people: patients with cardiovascular disease risk factors and cardiovascular disease events tended to be classified into high-incidence dementia groups. Early detection and management of these associated risk factors in the elderly may prevent or delay the deterioration of cognitive decline.
Dissertations / Theses on the topic "Patient's health trajectory modeling"
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Cissoko, Mamadou Ben Hamidou. "Adaptive time-aware LSTM for predicting and interpreting ICU patient trajectories from irregular data." Electronic Thesis or Diss., Strasbourg, 2024. https://publication-theses.unistra.fr/restreint/theses_doctorat/2024/CISSOKO_MamadouBenHamidou_2024_ED269.pdf.
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En médecine prédictive personnalisée, modéliser avec précision la maladie et les processus de soins d'un patient est crucial en raison des dépendances temporelles à long terme inhérentes. Cependant, les dossiers de santé électroniques (DSE) se composent souvent de données épisodiques et irrégulières, issues des admissions hospitalières sporadiques, créant des schémas uniques pour chaque séjour hospitalier.Par conséquent, la construction d'un modèle prédictif personnalisé nécessite une considération attentive de ces facteurs pour capturer avec précision le parcours de santé du patient et aider à la prise de décision clinique.LSTM sont efficaces pour traiter les données séquentielles comme les DSE, mais ils présentent deux limitations majeures : l'incapacité à interpréter les résultats des prédictions et à prendre en compte des intervalles de temps irréguliers entre les événements consécutifs. Pour surmonter ces limitations, nous introduisons de nouveaux réseaux neuronaux à mémoire dynamique profonde appelés Multi-Way Adaptive et Adaptive Multi-Way Interpretable Time-Aware LSTM (MWTA-LSTM etAMITA), conçus pour les données séquentielles collectées de manière irrégulière.L'objectif principal des deux modèles est de tirer parti des dossiers médicaux pour mémoriser les trajectoires de maladie et les processus de soins, estimer les états de maladie actuels et prédire les risques futurs, offrant ainsi un haut niveau de précision et de pouvoir prédictifIn personalized predictive medicine, accurately modeling a patient's illness and care processes is crucial due to the inherent long-term temporal dependencies. However, Electronic Health Records (EHRs) often consist of episodic and irregularly timed data, stemming from sporadic hospital admissions, which create unique patterns for each hospital stay. Consequently, constructing a personalized predictive model necessitates careful consideration of these factors to accurately capture the patient's health journey and assist in clinical decision-making. LSTM networks are effective for handling sequential data like EHRs, but they face two significant limitations: the inability to interpret prediction results and to take into account irregular time intervals between consecutive events. To address limitations, we introduce novel deep dynamic memory neural networks called Multi-Way Adaptive and Adaptive Multi-Way Interpretable Time-Aware LSTM (MWTA-LSTM and AMITA) designed for irregularly collected sequential data. The primary objective of both models is to leverage medical records to memorize illness trajectories and care processes, estimate current illness states, and predict future risks, thereby providing a high level of precision and predictive power
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Lin, Jielu. "From static to signal: New frontiers in trajectory modeling of health inequalities." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1401892405.
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Klotsche, Jens, Jens Peter Reese, Yaroslav Winter, Wolfgang H. Oertel, Hyacinth Irving, Hans-Ulrich Wittchen, Jürgen Rehm, and Richard Dodel. "Trajectory Classes of Decline in Health-Related Quality of Life in Parkinson’s Disease: A Pilot Study." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-112679.
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Objective: To analyze the change in health-related quality-of-life (HRQoL) in patients with Parkinson’s disease (PD) and to identify different classes of HRQoL decline.
Methods: A longitudinal cohort study was performed to assess clinical parameters (unified PD rating scale, Beck Depression Inventory) and HRQoL data (EuroQol, Parkinson’s Disease Questionnaire [PDQ]-39) at baseline, 3, 6, 12, 24, and 36 months. A total of 145 patients with PD were consecutively recruited in the county of Northern Hessia, Germany, between January and June 2000. A latent growth mixture model was applied to analyse the heterogeneity in HRQoL trajectories.
Results: We successfully applied latent mixture growth modeling in order to identify different classes of HRQoL trajectories in PD. Three growth models were developed and each resulted in a four-class model of distinct patterns using the generic EuroQol instruments’ outcomes (EuroQol-5 Dimensions and visual analogue scale) and the disease-specific PDQ- 39. The four classes were defined by individual trajectory characteristics. Classes one and two represented trajectories with moderate declines over 36 months, but with different initial intercepts. Class three consisted mainly of patients who passed away during the observation period and therefore had a large HRQoL decline. Class four was characterized by a low level of HRQoL at baseline and a significant subsequent decline.
Conclusions: The findings provide a more elaborate understanding of the variability in HRQoL reduction in PD over time. The classification of different HRQoL subgroups may help to explain the response of PD patients to the natural history of the disease. Future research will enable the identification of HRQoL responder subgroups on different treatment regimens.
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Klotsche, Jens, Jens Peter Reese, Yaroslav Winter, Wolfgang H. Oertel, Hyacinth Irving, Hans-Ulrich Wittchen, Jürgen Rehm, and Richard Dodel. "Trajectory Classes of Decline in Health-Related Quality of Life in Parkinson’s Disease: A Pilot Study." Technische Universität Dresden, 2011. https://tud.qucosa.de/id/qucosa%3A26861.
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Objective: To analyze the change in health-related quality-of-life (HRQoL) in patients with Parkinson’s disease (PD) and to identify different classes of HRQoL decline.
Methods: A longitudinal cohort study was performed to assess clinical parameters (unified PD rating scale, Beck Depression Inventory) and HRQoL data (EuroQol, Parkinson’s Disease Questionnaire [PDQ]-39) at baseline, 3, 6, 12, 24, and 36 months. A total of 145 patients with PD were consecutively recruited in the county of Northern Hessia, Germany, between January and June 2000. A latent growth mixture model was applied to analyse the heterogeneity in HRQoL trajectories.
Results: We successfully applied latent mixture growth modeling in order to identify different classes of HRQoL trajectories in PD. Three growth models were developed and each resulted in a four-class model of distinct patterns using the generic EuroQol instruments’ outcomes (EuroQol-5 Dimensions and visual analogue scale) and the disease-specific PDQ- 39. The four classes were defined by individual trajectory characteristics. Classes one and two represented trajectories with moderate declines over 36 months, but with different initial intercepts. Class three consisted mainly of patients who passed away during the observation period and therefore had a large HRQoL decline. Class four was characterized by a low level of HRQoL at baseline and a significant subsequent decline.
Conclusions: The findings provide a more elaborate understanding of the variability in HRQoL reduction in PD over time. The classification of different HRQoL subgroups may help to explain the response of PD patients to the natural history of the disease. Future research will enable the identification of HRQoL responder subgroups on different treatment regimens.
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Che, Sok-Leng, and 謝淑玲. "The Influence of Family Hardship on Health Trajectory among Middle-aged and Elderly in Taiwan: An Application of Hierarchical Growth Modeling." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/66179386460539791956.
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碩士國立臺灣大學
健康政策與管理研究所
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Background: Studies have shown that difficult life events in family have adverse impact on family members. However, most studies focused on a particular health outcome at a certain point of time, usually the endpoint of a follow-up period. This study aims to understand 1) the trajectories of self-rated health and health problems change from 2005 to 2014 in a sample of middle-aged and elderly subjects in Taiwan, 2) the impact of family hardships on the trajectories of self-rated health and health problems change, and whether there is age and gender-specific pattern of health change trajectories. Method: The study analyzes the data of Panel Study of Family Dynamics (PSFD) from 2005 to 2014. Multilevel growth models were conducted. Subjects aged above 40, have at least one family member in 2005, and has at least three time points of data were included in the study. A total of 1955 subjects were included. Result: Results indicated that, for all subjects, self-rated health deteriorated as time went by from 2005 to 2014. However, only for subjects who were aged 65 or above in 2005, health problems became worse as time went by from 2005 to 2014. Low quality of family relationship, widowhood and parental loss are the factors that predicted the poor health trajectory. For men, widowhood and parental loss are crucial predictors, yet poor quality of family relationship can predict poor health trajectory for women. Conclusion: The results suggest that poor health trajectories is associated with family hardships, and there are age and gender differences. We should be aware of the differences when implementing interventions, in order to fulfill different needs of different individuals.
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Thakar, Dhara Aniruddha. "Trajectories of mental health and acculturation among first year international graduate students from India." 2010. https://scholarworks.umass.edu/dissertations/AAI3427573.
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From 2001–2007, students from India have consistently comprised the largest ethnic group of international students on college campuses across the United States (Open Doors: Report on International Educational Exchange, 2007). Despite a number of studies that have researched the mental health of international students in the U.S., none have done so primarily with Indian graduate students. Theoretical and empirical literature regarding the psychological changes and acculturation patterns that international students undergo after their transition do not explore the possibility of multiple pathways of change. The current study identified four separate mental health trajectories for Indian international graduate students during their first year in the U.S. It also found three distinct patterns of acculturation for the Indian culture and four acculturation trajectories for the European American culture. The size of one’s adjustment, feelings about transition, gender role attitudes, and availability of out-group support were all significant contributors to the variability among empirically derived mental health trajectories.
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Sy, Ousmane. "Identification des trajectoires développementales de fréquence de la consommation d’alcool durant l’adolescence et relation entre ces trajectoires et la consommation excessive d’alcool épisodique à l’âge jeune adulte." Thèse, 2017. http://hdl.handle.net/1866/19459.
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Ce travail est principalement axé sur la modélisation par approche de groupe de trajectoires développementales de la consommation d'alcool des adolescents au Quebec.Contexte: L’impulsion à cette étude est le fait que la consommation d'alcool chez les adolescents a augmenté au cours des dernières années et que la consommation excessive d'alcool épisodique est de plus en plus fréquente chez les jeunes adultes. La prévention des problèmes de la consommation d’alcool chez les jeunes doit commencer par l'identification appropriée des sous-groupes d'adolescents à risque élevé. Objectifs: Cette étude avait trois objectifs: (i) identifier les trajectoires de développement de la consommation d'alcool chez les adolescents; (ii) d'examiner l'influence du sexe sur les trajectoires et (iii) d'examiner la relation entre les trajectoires de consommation d'alcool à l'adolescence et la consommation d'alcool excessive épisodique à l'âge jeune adulte. Méthode: Nous avons utilisé des données de l'étude longitudinale (n = 1294, 1999 - 2012) des adolescents âgés de 12 à 13 ans au début de l'étude de NDIT (Dépendance de la nicotine chez les adolescents). Les 1245 participants qui ont complété au moins trois des 22 cycles de NDIT ont été stratifiés selon leur statut alcoolique. C’est à dire ceux qui avaient consommé de l'alcool au début de l’étude, les buveurs (n = 497) et ceux qui n'avaient jamais consommé d'alcool au début de l’étude, les abstinents (n=748). Nous avons identifié les trajectoires de développement de la fréquence de la consommation d'alcool sur la base de 19 vagues de collecte de données après le début de l’étude de la 7e année (de 12 à 13 ans) à la 11e année (17-18 ans) en utilisant la modélisation semi-paramétrique basée sur l’approche des trajectoires par groupe (GBTM). Nous avons conduit des séries de régression logistique afin d'étudier l'association entre les trajectoires de développement de la consommation d'alcool et la consommation excessive épisodique à l'âge jeune adulte chez les buveurs d’alcool au début de l’étude et les abstinents au début de l’étude. Résultats: Pour l’ensemble de l’échantillon global (abstinents et buveurs) cinq groupes de trajectoires ont été identifiés. Ensuite, quatre groupes de trajectoires ont été identifiés parmi les abstinents au début de l’étude (‘faible’ consommateurs (28.3%, n=215), consommateurs tardifs croissants (21.5%, n=161), consommateurs modérés (29.9%, n=224) et consommateurs réguliers (20.3%, n=152). Parmi les consommateurs d’alcool (buveurs) au début de l’étude, les groupes de trajectoires comprenaient des consommateurs d’alcool peu fréquents (rares) (15.4%, n=76), des consommateurs en hausse (34.1%, n=170), des consommateurs réguliers (41.7%, n=207) et des consommateurs en baisse (8.8%, n=44). Les adolescents des groupes de trajectoires de fréquence de consommation d’alcool les plus élevées étaient plus susceptibles de pratiquer la consommation d’alcool excessive épisodique à l’âge jeune adulte. Conclusion: Cette étude montre la variabilité des trajectoires de développement de la consommation d'alcool des adolescents. Les données suggèrent que des approches multiples peuvent être nécessaires pour prévenir la consommation problématique d'alcool chez les adolescents et que ces approches devraient être nécessairement adaptées au sexe.
Background: The impetus for this study is that alcohol use among adolescents has increased in recent years and that heavy episodic or binge drinking is increasingly common in young adults. Prevention of problem drinking must begin with appropriate identification of sub-groups of adolescents at higher risk. Objective: This study had three objectives: (i) to identify the developmental trajectories of alcohol consumption among teens; (ii) to examine the influence of sex on trajectories and (iii) to examine the relationship between alcohol consumption trajectories in adolescence and binge drinking in young adulthood. Method: We used data from the NDIT (Nicotine Dependence In Teens) longitudinal study (n=1294; 1999 – 2012) of adolescents ages 12-13 years at inception. The 1245 participants who completed at least three of 22 NDIT cycles were stratified into those who had consumed alcohol at baseline, baseline drinkers (n=497) and those who had never consumed alcohol, baseline nondrinkers (n=748). We identified the developmental trajectories of the frequency of alcohol consumption based on 19 data collection waves after baseline from grade 7 (age 12-13) to grade 11 (age 17-18), using semi-parametric group-based trajectory modeling. Logistic regression was conducted to investigate the association between the alcohol consumption developmental trajectories and binge drinking in young adulthood among baseline drinkers and baseline nondrinkers. Results: Five trajectory were identified for the whole sample (baseline nondrinkers and baseline drinkers, n=1245). Then, four trajectory groups were identified among baseline nondrinkers (low consumers (28.3%, n=211), increasing late consumers (21.5%, n=161), moderate drinkers (29.9%, n=224), and regular users (20.3%, n=152). Among baseline drinkers, trajectory groups included experimenters (15.4%, n=76), increasing (34.1%, n=170), regular (41.7%, n=207) and decreasing consumers (8.8%, n=44). Participants in the higher trajectory groups were more likely to binge drink in young adulthood. Conclusion: This study shows variability among adolescents in alcohol consumption developmental trajectories. The data suggest that multiple approaches may be necessary to prevent problem alcohol consumption among adolescents and that these approaches may need to be sex-sensitive.
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Bilodeau, Karine. "La description de la pratique interprofessionnelle centrée sur le patient au cours de la trajectoire de soins en oncologie." Thèse, 2014. http://hdl.handle.net/1866/11269.
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Les orientations ministérielles du Québec encouragent une pratique interprofessionnelle centrée sur le patient (ICP), au cours de la trajectoire de soins, pour soutenir les patients diagnostiqués d’un cancer. Cette pratique assure une meilleure communication entre les professionnels et la sécurité des patients, et améliore les soins et l'accès aux services (Santé Canada, 2010). Cependant, les études rapportent généralement les perceptions des professionnels en regard des soins et des services et informent sur les facteurs organisationnels, procéduraux et relationnels liés à cette pratique. Considérant l’importance de celle-ci, il semble nécessaire de la documenter selon les perceptions de patients, de proches et de professionnels dans un contexte réel de soins. L’étude avait pour but de décrire la pratique ICP au cours de la trajectoire de soins en oncologie. Pour soutenir cette description, l’adaptation du cadre de référence Person centred nursing framework (PCNF) de McCormack et McCance (2010) a été réalisée en incluant l’interprofessionnalité, telle que définie par Couturier (2009) et utilisée.
Une étude qualitative de cas multiples a été réalisée auprès de deux équipes interprofessionnelles d’un centre hospitalier universitaire de la région de Montréal. L’échantillon (N=31) était composé de 8 patients, 3 proches, 18 professionnels et 2 gestionnaires. Vingt-huit entrevues ont été réalisées ainsi que 57,6 heures d’observation d’activités cliniques auxquelles participait le patient (ex., rendez-vous, traitement). Les résultats suggèrent que la pratique ICP des équipes est empreinte d’un dualisme de cultures (culture centrée sur le traitement versus culture davantage centrée sur le patient). De plus, les équipes étudiées ont présenté une pratique ICP fluctuante en raison de l’influence de nombreux facteurs tels le « fonctionnement de l’équipe », l’« environnement physique » et le « positionnement » des patients et des professionnels. Les résultats ont aussi suggéré que le déploiement des équipes de soins se fait à intensité variable au cours de la trajectoire. Il a été soulevé que les patients ont pu faire l’expérience d’une pratique ICP changeante, de bris dans la continuité des soins et de transition difficile entre les différentes périodes de la trajectoire. De plus, la description d’une pratique ICP souhaitée par les patients, leurs proches et les professionnels propose un accompagnement respectant le rythme du patient, sans prédominance des valeurs du professionnel ainsi qu’une assiduité dans la collaboration des membres de l’équipe.
Cette étude suggère que les sciences infirmières peuvent ajouter aux connaissances interprofessionnelles actuelles en utilisant une perspective centrée sur le patient, perspective cohérente avec ses valeurs disciplinaires. De plus, de nombreuses pistes de réflexion sont proposées pour la pratique, la recherche, la gestion et la formation.The ministerial orientations drawn up by the Quebec government promote the concept of interprofessional patient-centred practice (IPPC) throughout the care trajectory to support patients who have been diagnosed with cancer. That form of practice improves communication between professionals and patients’ sense of security, as well as improving care and access to services (Health Canada, 2010). However, studies generally report professionals’ perceptions of care and services and provide information on organizational, procedural and relationship factors connected to that practice. It is necessary to document this important practice based on the perceptions of patients, family members and professionals in a real context of care. This study was designed to describe IPPC practice throughout the trajectory of oncology care. The Person Centred Nursing Framework (PCNF) of McCormack and McCance (2010) was adapted by integrating interprofessionality, as defined by Couturier (2009), and used to support that description. A qualitative study of multiple cases was conducted with two interprofessional teams at a teaching hospital in the Montreal region. The sampling (N=31) consisted of 8 patients, 3 family members, 18 professionals and 2 managers. Twenty-eight interviews were conducted, as well as 57.6 hours of observation of clinical activities in which patients were participating (e.g. appointments, treatments). The results suggest that the teams’ IPPC practice reflected a duality of cultures (treatment-centred culture versus patient-centred culture). In addition, the IPPC practice of teams in the study fluctuated due to the influence of many factors, such as “how the team works,” “the physical environment” and the “stance” of patients and professionals. The results further suggested that the deployment of healthcare teams varied in intensity over the trajectory. The point was raised that patients experienced a variable IPPC practice, breakdowns in continuity of care, and difficult transitions between different periods in the trajectory. In addition, the description of the IPPC practice that patients, their family members and professionals would like to see suggests a form of accompaniment that would follow the patient’s own pace, without imposing professionals’ values, with assiduous collaboration from members of the team. This study suggests that nursing could advance current interprofessional knowledge by taking a patient-centred perspective, a perspective consistent with values in this discipline. In addition, many avenues for further reflection are put forward in terms of practice, research, management and training.
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Riglea, Teodora. "Physical activity and screen time trajectories in adolescents." Thèse, 2019. http://hdl.handle.net/1866/23576.
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Introduction : Seulement 7% des Canadiens de 17 ans et moins pratiquent 60 minutes d’activité physique modérée à vigoureuse quotidiennement. La majorité dépasse le temps d’écran quotidien recommandé de deux heures. Plusieurs études transversales indiquent que les deux comportements évoluent indépendamment et ne seraient que faiblement corrélés.
Objectifs et hypothèses : Les objectifs de ce mémoire est d’identifier des trajectoires d’activité physique et des trajectoires de temps devant l’écran durant l’adolescence, par sexe, et de décrire la distribution des individus selon l’appartenance simultanée aux deux trajectoires. Nos hypothèses stipulent que des trajectoires différentes seront observées pour les deux sexes et que les deux comportements ne s’influenceront pas.
Méthodes : Les données proviennent d’une étude prospective de 1294 adolescents de la région de Montréal, recrutés en 1999 à l’âge de 12-13 ans. Des questionnaires ont été remplis en classe à chaque 3 mois, de la première à la cinquième année du secondaire. Des modélisations de trajectoires de groupe ont identifié des trajectoires d’activité physique et de temps devant l’écran. Une modélisation de trajectoires jointes a rapporté des probabilités d’appartenance aux trajectoires des deux variables.
Résultats : Cinq groupes ont été identifiés pour les trajectoires d’activité physique pour les deux sexes. Quatre groupes ont été identifiés pour les garçons et 5 pour les filles concernant le temps passé devant l’écran. 57% des garçons et 46% des filles ont fait des activités physiques pendant 6-7 jours par semaine, durant toute l’adolescence. Toutes les trajectoires de temps devant l’écran accumulent plus de deux heures d’écran quotidiennement. Les probabilités conditionnelles suggèrent une relation entre l’activité physique et le temps devant l’écran.
Conclusion : Le développement de l’activité physique et du temps devant l’écran est hétérogène durant l’adolescence. Leur coévolution doit être prise en compte par les professionnels en santé publique.Introduction: Only 7% of Canadians age ≤ 17 years engage in the recommended 60 minutes or more of moderate-to-vigorous physical activity (PA) daily. Further, most youth surpass the recommended screen time maximum of 2-hours daily. Many cross-sectional studies suggest that PA and screen time are only weakly correlated and that they evolve independently. Objectives and hypotheses: The first objective of this MSc thesis was to model PA and screen time trajectories during adolescence, in boys and girls. The second objective was to describe the distribution of participants according to concurrent membership in the two sets of trajectories. Our hypotheses were that trajectories differ by sex and that PA trajectories are independent of screen time trajectories. Methods: Data were drawn from an ongoing longitudinal study of 1294 adolescents age 12-13 years recruited in 1999-2000 in 10 Montreal-area high schools. Self-report questionnaires were completed during class time, every 3 months from grade 7 to 11. Group-based trajectory modeling identified PA and screen time trajectories. Joint trajectory models provided membership probabilities in both PA and screen time trajectories. Results: Five groups of PA trajectories were identified in both sexes. Four and five screen time trajectory groups were identified in boys and girls, respectively. Half (57%) of boys and 46% of girls engaged in PA 6-7 days weekly during the entire 5-year follow-up. All screen time trajectories were above the recommended 2-hours daily. Conditional probabilities suggested weak associations between PA and screen time. Conclusion: Patterns of PA and screen time are heterogeneous during adolescence. Their co- evolution may need to be considered by public health practitioners.
Book chapters on the topic "Patient's health trajectory modeling"
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Gutierrez, Andres, Vamsi Krishna Guda, Stanley Mugisha, Christine Chevallereau, and Damien Chablat. "Trajectory Planning in Dynamics Environment: Application for Haptic Perception in Safe Human-Robot Interaction." In Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Health, Operations Management, and Design, 313–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06018-2_22.
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Pinaire, Jessica, Etienne Chabert, Jérôme Azé, Sandra Bringay, Pascal Poncelet, and Paul Landais. "Prediction of In-Hospital Mortality from Administrative Data: A Sequential Pattern Mining Approach." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210167.
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Study of trajectory of care is attractive for predicting medical outcome. Models based on machine learning (ML) techniques have proven their efficiency for sequence prediction modeling compared to other models. Introducing pattern mining techniques contributed to reduce model complexity. In this respect, we explored methods for medical events’ prediction based on the extraction of sets of relevant event sequences of a national hospital discharge database. It is illustrated to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). We mined sequential patterns from the French Hospital Discharge Database. We compared several predictive models using a text string distance to measure the similarity between patients’ patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. Indeed discrimination ranged from 0.71 to 0.99, together with a good overall accuracy. Thus, sequential patterns mining appear motivating for event prediction in medical settings as described here for ACS.
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D’Alessandro, Aline Almeida Barbaresco, Walmirton Bezerra D’Alessandro, Sávia Denise Silva Carlotto Herrera, Seyna Ueno Rabelo Mendes, Maykon Jhuly Martins de Paiva, Osvaldo Gonçalves Barbosa Junior, Francisco de Sousa Holanda, Layra Eugenio Pedreira, Mariana Gomes de Lima, and Isamara Alves dos Santos. "The use of Artificial Intelligence and 3D bio-printing for organ transplants." In Eyes on Health Sciences V.02. Seven Editora, 2024. http://dx.doi.org/10.56238/sevened2024.001-009.
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Technological evolution is driving revolutionary changes in the healthcare sector, especially in the field of organ transplants. Crucial innovations in this field include Artificial Intelligence (AI) and 3D printing, which, when combined, are paving the way for remarkable advancements in the creation of organs for transplants. This evolution has the potential to radically transform regenerative medicine and the treatment of chronic diseases. AI plays a fundamental role in processing and analyzing large amounts of data to optimize the design and functionality of artificial organs. It assists in modeling complex organic structures, predicts organ reactions in various environments, and facilitates the customization of organs according to the specific needs of patients. Furthermore, AI contributes to improving manufacturing processes and reducing costs, making the technology more accessible and effective. On the other hand, 3D printing, also known as bio-printing, enables the construction of three-dimensional structures using successive layers. In regenerative medicine, it is used to manufacture artificial organs and tissues from living cells. This technique has the potential to alleviate the shortage of donated organs, reducing dependence on human donors and the risk of transplant rejection, as organs can be created from the patient's own cells. The fusion of AI with 3D printing in organ transplant production is a promising field with enormous potential to save lives and improve the quality of life for patients. This interdisciplinary approach is redefining the boundaries of tissue and organ engineering, opening new horizons in the customization of medicine, research, and medical ethics. With the continuous advancement of these technologies, more significant progress is expected in the coming years.
Conference papers on the topic "Patient's health trajectory modeling"
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Caballero Barajas, Karla L., and Ram Akella. "Dynamically Modeling Patient's Health State from Electronic Medical Records." In KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2783258.2783289.
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Guiling, Li, Zhang Chundi, Ming Yue, Yang Liqun, and Zhang Lihua. "Modeling of Hypertensive Patient's Behavior Based on the Health Information." In 2015 Seventh International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). IEEE, 2015. http://dx.doi.org/10.1109/icmtma.2015.165.
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Chen, Jiayuan, Changchang Yin, Yuanlong Wang, and Ping Zhang. "Predictive Modeling with Temporal Graphical Representation on Electronic Health Records." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/637.
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Deep learning-based predictive models, leveraging Electronic Health Records (EHR), are receiving increasing attention in healthcare. An effective representation of a patient's EHR should hierarchically encompass both the temporal relationships between historical visits and medical events, and the inherent structural information within these elements. Existing patient representation methods can be roughly categorized into sequential representation and graphical representation. The sequential representation methods focus only on the temporal relationships among longitudinal visits. On the other hand, the graphical representation approaches, while adept at extracting the graph-structured relationships between various medical events, fall short in effectively integrate temporal information. To capture both types of information, we model a patient's EHR as a novel temporal heterogeneous graph. This graph includes historical visits nodes and medical events nodes. It propagates structured information from medical event nodes to visit nodes and utilizes time-aware visit nodes to capture changes in the patient's health status. Furthermore, we introduce a novel temporal graph transformer (TRANS) that integrates temporal edge features, global positional encoding, and local structural encoding into heterogeneous graph convolution, capturing both temporal and structural information. We validate the effectiveness of TRANS through extensive experiments on three real-world datasets. The results show that our proposed approach achieves state-of-the-art performance.
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Gutierrez, Robert, Joe Hart, Laura Barnes, and Mehdi Boukhechba. "Using Inertial Measurement Units (IMU) and Comparative Trajectory Analysis for Modeling Micro-level Human Motion Dysfunction." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001471.
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Ubiquitous sensing from smartphones and wearable devices has proven to be useful for applications ranging from sports to modern medicine. The aim of this paper is to propose a visualization framework to illustrate the points in time when a query trajectory is deviating the most from a reference trajectory. Validation is performed through the use of a novel post ACL reconstruction dataset. Validation is performed through wearable sensing data collected from 11 patients recovering from ACL reconstruction and 10 healthy participants. Results provide promising insights about how this method can be used to visualize anomalies in motion trajectories and to detect abnormal motion patterns.
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Han, Y., J. Han, S. Altmayer, K. Addetia, and A. Patel. "Group-Based Trajectory Modeling of Diastolic Function in Healthy Subjects and Patients with Pulmonary Arterial Hypertension." In American Thoracic Society 2019 International Conference, May 17-22, 2019 - Dallas, TX. American Thoracic Society, 2019. http://dx.doi.org/10.1164/ajrccm-conference.2019.199.1_meetingabstracts.a2817.
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Ranieri, Adriano, Ana Paula Lyra, Gustavo Lutz, Flavio Andrade, Francisco Santos, and Julio Pellegrini. "Introducing Resources in Oil Spill Trajectory Modeling - Contingency Analysis." In SPE Latin-American and Caribbean Health, Safety, Environment and Social Responsibility Conference. Society of Petroleum Engineers, 2013. http://dx.doi.org/10.2118/165591-ms.
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Minhas, S., A. Khanum, F. Riaz, S. A. Khan, and A. Alvi. "Trajectory based predictive modeling of conversion from mild cognitive impairment to Alzheimer's disease." In 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 2017. http://dx.doi.org/10.1109/bhi.2017.7897286.
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Gomez, S., B. Ivorra, R. Glowinski, and A. M. Ramos. "Modeling the Optimal Trajectory of a Skimmer Ship to Clean Oil Spills in the Open Sea." In SPE Latin American and Caribbean Health, Safety, Environment and Sustainability Conference. Society of Petroleum Engineers, 2015. http://dx.doi.org/10.2118/174150-ms.
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Cao, Weichong, Decai Li, Fengsheng Li, Lin Cheng, Shuyan Guo, and Guofeng Wang. "Center of Gravity Trajectory Modeling and Structural Stability Health Evaluation of Reclaimer Under Variable Load Conditions." In 2023 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). IEEE, 2023. http://dx.doi.org/10.1109/icsmd60522.2023.10491040.
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Seume, Erik, Jan Göing, and Jens Friedrichs. "A Literature Review on Data Sources and Methodologies for Enriching Gas Path Analysis With Earth Observation Data." In ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/gt2024-122772.
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Abstract Aero engine performance and condition deterioration are a function of operational severity. Operational severity is determined by various aspects such as airborne contaminants, derates, ambient temperature, humidity, and precipitation. More severe operating environments induce cost, which should be managed by using earth observation (EO) data in engine health monitoring. This paper presents an analytical approach that incorporates a broad set of EO data into engine health management and subsequent maintenance planning. Current approaches to gas path analysis are extended by incorporating EO data. The analytical approach is presented in combination with a literature review. The literature review covers the following topics: flight trajectory modeling, EO/environmental condition data, as well as engine performance, condition, and deterioration modeling. Methods and data sources in these different areas are reviewed while simultaneously presenting the currently researched analytical approach. The researched analytical approach creates high-resolution contamination profiles for a flight trajectory on a global scale. Weather conditions and the ingestion of aerosols and sand are monitored and correlated with aero engine condition and performance. A wide range of data sources are presented, which however only partly cater to the needs of the Maintenance, Repair and Overhaul community.