Personalized Patient Care Research Articles

The utility of personal wearable data in long COVID and personalized patient care

The utility of personal wearable data in long COVID and personalized patient care

Artificial intelligence in assessing progression of age-related macular degeneration

AbstractThe human population is steadily growing with increased life expectancy, impacting the prevalence of age-dependent diseases, including age-related macular degeneration (AMD). Health care systems are confronted with an increasing burden with rising patient numbers accompanied by ongoing developments of therapeutic approaches. Concurrent advances in imaging modalities provide eye care professionals with a large amount of data for each patient. Furthermore, with continuous progress in therapeutics, there is an unmet need for reliable structural and functional biomarkers in clinical trials and practice to optimize personalized patient care and evaluate individual responses to treatment. A fast and objective solution is Artificial intelligence (AI), which has revolutionized assessment of AMD in all disease stages. Reliable and validated AI-algorithms can aid to overcome the growing number of patients, visits and necessary treatments as well as maximize the benefits of multimodal imaging in clinical trials. Therefore, there are ongoing efforts to develop and validate automated algorithms to unlock more information from datasets allowing automated assessment of disease activity and disease progression. This review aims to present selected AI algorithms, their development, applications and challenges regarding assessment and prediction of AMD progression.

Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features.

Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. The Karnofsky Performance Status (KPS) score is a valuable tool for assessing patient functionality and contributes to the stratification of patients with poor prognoses. This study aimed to develop a 6-month postoperative KPS prediction model by combining clinical data with deep learning-based image features from pre- and postoperative MRI scans, offering enhanced personalized care for glioblastoma patients. Using 1,476 MRI datasets from the Brain Tumor Segmentation Challenge 2020 public database, we pretrained two variational autoencoders (VAEs). Imaging features from the latent spaces of the VAEs were used for KPS prediction. Neural network-based KPS prediction models were developed to predict scores below 70 at 6 months postoperatively. In this retrospective single-center analysis, we incorporated clinical parameters and pre- and postoperative MRI images from 150 newly diagnosed IDH wild-type glioblastoma, divided into training (100 patients) and test (50 patients) sets. In training set, the performance of these models was evaluated using the area under the curve (AUC), calculated through fivefold cross-validation repeated 10 times. The final evaluation of the developed models assessed in the test set. Among the 150 patients, 61 had 6-month postoperative KPS scores below 70 and 89 scored 70 or higher. We developed three models: a clinical-based model, an MRI-based model, and a multimodal model that incorporated both clinical parameters and MRI features. In the training set, the mean AUC was 0.785±0.051 for the multimodal model, which was significantly higher than the AUCs of the clinical-based model (0.716±0.059, P = 0.038) using only clinical parameters and the MRI-based model (0.651±0.028, P<0.001) using only MRI features. In the test set, the multimodal model achieved an AUC of 0.810, outperforming the clinical-based (0.670) and MRI-based (0.650) models. The integration of MRI features extracted from VAEs with clinical parameters in the multimodal model substantially enhanced KPS prediction performance. This approach has the potential to improve prognostic prediction, paving the way for more personalized and effective treatments for patients with glioblastoma.

NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes.

Accurate recognition and linking of oncologic entities in clinical notes is essential for extracting insights across cancer research, patient care, clinical decision-making, and treatment optimization. We present the Neuro-Symbolic System for Cancer (NSSC), a hybrid AI framework that integrates neurosymbolic methods with named entity recognition (NER) and entity linking (EL) to transform unstructured clinical notes into structured terms using medical vocabularies, with the Unified Medical Language System (UMLS) as a case study. NSSC was evaluated on a dataset of clinical notes from breast cancer patients, demonstrating significant improvements in the accuracy of both entity recognition and linking compared to state-of-the-art models. Specifically, NSSC achieved a 33% improvement over BioFalcon and a 58% improvement over scispaCy. By combining large language models (LLMs) with symbolic reasoning, NSSC improves the recognition and interoperability of oncologic entities, enabling seamless integration with existing biomedical knowledge. This approach marks a significant advancement in extracting meaningful information from clinical narratives, offering promising applications in cancer research and personalized patient care.

Artificial intelligence applications in cataract and refractive surgeries.

This review highlights the recent advancements in the applications of artificial intelligence within the field of cataract and refractive surgeries. Given the rapid evolution of artificial intelligence technologies, it is essential to provide an updated overview of the significant strides and emerging trends in this field. Key themes include artificial intelligence-assisted diagnostics and intraoperative support, image analysis for anterior segment surgeries, development of artificial intelligence-based diagnostic scores and calculators for early disease detection and treatment planning, and integration of generative artificial intelligence for patient education and postoperative monitoring. The impact of artificial intelligence on cataract and refractive surgeries is becoming increasingly evident through improved diagnostic accuracy, enhanced patient education, and streamlined clinical workflows. These advancements hold significant implications for clinical practice, promising more personalized patient care and facilitating early disease detection and intervention. Equally, the review also highlights the fact that only some of this work reaches the clinical stage, successful integration of which may benefit from our focus.

The Evolving Role Of Artificial Intelligence In Neurosurgical Planning And Outcome Prediction: A MetaAnalysis

Artificial Intelligence (AI) is transforming various medical fields, including neurosurgery. This review explores the evolving role of AI in neurosurgical planning and outcome prediction, with a focus on its applications in preoperative imaging, intraoperative navigation, and postoperative outcome forecasting. A meta-analysis of recent studies shows that AI enhances precision, reduces errors, and offers personalized patient care. However, significant challenges such as interpretability and integration into clinical practice remain. This article discussescurrent developments, limitations, and future innovations in the use of AI in neurosurgery.

Six clinical phenotypes with prognostic implications were identified by unsupervised machine learning in children and adolescents with SARS-CoV-2 infection: results from a German nationwide registry

ObjectivePhenotypes are important for patient classification, disease prognostication, and treatment customization. We aimed to identify distinct clinical phenotypes of children and adolescents hospitalized with SARS-CoV-2 infection, and to evaluate their prognostic differences.MethodsThe German Society of Pediatric Infectious Diseases (DGPI) registry is a nationwide, prospective registry for children and adolescents hospitalized with a SARS-CoV-2 infection in Germany. We applied hierarchical clustering for phenotype identification with variables including sex, SARS-CoV-2-related symptoms on admission, pre-existing comorbidities, clinically relevant coinfection, and SARS-CoV-2 risk factors. Outcomes of this study were: discharge status and ICU admission. Discharge status was categorized as: full recovery, residual symptoms, and unfavorable prognosis (including consequential damage that has already been identified as potentially irreversible at the time of discharge and SARS-CoV-2-related death). After acquiring the phenotypes, we evaluated their correlation with discharge status by multinomial logistic regression model, and correlation with ICU admission by binary logistic regression model. We conducted an analogous subgroup analysis for those aged < 1 year (infants) and those aged ⩾ 1 year (non-infants).ResultsThe DGPI registry enrolled 6983 patients, through which we identified six distinct phenotypes for children and adolescents with SARS-CoV-2 which can be characterized by their symptom pattern: phenotype A had a range of symptoms, while predominant symptoms of patients with other phenotypes were gastrointestinal (95.9%, B), asymptomatic (95.9%, C), lower respiratory tract (49.8%, D), lower respiratory tract and ear, nose and throat (86.2% and 41.7%, E), and neurological (99.2%, F). Regarding discharge status, patients with D and E phenotype had the highest odds of having residual symptoms (OR: 1.33 [1.11, 1.59] and 1.91 [1.65, 2.21], respectively) and patients with phenotype D were significantly more likely (OR: 4.00 [1.95, 8.19]) to have an unfavorable prognosis. Regarding ICU, patients with phenotype D had higher possibility of ICU admission than staying in normal ward (OR: 4.26 [3.06, 5.98]), compared to patients with phenotype A. The outcomes observed in the infants and non-infants closely resembled those of the entire registered population, except infants did not exhibit typical neurological/neuromuscular phenotypes.ConclusionsPhenotypes enable pediatric patient stratification by risk and thus assist in personalized patient care. Our findings in SARS-CoV-2-infected population might also be transferable to other infectious diseases.

Neuroschistosomiasis in Young Filipino Patient Presenting with Seizure.

Diagnostic challenge and insightThis case highlights the importance of considering a broad differential diagnosis in patients with atypical neurological presentations, particularly in those from regions where parasitic infections are endemic. The internist plays a crucial role in integrating clinical, serological, and imaging data to arrive at a correct diagnosis, in this case, neuroschistosomiasis.Tailored treatment strategyThe case illustrates the value of personalized patient care. Initiating empirical treatment based on a combination of clinical suspicion, high-risk patient demographics, and imaging findings, without invasive procedures, demonstrates the internist's role in managing complex cases where patient preferences must be balanced with medical necessity.

Cardiovascular Imaging: Recent Advances in Atherosclerosis Diagnosis

Recent advancements in cardiovascular imaging have significantly enhanced the diagnosis and management of atherosclerosis, a leading cause of cardiovascular disease. Techniques such as coronary computed tomography angiography (CCTA), magnetic resonance imaging (MRI), and positron emission tomography (PET) have improved our ability to detect and quantify atherosclerotic plaques, assess their stability, and predict adverse cardiovascular events. This review explores the latest developments in imaging modalities, their clinical applications, and the implications for personalized patient care.

Uncovering Prognostic Clinical Phenotypes in Children and Adolescents with SARS-CoV-2 Infection

Abstract Background Characterizing clinical phenotypes is important for classifying patients, predicting disease outcomes, and adapting treatment. We aimed to identify distinct clinical phenotypes among hospitalized children and adolescents with SARS-CoV-2 infection and to evaluate variations in prognosis across these phenotypes. Methods The DGPI (German Society of Pediatric Infectious Diseases) registry serves as a nationwide, prospective registry for children and adolescents hospitalized with a SARS-CoV-2 infection in Germany. We applied hierarchical clustering and integrated variables such as sex, SARS-CoV-2-related symptoms, pre-existing comorbidities, clinically relevant coinfection, and SARS-CoV-2 risk factors to describe distinct clinical phenotypes. Subsequently, we applied a multinomial logistic regression model to examine the association between these phenotypes and discharge status. Results The DGPI registry encompassed 6983 patients and facilitated the identification of six distinct phenotypes for children and adolescents with SARS-CoV-2. Phenotype A had a similar symptom pattern as the total sample. Predominant symptoms of patients with other phenotypes were gastrointestinal (95.9%, B), asymptomatic (95.9%, C), lower respiratory tract (49.8%, D), lower respiratory tract and ear, nose and throat (86.2% and 41.7%, E), and neurological (99.2%, F). Patients with phenotype C had lower odds of residual symptoms (OR: 0.10 [0.06, 0.15]) than those with phenotype A, whereas patients with phenotype D and E had higher odds of residual symptoms (OR: 1.33 [1.11, 1.59] and 1.91 [1.65, 2.21], respectively) than those with phenotype A. Patients with phenotype D were significantly more likely (OR: 4.00 [1.95, 8.19]) to have an unfavorable outcome, compared to patients with phenotype A. Conclusions We could show that the identified phenotypes could facilitate early risk assessment and aid in tailoring individualized patient management strategies. Key messages • Six distinct clinical phenotypes existed in children and adolescents with SARS-CoV-2 infection. • Phenotypes could assist in risk evaluation and help personalize patient care.

Leveraging digital platforms to improve Public Health in hospital smart buildings

Abstract The integration of digital platforms into hospital infrastructure represents a significant leap forward in public health management. This session explores how smart hospital buildings, equipped with advanced digital technologies, can enhance public health outcomes through more effective disease surveillance, improved environmental controls and enhanced patient care management. Smart hospital buildings utilize a range of new technologies such as IoT sensors, AI-driven analytics and big data solutions to monitor and optimize various aspects of hospital operations and care. These technologies enable real-time data collection on environmental conditions, patient health metrics and resource utilization, creating an ecosystem that not only supports the immediate health needs of patients but also contributes to broader public health goals. The session will address four key areas where digital platforms can make an impact: -Disease Surveillance and Management: Digital tools can track health data trends and patterns within hospital settings, enabling early detection of infectious outbreaks and more precise tracking of patient outcomes. -Environmental Monitoring: IoT sensors can continuously assess conditions such as air quality, temperature and humidity, which are critical for preventing hospital-acquired infections and ensuring a safe environment for patients and staff. -Resource Optimization: AI algorithms can analyse data to optimize resource allocation, ensuring that medical supplies and human resources are used efficiently, which is crucial in health emergencies. -Patient-Centred Care: Digital platforms can facilitate personalized patient care through real-time health monitoring and data analysis, ensuring timely medical interventions and better health management. This session will highlight case studies demonstrating successful implementation of digital technologies in smart hospital buildings and discuss the challenges and lessons learned from these initiatives.

Leveraging the variational Bayes autoencoder for survival analysis

Survival analysis in medical research has witnessed a growing interest in applying deep learning techniques to model complex, high-dimensional, heterogeneous, incomplete, and censored data. Current methods make assumptions about the relations between data that may not be valid in practice. Therefore, we introduce SAVAE (Survival Analysis Variational Autoencoder). SAVAE, based on Variational Autoencoders, contributes significantly to the field by introducing a tailored Evidence Lower BOund formulation, supporting various parametric distributions for covariates and survival time (if the log-likelihood is differentiable). It offers a general method that demonstrates robustness and stability through different experiments. Our proposal effectively estimates time-to-event, accounting for censoring, covariate interactions, and time-varying risk associations. We validate our model in diverse datasets, including genomic, clinical, and demographic tabular data, with varying levels of censoring. This approach demonstrates competitive performance compared to state-of-the-art techniques, as assessed by the Concordance Index and the Integrated Brier Score. SAVAE also offers an interpretable model that parametrically models covariates and time. Moreover, its generative architecture facilitates further applications such as clustering, data imputation, and synthetic patient data generation through latent space inference from survival data. This approach fosters data sharing and collaboration, improving medical research and personalized patient care.

Individualized, cross-validated prediction of future dementia using cognitive assessments in people with mild cognitive symptoms.

We aimed to develop an algorithm to predict the individualized risk of future dementia using brief cognitive tests suitable for primary care. We included 612 participants with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, assessed for at least 4 years or until progression to dementia. A logistic regression model, using cognitive tests as predictors and dementia progression as an outcome, stratified participants into low, intermediate, or high risk. A second model, including 1-year cognitive test changes, was applied to the intermediate group. The models were replicated in 392 SCD/MCI participants from the BioFINDER-1 study. The best two-step model for predicting dementia incorporated Trail Making Test B (attention/executive function), Animal Fluency (verbal fluency), Mini-Mental State Examination (global cognition), and 10-word list recall (memory). The model's positive predictive value in ADNI was 85.8% and negative predictive value was 92.2% versus 62.5% and 95.6%, respectively, in BioFINDER-1. This two-step model accurately predicts individualized dementia risk. To our knowledge, this is the first algorithm for predicting all-cause dementia using a novel two-step model utilizing brief cognitive tests. Applying a validated model including the Trail Making Test B, Animal Fluency, MMSE, Alzheimer's Disease Assessment Scale delayed, and immediate recall can robustly and accurately categorize individuals into low, intermediate, or high risk of dementia progression and can facilitate clinical decision-making and personalized patient care. We created an app that is available for research and educational purposes at https://brainapps.shinyapps.io/PredictAllCauseDementia to provide an individualized risk score for dementia progression.

Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review

: Artificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Through machine learning (ML), deep learning, natural language processing (NLP), and robotic automation, AI has enhanced efficiency, accuracy, and innovation in the field. The purpose of this review is to shed light on the practical applications and potential of AI in various pharmaceutical fields. These fields include medicinal chemistry, pharmaceutics, pharmacology and toxicology, clinical pharmacy, pharmaceutical biotechnology, pharmaceutical nanotechnology, pharmacognosy, and pharmaceutical management and economics. By leveraging AI technologies such as ML, deep learning, NLP, and robotic automation, this review delves into the role of AI in enhancing drug discovery, development processes, and personalized patient care. It analyzes AI's impact in specific areas such as drug synthesis planning, formulation development, toxicology predictions, pharmacy automation, and market analysis. Artificial intelligence integration into pharmaceutical sciences has significantly improved medicinal chemistry, drug discovery, and synthesis planning. In pharmaceutics, AI has advanced personalized medicine and formulation development. In pharmacology and toxicology, AI offers predictive capabilities for drug mechanisms and toxic effects. In clinical pharmacy, AI has facilitated automation and enhanced patient care. Additionally, AI has contributed to protein engineering, gene therapy, nanocarrier design, discovery of natural product therapeutics, and pharmaceutical management and economics, including marketing research and clinical trials management. Artificial intelligence has transformed pharmaceuticals, improving efficiency, accuracy, and innovation. This review highlights AI's role in drug development and personalized care, serving as a reference for professionals. The future promises a revolutionized field with AI-driven methodologies.

Systematic Review of the Application of Artificial Intelligence in Healthcare and Nursing Care.

This systematic review explores the complex relationship between artificial intelligence (AI) and healthcare, with an explicit focus on nursing care. Examining a range of studies from 2020, the research investigates the impact of AI on clinical decision-making, patient care and healthcare administration. Through a comprehensive literature review, the study highlights the potential benefits of AI integration in improving the efficiency and efficacy of healthcare. AI technologies offer opportunities for personalised patient care, predictive analytics and enhanced clinical processes, with the ultimate aim of transforming the healthcare system. However, ethical considerations and regulatory frameworks are crucial, emphasising patient privacy, autonomy and data security. The findings underscore the need for transparency, accountability and fairness in the application of AI in healthcare. While AI promises to improve patient outcomes and streamline healthcare delivery, careful consideration of ethical implications and regulatory compliance are essential for responsible implementation.

The impact of diagnosing provider type on longitudinal care for patients with newly diagnosed Huntington’s Disease

Aims This study evaluated the association between provider types for patients with newly diagnosed Huntington’s disease (HD) and healthcare resource utilization (HCRU), costs, and treatment patterns. Materials and methods This retrospective analysis used MarketScan® databases (1/1/2017-12/31/21) to identify provider type who diagnosed and managed US adult patients with HD. Patients with continuous enrollment 6 months pre- and 12 months post-diagnosis were included. Outcomes evaluated over 12 months post-diagnosis included hospitalizations, outpatient visits, antipsychotic or vesicular monoamine transporter 2 (VMAT2) inhibitor use, and total healthcare costs. Results 340 eligible patients had a mean age at diagnosis of 49 years. 56.5% were female; 71.5% had a Charlson Comorbidity Index of 0. Patients were diagnosed by neurologists (48.5%), primary care providers (PCP) (35.6%), psychiatrists (3.5%), or other providers (12.4%). Patients diagnosed by PCPs or neurologists received significantly more follow-ups by the same diagnosing provider type (P < 0.05). All-cause and HD-related outpatient visits at 12-month follow-up had more patients diagnosed by PCPs (23.9, 5.1) than neurologists (18.0, 2.4), psychiatrists (16.7, 1.67), or others (15.3, 2.4). HD-related mean costs totaled $2,489 ($1,179 inpatient and $1,310 outpatient). Patients diagnosed by neurologists had significantly lower HD-related total non-medication costs vs those diagnosed by PCPs (−$2,256; P < 0.05). Among patients diagnosed by neurologists vs PCPs, similar proportions received antipsychotics within the first year (55% vs 52%, respectively); more patients managed by neurologists received VMAT2 inhibitors (12% vs 7%, respectively). Limitations Our study includes limitations inherent to retrospective claims studies. Conclusions Patients with HD are most often diagnosed by neurologists or PCPs; the same diagnosing provider type typically manages follow-up. Patients diagnosed by neurologists had significantly fewer HD-related outpatient visits, lower HD-related non-drug costs, and more frequently received VMAT2 inhibitors vs those diagnosed by PCPs. Our findings show an integrated care team may provide evidence-based, personalized care for patients with HD.

Perceptions and Use of Automated Hospital Outcome Data by EMS Providers: A Pilot Study

Background: Our primary objective evaluated the perception of emergency medical service (EMS) providers’ review of automated hospital outcome data. Secondarily, we assessed participation in outcome review as a means of microlearning to obtain continuing education (CE). Methods: From October–December 2023, three high-volume EMS systems participated in a three-part intervention with results evaluated using a mixed-methods approach. First, EMS providers (emergency medical technicians and paramedics) were invited, via their electronic health record (EHR), to complete a presurvey evaluating their perceptions of reviewing outcomes. Then, EMS providers were notified about the opportunity to earn CE via a microlearning intervention, offering Commission on Accreditation for Pre-Hospital Continuing Education (CAPCE)-approved CE hours for completion of outcome reviews and associated learning modules. Finally, EMS providers were invited to complete a post-survey mirroring the pre-survey. Qualitative analyses identified themes among open-ended responses. Quantitative analyses examined perceptions between pre- and post- surveys. Results: Of 843 providers contacted, 217 responded to the pre-survey (25.7%). The most endorsed rationale for reviewing outcomes included improving clinical knowledge (95%), improving patient care (94%), and knowing whether care made a difference (93%). Nearly all (91%) reported being more likely to review outcomes if CE were awarded. Among the 67 who completed the open-ended items, the three dominant themes included enhance personal confidence and competence (43%); acquire personal knowledge (39%); and operations (21%). Of 211 providers who participated in the intervention, 56 (27%) were awarded CE. A total of 152 providers responded to the post-survey, and the percentage who agreed that reviewing outcomes improves job satisfaction rose from 89% to 95% between pre- and post-surveys (P = 0.05). Conclusion: EMS providers supported the personal and professional development and patient care improvement of reviewing patients’ outcomes with associated CE. Further study is warranted to evaluate the generalizability of these findings and the best user experience.

Current and Emerging Radiotracers in Molecular Cardiovascular Imaging.

Cardiovascular imaging has rapidly advanced over the past decades. Traditional imaging techniques such as echocardiography, computed tomography, and cardiovascular magnetic resonance are essential for assessing the structural and functional aspects of the cardiovascular system but often fall short in providing direct insights into disease activity. This gap is increasingly being bridged by molecular nuclear imaging techniques, including positron emission tomography and single-photon emission computed tomography, which enable the visualization of disease processes at the molecular and cellular levels. This review highlights the role of cardiovascular molecular imaging, emphasizing its current and potential applications in diagnosing and managing cardiovascular disease. With advancements in positron emission tomography scanners, novel radiotracers, and sophisticated imaging software, molecular imaging is set to play an essential role in precision medicine by enhancing our understanding of disease mechanisms, accelerating the development of targeted therapies, and facilitating personalized patient care.

Optimizing CT Protocols: Advanced Dose Reduction and Contrast Media Strategies for Personalized Patient Care – Today's Innovations and Tomorrow's Horizons

Optimizing CT Protocols: Advanced Dose Reduction and Contrast Media Strategies for Personalized Patient Care – Today's Innovations and Tomorrow's Horizons

The impact of business analytics on healthcare operations: A statistical perspective

This study examines the transformative potential of business analytics within the healthcare sector, highlighting its wide-ranging applications, the challenges it faces, and potential future developments. The primary goal is to evaluate how advanced analytical tools can enhance healthcare outcomes, improve decision-making, and optimize operational efficiency in healthcare organizations. Through an in-depth literature review, this study critically analyzes existing research and real-world case studies to identify the key trends and obstacles associated with implementing business analytics in healthcare. The findings reveal that business analytics plays a crucial role in personalizing patient care, enabling predictive modeling, and streamlining healthcare operations. These tools support more informed decision-making, leading to better patient outcomes and more efficient resource utilization. However, significant challenges hinder the widespread adoption of business analytics, such as difficulties in integrating data across diverse systems, concerns about patient data privacy, and the need for a cultural shift towards data-driven decision-making within healthcare institutions. In conclusion, while integrating business analytics in healthcare presents substantial benefits, it requires a comprehensive and multifaceted approach. This approach should include technological advancements, the establishment of robust data governance frameworks, and significant organizational changes. The study recommends that healthcare organizations invest in training programs to enhance data literacy among healthcare professionals, develop policies to promote interoperability across different data systems, and carefully address the ethical implications of data usage. Future research should focus on the long-term impacts of business analytics in healthcare, with a particular emphasis on the ethical challenges that may arise. Ensuring these powerful tools are used responsibly will be essential to improving healthcare delivery.