Category: Statistics

Stud Health Technol Inform. 2024 Aug 22;316:993-997. doi: 10.3233/SHTI240577.

ABSTRACT

Healthcare processes are complex and involve uncertainties to influence the service quality and health of patients. Patient transportation takes place between the hospitals or between the departments within the hospital (i.e., Inter- or Intra-Hospital Transportation respectively). The focus of our paper is route planning for transporting patients within the hospital. The route planning task is complex due to multiple factors such as regulations, fairness considerations (i.e., balanced workload amongst transporters), and other dynamic factors (i.e., transport delays, wait times). Transporters perform the physical transportation of patients within the hospital. In principle, each job allocation respects the transition time between the subsequent jobs. The primary objective was to determine the feasible number of transporters, and then generate the route plan for all determined transporters by distributing all transport jobs (i.e., from retrospective data) within each shift. Secondary objectives are to minimize the sum of total travel time and sum of total idle time of all transporters and minimize the deviations in total travel time amongst transporters. Our method used multi-staged Local Search Metaheuristics to attain the primary objective. Metaheuristics incorporate Mixed Integer Linear Programming to allocate fairly the transport jobs by formulating optimization constraints with bounds for satisfying the secondary objectives. The obtained results using formulated optimization constraints represent better efficacy in multi-objective route planning of Intra-Hospital Transportation of patients.

PMID:39176958 | DOI:10.3233/SHTI240577

Stud Health Technol Inform. 2024 Aug 22;316:929-933. doi: 10.3233/SHTI240563.

ABSTRACT

Predictive modeling holds a large potential in clinical decision-making, yet its effectiveness can be hindered by inherent data imbalances in clinical datasets. This study investigates the utility of synthetic data for improving the performance of predictive modeling on realistic small imbalanced clinical datasets. We compared various synthetic data generation methods including Generative Adversarial Networks, Normalizing Flows, and Variational Autoencoders to the standard baselines for correcting for class underrepresentation on four clinical datasets. Although results show improvement in F1 scores in some cases, even over multiple repetitions, we do not obtain statistically significant evidence that synthetic data generation outperforms standard baselines for correcting for class imbalance. This study challenges common beliefs about the efficacy of synthetic data for data augmentation and highlights the importance of evaluating new complex methods against simple baselines.

PMID:39176944 | DOI:10.3233/SHTI240563

Stud Health Technol Inform. 2024 Aug 22;316:868-872. doi: 10.3233/SHTI240549.

ABSTRACT

This study investigates the forecasting of cardiovascular mortality trends in Greece’s elderly population. Utilizing mortality data from 2001 to 2020, we employ two forecasting models: the Autoregressive Integrated Moving Average (ARIMA) and Facebook’s Prophet model. Our study evaluates the efficacy of these models in predicting cardiovascular mortality trends over 2020-2030. The ARIMA model showcased predictive accuracy for the general and male population within the 65-79 age group, whereas the Prophet model provided better forecasts for females in the same age bracket. Our findings emphasize the need for adaptive forecasting tools that accommodate demographic-specific characteristics and highlight the role of advanced statistical methods in health policy planning.

PMID:39176930 | DOI:10.3233/SHTI240549

Stud Health Technol Inform. 2024 Aug 22;316:719-723. doi: 10.3233/SHTI240515.

ABSTRACT

Automatic deidentification of Electronic Health Records (EHR) is a crucial step in secondary usage for biomedical research. This study introduces evaluation of an intricate hybrid deidentification strategy to enhance patient privacy in secondary usage of EHR. Specifically, this study focuses on assessing automatic deidentification using OpenDeID pipeline across diverse corpora for safeguarding sensitive information within EHR datasets by incorporating diverse corpora. Three distinct corpora were utilized: the OpenDeID v2 corpus containing pathology reports from Australian hospitals, the 2014 i2b2/UTHealth deidentification corpus with clinical narratives from the USA, and the 2016 CEGS N-GRID identification corpus comprising psychiatric notes. The OpenDeID pipeline employs a hybrid approach based on deep learning and contextual rules. Pre-processing steps involved harmonizing and addressing encoding and format issues. Precision, Recall, F-measure metrics were used to assess the performance. The evaluation metrics demonstrated the superior performance of the Discharge Summary BioBERT model. Trained on three corpora with a total of 4,038 reports, the best performing model exhibited robust deidentification capabilities when applied to EHR. It achieved impressive micro-averaged F1-scores of 0.9248 and 0.9692 for strict and relaxed settings, respectively. These results offer valuable insights into the model’s efficacy and its potential role in safeguarding patient privacy in secondary usage of EHR.

PMID:39176896 | DOI:10.3233/SHTI240515

Stud Health Technol Inform. 2024 Aug 22;316:705-709. doi: 10.3233/SHTI240511.

ABSTRACT

To address privacy and ethical issues in using health data for machine learning, we evaluate the scalability of advanced synthetic data generation methods like GANs, VAEs, copulaGAN, and transformer models specifically for patient service utilization data. Our study examines five models on data from a Canadian health authority, focusing on training and generation efficiency, data resemblance, and practical utility. Our findings indicate that statistical models excel in efficiency, while most models produce synthetic data that closely mirrors real data, and is also useful for real-world applications.

PMID:39176892 | DOI:10.3233/SHTI240511

Stud Health Technol Inform. 2024 Aug 22;316:695-699. doi: 10.3233/SHTI240509.

ABSTRACT

Annotated language resources derived from clinical routine documentation form an intriguing asset for secondary use case scenarios. In this investigation, we report on how such a resource can be leveraged to identify additional term candidates for a chosen set of ICD-10 codes. We conducted a log-likelihood analysis, considering the co-occurrence of approximately 1.9 million de-identified ICD-10 codes alongside corresponding brief textual entries from problem lists in German. This analysis aimed to identify potential candidates with statistical significance set at p < 0.01, which were used as seed terms to harvest additional candidates by interfacing to a large language model in a second step. The proposed approach can identify additional term candidates at suitable performance values: hypernyms MAP@5=0.801, synonyms MAP@5 = 0.723 and hyponyms MAP@5 = 0.507. The re-use of existing annotated clinical datasets, in combination with large language models, presents an interesting strategy to bridge the lexical gap in standardized clinical terminologies and real-world jargon.

PMID:39176890 | DOI:10.3233/SHTI240509

Stud Health Technol Inform. 2024 Aug 22;316:1962-1966. doi: 10.3233/SHTI240818.

ABSTRACT

Submitted genomic data for respiratory viruses reflect the emergence and spread of new variants. Although delays in submission limit the utility of these data for prospective surveillance, they may be useful for evaluating other surveillance sources. However, few studies have investigated the use of these data for evaluating aberration detection in surveillance systems. Our study used a Bayesian online change point detection algorithm (BOCP) to detect increases in the number of submitted genome samples as a means of establishing ‘gold standard’ dates of outbreak onset in multiple countries. We compared models using different data transformations and parameter values. BOCP detected change points that were not sensitive to different parameter settings. We also found data transformations were essential prior to change point detection. Our study presents a framework for using global genomic submission data to develop ‘gold standard’ dates about the onset of outbreaks due to new viral variants.

PMID:39176877 | DOI:10.3233/SHTI240818

Stud Health Technol Inform. 2024 Aug 22;316:1950-1954. doi: 10.3233/SHTI240815.

ABSTRACT

While it would be quite helpful to learn more about the daily fluctuations of fatigue and cognitive impairments of post-COVID patients, their condition can make investigating these especially challenging. By discussing these issues with post-COVID patients and clinical practitioners, we identified six challenges that specifically apply to daily computerized assessment of cognitive functions of post-COVID patients. We proposed solutions for each of the challenges which can be summarized as offering a carefully planned and flexible study design to participants and monitoring their well-being throughout the assessments. We argue that when the proposed precautions are taken, it is feasible to conduct a study that will generate valuable insights into the trajectories of (cognitive) post-COVID symptoms.

PMID:39176874 | DOI:10.3233/SHTI240815

Stud Health Technol Inform. 2024 Aug 22;316:1938-1942. doi: 10.3233/SHTI240812.

ABSTRACT

Burkina Faso has been facing a security crisis due to terrorism since 2015. This study aims to assess the impact of the attacks on the use of healthcare services. This is a secondary study on data from the country’s health data warehouse and the ACLED security data warehouse. After a description, generalized additive models were used to assess the impact of attacks on the use of health services. Between January 2015 and December 2022, 2449 kidnap/disappearance attacks, armed attacks, bombings and landmine explosions were perpetrated, causing 4965 deaths. The Sahel region was the most targeted (36.37% of attacks and 50.57% of deaths). Only population density had a significant impact on the use of health services (p<5%). The models were valid. Our study has shown that, despite the persistent insecurity in Burkina Faso, people are resilient and, above all, continue to seek out the most important healthcare services. It is therefore important to work to maintain the supply of these services.

PMID:39176871 | DOI:10.3233/SHTI240812

Stud Health Technol Inform. 2024 Aug 22;316:1891-1895. doi: 10.3233/SHTI240802.

ABSTRACT

INTRODUCTION: Autistic individuals, parents, organizations, and healthcare systems worldwide are actively sharing content aimed at increasing awareness about autism. This study aims at analyzing the type of contents presented in TikTok and YouTube Shorts videos under the hashtag #actuallyautistic and their potential to increase autism awareness.

METHODS: A sample of 60 videos were downloaded and analyzed (n=30 from TikTok and n=30 from YouTube Shorts). Video contents were analyzed using both thematic analysis and the AFINN sentiment analysis tool. The understandability and actionability of the videos were assessed with The Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT A/V).

RESULTS: The contents of these videos covered five main themes: Stigmatization; Sensory difficulties; Masking; Stimming; and Communication difficulties. No statistically significant differences were found on sentiment expressed on videos from both channels. TikTok videos received significantly more views, comments, and likes than videos on YouTube Shorts. The PEMAT A/V tool showed that there is a high level of understandability, but little reference to actionability.

DISCUSSION: Autistic people videos content spread valid and reliable information in hopes of normalizing difficulties and provide hope and comfort to others in similar situations.

CONCLUSIONS: Social media videos posted by autistic individuals provide accurate portrayals about autism but lack information on actionability. These shared personal stories can help increase public literacy about autism, dispel autism stigmas and emphasize individuality.

PMID:39176861 | DOI:10.3233/SHTI240802