Background: Automatic text summarization (ATS) enables users to retrieve meaningful evidence from big data of biomedical repositories to make complex clinical decisions. Deep neural and recurrent networks outperform traditional machine-learning techniques in areas of natural language processing and computer vision; however, they are yet to be explored in the ATS domain, particularly for medical text summarization. Objective: Traditional approaches in ATS for biomedical text suffer from fundamental issues such as an inability to capture clinical context, quality of evidence, and purpose-driven selection of passages for the summary. We aimed to circumvent these limitations through achieving precise, succinct, and coherent information extraction from credible published biomedical resources, and to construct a simplified summary containing the most informative content that can offer a review particular to clinical needs. Methods: In our proposed approach, we introduce a novel framework, termed Biomed-Summarizer, that provides quality-aware Patient/Problem, Intervention, Comparison, and Outcome (PICO)-based intelligent and context-enabled summarization of biomedical text. Biomed-Summarizer integrates the prognosis quality recognition model with a clinical context–aware model to locate text sequences in the body of a biomedical article for use in the final summary. First, we developed a deep neural network binary classifier for quality recognition to acquire scientifically sound studies and filter out others. Second, we developed a bidirectional long-short term memory recurrent neural network as a clinical context–aware classifier, which was trained on semantically enriched features generated using a word-embedding tokenizer for identification of meaningful sentences representing PICO text sequences. Third, we calculated the similarity between query and PICO text sequences using Jaccard similarity with semantic enrichments, where the semantic enrichments are obtained using medical ontologies. Last, we generated a representative summary from the high-scoring PICO sequences aggregated by study type, publication credibility, and freshness score. Results: Evaluation of the prognosis quality recognition model using a large dataset of biomedical literature related to intracranial aneurysm showed an accuracy of 95.41% (2562/2686) in terms of recognizing quality articles. The clinical context–aware multiclass classifier outperformed the traditional machine-learning algorithms, including support vector machine, gradient boosted tree, linear regression, K-nearest neighbor, and naïve Bayes, by achieving 93% (16127/17341) accuracy for classifying five categories: aim, population, intervention, results, and outcome. The semantic similarity algorithm achieved a significant Pearson correlation coefficient of 0.61 (0-1 scale) on a well-known BIOSSES dataset (with 100 pair sentences) after semantic enrichment, representing an improvement of 8.9% over baseline Jaccard similarity. Finally, we found a highly positive correlation among the evaluations performed by three domain experts concerning different metrics, suggesting that the automated summarization is satisfactory. Conclusions: By employing the proposed method Biomed-Summarizer, high accuracy in ATS was achieved, enabling seamless curation of research evidence from the biomedical literature to use for clinical decision-making.

Predicting length of stay (LoS) and understanding its underlying factors is essential to minimizing the risk of hospital-acquired conditions, improving financial, operational, and clinical outcomes, and better managing future pandemics. The purpose of this study was to forecast patients’ LoS using a deep learning model and to analyze cohorts of risk factors reducing or prolonging LoS. We employed various preprocessing techniques, SMOTE-N to balance data, and a TabTransformer model to forecast LoS. Finally, the Apriori algorithm was applied to analyze cohorts of risk factors influencing hospital LoS. The TabTransformer outperformed the base machine learning models in terms of F1 score (0.92), precision (0.83), recall (0.93), and accuracy (0.73) for the discharged dataset and F1 score (0.84), precision (0.75), recall (0.98), and accuracy (0.77) for the deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to laboratory, X-ray, and clinical data, such as elevated LDH and D-dimer levels, lymphocyte count, and comorbidities such as hypertension and diabetes. It also reveals what treatments have reduced the symptoms of COVID-19 patients, leading to a reduction in LoS, particularly when no vaccines or medication, such as Paxlovid, were available.

BACKGROUND Making clinical decisions about the treatment of Intracranial Aneurysms (IA) is not straightforward: small IAs in certain arteries may rupture while larger at others do not. Since many IA’s may not rupture for many years the risk of side effects from treatment prior to rupture must be weighed against the risk of SAH. Existing statistical and traditional approaches neither provide an accurate prediction of aneurysmal rupture nor offer a quantitative comparison among a group of SAH risk factors. OBJECTIVE This paper evaluates the shortcomings of the PHASES and UIATS scores on retrospective data. Additionally, to address the complex challenge of SAH prediction, we develop an interpretable machine learning and data mining-based framework for the individualized rupture risk prediction of saccular intracranial aneurysm (SIA), and the aneurysmal rupture criticality prediction index for a set of risk factors to perform its relative comparison with other cases. METHODS The proposed AI framework predicts the 5-year and lifetime rupture risk of intracranial aneurysms by training machine learning models based on an aneurysm’s location and shape. Additionally, it uses an ensemble learning model that is trained on data of all potential IA locations. Next, we employ the longitudinal data of 30 patients to develop a linear regression-based model to predict an aneurysm’s growth score. We use the Apriori algorithm to identify risk factors which carry a strong association with aneurysmal rupture for each combination of location and associated rupture risk factors. We compare the results not only with PHASES and UIATS scores but also with the scores of a multidisciplinary team of neurosurgeons. RESULTS The PHASES and UIATS scores show sensitivities of 22%, and 35%, and specificities of 76% and 79%, respectively. For the proposed framework, location-specific models show precision and recall of 93% and 90% for the Middle Cerebral Artery, 83% and 80% for the Anterior Communicating Artery, and 80% and 80% for the Supraclinoid Internal Carotid Artery. The ensemble method shows both precision and recall of 80%. The validation of the models on unseen data shows that the proposed framework performs better than our control group of neurosurgeons. Data-driven knowledge produces comparisons among 61 risk factor combinations, 11 ranked minor, 8 moderate, 41 severe, and one of which is a critical factor. CONCLUSIONS The PHASES and UIATS scores may only provide weak assistance in the clinical decision-making process of aneurysm treatment, particularly when it comes to the prediction of tiny aneurysms (those less than 5mm). The validation of the proposed framework, using an independent cohort (n=148), shows that it is possible to predict individualized rupture risk of saccular intracranial aneurysms of different sizes, including tiny ones. Apart from determining rupture probability in specific cases, the proposed Aneurysmal Rupture Criticality Prediction (ARCP) score could be used in training residents, both in understanding and in the ability to explain how the group of risk factors contribute to both 5-year and lifetime rupture risk.

To practice the evidence-based medicine, clinicians are interested to find the most suitable research for the clinical decision making. The use of knowledge graphs (KGs) in evidence-based clinical decision support systems is becoming increasingly popular. However, existing KG construction frameworks are not fully automated and contextualized, thus unable to adapt to new domains and incorporate constantly changing information into their knowledge base, resulting in loss of relevance over time. Furthermore, existing KGs construction frameworks don't generate KG that provide relevant information within an acceptable response time for evidence-based practitioners because the organization of constructed subgraphs is neither topic-specific nor evidence-based PICO (Participants/Problem P, Intervention-I, Comparison C, Outcome O) query-friendly. By employing concept extraction, semantic enrichment, optimized clustering, and state of art Recurrent Neural Networks (RNNs) with BioBERT based encoded representation to categorize PICO elements and predict relationships between concepts using huge corpus of publicly available literature on COVID19 and cerebral aneurysm, this paper proposes a topic specific, PICO enabled, and fully automated framework to curate information and create KG of different clinical domains. The evaluation shows that the proposed framework achieves significant improvement over baseline models and has 93%, and 82% accuracy on aneurysm and covid data set respectively for PICO classification. Also, the relationship extraction module has an accuracy of 96% with precision and recall being 92%, and 90% respectively.