Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures.

Using clustering analysis for early vital signs, unique patient phenotypes with distinct pathophysiological signatures and clinical outcomes may be revealed and support early clinical decision-making. Phenotyping using early vital signs has proven challenging, as vital signs are typically sampled sporadically. We proposed a novel, deep temporal interpolation and clustering network to simultaneously extract latent representations from irregularly sampled vital signs and derive phenotypes. Four distinct clusters were identified. Phenotype A (18%) had the greatest prevalence of comorbid disease with increased prevalence of prolonged respiratory insufficiency, acute kidney injury, sepsis, and long-term (3-year) mortality. Phenotypes B (33%) and C (31%) had a diffuse pattern of mild organ dysfunction. Phenotype B's favorable short-term clinical outcomes were tempered by the second highest rate of long-term mortality. Phenotype C had favorable clinical outcomes. Phenotype D (17%) exhibited early and persistent hypotension, high incidence of early surgery, and substantial biomarker incidence of inflammation. Despite early and severe illness, phenotype D had the second lowest long-term mortality. After comparing the sequential organ failure assessment scores, the clustering results did not simply provide a recapitulation of previous acuity assessments. This tool may impact triage decisions and have significant implications for clinical decision-support under time constraints and uncertainty.

Application Exploration of Medical Image-aided Diagnosis of Breast Tumour Based on Deep Learning.

BACKGROUND: Nowadays, people attach increasing importance to accurate and timely disease diagnosis and personalized treatment. Because of the uncertainty and latency of the pathogenesis, it is difficult to detect breast tumour early. With higher resolution, magnetic resonance imaging (MRI) has become an important method for early detection of cancer in recent years. At present, DL technology can automatically study imaging features of different depths. OBJECTIVE: This work aimed to use DL to study medical image-assisted diagnosis. METHODS: The image data were collected from the patients. ROI (region of interest) containing the complete tumor area in the medical image was generated. The ROI image was extracted, and the extracted feature data were expanded. By constructing a three-dimensional (3D) CNN model, the evaluation indicators of breast tumour diagnosis results have been proposed. In the experiment part, 3D CNN model and other models have been used to diagnose the medical image of breast tumour. RESULTS: The 3D CNN model exhibited good ROI region extraction effect and breast tumor image diagnosis effect, and the average diagnostic accuracy of breast tumor image diagnosis was 0.736, which has been found to be much higher than other models and could be applied to breast tumor medical image-aided diagnosis. CONCLUSION: The 3D CNN model has been trained by combining the two-dimensional CNN training mode, and the evaluation index of diagnostic results has been established. The experimental part verified the medical image diagnosis effect of the 3D CNN model. The model had exhibited a high ROI region extraction effect and breast tumor image diagnosis effect.

Bilateral renal artery stenosis impacts postoperative complications after major vascular surgery.

Background: Incidental atherosclerotic renal artery stenosis (RAS) is common in patients undergoing vascular surgery and has been shown to be associated with postoperative AKI among patients undergoing major non-vascular surgeries. We hypothesized that patients with RAS undergoing major vascular procedures would have a higher incidence of AKI and postoperative complications than those without RAS. Methods: A single-center retrospective cohort study of 200 patients who underwent elective open aortic or visceral bypass surgery (100 with postoperative AKI; 100 without AKI) were identified. RAS was then evaluated by review of pre-surgery CTAs with readers blinded to AKI status. RAS was defined as ≥50 % stenosis. Univariate and multivariable logistic regression was used to assess association of unilateral and bilateral RAS with postoperative outcomes. Results: 17.4 % (n = 28) of patients had unilateral RAS while 6.2 % (n = 10) of patients had bilateral RAS. Patients with bilateral RAS had similar preadmission creatinine and GFR as compared to unilateral RAS or no RAS. 100 % (n = 10) of patients with bilateral RAS had postoperative AKI compared with 45 % (n = 68) of patients with unilateral or no RAS (p < 0.05). In adjusted logistic regression models, bilateral RAS predicted severe AKI (OR 5.82; CI 1.33, 25.53; p = 0.02), in-hospital mortality (OR 5.71; CI 1.03, 31.53; p = 0.05), 30-day mortality (OR 10.56; CI 2.03, 54.05; p = 0.005) and 90-day mortality (OR 6.88; CI 1.40, 33.87; p = 0.02). Conclusions: Bilateral RAS is associated with increased incidence of AKI as well as in-hospital, 30-day, and 90-day mortality suggesting it is a marker of poor outcomes and should be considered in preoperative risk stratification.

A deep learning-based dynamic model for predicting acute kidney injury risk severity in postoperative patients.

BACKGROUND: Acute kidney injury is a common postoperative complication affecting between 10% and 30% of surgical patients. Acute kidney injury is associated with increased resource usage and chronic kidney disease development, with more severe acute kidney injury suggesting more aggressive deterioration in clinical outcomes and mortality. METHODS: We considered 42,906 surgical patients admitted to University of Florida Health (n = 51,806) between 2014 and 2021. Acute kidney injury stages were determined using the Kidney Disease Improving Global Outcomes serum creatinine criteria. We developed a recurrent neural network-based model to continuously predict acute kidney injury risk and state in the following 24 hours and compared it with logistic regression, random forest, and multi-layer perceptron models. We used medications, laboratory and vital measurements, and derived features from past one-year records as inputs. We analyzed the proposed model with integrated gradients for enhanced explainability. RESULTS: Postoperative acute kidney injury at any stage developed in 20% (10,664) of the cohort. The recurrent neural network model was more accurate in predicting nearly all categories of next-day acute kidney injury stages (including the no acute kidney injury group). The area under the receiver operating curve and 95% confidence intervals for recurrent neural network and logistic regression models were for no acute kidney injury (0.98 [0.98-0.98] vs 0.93 [0.93-0.93]), stage 1 (0.95 [0.95-0.95] vs. 0.81 [0.80-0.82]), stage 2/3 (0.99 [0.99-0.99] vs 0.96 [0.96-0.97]), and stage 3 with renal replacement therapy (1.0 [1.0-1.0] vs 1.0 [1.0-1.0]. CONCLUSION: The proposed model demonstrates that temporal processing of patient information can lead to more granular and dynamic modeling of acute kidney injury status and result in more continuous and accurate acute kidney injury prediction. We showcase the integrated gradients framework's utility as a mechanism for enhancing model explainability, potentially facilitating clinical trust for future implementation.

Analysis of Signs and Effects of Surgical Breast Cancer Patients Based on Big Data Technology.

Big data in health care has gained popularity in recent years for disease prediction. Breast cancer infections are the most common cancer in urban Indian women, as well as women internationally, and are impacted by many events across countries and regions. Breast malignant growth is a notable disease among Indian women. According to the WHO, it represents 14% of all malignant growth tumors in women. A couple of studies have been directed utilizing big data to foresee breast malignant growth. Big data is causing a transformation in healthcare, with better and more ideal results. Monstrous volumes of patient-level data are created by using EHR (Electronic Health Record) systems data because of fast mechanical upgrades. Big data applications in the healthcare business will assist with improving results. Conventional forecast models, then again, are less productive in terms of accuracy and error rate because the exact pace of a specific calculation relies upon different factors such as execution structure, datasets (little or enormous), and kinds of datasets utilized (trait-based or picture based). This audit article looks at complex information mining, AI, and profound learning models utilized for recognizing breast malignant growth. Since "early identification is the way to avoidance in any malignant growth," the motivation behind this audit article is to support the choice of fitting breast disease expectation calculations, explicitly in the big information climate, to convey powerful and productive results. This survey article analyzes the precision paces of perplexing information mining, AI, and profound learning models utilized for distinguishing breast disease on the grounds that the exactness pace of a specific calculation relies upon different factors such as execution structure, datasets (little or enormous), and dataset types (quality based or picture based). The reason for this audit article is to aid the determination of suitable breast disease expectation calculations, explicitly in the big information climate, to convey successful and productive outcomes. Thus, "Early discovery is the way to counteraction in the event of any malignant growth."

Artificial intelligence-enabled decision support in nephrology.

Kidney pathophysiology is often complex, nonlinear and heterogeneous, which limits the utility of hypothetical-deductive reasoning and linear, statistical approaches to diagnosis and treatment. Emerging evidence suggests that artificial intelligence (AI)-enabled decision support systems - which use algorithms based on learned examples - may have an important role in nephrology. Contemporary AI applications can accurately predict the onset of acute kidney injury before notable biochemical changes occur; can identify modifiable risk factors for chronic kidney disease onset and progression; can match or exceed human accuracy in recognizing renal tumours on imaging studies; and may augment prognostication and decision-making following renal transplantation. Future AI applications have the potential to make real-time, continuous recommendations for discrete actions and yield the greatest probability of achieving optimal kidney health outcomes. Realizing the clinical integration of AI applications will require cooperative, multidisciplinary commitment to ensure algorithm fairness, overcome barriers to clinical implementation, and build an AI-competent workforce. AI-enabled decision support should preserve the pre-eminence of wisdom and augment rather than replace human decision-making. By anchoring intuition with objective predictions and classifications, this approach should favour clinician intuition when it is honed by experience.

Reinforcement learning in surgery.

Patients and physicians make essential decisions regarding diagnostic and therapeutic interventions. These actions should be performed or deferred under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. This may lead to cognitive and judgment errors. Reinforcement learning is a subfield of machine learning that identifies a sequence of actions to increase the probability of achieving a predetermined goal. Reinforcement learning has the potential to assist in surgical decision making by recommending actions at predefined intervals and its ability to utilize complex input data, including text, image, and temporal data, in the decision-making process. The algorithm mimics a human trial-and-error learning process to calculate optimum recommendation policies. The article provides insight regarding challenges in the development and application of reinforcement learning in the medical field, with an emphasis on surgical decision making. The review focuses on challenges in formulating reward function describing the ultimate goal and determination of patient states derived from electronic health records, along with the lack of resources to simulate the potential benefits of suggested actions in response to changing physiological states during and after surgery. Although clinical implementation would require secure, interoperable, livestreaming electronic health record data for use by virtual model, development and validation of personalized reinforcement learning models in surgery can contribute to improving care by helping patients and clinicians make better decisions.

Shortest path counting in probabilistic biological networks.

BACKGROUND: Biological regulatory networks, representing the interactions between genes and their products, control almost every biological activity in the cell. Shortest path search is critical to apprehend the structure of these networks, and to detect their key components. Counting the number of shortest paths between pairs of genes in biological networks is a polynomial time problem. The fact that biological interactions are uncertain events however drastically complicates the problem, as it makes the topology of a given network uncertain. RESULTS: In this paper, we develop a novel method to count the number of shortest paths between two nodes in probabilistic networks. Unlike earlier approaches, which uses the shortest path counting methods that are specifically designed for deterministic networks, our method builds a new mathematical model to express and compute the number of shortest paths. We prove the correctness of this model. CONCLUSIONS: We compare our novel method to three existing shortest path counting methods on synthetic and real gene regulatory networks. Our experiments demonstrate that our method is scalable, and it outperforms the existing methods in accuracy. Application of our shortest path counting method to detect communities in probabilistic networks shows that our method successfully finds communities in probabilistic networks. Moreover, our experiments on cell cycle pathway among different cancer types exhibit that our method helps in uncovering key functional characteristics of biological networks.

Identification of jointly correlated gene sets.

Associations between expressions of genes play a key role in deciphering their functions. Correlation score between pairs of genes is often utilized to associate two genes. However, the relationship between genes is often more complex; multiple genes might collaborate to control the transcription of a gene. In this paper, we introduce the problem of searching pairs of genes, which collectively correlate with another gene. This problem is computationally much harder than the classical problem of identifying pairwise gene associations. Exhaustive search is infeasible for transcriptomic datasets also; since for [Formula: see text] genes, there are [Formula: see text] possible gene combinations. Our method builds three filters to avoid computing the association for a large fraction of the gene combinations, which do not produce high correlation. Our experiments on a synthetic dataset and a prostate cancer dataset demonstrate that our method produces accurate results at the transcriptome level in practical time. Moreover, our method identifies biologically novel results which classical pairwise gene association studies are unlikely to discover.

ProMotE: an efficient algorithm for counting independent motifs in uncertain network topologies.

BACKGROUND: Identifying motifs in biological networks is essential in uncovering key functions served by these networks. Finding non-overlapping motif instances is however a computationally challenging task. The fact that biological interactions are uncertain events further complicates the problem, as it makes the existence of an embedding of a given motif an uncertain event as well. RESULTS: In this paper, we develop a novel method, ProMotE (Probabilistic Motif Embedding), to count non-overlapping embeddings of a given motif in probabilistic networks. We utilize a polynomial model to capture the uncertainty. We develop three strategies to scale our algorithm to large networks. CONCLUSIONS: Our experiments demonstrate that our method scales to large networks in practical time with high accuracy where existing methods fail. Moreover, our experiments on cancer and degenerative disease networks show that our method helps in uncovering key functional characteristics of biological networks.