Automated Protocoling for MRI Exams-Challenges and Solutions.

Automated protocoling for MRI examinations is an amendable target for workflow automation with artificial intelligence. However, there are still challenges to overcome for a successful and robust approach. These challenges are outlined and analyzed in this work. Through a literature review, we analyzed limitations of currently published approaches for automated protocoling. Then, we assessed these limitations quantitatively based on data from a private radiology practice. For this, we assessed the information content provided by the clinical indication by computing the overlap coefficients for the sets of ICD-10-coded admitting diagnoses of different MRI protocols. Additionally, we assessed the heterogeneity of protocol trees from three different MRI scanners based on the overlap coefficient, on MRI protocol and sequence level. Additionally, we applied sequence name standardization to demonstrate its effect on the heterogeneity assessment, i.e., the overlap coefficient, of different protocol trees. The overlap coefficient for the set of ICD-10-coded admitting diagnoses for different protocols ranges from 0.14 to 0.56 for brain/head MRI exams and 0.04 to 0.57 for spine exams. The overlap coefficient across the set of sequences used at two different scanners increases when applying sequence name standardization (from 0.81/0.86 to 0.93). Automated protocoling for MRI examinations has the potential to reduce the workload for radiologists. However, an automated protocoling approach cannot be solely based on admitting diagnosis as it does not provide sufficient information. Moreover, sequence name standardization increases the overlap coefficient across the set of sequences used at different scanners and therefore facilitates transfer learning.

Automating Time-Consuming and Error-Prone Manual Nursing Management Documentation Processes.

A German regulation requires nursing managers to document patient-nurse ratios. They have to combine heterogeneous hospital data from different sources. Missing documentation or ratios that are too high lead to sanctions. Automated approaches are needed to accelerate the time-consuming and error-prone documentation process. A documentation and visualization system was implemented. The system allows nursing managers to quickly and automatically create the documentation required by the regulation. Interactive visualization dashboards assist with the analysis of patient and staff numbers. The developed method was effectively used in nursing management tasks. No changes to the information technology infrastructure were needed. The new process is around 35 hours per month faster and less error-prone. The documentation functionality automatically reads the required information and correctly calculates the documentation. The visualization functionality allows nursing managers to assess the current patient-nurse ratios before the documentation is submitted. The method scales to multiple wards and locations. It calculates the sanctions to expect and is easily updatable. The proposed method is expected to decrease nursing administration workloads and facilitate the analysis of nursing management data in a cost-effective way.

Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care.

We propose a novel method that uses associative classification and odds ratios to predict in-hospital mortality in emergency and critical care. Manual mortality risk scores have previously been used to assess the care needed for each patient and their need for palliative measures. Automated approaches allow providers to get a quick and objective estimation based on electronic health records. We use association rule mining to find relevant patterns in the dataset. The odds ratio is used instead of classical association rule mining metrics as a quality measure to analyze association instead of frequency. The resulting measures are used to estimate the in-hospital mortality risk. We compare two prediction models: one minimal model with socio-demographic factors that are available at the time of admission and can be provided by the patients themselves, namely gender, ethnicity, type of insurance, language, and marital status, and a full model that additionally includes clinical information like diagnoses, medication, and procedures. The method was tested and validated on MIMIC-IV, a publicly available clinical dataset. The minimal prediction model achieved an area under the receiver operating characteristic curve value of 0.69, while the full prediction model achieved a value of 0.98. The models serve different purposes. The minimal model can be used as a first risk assessment based on patient-reported information. The full model expands on this and provides an updated risk assessment each time a new variable occurs in the clinical case. In addition, the rules in the models allow us to analyze the dataset based on data-backed rules. We provide several examples of interesting rules, including rules that hint at errors in the underlying data, rules that correspond to existing epidemiological research, and rules that were previously unknown and can serve as starting points for future studies.

Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios.

HIV/AIDS is an ongoing global pandemic, with an estimated 39 million infected worldwide. Early detection is anticipated to help improve outcomes and prevent further infections. Point-of-care diagnostics make HIV/AIDS diagnoses available both earlier and to a broader population. Wide-spread and automated HIV risk estimation can offer objective guidance. This supports providers in making an informed decision when considering patients with high HIV risk for HIV testing or pre-exposure prophylaxis (PrEP). We propose a novel machine learning method that allows providers to use the data from a patient's previous stays at the clinic to estimate their HIV risk. All features available in the clinical data are considered, making the set of features objective and independent of expert opinions. The proposed method builds on association rules that are derived from the data. The incidence rate ratio (IRR) is determined for each rule. Given a new patient, the mean IRR of all applicable rules is used to estimate their HIV risk. The method was tested and validated on the publicly available clinical database MIMIC-IV, which consists of around 525,000 hospital stays that included a stay at the intensive care unit or emergency department. We evaluated the method using the area under the receiver operating characteristic curve (AUC). The best performance with an AUC of 0.88 was achieved with a model consisting of 53 rules. A threshold value of 0.66 leads to a sensitivity of 98% and a specificity of 53%. The rules were grouped into drug abuse, psychological illnesses (e.g., PTSD), previously known associations (e.g., pulmonary diseases), and new associations (e.g., certain diagnostic procedures). In conclusion, we propose a novel HIV risk estimation method that builds on existing clinical data. It incorporates a wide range of features, leading to a model that is independent of expert opinions. It supports providers in making informed decisions in the point-of-care diagnostics process by estimating a patient's HIV risk.

Predicting Anxiety in Routine Palliative Care Using Bayesian-Inspired Association Rule Mining.

We propose a novel knowledge extraction method based on Bayesian-inspired association rule mining to classify anxiety in heterogeneous, routinely collected data from 9,924 palliative patients. The method extracts association rules mined using lift and local support as selection criteria. The extracted rules are used to assess the maximum evidence supporting and rejecting anxiety for each patient in the test set. We evaluated the predictive accuracy by calculating the area under the receiver operating characteristic curve (AUC). The evaluation produced an AUC of 0.89 and a set of 55 atomic rules with one item in the premise and the conclusion, respectively. The selected rules include variables like pain, nausea, and various medications. Our method outperforms the previous state of the art (AUC = 0.72). We analyzed the relevance and novelty of the mined rules. Palliative experts were asked about the correlation between variables in the data set and anxiety. By comparing expert answers with the retrieved rules, we grouped rules into expected and unexpected ones and found several rules for which experts' opinions and the data-backed rules differ, most notably with the patients' sex. The proposed method offers a novel way to predict anxiety in palliative settings using routinely collected data with an explainable and effective model based on Bayesian-inspired association rule mining. The extracted rules give further insight into potential knowledge gaps in the palliative care field.

Effects of a high-fat diet on body composition in cancer patients receiving chemotherapy: a randomized controlled study.

BACKGROUND: Patients with cancer are characterized by a profound impairment of glucose utilization, with lipids being the preferred metabolic fuel. In contrast, the energy needs of malignant tumors are almost entirely met by glucose. We therefore studied the effects of a high-fat diet, particularly on body composition. PATIENTS AND METHODS: Twenty-three moderately malnourished patients with gastrointestinal carcinomas were randomized to receive either a conventional diet supplying 35 nonprotein kcal and 1.1 g of protein/kg per day (group A, n = 11) or a fat-enriched artificial liquid diet (20 nonprotein kcal/kg per day) plus normal meals (group B, n = 12) for a period of eight weeks, i.e., from the first to the third chemotherapy cycle. The fat content of the artificial diet was 66% of the nonprotein calories. The day before the nutritional interventions, and again after four and eight weeks, body compartments were determined using bioelectrical impedance analysis, lymphocyte subpopulations were quantified using flow cytometry, and some aspects of the quality of life were rated using four linear analog self-assessment (LASA) scales. The statistical calculations were done as an exploratory data analysis. RESULTS: The consumption of non-protein calories did not differ significantly between the two patient groups. An average weight gain in group B contrasted with an average weight loss in group A after four (P < 0.01) and eight weeks (P < 0.05). Fat-free mass showed an intergroup difference in favor of group B after eight weeks (P < 0.05). Body cell mass was maintained throughout the study in group B, but declined significantly up to weeks 4 and 8 in group A (intergroup difference: P < 0.05 and 0.01, respectively). A decrease in the total lymphocyte count by 559 cells/mul occurred with the fat-enriched diet (P < 0.05). Several aspects of the quality of life were rated to be better in group B than in group A, although not all differences reached statistical significance. CONCLUSION: In patients with cancer, a high-fat diet may possibly support the maintenance of both body weight and body cell mass. However, monitoring the lymphocyte count is advisable.

Disc height and anteroposterior translation in fused and adjacent segments after lumbar spine fusion.

In a series of 46 patients the effects of spinal fusion upon intervertebral height and sagittal alignment in operated and non-operated segments were retrospectively evaluated on digitized radiographs. Data was compared with age- and gender-normalized standard values. The objective was to evaluate the influence of different types of spine fusions primarily upon adjacent segments, particularly in terms of degeneration and sagittal profile of the lumbar spine. Incidence of adjacent segment degeneration (ASD) is still highly controversial. However, not every degeneration adjacent to spinal fusion must be caused by the fusion and responsibility of the fusion for ASD may vary with its range and type. Distortion Corrected Roentgen Analysis (DCRA) was utilized. DCRA is a proven valid, reliable, observer-independent, and accurate tool for assessment of these parameters over time and in comparison with "normal" cohorts. With this method the exact posture of the patients needs not to be known. There was little evidence for serious fusion-related ASD within an average of 40 months follow-up. No difference could be detected for rigid vs. non-rigid fusion and instrumented vs. non-instrumented techniques. Temporary postoperative distraction effects could be detected in operated and non-operated segments. Absolute preoperative values for intervertebral height and vertebral slip were age-related. Retrospectively, the choice of segments for fusion was clearly based upon radiological criteria. Thus we conclude that radiological parameters have an obvious clinical relevance for decision-making and need to be quantified. Within the limitations of this pilot study, true fusion related ASD seems to be infrequent.