Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients.

Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients. We conducted a prognostic study using data on 93,260 HF patients continuously enrolled for 2-years in a large U.S. commercial insurer to develop and validate prediction models for three outcomes of interest: preventable hospitalizations, preventable emergency department (ED) visits, and preventable costs. Patients were split into training, validation, and testing samples. Outcomes were modeled using traditional and enhanced LR and compared to gradient boosting model and deep learning models using sequential and non-sequential inputs. Evaluation metrics included precision (positive predictive value) at k, cost capture, and Area Under the Receiver operating characteristic (AUROC). Deep learning models consistently outperformed LR for all three outcomes with respect to the chosen evaluation metrics. Precision at 1% for preventable hospitalizations was 43% for deep learning compared to 30% for enhanced LR. Precision at 1% for preventable ED visits was 39% for deep learning compared to 33% for enhanced LR. For preventable cost, cost capture at 1% was 30% for sequential deep learning, compared to 18% for enhanced LR. The highest AUROCs for deep learning were 0.778, 0.681 and 0.727, respectively. These results offer a promising approach to identify patients for targeted interventions.

Development of a machine learning algorithm for early detection of opioid use disorder.

BACKGROUND: Opioid use disorder (OUD) affects an estimated 16 million people worldwide. The diagnosis of OUD is commonly delayed or missed altogether. We aimed to test the utility of machine learning in creating a prediction model and algorithm for early diagnosis of OUD. SUBJECTS AND METHODS: We analyzed data gathered in a commercial claim database from January 1, 2006, to December 31, 2018 of 10 million medical insurance claims from 550 000 patient records. We compiled 436 predictor candidates, divided to six feature groups - demographics, chronic conditions, diagnosis and procedures features, medication features, medical costs, and episode counts. We employed the Word2Vec algorithm and the Gradient Boosting trees algorithm for the analysis. RESULTS: The c-statistic for the model was 0.959, with a sensitivity of 0.85 and specificity of 0.882. Positive Predictive Value (PPV) was 0.362 and Negative Predictive Value (NPV) was 0.998. Significant differences between positive OUD- and negative OUD- controls were in the mean annual amount of opioid use days, number of overlaps in opioid prescriptions per year, mean annual opioid prescriptions, and annual benzodiazepine and muscle relaxant prescriptions. Notable differences were the count of intervertebral disc disorder-related complaints per year, post laminectomy syndrome diagnosed per year, and pain disorders diagnosis per year. Significant differences were also found in the episodes and costs categories. CONCLUSIONS: The new algorithm offers a mean 14.4 months reduction in time to diagnosis of OUD, at potential saving in further morbidity, medical cost, addictions and mortality.

Machine learning algorithm for early detection of end-stage renal disease.

BACKGROUND: End stage renal disease (ESRD) describes the most severe stage of chronic kidney disease (CKD), when patients need dialysis or renal transplant. There is often a delay in recognizing, diagnosing, and treating the various etiologies of CKD. The objective of the present study was to employ machine learning algorithms to develop a prediction model for progression to ESRD based on a large-scale multidimensional database. METHODS: This study analyzed 10,000,000 medical insurance claims from 550,000 patient records using a commercial health insurance database. Inclusion criteria were patients over the age of 18 diagnosed with CKD Stages 1-4. We compiled 240 predictor candidates, divided into six feature groups: demographics, chronic conditions, diagnosis and procedure features, medication features, medical costs, and episode counts. We used a feature embedding method based on implementation of the Word2Vec algorithm to further capture temporal information for the three main components of the data: diagnosis, procedures, and medications. For the analysis, we used the gradient boosting tree algorithm (XGBoost implementation). RESULTS: The C-statistic for the model was 0.93 [(0.916-0.943) 95% confidence interval], with a sensitivity of 0.715 and specificity of 0.958. Positive Predictive Value (PPV) was 0.517, and Negative Predictive Value (NPV) was 0.981. For the top 1 percentile of patients identified by our model, the PPV was 1.0. In addition, for the top 5 percentile of patients identified by our model, the PPV was 0.71. All the results above were tested on the test data only, and the threshold used to obtain these results was 0.1. Notable features contributing to the model were chronic heart and ischemic heart disease as a comorbidity, patient age, and number of hypertensive crisis events. CONCLUSIONS: When a patient is approaching the threshold of ESRD risk, a warning message can be sent electronically to the physician, who will initiate a referral for a nephrology consultation to ensure an investigation to hasten the establishment of a diagnosis and initiate management and therapy when appropriate.

Do Male Patients Benefit from Laparoscopic Adjustable Gastric Banding More than Female Patients? A Retrospective Cohort Study.

BACKGROUND: Clinical trials in the field of bariatrics, and specifically laparoscopic adjustable gastric banding (LAGB), have frequently been gender imbalanced, with males representing only 20% of examinees. Long-term gender-oriented results, and specifically quality of life (QOL) parameters, have not been addressed sufficiently. The aim of our study was to examine the long-term gender association with outcome of LAGB including the impact on QOL. METHODS: A retrospective cohort study of patients who underwent LAGB between 2006 and 2014 by a single surgeon was conducted. Data were collected from the hospital registry and a telephone interview that included a standardized questionnaire. Outcomes including BMI reduction, evolution of comorbidities, complications, reoperations, and QOL were compared according to the Bariatric Analysis and Reporting Outcome System (BAROS). RESULTS: Included were 114 males and 127 females, with a mean age of 38.2 years at surgery, and an average post-surgery follow-up of 6.5 years. Similar BMI reduction (p = 0.68) and perioperative complication rates (p = 0.99) were observed. Males had a greater improvement in comorbidities (p < 0.001), less band slippage (p = 0.006), underwent fewer reoperations (p = 0.02), and reported higher QOL scores (p = 0.02) than females. The total BAROS score was significantly higher for males than females (p < 0.001). CONCLUSIONS: LAGB surgery results in better outcomes for male than female patients as measured by the BAROS, despite a similar BMI reduction. Gender-specific outcomes should be taken into consideration in optimizing patient selection and preoperative patient counseling.