Multimodal Machine Learning for Automated ICD Coding

This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.Comment: Machine Learning for Healthcare 201

Patient, Operator, and Procedural Characteristics of Guidewire Retention as a Complication of Vascular Catheter Insertion

OBJECTIVES:. Guidewire retention after intravascular catheter insertion is considered a “never event.” Prior reports attribute this complication to various characteristics including uncooperative patients, operator inexperience, off-hour or emergent insertion, and underutilization of ultrasound guidance. In this descriptive analysis of consecutive events, we assessed the frequency of patient, operator, and procedural factors in guidewire retention. DESIGN:. Pre-specified observational analysis as part of a quality improvement study of consecutive guidewire retention events across a multihospital health system from August 2007 to October 2015. SETTING:. Ten hospitals within the Cleveland Clinic Health System in Ohio, United States. PATIENTS:. Consecutive all-comers who experienced guidewire retention after vascular catheter insertion. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. Data were manually obtained from the electronic medical records and reviewed for potential contributing factors for guidewire retention, stratified into patient, operator, and procedural characteristics. A total of 24 events were identified. Overall, the median age was 74 years, 58% were males, and the median body mass index was 26.5 kg/m2. A total of 12 (50%) individuals were sedated during the procedure. Most incidents (10 [42%]) occurred in internal jugular venous access sites. The majority of cases (13 [54%]) were performed or supervised by an attending. Among all cases, three (12%) were performed by first-year trainees, seven (29%) by residents, three (12%) by fellows, and four (17%) by certified nurse practitioners. Overall, 16 (67%) events occurred during regular working hours (8 amto 5 pm). In total, 22 (92%) guidewires were inserted nonemergently, with two (8%) during a cardiac arrest. Ultrasound guidance was used in all but one case. CONCLUSIONS:. Guidewire retention can occur even in the presence of optimal patient, operator, and procedural circumstances, highlighting the need for constant awareness of this risk. Efforts to eliminate this important complication will require attention to issues surrounding the technical performance of the procedure