N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit.

BACKGROUND: Existing risk adjustment models for intensive care unit (ICU) outcomes rely on manual abstraction of patient-level predictors from medical charts. Developing an automated method for abstracting these data from free text might reduce cost and data collection times. OBJECTIVE: To develop a support vector machine (SVM) classifier capable of identifying a range of procedures and diagnoses in ICU clinical notes for use in risk adjustment. MATERIALS AND METHODS: We selected notes from 2001-2008 for 4191 neonatal ICU (NICU) and 2198 adult ICU patients from the MIMIC-II database from the Beth Israel Deaconess Medical Center. Using these notes, we developed an implementation of the SVM classifier to identify procedures (mechanical ventilation and phototherapy in NICU notes) and diagnoses (jaundice in NICU and intracranial hemorrhage (ICH) in adult ICU). On the jaundice classification task, we also compared classifier performance using n-gram features to unigrams with application of a negation algorithm (NegEx). RESULTS: Our classifier accurately identified mechanical ventilation (accuracy=0.982, F1=0.954) and phototherapy use (accuracy=0.940, F1=0.912), as well as jaundice (accuracy=0.898, F1=0.884) and ICH diagnoses (accuracy=0.938, F1=0.943). Including bigram features improved performance on the jaundice (accuracy=0.898 vs 0.865) and ICH (0.938 vs 0.927) tasks, and outperformed NegEx-derived unigram features (accuracy=0.898 vs 0.863) on the jaundice task. DISCUSSION: Overall, a classifier using n-gram support vectors displayed excellent performance characteristics. The classifier generalizes to diverse patient populations, diagnoses, and procedures. CONCLUSIONS: SVM-based classifiers can accurately identify procedure status and diagnoses among ICU patients, and including n-gram features improves performance, compared to existing methods.

Hyperbilirubinemia in the term newborn.

Hyperbilirubinemia is one of the most common problems encountered in term newborns. Historically, management guidelines were derived from studies on bilirubin toxicity in infants with hemolytic disease. More recent recommendations support the use of less intensive therapy in healthy term newborns with jaundice. Phototherapy should be instituted when the total serum bilirubin level is at or above 15 mg per dL (257 micromol per L) in infants 25 to 48 hours old, 18 mg per dL (308 micromol per L) in infants 49 to 72 hours old, and 20 mg per dL (342 micromol per L) in infants older than 72 hours. Few term newborns with hyperbilirubinemia have serious underlying pathology. Jaundice is considered pathologic if it presents within the first 24 hours after birth, the total serum bilirubin level rises by more than 5 mg per dL (86 micromol per L) per day or is higher than 17 mg per dL (290 micromol per L), or an infant has signs and symptoms suggestive of serious illness. The management goals are to exclude pathologic causes of hyperbilirubinemia and initiate treatment to prevent bilirubin neurotoxicity.

Icterícias näo colestáticas do recém-nascido / Non-colestatic neonatal jaundice

Este artigo relembra alguns conceitos básicos e faz uma revisäo do que há de novo no conhecimento e abordagem das icterícias neonatais näo colestáticas

Management of neonatal jaundice.

Jaundice is the most commonly encountered neonatal clinical problem; 80 percent of neonates become clinically jaundiced, while 5 percent develop serum bilirubin levels above currently recommended treatment standards. This article outlines theories about the pathophysiology of neonatal jaundice and presents a logical approach to its management. First, the health care provider must distinguish between physiologic, exaggerated and pathologic jaundice. The treatment modalities of exchange transfusion, phototherapy and cessation of nursing are discussed.