Generating Evidence Based Interpretation of Hematology Screens via Anomaly Characterization

ABSTRACT

Introduction: We propose a simple, workable algorithm that provides assistance for interpreting any set of data from the screen of a blood analysis with high accuracy, reliability, and inter-operability with an electronic medical record. This has been made possible at least recently as a result of advances in mathematics, low computational costs, and rapid transmission of the necessary data for computation.

Materials and Methods: The database used for this study is a file of 22,000 laboratory hemograms generated by two Beckman-Coulter Gen-S analyzers over a two month period in a 630 bed acute care facility in Brooklyn. All control samples, patient identifiers, and patients under 23 years old were stripped from the dataset. An experienced medical practitioner reviewed all of the data used in generating the algorithm described. The differential diagnoses were outlined prior to beginning the study, and preliminary studies were done to determine the reference ranges for each predictor. An algorithm for anomaly detection and classification via anomaly characterization is proposed. For each patient, the algorithm characterizes its anomalous profile and builds a differential metric to identify similar patients who are mapped into a classification.

Results: The algorithm successfully classified patients into the diagnosis that were sufficient in sample size, and others are still under observation. The algorithm correctly classified the patients as follows: Microcytic Anemia - 99.63%, Normocytic Anemia - 98.03%, Mild SIRS - 73.42%, Thrombocytopenia - 99.52%, Leukocytopenia - 84.83%, Moderate / Severe SIRS - 96.69% and Normal - 93.18%.

Discussion: This limited analysis of automated hematological results can be extended to the case of more complicated conditions than presented, and can be extended to a combination of chemistry, hematology, immunology, and other data.