Recognition of printed Chinese characters

This method of character recognition is according to the topological features of a.given character. First store the image of a Chinese character into the storage of the computer. Each image of the character appears as a 20 x 20 binary matrix. Each small square in the matrix is designated as one if the reflected light is more than 50% of that of a blank point, otherwise it is zero. The encoding method is as follows: (A) Preprocessor: This process includes three operations. These are Cleaning, Thinning and Connecting. (B) Preliminary Classification: First of all count all the " 1 " points in each column of the binary matrix from left to right. This list of digits is named as the Original Digit Code (ODC). From the ODC curve, by recording the extreme points, we get a Modified Digit Code (MDC). (C) Fundamental Classification: Choosing the longest line in each column of a binary matrix from left to right form the Longest Line Code (LLC), Plot the LLC against column number, to get the LLC curve. From the LLC curve, pick up the maximum points as the Largest Digit Code (LDC) and also record the number of digits between the two largest digits in LLC as the Distance Code (DC). In order to search easily for the English translation of a given character, the assigning of the order number to each digit in LDC is more important than the LDC itself. We call these digits as the Digit Order of LDC (DOL). According to the MDC, DC and DOL, the given character can be easily recognized by the computer

Automated clinical coding using off-the-shelf large language models

The task of assigning diagnostic ICD codes to patient hospital admissions is typically performed by expert human coders. Efforts towards automated ICD coding are dominated by supervised deep learning models. However, difficulties in learning to predict the large number of rare codes remain a barrier to adoption in clinical practice. In this work, we leverage off-the-shelf pre-trained generative large language models (LLMs) to develop a practical solution that is suitable for zero-shot and few-shot code assignment, with no need for further task-specific training. Unsupervised pre-training alone does not guarantee precise knowledge of the ICD ontology and specialist clinical coding task, therefore we frame the task as information extraction, providing a description of each coded concept and asking the model to retrieve related mentions. For efficiency, rather than iterating over all codes, we leverage the hierarchical nature of the ICD ontology to sparsely search for relevant codes.Comment: Accepted to the NeurIPS 2023 workshop Deep Generative Models For Health (DGM4H). 9 pages, 3 figure

Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.

The early detection of relapse following primary surgery for non-small-cell lung cancer and the characterization of emerging subclones, which seed metastatic sites, might offer new therapeutic approaches for limiting tumour recurrence. The ability to track the evolutionary dynamics of early-stage lung cancer non-invasively in circulating tumour DNA (ctDNA) has not yet been demonstrated. Here we use a tumour-specific phylogenetic approach to profile the ctDNA of the first 100 TRACERx (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy (Rx)) study participants, including one patient who was also recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release and analyse the tumour-volume detection limit. Through blinded profiling of postoperative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients who are very likely to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastasis, providing a new approach for ctDNA-driven therapeutic studies

Characteristics and Management of Patients with Chronic Hepatitis B in an Integrated Care Setting

BACKGROUND: Few population-based studies have described characteristics and management of patients with chronic hepatitis B (CHB) in the USA. METHODS: We retrospectively studied adults with CHB in the Northern California Kaiser Permanente Medical Care Program (KPNC) from July 2009 to December 2010 (n = 12,016). Laboratory tests, treatment patterns, and hepatocellular carcinoma (HCC) surveillance were ascertained during a “recent” 18-month study window (July 2009–December 2010), or as “ever” based on records dating to 1995. RESULTS: The mean age was 49 years; 51 % were men, 83 % Asian, and 87 % KPNC members >5 years. Overall, 51 % had ≥1 liver-related visit, 14 % with gastroenterology or infectious disease specialists, and 37 % with primary care providers (PCP) only. Less than 40 % of patients had both hepatitis B virus (HBV) DNA and ALT testing conducted recently, while 56 % of eligible patients had received HCC surveillance. Recent laboratory testing and HCC surveillance were more frequent in patients seen by a specialist versus PCP only (90 vs. 47 % and 92 vs. 73 %, respectively, p values <0.001). During the study period, 1,649 (14 %) received HBV treatment, while 5 % of untreated patients had evidence of treatment eligibility. Among 599 patients newly initiated on HBV therapy, 76 % had guideline-based indications for treatment. CONCLUSIONS: Most patients initiated on HBV treatment met eligibility, and very few patients with evidence of needing treatment were left untreated. However, monitoring of ALT and HBV DNA levels, as well as HCC surveillance, were not frequent, underestimating the proportion of patients that warranted HBV therapy. Viral monitoring and cancer surveillance are therefore important targets for improving the scope of CHB care in the community setting