Automated ICD coding via machine learning that focuses on some specific diseases has been a hot topic. As one of the leading causes of death, coronary heart diseases (CHD) have seldom been specifically studied by related research, probably due to lack of data concretely targeting at the diseases. Based on Fuwai-CHD and MIMIC-III-CHD, which are a private dataset from Fuwai Hospital and the CHD-related subset of a public dataset named MIMIC-III respectively, this study aimed at automated CHD coding by a deep learning method, which mainly consists of three modules. The first is a B ERT variant module responsible for encoding clinical text. In the module, we fine-tuned BERT variants with masked language model on clinical text, and proposed a truncation method to tackle the problem that BERT variants generally cannot handle sequences containing more than 512 tokens. The second is a word2vec module for encoding code titles and the third is a label-attention module for integrating the embeddings of clinical text and code titles. In short, we named the method BW_att. We compared BW_att against some widely studied baselines, and found that BW_att performed best in most of the coding missions. Specifically, BW_att reached a Macro-F1 of 96.2% and a Macro-AUC of 98.9% for the top-100 most frequent codes in Fuwai-CHD, which covered 89.2% of the total code occurrences. When predicting the top-50 most frequent codes in MIMIC-III-CHD, BW_att reached a Macro-F1 of 40.5% and a Macro-AUC of 66.1%. Moreover, BW_att was capable of locating informative tokens from clinical text for predicting the target codes. In summary, BW_att can not only suggest CHD codes accurately, but also possess robust interpretability, hence has great potential in facilitating CHD coding in practice.

Phosphorus circulation in Taihu Lake has attracted extensive attention, but the contribution of Microcystis to phosphorus circulation in this area is unknown. In this study, the phosphorus concentrations in algal samples collected from the lake in 2015–2016 were determined in the laboratory. From the concentration data, the total quantity of algal phosphorus was calculated and the seasonal variations in algal phosphorus were examined. The results indicated that the intracellular phosphorus content of Microcystis in Taihu varied from 0.044 to 0.130 pg/cell and tended to be high in spring and low in summer. The total amount of algal phosphorus in Taihu Lake ranged between 7.78 and 97.32 t over the study period. Algal phosphorus only accounted for between 1.5 and 18.5% of the phosphorus stock in the water. Because Microcystis accumulated downwind, there was a tendency for the total phosphorus concentrations to be low in the east of the lake and high in the west of the lake. This new information about the spatial and temporal distribution of algal phosphorus contributes to our understanding of how phosphorus in Microcystis contributes to phosphorus circulation in Taihu Lake.