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Author: shaoxiongji

README.md

@@ -1 +1,50 @@
-# awesome
+# DCAN
+
+Dilated Convolutional Attention Network (DCAN), integrating dilated convolutions, residual connections, and label attention, for medical code assignment. It adopts dilated convolutions to capture complex medical patterns with a receptive field which increases exponentially with dilation size.
+
+## Data
+Download MIMIC-III dataset from [physionet](https://mimic.physionet.org).
+
+Organize your data using the following structure
+
+```
+data
+|   D_ICD_DIAGNOSES.csv
+|   D_ICD_PROCEDURES.csv
+|   ICD9_descriptions
+└───mimic3/
+|   |   NOTEEVENTS.csv
+|   |   DIAGNOSES_ICD.csv
+|   |   PROCEDURES_ICD.csv
+|   |   *_hadm_ids.csv
+```
+
+
+`ICD9_descriptions` is avaiable [in this repo](https://github.com/jamesmullenbach/caml-mimic/blob/master/mimicdata/ICD9_descriptions), and 
+`*_hadm_ids.csv` are avaiable [here](https://github.com/jamesmullenbach/caml-mimic/tree/master/mimicdata/mimic3).
+`MIMIC_RAW_DSUMS` is available [here](https://physionet.org/works/ICD9CodingofDischargeSummaries/), while the rest file for MIMIC2 can be generated with their code. 
+If you use Python3 `consctruct_datasest.py` in `ICD9_Coding_of_Discharge_Summaries` to create data files, remember to convert dict object to list (line 82&83) and use `dict.items()` instead of `dict.iteritems()`.
+Assign the directories of MIMIC data using `MIMIC_3_DIR`.
+
+## Run
+``python3 main.py``
+
+Configs available at `options.py`.
+
+Requirements:
+- python 3.7
+- pytorch 1.5.0
+
+## Citation
+```
+@inproceedings{ji2020dilated,
+  title={Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text},
+  author={Ji, Shaoxiong and Cambria, Erik and Marttinen, Pekka},
+  booktitle={3rd Clinical Natural Language Processing Workshop at EMNLP},
+  year={2020}
+}
+```
+
+## References
+- https://github.com/jamesmullenbach/caml-mimic
+- https://github.com/foxlf823/Multi-Filter-Residual-Convolutional-Neural-Network

dataloader.py

@@ -0,0 +1,193 @@
+import csv
+import torch
+import numpy as np
+from collections import defaultdict
+from transformers import AutoTokenizer
+from torch.utils.data import Dataset
+from elmo import elmo
+
+
+def load_vocab_dict(args, vocab_file):
+    """
+    Load vocabulary dictionary from file: vocab_file
+    """
+    vocab = set()
+    with open(vocab_file, 'r') as vocabfile:
+        for i, line in enumerate(vocabfile):
+            line = line.rstrip()
+            if line != '':
+                vocab.add(line.strip())
+    ind2w = {i + 1: w for i, w in enumerate(sorted(vocab))}
+    w2ind = {w: i for i, w in ind2w.items()}
+    return ind2w, w2ind
+
+
+def load_full_codes(train_path, mimic2_dir, version='mimic3'):
+    """
+    Load full set of ICD codes
+    """
+    if version == 'mimic2':
+        ind2c = defaultdict(str)
+        codes = set()
+        with open(mimic2_dir, 'r') as f:
+            r = csv.reader(f)
+            next(r) # skip header
+            for row in r:
+                codes.update(set(row[-1].split(';')))
+        codes = set([c for c in codes if c != ''])
+        ind2c = defaultdict(str, {i:c for i,c in enumerate(sorted(codes))})
+    else:
+        codes = set()
+        for split in ['train', 'dev', 'test']:
+            with open(train_path.replace('train', split), 'r') as f:
+                lr = csv.reader(f)
+                next(lr)
+                for row in lr:
+                    for code in row[3].split(';'):
+                        codes.add(code)
+        codes = set([c for c in codes if c != ''])
+        ind2c = defaultdict(str, {i:c for i,c in enumerate(sorted(codes))})
+    return ind2c
+
+
+def load_lookups(args):
+    """
+    Load lookup dictionaries: index2word, word2index, index2code, code2index 
+    """
+    ind2w, w2ind = load_vocab_dict(args, args.vocab)
+    if args.Y == 'full':
+        ind2c = load_full_codes(args.data_path, '%s/proc_dsums.csv' % args.MIMIC_2_DIR, version=args.version)
+    else:
+        codes = set()
+        with open("%s/TOP_%s_CODES.csv" % (args.MIMIC_3_DIR, str(args.Y)), 'r') as labelfile:
+            lr = csv.reader(labelfile)
+            for i, row in enumerate(lr):
+                codes.add(row[0])
+        ind2c = {i:c for i,c in enumerate(sorted(codes))}
+    c2ind = {c:i for i,c in ind2c.items()}
+    dicts = {'ind2w': ind2w, 'w2ind': w2ind, 'ind2c': ind2c, 'c2ind': c2ind}
+    return dicts
+
+
+def prepare_instance(dicts, filename, args, max_length):
+    # filename: data/mimic[2/3]/[train/dev/test]_[50/full].csv, e.g., data/mimic3/train_50.csv
+    ind2w, w2ind, ind2c, c2ind = dicts['ind2w'], dicts['w2ind'], dicts['ind2c'], dicts['c2ind']
+    instances = []
+    num_labels = len(dicts['ind2c'])
+    with open(filename, 'r') as infile:
+        r = csv.reader(infile)
+        next(r)     # skip header
+        for row in r:
+            text = row[2]
+            labels_idx = np.zeros(num_labels)
+            labelled = False
+            for l in row[3].split(';'):
+                if l in c2ind.keys():
+                    code = int(c2ind[l])
+                    labels_idx[code] = 1
+                    labelled = True
+            if not labelled:
+                continue
+            tokens_ = text.split()
+            tokens = []
+            tokens_id = []
+            for token in tokens_:
+                if token == '[CLS]' or token == '[SEP]':
+                    continue
+                tokens.append(token)
+                token_id = w2ind[token] if token in w2ind else len(w2ind) + 1
+                tokens_id.append(token_id)
+            if len(tokens) > max_length:
+                tokens = tokens[:max_length]
+                tokens_id = tokens_id[:max_length]
+            dict_instance = {'label': labels_idx, 'tokens': tokens, "tokens_id": tokens_id}
+            instances.append(dict_instance)
+    return instances
+
+
+def prepare_instance_bert(dicts, filename, args, max_length):
+    ind2w, w2ind, ind2c, c2ind = dicts['ind2w'], dicts['w2ind'], dicts['ind2c'], dicts['c2ind']
+    instances = []
+    num_labels = len(dicts['ind2c'])
+    wp_tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
+    with open(filename, 'r') as infile:
+        r = csv.reader(infile)
+        next(r)
+        for row in r:
+            text = row[2]
+            labels_idx = np.zeros(num_labels)
+            labelled = False
+            for l in row[3].split(';'):
+                if l in c2ind.keys():
+                    code = int(c2ind[l])
+                    labels_idx[code] = 1
+                    labelled = True
+            if not labelled:
+                continue
+            tokens_ = text.split()
+            tokens = []
+            for token in tokens_:
+                if token == '[CLS]' or token == '[SEP]':
+                    continue
+                wps = wp_tokenizer.tokenize(token)
+                tokens.extend(wps)
+            tokens_max_len = max_length-2 # for CLS SEP
+            if len(tokens) > tokens_max_len:
+                tokens = tokens[:tokens_max_len]
+            tokens.insert(0, '[CLS]')
+            tokens.append('[SEP]')
+            tokens_id = wp_tokenizer.convert_tokens_to_ids(tokens)
+            masks = [1] * len(tokens)
+            segments = [0] * len(tokens)
+            dict_instance = {'label':labels_idx, 'tokens':tokens, "tokens_id":tokens_id, 
+                             "segments":segments, "masks":masks}
+            instances.append(dict_instance)
+    return instances
+
+
+class MyDataset(Dataset):
+    def __init__(self, X):
+        self.X = X
+
+    def __len__(self):
+        return len(self.X)
+
+    def __getitem__(self, idx):
+        return self.X[idx]
+
+
+def pad_sequence(x, max_len, type=np.int):
+    padded_x = np.zeros((len(x), max_len), dtype=type)
+    for i, row in enumerate(x):
+        padded_x[i][:len(row)] = row
+    return padded_x
+
+
+def my_collate(x):
+    words = [x_['tokens_id'] for x_ in x]
+    seq_len = [len(w) for w in words]
+    masks = [[1]*len(w) for w in words]
+    max_seq_len = max(seq_len) # TODO 
+    # max_seq_len = args.MAX_LENGTH # TODO for capsule network
+
+    inputs_idx = torch.LongTensor(pad_sequence(words, max_seq_len))
+    inputs_mask = torch.LongTensor(pad_sequence(masks, max_seq_len))
+    labels = torch.FloatTensor([x_['label'] for x_ in x])
+    inputs_text = [x_['tokens'] for x_ in x]
+    inputs_text = elmo.batch_to_ids(inputs_text)
+    return inputs_idx, labels, inputs_text, inputs_mask
+
+
+def my_collate_bert(x):
+    words = [x_['tokens_id'] for x_ in x]
+    segments = [x_['segments'] for x_ in x]
+    masks = [x_['masks'] for x_ in x]
+    seq_len = [len(w) for w in words]
+    max_seq_len = max(seq_len)    # max of batch
+
+    inputs_idx = torch.LongTensor(pad_sequence(words, max_seq_len))
+    segments = torch.LongTensor(pad_sequence(segments, max_seq_len))
+    masks = torch.LongTensor(pad_sequence(masks, max_seq_len))
+    labels = torch.FloatTensor([x_['label'] for x_ in x])
+    return inputs_idx, segments, masks, labels
+